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# Lab 2 - 定制一个新的张量运算
## 实验目的
1. 理解DNN框架中的张量算子的原理
2. 基于不同方法实现新的张量运算，并比较性能差异
## 实验环境
* PyTorch==1.5.0
## 实验原理
1. 深度神经网络中的张量运算原理
2. PyTorch中基于Function和Module构造张量的方法
3. 通过C++扩展编写Python函数模块
## 实验内容
### 实验流程图
### 具体步骤
1. 在MNIST的模型样例中，选择线性层（Linear）张量运算进行定制化实现
2. 理解PyTorch构造张量运算的基本单位：Function和Module
3. 基于Function和Module的Python API重新实现Linear张量运算
1. 修改MNIST样例代码
2. 基于PyTorch Module编写自定义的Linear 类模块
3. 基于PyTorch Function实现前向计算和反向传播函数
4. 使用自定义Linear替换网络中nn.Linear() 类
5. 运行程序，验证网络正确性
4. 理解PyTorch张量运算在后端执行原理
5. 实现C++版本的定制化张量运算
1. 基于C++，实现自定义Linear层前向计算和反向传播函数，并绑定为Python模型
2. 将代码生成python的C++扩展
3. 使用基于C++的函数扩展，实现自定义Linear类模块的前向计算和反向传播函数
4. 运行程序，验证网络正确性
6. 使用profiler比较网络性能：比较原有张量运算和两种自定义张量运算的性能
7. 【可选实验，加分】实现卷积层（Convolutional）的自定义张量运算
## 实验报告
### 实验环境
||||
|--------|--------------|--------------------------|
|硬件环境|CPU（vCPU数目）| |
||GPU(型号，数目)||
|软件环境|OS版本||
||深度学习框架<br>python包名称及版本||
||CUDA版本||
||||
### 实验结果
|||
|---------------|---------------------------|
| 实现方式（Linear层为例）| 性能评测 |
|<br/> <br/>PyTorch原有张量运算<br/> <br/> | |
|<br/> <br/>基于Python API的定制化张量运算<br/> <br/> ||
|<br/> <br/>基于C++的定制化张量运算<br/> <br/> ||
||||
## 参考代码
1. 基于Python API实现定制化张量运算Linear
代码位置：`Lab2/mnist_custom_linear.py`
运行命令：`python mnist_custom_linear.py`
2. 基于C++ API实现定制化张量运算Linear
代码位置：`Lab2/mnist_custom_linear_cpp.py`
运行命令：
```
cd mylinear_cpp_extension
python setup.py install --user
cd ..
python mnist_custom_linear_cpp.py
```
## 参考资料
* EXTENDING PYTORCH: https://pytorch.org/docs/master/notes/extending.html
|
AI-System/Labs/BasicLabs/Lab2/README.md/0
|
{
"file_path": "AI-System/Labs/BasicLabs/Lab2/README.md",
"repo_id": "AI-System",
"token_count": 1897
}
| 11 |
## 环境设置
### 1 前置依赖
推荐操作系统：Ubuntu 16.04 or 18.04
### 1.1 配置你的电脑或服务器
Docker的官方文档中的*getting started*有关于不同操作系统的Docker细节配置方法
* [Linux](https://docs.docker.com/engine/installation/linux/)
* [Ubuntu](https://docs.docker.com/engine/install/ubuntu/)
* [Mac](https://docs.docker.com/docker-for-mac/)
* [Windows](https://docs.docker.com/docker-for-windows/)
*如果你使用Docker for Windows* 请确保已经 [共享驱动](https://docs.docker.com/docker-for-windows/#shared-drives).
*注意事项* 如果你使用的是旧版本的Windows或者Mac系统，可能你需要使用[Docker Machine](https://docs.docker.com/machine/overview/)进行替代.
*以下的命令可以在bash或者 Powershell on Windows中执行*
如果你已经安装好Docker，通过以下命令测试你的环境已经安装成功:
```
$ docker run hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
03f4658f8b78: Pull complete
a3ed95caeb02: Pull complete
Digest: sha256:8be990ef2aeb16dbcb9271ddfe2610fa6658d13f6dfb8bc72074cc1ca36966a7
Status: Downloaded newer image for hello-world:latest
Hello from Docker.
This message shows that your installation appears to be working correctly.
...
```
## 下一步
点击进入本教程的下一步 [2. 运行你的第一个容器](alpine.md)
|
AI-System/Labs/BasicLabs/Lab5/setup.md/0
|
{
"file_path": "AI-System/Labs/BasicLabs/Lab5/setup.md",
"repo_id": "AI-System",
"token_count": 665
}
| 12 |
#include <iostream>
#include <stdio.h>
using namespace std;
int main()
{
int M = 3;
int N = 3;
int K = 3;
int A[M][K];
int B[K][N];
int C[M][N];
for (int m = 0; m < M; m++) {
for (int n = 0; n < N; n++) {
C[m][n] = 0;
for (int k = 0; k < K; k++) {
C[m][n] += A[m][k] * B[k][n];
printf("%d \n", &A[m][k]);
printf("%d \n", &B[k][n]);
printf("%d \n", &C[m][n]);
}
}
}
return 0;
}
|
AI-System/Textbook/第1章-人工智能系统概述/src/gemm_locality.cpp/0
|
{
"file_path": "AI-System/Textbook/第1章-人工智能系统概述/src/gemm_locality.cpp",
"repo_id": "AI-System",
"token_count": 304
}
| 13 |
<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
- [3.2 神经网络计算中的控制流](#32-神经网络计算中的控制流)
- [3.2.1 背景](#321-背景)
- [3.2.2 静态图：向数据流图中添加控制流原语](#322-静态图向数据流图中添加控制流原语)
- [3.2.3 动态图：复用宿主语言控制流语句](#323-动态图复用宿主语言控制流语句)
- [3.2.4 动态图转换为静态图](#324-动态图转换为静态图)
- [3.2.5 小结与讨论](#325-小结与讨论)
- [参考文献](#参考文献)
# 3.2 神经网络计算中的控制流
## 3.2.1 背景
深度学习框架是一个可编程系统，框架在设计时一个首要设计选择是如何让前端用户能够独立于后端实现细节以最自然的方式描述出各类神经网算法的计算过程。描述的完备性不仅影响深度学习框架能所够支持的神经网络结构，决定了前端用户在编程深度学习算法时能够享有的灵活性，也影响了一个深度学习框架在中端和后端能够应用优化技术，以及如何对系统进行扩展。主流深度学习框架都将神经网络计算抽象为由基本原语构成的有向无环图，计算图中的结点是由后端提供高效实现的基本操作原语，是一个解耦到底层编程模型去进一步进行性能优化的性能域；边表示原子操作之间的数据依赖关系。这种使用有向无环图刻画神经网络计算的视角，十分符合算法开发者眼中神经网络的概念模型：算子间拓扑结构对学习特性有重要影响，足以毫不费力地描述出大多数通过堆叠深度或多分枝形成的复杂神经网络。
然而，随着神经网络算法研究的发展，一些新颖的神经网络结构很难自然地表示为纯数据流图。这一节我们以循环神经网络和注意力机制为例，来看看使用最自然地方式描述这些算法对深度学习框架会带来什么新的描述性要求。循环神经网络和注意力机制是两类存在诸多变种的神经网络算法，图1（a）（b）的左侧分别是一个通用循环神经网络和注意力机制中关键步骤元素对间相似度计算过程的示意图，右侧对应了使用最自然的方式描述这一算法计算过程的伪代码。
<p align="center">
<img src="img/control-flow-examples.png" width=60%><br>
图1. 循环神经网络和注意力机制计算过程示意图
</p>
循环神经网络是应用于序列处理任务的经典模型。在循环神经网络设计中，算法设计者的关注点是通过对基本计算原语的组合，定义出单时间步内的计算函数，然后将其重复应用于序列中的每个元素。这个处理函数上一时间步的输出会成为下一时间步的输入，顾名思义称之为循环神经网络。注意力机制[<sup>[2]</sup>](#attn-2)最初用于学习两个序列中元素之间的对齐关系。两个序列长度分别为$n$和$m$的序列计算笛卡尔积会得到一个含有$m\times n$个元素的元素对对集合。注意力机制的核心步骤之一是将一个用户自定义的，用于计算元素对相似性分值的神经网络，应用这个$m \times n$个元素构成的元素对集合。从图1左侧的伪代码描述中可以看到，想要以通用的方式，自然地描述出循环神经网络和注意力机制的算法框架，均依赖于循环控制。
为了能够支持如自定义循环神经网络这类计算过程中天生就含有控制流结构的神经网络计算，主流深度学习框架不约而同的引入了对动态控制流这一语言结构（Language Construct）的支持。目前在控制流解决方案上，主流框架采用了两类设计思路：后端对控制流语言结构进行原生支持，计算图中允许数据流和控制流的混合；复用前端语言的控制流语言结构，用前端语言中的控制逻辑驱动后端数据流图的执行。前者以TensorFlow为典型代表，后者以PyTorch为典型代表。
## 3.2.2 静态图：向数据流图中添加控制流原语
声明式编程由于能够在运行计算之前得到全计算过程的统一描述，使得编译期优化器能够利用全计算过程信息进行更激进的推断，同时，执行流无需在前端语言与运行时之间反复切换，避免了跨越语言边界的调用开销，因此往往有着更高的执行效率。基于这一设计理念，主流深度学习框架TensorFlow在解决控制流需求时，选择向数据流图中加入如图2所示的5个底层控制流原语：Enter，Switch，Exit，Merge，NextIteration对计算图进行扩展，并由运行时系统对控制流原语以第一等级（First-class）进行实现支持[<sup>[1]</sup>](#tf-control-1)[<sup>[5]</sup>](#tf-control-impl-5)。这些控制流原语的设计深受数据流（Dataflow）编程语言研究的影响[<sup>[3]</sup>](#dataflow-language-3)[<sup>[4]</sup>](#dataflow-language-4)。
<p align="center">
<img src="img/control-flow-primitives-tensorflow.png" width=50%><br>
图 2. TensorFlow中的控制流原语
</p>
在TensorFlow的计算图，每个算子的执行都位于一个执行帧中（Execution Frame）中，每个执行帧具有全局唯一的名字作为标识符。可以将执行帧类比为程序语言中的域（Scope），其中通过键-值（Key-Value）表保存着执行算子所需的上下文信息，如输入输出变量存储位置等。当计算图中引入控制流后，每个算子有可能被多次执行，控制流原语会在运行时创建这些执行帧，执行帧可以嵌套，对应了前端用户写出的嵌套控制流。例如，tf.while_loop的循环体是一个用户自定义计算子图，位于同一个计算帧中，嵌套的tf.while_loop对应嵌套的计算帧，位于不同计算帧中的算子，只要它们之间不存在数据依赖，有能够被运行时调度并发执行。
在图2中：
- **Switch**原语根据谓词算子p的结果是否为真将输入边上流入的张量d传递给两个输出边之一。只有在两个输入p和d同时可得时，Switch原语才会被运行时调度执行。
- **Merge**原语将两个输入边中任意一个边上流入的张量d传递给输出边。只要在任何一个输入边上有张量d可得，Merge原语就会被运行时调度执行，当两个输如边上同时有张量流入时，输出边上张量输入的顺序完全由运行时调度决定，编译期分析无法预知。
- **Enter**原语将输入边上流入的张量d传递给一个由名字决定的执行帧。Enter原语用于实现将一个张量从父执行帧传递入子执行帧。当一个执行帧的Enter原语第一次被执行时，一个新的执行帧将会被创建并由运行时进行管理。只要输入边上有张量d可得时，Enter原语就会被运行时调度执行。
- **Exit**原语的行为与Enter相反，Exit原语用于将一个张量返回给父执行帧。只要输入边上有张量d可得时，Exit原语就会被运行时调度执行。
- **NextIteration**原语用于将输入边上流入的张量d传递给执行帧的下一次执行。只要输入边上有张量d可得时，NextIteration原语就会被运行时调度执行。NextIteration原语的行为可以简单地理解为在循环体的多次执行之间传递数据。
Switch和Merge的混合使用用于表达条件分枝，全部5个原语的混合使用用于表达循环执行。为了提高可理解性和编程效率避免前端用户直接操作底层算子，图2中这些计算图中的控制流原语会被进一步封装为前端的控制流AIP。图3是用户使用前端基础控制流API编写带条件和循环的计算，以及它们所对应的计算图表示[<sup>[5]</sup>](#tf-control-impl-5)。
<p align="center">
<img src="img/control-flow-API-to-ops.png" width=70%><br>
图 3. TensorFlow中控制流API到计算图
</p>
向计算图中引入控制流算子有利于编译期得到全计算过程描述，从而发掘更多改善运行时开销的机会。由于控制流原语语义的设计首要服务于运行时系统的并发执行模型，与前端用户在描述算法时直觉中的神经网络概念模型有很大的语义鸿沟，对前端用户来说存在一定的易用性困扰。因此，需要对控制流原语进行再次封装，以控制流API的方式供前端用户使用，这也导致了构建计算图步骤相对复杂。随着神经网络算法的发展，框架的设计者也希望能够将尽可能多的优化机会放在编译期，由一个独立的优化器组件完成，而这些底层控制流API的复杂性又让控制结构的识别十分困难，因此，为了简化识别计算图中的控制结构，TensorFlow在后期又在底层控制流原语的基础上引入了一层高层次Functional控制流算子，同时添加了高层次控制流算子向底层控制流算子的转换。
<p align="center">
<img src="img/control-flow-tensorflow.png" width=40%><br>
图 4. TensorFlow控制流解决方案概况
</p>
图4是TensorFlow中控制流方案的概况，整体分为：暴露给前端用户用于构建计算图的前端API，这些API会被转换成更低等级的控制流原语，再由计算图优化器进一步进行改写。为了平衡编程的易用性和优化器设计中保留更多易于被识别出的优化机会，TensorFlow提供了多套有着不同抽象等级的前端API以及计算图上的控制流原语。
## 3.2.3 动态图：复用宿主语言控制流语句
与向计算图中引入控制流算子这种解决方案相对的是以PyTorch[<sup>[8]</sup>](#pytorch-jit-8)为代表的复用宿主语言(在机器学习任务中，Python是最流行的宿主语言)控制流构建动态图。下面的代码片断是图3中代码对应的动态图版本，这时框架不再维护一个全局的神经网络算法描述，神经网络变成一段Python代码，后端的张量计算以库的形式提供，维持了与numpy[<sup>[9]</sup>](#numpy-9)一致的编程接口。
```python
from torch import Tensor
def foo1(x: Tensor, y: Tensor, z: Tensor) -> Tensor:
if x < y:
s = x + y
else:
s = torch.square(y)
return s
def foo2(s: Tensor) -> Tensor:
for i in torch.range(10):
s += i
return s
```
由于用户能够自由地使用前端宿主语言（往往是如Python这样的高级脚本语言）中的控制流语言，即时输出张量计算的求值结果，这种复用宿主语言控制流驱动后端执行的方式有着更好的交互性，用户体验更加友好。为用户带来一种使用体验上的错觉：定义神经网络计算就像是编写真正的程序，但缺点也是明显的：用户容易滥用前端语言特性，带来更复杂且难以优化性能问题。并且，在这种设计选择之下，一部分控制流和数据流被严格地隔离在前端语言和后端语言之中，跨语言边界的优化十分困难，执行流会在语言边界来回跳转，带来十分严重的运行时开销。
## 3.2.4 动态图转换为静态图
静态图易于优化但灵活性低，动态图灵活性高但由于缺少统一的计算过程表示难以在编译期进行分析，两者的优缺点相对。那么，是否有可能模糊两种解决方案之间的边界，兼具动态图的灵活性以及静态图的性能优势？答案是肯定的。以TensorFlow的Auto-graph[<sup>[6]</sup>](#auto-graph-6)和PyTorch的JIT[<sup>[8]</sup>](#pytorch-jit-8)为代表，主流深度学习框架最终都走向了探索动态图与静态图的融合：前端用户使用宿主语言中的控制流语句编写神经网络程序，调试完毕后，由框架自动转换为静态图网络结构。动态图向静态图转换分为基于追踪（Tracing）和基于源代码解析（Parsing）两种方式。
- **基于追踪的方式**会直接执行用户代码，记录下算子调用序列，将这个算子调用序列保存为静态图模型，再以后的执行中脱离前端语言环境，完全交由运行时系统按照静态图调度。
- **基于源代码解析**的方式，以宿主语言的抽象语法树（Abstract Syntax Tree, AST）为输入。这一步首先需要严格地筛选宿主语言语法要素，往往只会解析宿主语言一个十分小的子集，将宿主语言的抽象语法树首先整理成一个内部的抽象语法树表示，再从这个内部语法树开始经过别名分析，SSA（Static Single Value Assignment）化，类型推断等重要分析，最终转换为计算图表示。
```python
@torch.jit.script
def foo1(x: Tensor, y: Tensor, z: Tensor) -> Tensor:
if x < y:
s = x + y
else:
s = torch.square(y)
return s
@torch.jit.script
def foo2(s: Tensor) -> Tensor:
for i in torch.range(10):
s += i
return s
```
上面的代码片断是使用PyTroch的Script模式（基于源代码解析）将上一节中的动态图转换为静态图执行，图5是框架背后的处理流程。
<p align="center">
<img src="img/pytorch-jit-overview.png" width=50%><br>
图 5. TorchScript基于源代码转换的动态图转静态图
</p>
基于追踪的方式原理十分简单易于实现，能够更广泛地支持宿主语言中的各种动态控制流语句，例如：函数调用，函数嵌套，函数递归等等。但是直接执行程序一次，只能保留程序的一条执行轨迹，并将其线性化，得到的静态图已经失去了用户源程序中的控制结构，使用场景非常有限。对基于源代码解析的方式，由于所有深度学习框架的运行时系统在设计时，为了性能考虑，始终存在诸多静态性要求。由于后端实现限制的存在，宿主语言的控制流语句并不总是能成功映射到后端运行时系统的静态图表示，因此对宿主语言的语法要素有着十分严格的要求，一旦遇到过度灵活的动态控制流语句，运行时系统依然会退回到“由前端语言跨语言调用驱动后端执行”这种动态执行策略。
## 3.2.5 小结与讨论
1. 在控制流支持上采用的不同设计选择将主流深度学习框架分裂为声明式编程模型和静态图，以及命令式编程模型和动态图两大阵营。前者有利于为编译期优化器提供全计算过程描述从而发掘更多优化机会，但是由于静态性限制，计算图上的控制流原语与前端用户的神经网络概念模型存在语义鸿沟等问题，需要遵从后端系统实现引入的语义限制，导致灵活性和易用性受限。
1. 相比之下，命令式编程模型中能够自由地复用宿主语言中的控制流原语，但执行过程是由前端语言驱动对后端张量计算库的跨语言调用，编译期分析也失去了对全计算过程分析的机会。
1. 主流深度学习框架都支持通过源代码转换的方式实现自动将动态图转换为静态图，从而达到易用性和性能的兼顾：限制能够使用的前端语言语法要素，通过语法分析器（Parser）自动解析出对后端优化更加友好的静态子图。但这种自动转换只是提供了一种编程体验的改善，而控制流的优化难题并没有完全解决。操作控制流结构实现程序优化往往依赖于设计精巧的语义模型来保证变换前后的语义一致，否则优化器不得不选择最简单的策略将前端语言的控制流直接翻译到后端控制流，能够减少的只是执行流在语言间切换以及调度开销。
# 参考文献
<div id="tf-control-1"></div>
1. Yu, Yuan, et al. "[Dynamic control flow in large-scale machine learning](https://arxiv.org/pdf/1805.01772.pdf)." Proceedings of the Thirteenth EuroSys Conference. 2018.
<div id="attn-2"></div>
2. Bahdanau, Dzmitry, Kyung Hyun Cho, and Yoshua Bengio. "[Neural machine translation by jointly learning to align and translate](http://www.wins.or.kr/DataPool/Board/4xxxx/458xx/45877/1409.0473[1].pdf)." 3rd International Conference on Learning Representations, ICLR 2015. 2015.
<div id="dataflow-language-3"></div>
3. Johnston, Wesley M., JR Paul Hanna, and Richard J. Millar. "[Advances in dataflow programming languages](https://www.cs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/DataFlowProgrammingLanguages.pdf)." ACM computing surveys (CSUR) 36.1 (2004): 1-34.
<div id="dataflow-language-4"></div>
4. Veen, Arthur H. "[Dataflow machine architecture](https://course.ece.cmu.edu/~ece740/f13/lib/exe/fetch.php?media=veen86.pdf)." ACM Computing Surveys (CSUR) 18.4 (1986): 365-396.
<div id="tf-control-impl-5"></div>
5. [Implementation of Control Flow in TensorFlow](http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf)
<div id="auto-graph-6"></div>
6. Moldovan, Dan, et al. "[AutoGraph: Imperative-style Coding with Graph-based Performance](https://arxiv.org/pdf/1810.08061.pdf).(oct 2018)." arXiv preprint arXiv:1810.08061 (2018).
<div id="pytorch-7"></div>
7. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). [Pytorch: An imperative style, high-performance deep learning library](https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf). Advances in neural information processing systems, 32.
<div id="pytorch-jit-8"></div>
8. [PyTorch JIT Technical Overview](https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md#jit-technical-overview)
<div id="numpy-9"></div>
9. Van Der Walt, Stefan, S. Chris Colbert, and Gael Varoquaux. "[The NumPy array: a structure for efficient numerical computation](https://arxiv.org/pdf/1102.1523.pdf%C3%AB%C2%A5%C2%BC)." Computing in science & engineering 13.2 (2011): 22-30.
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<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 4.1 深度学习的计算模式
在理解深度学习体系结构之前，分析深度学习计算中常见的计算模式或模型结构至关重要。目前，深度学习已经广泛应用在计算机视觉、自然语言处理、语音处理等应用领域中，尽管模型结构在不断的演进，我们仍可以粗略的将大部分深度学习模型归纳为几大类常见的结构，如全连接层（Fully-connected layer）、卷积层（Convolution layer）、循环网络层（Recurrent neral network layer）和注意力层（Attention layer）等。在本节中，我们针对每一类模型结构去分析和总结一下其核心的运算模式。
## 4.1.1 全连接层
全连接层是深度学习模型中最简单也是最常见的一种网络结构，每一层中的一个值就等于前一层所有值的加权求和，权值就是每条对应边上的权值，也是神经网络中需要学习的参数，如图4-1-1所示，第二层中的每一个$y_j$的值就等于
$y_j = \sum_{i=0}^{2}x_iw_{ij}$
因此，一层所有值的计算就可以表示为一个矩阵运算
$Y=W^TX$
其中
$X=
\begin{bmatrix}
x_0\\
x_1\\
x_2\\
\end{bmatrix},
W=
\begin{bmatrix}
w_{0,0} & ... & w_{0,3}\\
w_{1,0} & ... & w_{1,3}\\
w_{2,0} & ... & w_{2,3}\\
\end{bmatrix},
Y=
\begin{bmatrix}
y_0\\
y_1\\
y_2\\
\end{bmatrix}
$.
<center> <img src="./img/4-1-1-fc.png" /></center>
<center>图4-1-1. 全连接层示意图</center>
这时，一层全连接层的计算就变成一个矩阵乘以向量的运算，在深度学习的训练或离线推理中，输入层还可以是多个样本的批量输入，这时候一层的计算就可以转化成一个矩阵乘以矩阵的运算。由于矩阵乘法是一个经典的计算，无论是在CPU还是GPU中等有非常成熟的软件库，如MKL，CUBLAS等，因此其可以直接高效的被各种硬件支持。
## 4.1.2 卷积层
卷积神经网络层是在计算机视觉应用中最常见也是各类视觉神经网络中最主要的计算部分。其计算逻辑是将一个滤波器（Filter）在输入矩阵上通过滑动窗口的方式作用整个输入矩阵，在每一个窗口内计算输入数据和滤波器的加权和。如下图左半部分示意的一个简单卷积层计算过程，其中转出举证的元素1是由滤波器中的每个元素和输入矩阵中的元素1、2、4、5所相乘并求和计算所得。
<center> <img src="./img/4-1-2-conv.png" /></center>
<center>图4-1-2. 卷积神经网络层（左）和对应的矩阵乘法（右）示意图</center>
为了高效的计算上述过程，卷积层的计算可以通过对输入矩阵的重组而等价变化成一个矩阵相乘的形式，即通过将滤波器滑动窗口对应的每一个输入子矩阵作为新的矩阵的一列，也就是img2col的方法。图4-1-2的右图所示就是对应左图的卷积层通过对输入矩阵重组后的矩阵乘的形式。通过这样的变化，卷积层的计算就可以高效的利用到不同硬件平台上的矩阵加速库了。值得注意的是，这样的矩阵实现在实际中并不一定是最高效的，因为重组的输入矩阵的元素个数比原始矩阵变多了，也就意味着计算过程中要读取更多的数据，同时重组的过程也会引入一次内存复制的开销。为了优化后者，一种隐式矩阵乘法的实现就是在计算过程中在高级存储层中重组矩阵，从而减少对低级内存的访问量。
```
思考：请计算一下通过将卷积算子变化成矩阵乘法后，需要要读取的数据量和卷积算子的形状之前的关系，并思考一下，隐式矩阵乘法是如何减少数据访问量的。
```
## 4.1.3 循环网络层
循环神经网络层善于处理序列性的数据，在自然语言处理、语音识别、时序数据分析等应用中广泛采用。循环神经网络层中的主要计算就是其循环单元（Cell）的计算，如图4-1-3展示的是一个常用的GRU单元的计算流图，其中每个图形表示一个算子，M表示矩阵乘算子，其它均为一些轻量的point-wise算子。可以看到，循环神经网络层中的主要计算部分也是矩阵乘法，例如一个GRU单元里的就有6个矩阵乘算子。
<center> <img src="./img/4-1-3-gru.png" /></center>
<center>图4-1-3. 循环神经网络层的GRU单元示意图</center>
## 4.1.4 注意力层
注意力机制（Attention）在接循环神经网络之后成为一种被广泛使用在自然语言处理中的模型结构，最近也在计算机视觉等其它应用中取得不错的效果。其核心是建模不同符号（token）之间的联系，如一句话中不同词之间的联系。图4-1-4为注意力机制中最基本的单元的数据流图，可以看到与其它算子的计算量相比，其核心的算子也是矩阵乘算子。
<center> <img src="./img/4-1-4-att.png" /></center>
<center>图4-1-4. 注意力机制层的示意图</center>
## 4.1.5 小结与讨论
通过上述小节对不同的主流模型结构的分类分析，我们发现一个深度学习模型的共同特点，就是大部分模型结构的核心计算模式都可以直接或间接的表示为矩阵乘法。这样的结果虽然有些巧合，但其背后却蕴含着深度学习模型的发展和支持其计算的软硬件发展之间相辅相成的关系。一方面，能够被广泛应用的模型结构必然要能够在现有体系结构中得到比较好的支持，矩阵乘作为经典计算已经被不同平台良好的支持，因此模型的设计者会倾向于尽可能利用这样的软硬件优势。另一方面，模型一旦取得比较好的结果，新的体系结构也会超着能更好的支持主流模型的方向上发展，这也进一步强化了现有硬件在支持诸如矩阵乘法上的力度，如近年来在GPU上出现的用来加速矩阵乘法的张量核（Tensor Core）就是一个这样的例子。当然，这也不能完全成为深度学习甚至更广泛的机器学习的唯一发展方向，针对其它计算模式的模型设计和体系结构支持都是非常有必要的。为了简单起见，本章节后续内容会以矩阵乘在不同体系结构中的实现和优化作为例子来分析体系结构的变化趋势。
请读者思考，除了矩阵乘法之外，还有哪些你认为深度学习模型中常用到的计算模式或算子呢？ 这些算子在不同的硬件平台上是否有较好的软件库支持呢？
## 参考文献
1. https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-mkl-for-dpcpp/top.html
2. https://developer.nvidia.com/cublas
3. https://en.wikipedia.org/wiki/Toeplitz_matrix#Discrete_convolution
4. https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html
5. https://en.wikipedia.org/wiki/Recurrent_neural_network
6. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
7. [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
|
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<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 分布式训练算法与系统 (Algorithms and Systems for Distributed Parallel Training)
# 简介
深度机器学习以其强大的数学统计能力，在众多领域的不同任务中显著超越了传统方法，从而广泛应用于我们生活生产的各个方面。除了有赖于模型设计的不断发展之外，这一切进步要着重归功于背后起支撑作用的巨大计算力。正如[第1章](../%E7%AC%AC1%E7%AB%A0-%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E7%B3%BB%E7%BB%9F%E6%A6%82%E8%BF%B0/1.4-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E6%A0%B7%E4%BE%8B%E8%83%8C%E5%90%8E%E7%9A%84%E7%B3%BB%E7%BB%9F%E9%97%AE%E9%A2%98.md)中所述，在模型和数据不断增长的背景下，单设备的存储和计算能力逐渐无法满足这样的需求。
因此，分布式计算也从传统的高性能计算和大数据计算领域，扩展到深度学习的助力上。
<center><img src="./img/image0.png" width="500" height="" /></center>
<center>图6-0-1: 分布式机器学习训练与算法内容结构 </center>
如上图，本章将会首先简要介绍分布式深度学习计算出现的因由以及相关的并行性理论。
之后我们从算法方面展示不同的分布式策略以及之间的比较。
同样的分布式算法可能对应不同的同步方式，具体会在*深度学习并行训练同步方式*中进行讲述与讨论。
承载算法和通信方式的是分布式训练系统，这里我们会介绍目前流行的训练系统和使用方式。
综上技术，我们在本章中会展示现今技术如何利用分布式计算有效地组织多个计算和通信设备，提供高效的计算能力，从而满足日益增长的深度学习模型应用需求。
# 内容概览
本章包含以下内容：
- [6.1 分布式深度学习计算简介](6.1-分布式深度学习计算简介.md)
- [6.2 分布式训练算法分类](6.2-分布式训练算法分类.md)
- [6.3 深度学习并行训练同步方式](6.3-深度学习并行训练同步方式.md)
- [6.4 分布式训练系统简介](6.4-分布式训练系统简介.md)
- [6.5 分布式训练的通信协调](6.5-分布式训练的通信协调.md)
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<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 7.4 面向深度学习的集群管理系统
之前的章节已经介绍经典的调度算法在运行深度学习作业的集群调度中的应用。但是这些算法本身没有考虑深度学习作业自身的特点，也没有利用 GPU 服务器中 GPU 的拓扑结构等硬件体系结构。
本章将围绕前沿的针对深度学习负载和 GPU 服务器特点而设计的平台调度算法进行介绍，期望让读者了解新负载和硬件的特点和调度管理需求，启发新的工作。
本章节的很多工作目前没有严格定论，还属于研究的前沿，本章内容中更重要的是总结当前负载，硬件和平台本身的新问题和设计动机（Motivation），启发读者未来可以思考新的研究和工程工作。
- [7.4 面向深度学习的集群管理系统](#74-面向深度学习的集群管理系统)
- [7.4.1 深度学习工作负载的需求](#741-深度学习工作负载的需求)
- [思考题](#思考题)
- [7.4.2 异构硬件的多样性](#742-异构硬件的多样性)
- [7.4.3 深度学习平台的管理与运维需求](#743-深度学习平台的管理与运维需求)
- [思考与实验设计](#思考与实验设计)
- [7.4.4 深度学习负载与异构硬件下的调度设计](#744-深度学习负载与异构硬件下的调度设计)
- [7.4.5 开源和云异构集群管理系统简介](#745-开源和云异构集群管理系统简介)
- [7.4.6 部署异构资源集群管理系统实验](#746-部署异构资源集群管理系统实验)
- [7.4.6.1 实验目的](#7461-实验目的)
- [7.4.6.2 实验环境](#7462-实验环境)
- [7.4.6.3 实验原理](#7463-实验原理)
- [7.4.6.4 实验内容](#7464-实验内容)
- [7.4.6.5 实验报告](#7465-实验报告)
- [小结与讨论](#小结与讨论)
- [参考文献](#参考文献)
## 7.4.1 深度学习工作负载的需求
已有的深度学习作业调度工作（例如，[Gandiva OSDI '18](https://dl.acm.org/doi/10.5555/3291168.3291212) [<sup>[2]</sup>](#gandiva)）对当前深度学习作业负载的特点进行了分析与总结。我们使用的集群管理系统管理的大多数作业是深度学习的训练作业而不是推理作业。深度学习训练作业相比传统的数据中心批处理作业（例如，大数据处理和分析作业）有一些新的特点和不同：
“
- 单个深度学习训练作业特点：
- 执行时间长：训练时间持续数小时甚至几天。
- 迭代计算：作业主干部分是迭代的计算，每轮迭代可以切分为小时间窗口的任务。这样让作业本身有机会以更小的任务粒度触发调度和抢占策略。
- 内存数据量动态变化：在训练过程中不同的时间点做检查点有不同的内存数据量，这给做检查点提供了优化机会。如果有检查点的支持，可以让平台或框架本身支持更加激进的调度策略，例如，动态迁移作业，装箱作业到同一块 GPU，时分复用等。
- 性能可预测性：资源消耗可预测性，可以通过运行时监控获取。由于其算法为迭代过程且具有一定的可预测性，让调度器有机会可以根据资源消耗做更加优化的作业放置和装箱。
- 分布式深度学习训练作业特点：
- 对 GPU 拓扑结构敏感：数据并行策略通信传递梯度，模型并行策略通信传递中间结果张量，GPU 与 GPU 之间传输带宽容易形成瓶颈。所以考虑 GPU 亲和性（Affinity）的任务放置策略，对降低分布式训练作业的完工时间有帮助。但会引发新的问题，由于调度是一批 GPU，满足亲和性的同时，可能会产生更多资源碎片。
- 批量深度学习训练作业特点：
- 反馈驱动探索：自动化机器学习场景下，用户会一次性提交大量的深度学习作业。自动机器学习训练作业的一个关键特征是反馈驱动探索。由于深度学习实验固有的反复试验方法，用户通常会尝试多个作业配置（多项工作），并利用这些工作的早期反馈（准确度，误差等）来决定是否优先考虑或终止其中的某些作业。这种有条件的探索，称为超参数搜索或神经网络结构搜索，可以是手动的，也可以是系统自动调度。所以我们经常可以看到集群中有大量相似作业和被提前取消的作业。
”
根据深度学习作业特点，软件栈和硬件栈的支持，框架或平台可以协同设计面向深度学习的作业调度策略，提升资源利用率等指标。
#### 思考题
总结思考深度学习作业和传统操作系统作业以及大数据平台作业的异同点？
## 7.4.2 异构硬件的多样性
深度学习作业训练时主要的计算单元是 GPU，所使用的服务器一般会挂载多块 GPU。相比传统的大数据作业使用的服务器硬件有一些新的特点。GPU 服务器集群运行深度学习问题与挑战：
（1）通信代价：由于多块 GPU 之间的互联方式多样，造成作业的不同放置方式受到 GPU 拓扑结构影响，进而影响数据通信代价，影响性能。GPU 根据一定拓扑挂载在 PCIe 总线或交换机上，GPU 与 GPU 之间通信可能节点内跨越 PCIe，PCIe 交换机，节点之间可能跨越 InfiniBand 或以太网。“距离”最近的 GPU 之间通信代价越低。
（2）资源争用：同时，作业本身由于可能共享服务器，数据总线等资源也受到服务器上同时运行作业的争用和干扰。
拓扑结构与任务的放置会影响多卡与分布式作业的训练性能。
所以针对硬件特点可以设计启发优化策略：考虑集群和服务器节点的 GPU 拓扑结构的亲和性（Affinity）调度。这点和传统 NUMA 架构中考虑[处理器亲和性（Processor Affinity）](https://en.wikipedia.org/wiki/Processor_affinity)的问题与优化有异曲同工之处。我们可以看到，对系统问题，我们可以从传统的操作系统的经典设计中找到设计原则，对新工作形成指导和借鉴。
## 7.4.3 深度学习平台的管理与运维需求
深度学习平台对上管理深度学习模型训练作业，对下管理以 GPU，InfiniBand 为代表的异构硬件，平台管理与运维也遇到了不小的挑战。平台管理员相比机器学习工程师，数据科学家等使用平台的用户更加关注以下的设计目标：
- 效率
- GPU集群价格昂贵，更新换代频繁，如何规划好集群，提升投入产出比，如何在现有集群中，减少资源碎片，提升利用率也有很大的挑战。调度算法在一定程度上能优化和提升集群的资源利用率。
- 公平性
- 目前使用深度学习平台的用户既有工程目的也有很多是科研目的。在训练生产模型的同时，也有一些是研究投稿赶论文截止的需求，造成相比传统批处理调度场景，用户有了类似特定时段的峰值资源使用需求。平台需要保证各组资源使用的公平性，同时提前规划好用户的资源使用同时兼顾峰值利用需求，需要管理员设计好相应的策略。
- 稳定性
- 由于深度学习框架的设计者在初始没有像大数据社区一样把容错当成第一要义，框架提供基础的检查点机制，但是需要用户控制，没有自动备份与恢复的支持，在之后的设计版本和社区工具中才有弹性等功能的支持。对底层平台来说造成比较大的运维负担。
- 由于节点上的异构硬件也有一定概率产生硬件问题（例如，[GPU 故障（Failure）](https://ieeexplore.ieee.org/abstract/document/7056044)）[<sup>[1]</sup>](#failure)，造成平台稳定性的挑战。如何高效，敏捷的发现和修复故障，除了工具的支持，还需要系统化的系统设计，开发流程设计与管理策略设计共同作用。
- 可维护性
- 平台团队同时在开发和运维平台，可维护性也是平时减少运维负担的一个重要考虑的因素。通过微服务等手段(回顾操作系统[微内核](https://en.wikipedia.org/wiki/Microkernel)的设计思想)将功能模块尽可能的拆分，能够让故障的定位与修复最小化，同时良好的 DevOps 流程搭建，敏捷的开发与项目管理也为平台的可维护性提升起到关键的作用。
- 用户体验
- 用户体验良好并统一的作业提交，作业管理与调试工具，能大幅提升用户的开发生产力，同时也能减轻平台运维工程师的负担。
除了以上指标，平台也会关注性能（吞吐，完工时间等）指标。综上所述我们看到，平台本身模块众多，涉及的外部交互的软硬件多样，使用和维护的用户也很多，所以其面对的问题场景较为复杂，作为平台设计者和使用者需要通盘考量，性能只其中一个环节，我们还要以系统化的视角去设计和管理整个异构资源，为上层应用负载与用户提供更加透明与便捷的用户体验。
### 思考与实验设计
思考，如果需要兼顾以上指标，新一代深度学习调度与平台设计应该朝着哪个方向设计与发展？
请读者设计算法或策略，保证公平性的同时，最大限度提升集群效率，可以上一小节的日志痕迹（Trace）进行实验设计与算法验证。
## 7.4.4 深度学习负载与异构硬件下的调度设计
接下来，我们首先将从深度学习平台的调度算法入手，将介绍考虑不同设计目标和侧重点的调度算法设计。这些调度器由于设计目标不同，且基于能获取信息的 ***假设*** 也不同，同时实现和对作业的入侵性也不同，读者在选用和设计调度器时，需要考虑不同算法的优劣势并根据平台现状酌情选择。
我们总结当前有以下几类的调度器设计思路：
1. 兼顾新负载特点扩展经典调度器的设计
2. 框架与平台协同设计的调度器设计
3. 历史作业数据驱动的调度器设计
4. 面向特定场景问题（多租）的调度器设计
- ***兼顾新负载特点扩展经典调度器的设计***
本小节介绍的调度算法有以下特点：
- 基于经典集群调度算法和调度器
- 根据新负载特点（例如，GPU亲和性）进行策略扩展
深度学习系统社区有很多有大数据系统和平台背景的工程师，同时深度学习训练负载从作业性质上可以归纳为[批处理作业（Batch Job）](https://research.google/pubs/pub43438/)，那么以往成熟的大数据批处理作业集群管理系统 [YARN](https://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html) 中的调度器，无疑在平台设计初期是一个较好的入手点和起点，因为起经过大规模的生产环境检验，提供容量调度，虚拟集群等机制的支持，社区活跃，应用范围广。所以有些工作 [YARN-CS](https://dl.acm.org/doi/10.5555/3358807.3358888))[<sup>[5]</sup>](#yarncs) 和开源系统 [OpenPAI](https://github.com/microsoft/pai) 的初期采用从经典调度器的基础上进行扩展深度学习负载调度特点功能支持的思路进行设计。[YARN-CS](https://dl.acm.org/doi/10.5555/3358807.3358888) 研究作业在不同 GPU 拓扑下的性能影响，进而通过分析作业调度日志，证明分布式作业尽可能考虑局部性（Locality），将作业内的任务调度到“离得近”且通信代价小的一批 GPU 和节点上，能够降低作业的完工时间。同时其还通过观察和实验发现，过于追求严格的局部性，会造成较长的排队延迟（Queuing Delay），其称为碎片延迟（Fragmentation Delay），适当放松局部性约束是有意义的，随着时间的推移，以减轻分布式作业的排队延迟。但是容量调度产生的为公平性共享（Fare-Share）产生的排队延迟（这种延迟一定程度可以通过抢占式调度降低），机器学习任务的群调度和局部性需求会产生碎片延迟（Fragmentation Delay），这些洞察也启发之后会介绍的 HiveD 等延续性工作。
- ***框架与平台协同设计的调度器设计***
本小节介绍的调度器有以下特点：
- 入侵性强，需要框架与平台协同设计
- 利用深度学习作业特点和运行时性能数据
- 以降低作业延迟和提升效率为目标
[Gandiva OSDI '18](https://dl.acm.org/doi/10.5555/3291168.3291212) [<sup>[2]</sup>](#gandiva)根据深度学习作业特点（早反馈，分布式作业对拓扑敏感等），以及硬件和操作系统对 GPU 计算的进程上下文切换支持的不足进行弥补，通过框架和平台协同设计进而提升整体集群资源利用率。但是其假设是框架提供支持基本检查点和迁移的功能原语，所以具体实现需要入侵性的进行框架的修改。
Gandiva 设计了两种模式：
一种是 ***反应模式（Reactive Mode）*** ：
类似传统调度器事件驱动的设计，根据不同事件和状态（作业到达（Arrivals）, 离开（Departures）, 失效（Failures））触发调度策略，可以抽象理解其整体策略为一个状态机（State Machine）。
<center> <img src="./img/4/affinity.png" /></center>
<center>图 7.4.1 分布式作业调度受局部性影响 (<a href="https://www.usenix.org/sites/default/files/conference/protected-files/osdi20_slides_zhao.pdf">图片引用 HiveD OSDI '20</a>)</center>
通过图中可以看到：“将同样需要 2 块 GPU 卡的作业分别调度在相同 PCIe 交换机（Switch），跨交换机和跨节点下进行部署运行，会产生 40% ~ 5x 的降速。所以对于多卡作业，考虑部署的局部性，通过亲和性调度，可以让作业执行更快，节省更多的资源执行其他作业，进而对整体完工时间受益，进而提升资源利用率。”
当触发调度时，其使用考虑亲和性（Affinity）的调度策略：
在调度过程中按以下优先级考虑和排序节点进行作业分配，这样能够更多的考虑 GPU 的亲和性和拓扑结构，让深度学习作业减少数据 I/O 的开销，提升性能。
其优先考虑的待分配节点优先级为：
“
1. 拥有相同亲和性的节点。
2. 还未标注亲和性的节点。
3. 有不同亲和性的节点。
4. 进行超额订阅（Oversubscription），在有相同亲和性的节点暂停和恢复其他作业。
5. 不满足之前条件，则作业排队等待。
”
例如，如图 7.4.2 所示：“调度器将需要 1 个 GPU 的作业放在一起，但需要 2 或 4 个 GPU 的作业放置在不同的服务器上。此外，我们可以通过选择负载最小的服务器，试图平衡每台服务器上的超额订阅（Over-Suscription）负载（例如，图中防止 1 个 GPU 需求作业的服务器中，各有 6 个 1 个 GPU 需求的作业）。”
<center> <img src="./img/4/7-4-6-gandivaplacement.png" ch="500" width="700" height="400" /></center>
<center>图 7.4.2 16 块 GPU 集群中，Gandiva 调度实例 (<a href="https://www.usenix.org/system/files/osdi18-xiao.pdf">图片引用 Gandiva OSDI '18</a>)</center>
另一种是 ***内省模式（Introspective Mode）*** ：
这种模型应用在作业执行后，持续监控并定期优化当前作业的放置 (Placement)，同时通过扩展框架支持细粒度的检查点和恢复功能，对后续备份与迁移策略提供基础原语的支持。通过不断监控作业利用率和节点资源利用率，不断进行作业的，装箱（Bin Packing），迁移（Migration），增长收缩（Grow-Shrink），超额订阅和时间切片（Time Slicing），进而提升整体资源利用率，降低作业的完工时间（Makespan）。
- 装箱 (Bin Packing）：在保证 GPU 显存约束的情况下，根据浮点运算量，将更多的作业装箱到相同 GPU，提升资源利用率。
- 时分复用（Time Slicing）：利用框架层或底层实现的检查点和恢复机制，多个作业可以通过时分复用，共享单块GPU。我们可以类比于一种粗粒度的进程的上下文切换（Context Switching）机制。
- 迁移 (Migration）：利用框架层或底层实现的检查点和恢复机制，当有空闲资源或奖励资源，动态迁移作业使用奖励资源，加速训练。当作业需要被抢占以归还资源，迁移作业保证作业之前训练不失效。
图 7.4.3 展示了集群实验的一个示例。“当多作业调度执行的场景，其中有 4 个均需要 2 块 GPU 的作业，这些作业都已经被调度，但是其中 3 个作业都没有好的亲和性（J1，J2 和 J3），只有 J0 的 GPU 被打包分配到了相同的节点。
3 分钟后，一个使用 DeepSpeed 框架训练的作业（图中绿色圆圈表示）训练完成并释放 8 块 GPU，其中 3 块在图中以绿色圆圈表示，并分布在不同服务器，这三块 GPU 有潜力提升当前多个作业的训练效率。Gandiva 调度器因此启动了迁移流程，重新分配 J1，J2 和 J3 到放置在一起的 GPU。为了减少碎片，我们选择迁移空闲GPU最多的服务器上的作业进行迁移。之后开始迁移运行的作业从当前服务器（空闲 GPU 更多的）到另一个服务器（空闲 GPU 更少的），进而同作业的任务可以执行在相同服务器的 GPU。Gandiva 不断重复这个过程，直到非空闲服务器上的空闲GPU数量小于一定阈值（实验中使用 3/4 作为阈值），或者直到没有作业能够受益于作业迁移。”
<center> <img src="./img/4/7-4-7-jobmitivationgandiva.png" ch="500" width="700" height="300" /></center>
<center>图 7.4.3 共享资源集群中，Gandiva 进行作业迁移实例 (<a href="https://www.usenix.org/system/files/osdi18-xiao.pdf">图片引用 Gandiva OSDI '18</a>)</center>
Gandiva[<sup>[2]</sup>](#gandiva) 的设计方法能大幅度降低作业执行延迟和提升资源利用率，但需要结合其他调度策略才能兼顾公平性等目标，同时需要框架和平台提供功能支持。同时也存在很多其他挑战，例如：需要能够监控深度学习作业负载信息，同时越来越多样的资源使用状况（例如，动态性的模型训练）的深度学习作业会产生新的挑战。
Gandiva[<sup>[2]</sup>](#gandiva) 本身需要框架或硬件提供检查点（Checkpoint）和实时迁移 (Live Migrtation)的支持，这点假设对现有平台挑战较大，因为框架与平台本身是分离的软件。需要框架和平台协同设计，对一些公司或平台两者为分离团队，协同开发和维护也有新的挑战。
利用运行时信息反馈和框架与平台协同设计的调度器工作还有：
- 基于在线（Online）作业信息反馈，进行调度算法优化设计的调度器 [Optimus EuroSys '18](https://dl.acm.org/doi/10.1145/3190508.3190517)[<sup>[3]</sup>](#optimus) 等。
- 需要框架和平台协同设计提供支持的调度器还有 [AntMan OSDI '20](https://dl.acm.org/doi/abs/10.5555/3488766.3488796)[<sup>[4]</sup>](#antman) 等。
但是请读者思考，当前这类调度器本质依赖框架协同，本质属于框架与平台的协同设计（Co-Design）的产物，在很多假设或者条件不能满足的情况下，很难实现或部署。例如，当前框架多样，如果平台本身难以限定用户使用唯一的框架进行模型训练，以上的功能也就难以完全落地与应用。
***经典回顾***
同时我们也可以看到数据驱动（Data-Driven）的设计思路和检查点与迁移等经典机制再次在新的场景下的应用与创新，数据驱动的内省模式是经典的系统优化策略，和 JIT（Just-In-Time）优化利用运行时信息进行优化有异曲同工之妙，同时检查点和实时迁移是操作系统中进程，线程上下文切换的经典设计，其在提升资源复用的设计目标上是基础机制。
[处理器亲和性（Processor Affinity）](https://en.wikipedia.org/wiki/Processor_affinity)：“处理器亲和性可以将进程或线程绑定和解除绑定到中央处理单元 (CPU) 或一系列 CPU。传统的处理器亲和性更多是缓存亲和性减少缓存失效，上面介绍的工作中更多的是通过亲和性减少通信 I/O，这点更像 NUMA 中考虑的亲和性问题，也像之前介绍的数据读写的局部性（Locality）原则所要解决的问题，减少数据跨节点搬运。”
[时间片（Time Slice）](https://en.wikipedia.org/wiki/Preemption_(computing)#Time_slice)：调度中断以允许操作系统内核在它们的时间片到期时在进程之间切换，从而有效地允许处理器的时间在多个任务之间共享。在上面的工作实现中需要模拟操作系统的中断机制，通过信号控制进程。
[进程迁移（Process Migration）](https://en.wikipedia.org/wiki/Process_migration)：在计算中，进程迁移是一种特殊的操作系统进程管理形式，通过这种形式，进程从一个计算环境移动到另一个计算环境。进程迁移作为进程调度的标准部分发生，并且在给定机器内迁移进程非常容易，因为大多数资源（内存、文件、套接字）并不需要改变的只需要执行上下文（主要是程序计数器和寄存器）切换。所以在上面的工作实现中需要模拟操作系统的上下文切换，通过迁移进程“上下文”，进而在其他节点恢复进程。
- ***历史作业数据驱动的调度器设计***
本小节介绍的调度算法有以下特点和假设：
- 基于历史作业数据
- 目标：降低完工时间和提升资源利用率
[Tiresias NSDI '19](https://dl.acm.org/doi/10.5555/3323234.3323274)[<sup>[6]</sup>](#tiresias) 调度器：“对集群历史作业的资源分配和完工时间日志痕迹(Log Trace)进行统计分析，通过数据驱动的设计思路，预估作业执行时间，并利用这些信息指导调度，优先调度短时作业，降低作业的完工时间。并借鉴经典的基廷斯系数（[Gittins index](https://en.wikipedia.org/wiki/Gittins_index)）等理论进行优化问题的抽象与策略设计，优先调度大量短作业，提升整体完工时间。另外同时对分布式深度学习作业进行基准测试，启发对亲和性敏感的负载进行策略设计，提升利用率，同时也抛出亲和性影响排队时间的伏笔，为后续 HiveD 等的研究工作铺垫问题。同时其在论文中也提供了表 7.4.1 中调度器的比较。相比于基于 Apache YARN 的容量调度程序（YARN-CS）和 Gandiva，Tiresias 旨在最小化平均作业完成时间(Job Completion Times)。与 Optimus 不同，Tiresias 可以有效地安排工作利用或不利用部分先验知识（表 7.4.1）。此外，Tiresias 可以根据 Tiresias 分析器自动捕获的拓扑结构，智能放置分布式深度学习作业。”
| |YARN-CS|Gandiva|Optimus|Tiresias（Gittins index）|Tiresias（LAS）|
|------|------|------|------|------|------|
|先验知识（Prior Knowledge） |None |None| 完工时间（JCT）预测| 完工时间（JCT）分布 |None|
|调度算法（Scheduling Algorithm） |先进先出（FIFO） |分时（Time-sharing） |剩余时间驱动（Remaining-time-driven）| 吉廷斯指数（Gittins index） |最少获得服务（LAS）
|调度输入（Scheduling Input） |到达时间（Arrival time） |N/A| 剩余时间（Remaining Time） |获得服务（Attained service），例如空间和时间维度 |获得服务（Attained service）
|调度维度（Schedule Dimensions） |时间的（Temporal），执行多久| None| 时间的（Temporal） |空间的（Spatial） & 时间的（temporal）| 空间的（Spatial），需要多少GPU & 时间的（temporal）
|调度优先级（Job Priority） |连续的（Continuous） |连续的（Continuous）|连续的（Continuous）|离散队列（Discretized queues）| 离散队列（Discretized queues）
|作业强占（Job Preemption） |N/A| 上下文切换（Context switch）|模型检查点（Model checkpoint）| 模型检查点（Model checkpoint）| 模型检查点（Model checkpoint）|
|最小化平均作业完工时间（Minimizing Average JCT） |No| No| Yes| Yes |Yes
|饥饿避免（Starvation Avoidance） |N/A |N/A |动态资源（Dynamic resource） |如达到饥饿阈值，提升到最高优先级Q1 |如达到饥饿阈值，提升到最高优先级Q1|
|作业放置（Job Placement） |合并（Consolidation） |试错（Trial-and-error）|基于容量（ Capacity-based） |基于观测的（Profile-based） |基于观测的（Profile-based）
<center>表 7.4.1 Tiresias 对深度学习调度器比较总结 (<a href="https://www.usenix.org/system/files/nsdi19-gu.pdf">表格引用 Tiresias NSDI '19</a>)</center>
此类工作在实施过程中假设集群已经积累了大量的历史作业，且历史作业模式较为稳定（如果变化较快需要持续更新），不适合领启动集群或作业模型更新迭代较快的集群。其工作负载驱动的调度思想相比 Gandiva 运行时监控测试运行来说，在作业调度之前为调度器获取了更多信息，进而可以求解信息更全面的优化问题。
- ***面向特定场景问题（多租）的调度器设计***
在本小节介绍的调度算法有以下特点：
- 面向多租虚拟集群环境
- 优化目标：降低由于亲和性调度造成排队延迟问题和资源分配碎片问题
<center> <img src="./img/4/queuedelay.png" ch="500" width="1000" height="300" /></center>
<center>图 7.4.4 排队延迟问题 (<a href="https://www.usenix.org/sites/default/files/conference/protected-files/osdi20_slides_zhao.pdf">图片引用 OSDI '20</a>)</center>
如图所示：“图 7.4.4 展示 2 个月的日志数据，2232 个 GPU 的集群，11 个租户的情况下的私有（Private）集群，共享多租(Shared）集群和 HiveD 优化后的共享多租集群情况，作业的排队时间延迟问题，其中红色的线中我们观察到，平均有 7 倍的延迟由于多租环境下考虑要求作业满足节点亲和性硬约束（尽可能将作业调度到通信距离更近的节点）造成作业延迟调度。”
[HiveD OSDI '20](https://dl.acm.org/doi/10.5555/3488766.3488795)[<sup>[7]</sup>](#hived)：“提出如果假设调度深度学习作业同时考虑多租环境，不同租户（Tenants）之间会配置不同的资源配额，以配额被分配相应的资源，但是如果考虑 GPU 亲和性的调度策略，会尽可能将亲和性高的资源整体分配给作业，这就会造成集群调度容易出现排有些作业队延迟较高的异常。HiveD 通过设计多级单元格（Cell）结构，设计了伙伴单元格分配（Buddy Cell Allocation）算法去保证在之前的约束下，资源能够高效分配，降低排队时间和资源碎片。同时HiveD能够和现有的调度器进行较好的集成。”
HiveD 考虑到兼容性设计，能够与其他调度器兼容的集成使用。图 7.4.4 展示了一个示例:“其中有 4 个级别的单元格结构（Cell Structure）：GPU (Level-1)、PCIe 交换机（Switch）(Level-2)、CPU 套接字（Socket）（Level-3）和节点级别（Level-4）。当前实例集群有一个机架，由四个 8-GPU 节点组成，由三个租户 A、B 和 C 共享。每个租户的单元格资源分配和租户的虚拟集群（VC）情况总结在图 3 的表格中。租户 A 和 B 的 VC 都保留了一个 3 级（Level）单元格（4 个 GPU 在同一个 CPU 插槽），一个2级单元格（2 个 GPU 在同一个 PCIe 交换机）和一个 1 级单元格（单个 GPU）。租户 C 是一个更大的租户，它保留了两个 4 级单元格（节点级别）和一个 2 级单元格。给定图 7-4-3 中定义的 VC 视图，HiveD 可以采用第三方调度器（例如，7.3 和 7.4 中介绍的调度器）来在当前租户分配的资源视图下继续进行租户内的作业资源调度。从第三方调度器的角度来看，VC 视图和私有集群没有区别，由不同大小的节点组成（即不同级别单元格）。例如，调度程序可以将租户 C 视为具有两个 8-GPU 节点和一个 2-GPU 节点的私有集群，尽管 2-GPU 节点实际上是一个 2 级单元。请注意，第三方调度程序可以使用任何 GPU 分配的单元格。例如，它可以调度两个 2-GPU 作业到一个 4-GPU (level-3) 单元：一个单元格是资源的粒度在 VC 和物理集群中保留，但不是必须第三方调度器进行作业调度的粒度。”
“在单元层次结构中，第 k 级单元 c 由一个 S 集合其中包含一组第 k-1 级单元格构成。S 中的单元格称为伙伴单元格（Buddy Cells）。伙伴单元格可以合并到更高级别的单元格中（例如第 k 级）。我们假设单元格展示了分层统一的可组合性：(1) 在满足租户方面，所有 k 级单元都是等效的，请求第 k 级单元，并且 (2) 所有第 k 级单元可以被拆分到相同数量的第 k - 1 级单元格。”
<center> <img src="./img/4/7-4-5-hivedmotivatingexample.png" ch="500" width="700" height="800" /></center>
<center>图 7.4.5 HiveD机架的多级单元分配示例。 (<a href="https://www.usenix.org/system/files/osdi20-zhao_hanyu.pdf">图片引用 HiveD OSDI '20</a>)</center>
HiveD 假设平台已经提供多租虚拟集群机制，以及能在平台获取到 GPU 拓扑，同时多卡分布式作业多，有需求考虑作业的 GPU 亲和性问题。
由于篇幅所限，我们暂不列出调度算法细节，而主要介绍其相比其他算法的较大不同点，动机，和方法设计和启发实例，读者如果对 HiveD 的调度算法策略感兴趣，可以参考其[ HiveD OSDI '20 论文](https://github.com/microsoft/hivedscheduler)。或使用和测试其已[开源的HiveD调度器](https://github.com/microsoft/hivedscheduler)。
***经典回顾***
同时我们也可以看到经典的系统算法再次在新的场景下的应用与创新，伙伴系统是资源分配算法中常常使用的机制用于减少资源碎片的发生，常常用于内存分配器或资源调度器中。例如，经典的 [Buddy memory allocation](https://en.wikipedia.org/wiki/Buddy_memory_allocation)：“伙伴内存分配技术是一种内存分配算法，它将内存划分为多个分区，以尽可能地满足内存请求。该系统利用将内存不断分成两半来尝试提供最佳匹配的块尺寸，同时内存回收时有利于相邻内存块合并。伙伴系统优势是，易于实现，很容易合并相邻的孔（Holes）减少外碎片（External Fragmentation），快速分配内存和释放内存。劣势是，它要求所有的分配单元都是 2 的幂，容易导致内部碎片化（Internal Fragmentation）。”
***调度问题的约束***
通过以上调度经典算法的脉络，我们可以看到，在深度学习集群调度问题中充满了不同的设计目标的权衡和需要满足的约束，之前我们已经总结过调度算法的设计目标，接下来我们可以总结调度问题设计过程中常常可以追加和需要满足的硬约束（Hard constraint）和软约束（Soft constraint）。
- 配额（Quota）约束：虚拟集群等多租环境有严格的配额约束保证公平性。保证作业的 GPU，GPU 内存，CPU，主存等资源需要有空闲资源保证能分配给作业使用。此类约束一般可以设计为硬约束（Hard Constraint），需要必须满足。但是可以通过抢占等底层机制的支持，适当放松。
- 最小资源保证约束：容量调度等场景有虚拟集群的最小资源佩配额约束保证公平性。此类约束一般可以设计为硬约束（Hard Constraint），需要必须满足。
- 资源局部性（Locality）：GPU 亲和性约束有助于降低分布式或多卡训练作业的通信开销，降低完工时间。此类约束一般可以设计为软约束（Soft Constraint），不需要必须满足。
- 完工时间约束：例如，保证排队时间低于一定条件，对排队较久或执行时间更短的作业优先调度。此类约束一般可以认为是软约束（Soft Constraint）。
随着新技术的发展，还会有新的调度算法设计和产生，最新的深度学习集群调度算法研究工作，读者可以关注 OSDI，SOSP，ATC，NSDI，SoCC 等计算机系统与网络会议的最新进展。
总结起来，调度器的算法求解的是基于不同的可获取（例如，在线，离线获取）的作业（时间）和硬件拓扑（空间）上下文信息假设，针对优化目标（例如，完工时间，平均完工时间等），满足特定的约束（例如，局部性）的作业资源分配的多约束优化问题。在历史的长河中，涌现出非常多的经典算法设计，同时随着深度学习的发展，还会不断涌现新的调度算法设计，感兴趣的读者可以思考，应用和研究相关工作，为未来平台管理打下坚实基础。
## 7.4.5 开源和云异构集群管理系统简介
本小节我们将介绍代表性的开源和企业内部部署的大规模异构集群管理系统。基于开源系统，企业可以部署和构建自己的平台，提升资源管理能力，资源利用率和开发效率。参考已经发表的大规模异构集群管理系统文献，企业可以较早的规划和采取最佳实践策略，将未来可预见的问题较早规避。
- 开源中立人工智能平台-[OpenPAI](https://github.com/microsoft/pai)
开源的中立人工智能平台相比其他场景除公有的挑战和功能需求，其还需要保证：
（1）组件化设计，支持用户部署需求与定制二次开发，替换其中组件（类似 Micro-Kernel 的设计思想）。
（2）尽可能利用社区可用的最佳开源系统模块，不绑定特定开源系统或社区。
（3）尽可能的全面功能支持，让用户开箱即用。
<center> <img src="./img/4/pai.png" ch="500" width="500" height="600" /></center>
<center>图 7.4.6 OpenPAI架构图 (<a href="https://github.com/microsoft/pai">图片引用 OpenPAI 文档</a>)</center>
OpenPAI 是由微软亚洲研究院系统组和微软（亚洲）互联网工程院联合研发的，支持多种深度学习、机器学习及大数据任务，可提供大规模 GPU 集群调度、集群监控、任务监控、分布式存储等功能，且用户界面友好，易于操作。OpenPAI 正在转向更健壮、更强大和更轻量级的架构。OpenPAI 还提供了许多 AI 用户友好的功能，使最终用户和管理员更容易完成日常的人工智能作业。OpenPAI 通过统一的框架控制器（Framework Controller），类似 YARN 提供统一的 AppMaster，提供对各个深度学习或大数据框架的作业部署，监控，重试（Retry）的支持。同时提供丰富的运行时信息监控，服务状态监控，调试（例如，远程SSH）等功能支持，功能和组件较为丰富。同时还提供应用市场，插件化支持和扩展平台上可以运行的应用。
- 基于 Kubernetes 社区原生开源平台-[Kubeflow](https://www.kubeflow.org/)
原生的平台相比其他场景除公有的挑战和功能需求，其还需要保证：
（1）对 Kuberenetes 的兼容性支持，生态绑定和新特性的支持。
（2）尽可能利用社区的原生组件进行组合。
（3）开源系统更完善的社区支持，培训，用户培育与二次开发支持。
Kubeflow 是由 Google 开源的平台项目，该项目致力于使机器学习工作流在 Kubernetes 上的部署变得简单、便携和可扩展。由于 Kubeflow 是 Kubernetes 社区原生支持，一经推出，社区发展就非常迅速，扩展组件（例如，调度，工作流管理与可视化等）发展迅速。Kubeflow 的设计目标不是重新创建其他服务，而是提供一种直接的方法，将用于机器学习和深度学习的同类最佳开源系统部署到不同的基础设施。无论用户在何处运行 Kubernetes，都可以运行 Kubeflow。Kubeflow 通过定制化各个框架的 Operator，提供对各个深度学习或大数据框架的自动部署，监控，重试（Retry）的支持，但是区别是，没有像 YARN 一样提供统一的 AppMaster，需要各个框架依赖社区构建，为之后的维护和功能支持会增加额外负担。
- 企业内面向第一方（First-party）用户的平台-[Philly](https://dl.acm.org/doi/10.5555/3358807.3358888)
面向第一方（First-party）的平台相比第三方平台，其还需要保证：
（1）定制化团队的极致性能支持。头部公司更大规模的场景支持。
（2）较好的资源利用与多路复用（Multiplexing）需求。
（3）根据团队需求特点的硬件与规格规划，支持定制化改造与功能提供。对内部框架的更好的支撑。
Philly 是微软内部使用的大规模深度学习训练作业平台，Philly 旨在支持执行有监督式机器学习的训练工作负载。这包括培训来自开发产品的生产团队的工作，这些产品使用用于图像分类、语音识别等的模型。有相关研究工作对 Philly 上的[资源分配，深度学习作业性能特点](https://dl.acm.org/doi/10.5555/3358807.3358888)[<sup>[5]</sup>](#yarncs)和[深度学习程序缺陷](https://dl.acm.org/doi/10.1145/3377811.3380362)[<sup>[8]</sup>](#programfail)进行研究，从中我们可以观察和了解大规模生产环境中作业资源争用，资源利用率，调度策略设计和程序缺陷等问题及启发相关新的研究工作。
- 公有云面向第三方（Third-party）人工智能平台服务-[Singularity](https://arxiv.org/abs/2202.07848)[<sup>[9]</sup>](#singularity)
公有云面向第三方（Third-party）的平台相比其他场景除公有的挑战和功能需求，其还需要保证：（1）更严格认证与权限管理。（2）更严格的隔离性保证。（3）不同地理位置的客户部署需求。（4）出于更低价格的竞争优势，底层有高效的资源利用与多路复用（Multiplexing）。（5）为满足服务等级协议的稳定性和可用性支持。
Singularity 是微软 Azure 提供的大规模云端 AI 平台服务，“Singularity 旨在支持调度并执行跨数据中心，抢占式和弹性调度的深度学习作业。Singularity 的核心是一种工作负载感知的调度程序，它可以透明地抢占和弹性扩展深度学习工作负载，以在不影响其正确性或性能的情况下在全球范围内的 AI 加速器（例如 GPU、FPGA）中提高利用率。默认情况下，Singularity 中的所有作业都是可抢占、可迁移和动态调整大小（具备弹性）。作业可以动态且透明地 (a) 抢占并迁移到一组不同的节点、集群、数据中心或区域，并准确地从执行被抢占的点，以及 (b) 在给定类型的一组不同的加速器上调整大小（即弹性地放大/缩小）。Singlularity 通过底层实现设备代理（Device Proxy）机制，通过在驱动层实现透明的检查点（Checkpointing）功能支持，进而支持作业的抢占，迁移与弹性调整。从这里我们可以看到很多高层应用的功能十分依赖底层基础机制的支持。”相较于 Gandiva 对 GPU 备份在框架层设计检查点，Singularity 在内核驱动层面设计检查点，可以做到透明模型检查点和弹性作业的支持，但是难以获取上层语义，可能潜在产生冗余备份。
同性质的公有云代表性人工智能平台还有，亚马逊 AWS SageMaker，阿里云 PAI 等。
在工业界，有大规模人工智能应用场景的公司，都需要购买并部署大规模异构资源管理平台，并进行定制化的功能开发与支持，感兴趣的读者可以关注开源和业界的公开分享，并不断设计和重构公司的平台系统，让平台更加稳定与高效。
面向深度学习的集群管理系统目前仍是学术界和工业界的前沿和重要的研究和工程实践的方向，仍在不断发展和迭代，以上内容很多经典问题场景已经有很多共识，但仍有很多问题各家机构呈现出不同的方案，解决方法仍在演化。请感兴趣的读者或者从业者密切关注社区的发展与前沿，一起推动人工智能领域的集群资源管理不断朝着更加成熟，稳定与高效发展。
## 7.4.6 部署异构资源集群管理系统实验
请大家参考 [AI-System Lab6](https://github.com/microsoft/AI-System/tree/main/Labs/AdvancedLabs/Lab6) 进行集群管理系统 OpenPAI 的部署练习。
大家通过当前实例可以练习和感受以下任务：
- 部署异构资源集群管理系统
- 监控异构资源集群管理系统
- 提交作业与监控作业
- 调整调度器配置，观察调度器的影响
### 7.4.6.1 实验目的
以 [Microsoft Open Platform for AI (OpenPAI)](https://github.com/microsoft/pai) 为例，学习搭建并使用面向深度学习的异构计算集群调度与资源管理系统。
### 7.4.6.2 实验环境
本实验为分组实验，3~4 位同学一组，实验内容略有差别， 实验流程中将以 Alice, Bob, Carol, Dan 指代（3人组请忽略 Dan），每位同学的环境均为：
* Ubuntu 18.04 LTS
* NVIDIA GPU (已装好驱动)
* Docker Engine
* nvidia-container-runtime
* ssh and sshd
* [OpenPAI v1.2.0](https://github.com/microsoft/pai/releases/tag/v1.2.0)
### 7.4.6.3 实验原理
1. 深度学习集群管理系统
OpenPAI（如上面介绍的架构图）本身是基于 Kubernetes 的深度学习集群管理系统，在其基础上构建针对深度学习作业的调度器，作业框架控制器，监控与报警平台，运行时等。Kubernetes 是一个可移植的、可扩展的开源平台，用于管理容器化的工作负载和服务，可促进声明式配置和自动化。一个 Kubernetes 集群由一组被称作节点的机器组成。这些节点上运行 Kubernetes 所管理的容器化应用，集群具有至少一个主节点和至少一个工作节点。
***主节点*** 管理集群中的工作节点和 Pod，通常运行控制组件，对集群做出全局决策（比如调度），以及检测和响应集群事件，主要包括以下组件（更详细的功能介绍请参考[官方文档](https://kubernetes.io/zh/docs/concepts/overview/components/)）：
- kube-apiserver
主节点上负责提供 Kubernetes API 服务的组件。
- etcd
etcd 是兼具一致性和高可用性的键值数据库，可以作为保存 Kubernetes 所有集群数据的后台数据库。
- kube-scheduler
主节点上的组件，该组件监视那些新创建的未指定运行节点的 Pod，并选择节点让 Pod 在上面运行。
- kube-controller-manager
在主节点上运行控制器的组件。
- cloud-controller-manager
与 kube-controller-manager 类似，cloud-controller-manager 将嵌入特定云平台的控制逻辑，在主节点上运行的云服务器控制器的组件。
***工作节点*** 托管作为应用程序组件的 Pod，维护运行的 Pod 并提供 Kubernetes 运行环境，主要包括以下组件：
- kubelet
kubelet 是一个在集群中每个节点上运行的代理，保证容器都运行在 Pod 中。
- kube-proxy
kube-proxy 是集群中每个节点上运行的网络代理，维护节点上的网络规则。
- Container Runtime
容器运行环境是负责运行容器的软件，例如 Docker.
<center> <img src="./img/4/7-4-4-components-of-kubernetes.svg" ch="500" width="700" height="500" /></center>
<center>图 7.4.7 Kuberenetes控制平面组件（Control Plane Components）
(<a href="https://kubernetes.io/zh/docs/concepts/overview/components">图片引用 Kubernetes 文档</a>)</center>
2. [HiveD](https://www.usenix.org/system/files/osdi20-zhao_hanyu.pdf) 调度器（Scheduler）与调度算法（Scheduling Algorithm）
HiveD 调度器（Scheduler） 是一个适用于多租户 GPU 集群的 Kubernetes 调度器扩展（Scheduler Extender）. 多租户 GPU 群集假定多个租户（团队）在单个物理集群（Physical Cluster）中共享同一 GPU 池，并为每个租户提供一些资源保证。HiveD 将每个租户创建一个虚拟集群（Virtual Cluster），以便每个租户可以像使用私有群集一样使用自己的虚拟集群 VC，同时还可以较低优先级地使用其他租户 VC 的空闲资源。
HiveD 为 VC 提供资源保证，不仅是资源的数量保证，还提供资源拓扑结构的保证。例如，传统的调度算法可以确保 VC 使用 8 块 GPU，但是它不知道这 8 块 GPU 的拓扑结构，即使在其 VC 仍有 8 个空闲 GPU 的情况下也可能因为这些 GPU 在不同的机器上，无法分配在单个机器上运行的 8 卡训练任务。HiveD 可以为 VC 提供 GPU 拓扑结构的保证，例如保证 VC 可以使用在同一个机器上的 8 块 GPU.
HiveD 通过 单元格（Cell） 单元来分配资源，一个 单元格（Cell） 单元包含用户自定义的资源数量和硬件的拓扑结构信息。例如用户可以定义一个包含 8 GPU 的节点，并把一个这样的 单元格（Cell） 分配给 VC，这样 HiveD 可以保证该 VC 一定有一个可分配的 8 GPU 机器，不管其它 VC 的资源分配情况怎样。HiveD 支持灵活的（单元格（Cell） 单元定义，来保证细粒度的资源分配。例如，用户可以针对不同的 AI 硬件（例如 NVIDIA V100, AMD MI50, Google Cloud TPU v3）或网络配置（例如 InfiniBand）在多个拓扑层级（例如 PCIe switch, NUMA）定义 单元格（Cell） 单元。VC 可以包含各种层级的 单元格（Cell） 单元，HiveD 可以保证所有 单元格（Cell） 单元的资源。
### 7.4.6.4 实验内容
***实验流程图***
<center> <img src="./img/6/Lab6-flow.png" /></center>
<center>图 7.4.8 实验流程图(<a href="">图片来源</a>)</center>
***具体步骤***
1. 安装环境依赖
（以下步骤在 Alice, Bob, Carol, Dan 的机器上执行）
1. 安装 Docker Engine
参照 [Docker Engine 文档](https://docs.docker.com/engine/install/ubuntu/) 在 Ubuntu 上安装 Docker Engine.
2. 安装NVIDIA容器运行时 nvidia-container-runtime
参照 [Installation 文档](https://github.com/NVIDIA/nvidia-container-runtime#installation) 在 Ubuntu 上安装 nvidia-container-time
参照[文档](https://github.com/NVIDIA/nvidia-container-runtime#daemon-configuration-file) 修改 Docker daemon 配置文件，将 `default-runtime` 设为 `nvidia`，配置文件修改后需要使用 `sudo systemctl restart docker` 重启 Docker daemon.
3. 验证安装结果
* 通过 `sudo docker info` 检查是否有 "Default runtime: nvidia" （默认为 runc）.
* 通过 `sudo docker run nvidia/cuda:10.0-base nvidia-smi` 运行一个 GPU Docker 看是否能正确看到 GPU 信息。
4. 新建 Linux 用户
新建相同的 Linux 用户，例如 username: openpai, password: paiopen, 并将该用户加到 sudo 组里。
```sh
sudo useradd openpai
sudo usermod -a -G sudo openpai
```
2. 部署 OpenPAI
在部署的集群中：Alice 的机器为 dev-box（管理员用来操作集群，不在集群中），Bob 的机器为 master（在集群中，不跑具体的任务），Carol 和 Dan 的机器为 worker（在集群中，用来跑用户的任务）。
（以下步骤只在 Alice 的机器上执行）
1. 准备配置文件
* `~/master.csv`:
```
hostname-bob,10.0.1.2
```
"hostname-bob" 是在 Bob 的机器上执行 `hostname` 的结果，10.0.1.2 替换为 Bob 的机器的 ip 地址。
* `~/worker.csv`:
```
hostname-carol,10.0.1.3
hostname-dan,10.0.1.4
```
"hostname-carol" 是在 Carol 的机器上执行 `hostname` 的结果，10.0.1.3 替换为 Carol 的机器的 ip 地址。Dan 同理。
* `~/config.yaml`:
```yaml
user: openpai
password: paiopen
branch_name: v1.2.0
docker_image_tag: v1.2.0
```
"user" 和 "password" 是新建的 Linux 用户的 username 和 password.
2. 部署 OpenPAI
1. 克隆 OpenPAI 的代码
```sh
git clone -b v1.2.0 https://github.com/microsoft/pai.git
cd pai/contrib/kubespray
```
2. 部署 Kubernetes
```sh
bash quick-start-kubespray.sh -m ~/master.csv -w ~/worker.csv -c ~/config.yaml
```
3. 启动 OpenPAI 服务
```sh
bash quick-start-service.sh -m ~/master.csv -w ~/worker.csv -c ~/config.yaml
```
如果部署成功，会看到如下信息：
```
Kubernetes cluster config : ~/pai-deploy/kube/config
OpenPAI cluster config : ~/pai-deploy/cluster-cfg
OpenPAI cluster ID : pai
Default username : admin
Default password : admin-password
You can go to http://<your-master-ip>, then use the default username and password to log in.
```
在浏览器中访问 http://bob-ip，使用 admin 和 admin-password 登陆。
3. 运行 dev-box Docker 管理集群
运行 dev-box Docker 容器：
```sh
sudo docker run -itd \
-v /var/run/docker.sock:/var/run/docker.sock \
-v ${HOME}/pai-deploy/cluster-cfg:/cluster-configuration \
-v ${HOME}/pai-deploy/kube:/root/.kube \
--privileged=true \
--name=dev-box \
openpai/dev-box:v1.2.0
```
执行 dev-box Docker 容器：
```sh
sudo docker exec -it dev-box bash
```
列出集群中的节点：
```sh
kubectl get nodes
```
使用 `paictl` 管理 OpenPAI 服务：
```sh
cd /pai
python paictl.py config get-id
```
> 注：详细的部署说明请参考 [Installation Guide](https://openpai.readthedocs.io/en/latest/manual/cluster-admin/installation-guide.html#installation-from-scratch)，部署过程中遇到的问题可以参考 [troubleshooting](https://openpai.readthedocs.io/en/latest/manual/cluster-admin/installation-faqs-and-troubleshooting.html#troubleshooting) 或在 [GitHub issue](https://github.com/microsoft/pai/issues) 上提问。
3. 使用 OpenPAI
1. 新建 OpenPAI 用户
（Bob 执行）
Bob 访问 http://bob-ip, 在 Administration -> User Management 页面中，给 Alice, Carol, Dan 分别新建账号。
2. 提交集群任务
（Alice, Bob, Carol, Dan 都执行）
在浏览器中访问 http://bob-ip, 在 Submit Job 页面提交 Single Job。观察集群中任务的等待和执行情况。
4. 更改调度器配置并使用不同调度策略
1. 更改调度器配置
（Alice 执行）
将两个 GPU 机器配置成两个不同的 VC，在 dev-box Docker 容器中更改 `/cluster-configuration/service-configuration.yaml` 文件中的 `hivedscheduler`:
```yaml
hivedscheduler:
config: |
physicalCluster:
skuTypes:
GPU-C:
gpu: 1
cpu: 2
memory: 4096Mi
GPU-D:
gpu: 1
cpu: 2
memory: 4096Mi
cellTypes:
GPU-C-NODE:
childCellType: GPU-C
childCellNumber: 1
isNodeLevel: true
GPU-C-NODE-POOL:
childCellType: GPU-C-NODE
childCellNumber: 1
GPU-D-NODE:
childCellType: GPU-D
childCellNumber: 1
isNodeLevel: true
GPU-D-NODE-POOL:
childCellType: GPU-D-NODE
childCellNumber: 1
physicalCells:
- cellType: GPU-C-NODE-POOL
cellChildren:
- cellAddress: hostname-carol #TODO change to Carol's
- cellType: GPU-D-NODE-POOL
cellChildren:
- cellAddress: hostname-dan #TODO change to Dan's
virtualClusters:
default:
virtualCells:
- cellType: GPU-C-NODE-POOL.GPU-C-NODE
cellNumber: 1
vc1:
virtualCells:
- cellType: GPU-D-NODE-POOL.GPU-D-NODE
cellNumber: 1
```
然后使用 `paictl` 更新配置文件并重启相应的服务（提示输入的 cluster-id 为 "pai"）：
```sh
python paictl.py service stop -n rest-server hivedscheduler
python paictl.py config push -p /cluster-configuration -m service
python paictl.py service start -n rest-server hivedscheduler
```
2. 虚拟集群安全（VC） 安全 (VC Safety)
（Alice, Bob, Carol, Dan 都执行，可同时进行）
同时向 `vc1` 提交任务（任务配置文件可参考 `job-config-0.yaml`），观察任务的运行情况：提交的任务会在哪个机器上运行，当有多个任务在等待并且集群中的 `default` VC 空闲时任务会被怎样调度？
3. 优先级和抢占 (Priority and Preemption)
（Alice, Bob, Carol, Dan 按顺序依次实验，实验时确保集群中没有其它未结束的任务）
先向 `vc1` 提交一个优先级 `jobPriorityClass` 为 `test` 的任务（任务配置文件可参考 `job-config-1.yaml`），在其运行时再向 `vc1` 提交一个优先级为 `prod` 的任务（任务配置文件可参考 `job-config-2.yaml`），观察任务的运行情况：后提交的任务是否在先提交的任务运行完成之后运行，什么时候两个任务都运行结束？
4. 低优先级任务 ，也叫机会任务(Opportunistic Job)
（Alice, Bob, Carol, Dan 按顺序依次实验，实验时确保集群中没有其它未结束的任务）
先向 `vc1` 提交一个优先级 `jobPriorityClass` 为 `prod` 的任务（任务配置文件可参考 `job-config-3.yaml`），在其运行时再向 `vc1` 提交一个优先级为 `oppo`（最低优先级）的任务（任务配置文件可参考 `job-config-4.yaml`），观察任务的运行情况：后提交的任务什么时候开始运行，是否会等高优先级的任务运行完？如果在后提交的任务运行时再向 `default` VC 提交优先级为 `test` 的任务会被怎样调度？
5. 更改调度器配置
（Alice 执行）
将两个 GPU 机器配置在相同 VC 里，在 dev-box Docker 容器中更改 `/cluster-configuration/service-configuration.yaml` 文件中的 `hivedscheduler`:
```yaml
hivedscheduler:
config: |
physicalCluster:
skuTypes:
GPU:
gpu: 1
cpu: 2
memory: 4096Mi
cellTypes:
GPU-NODE:
childCellType: GPU
childCellNumber: 1
isNodeLevel: true
GPU-NODE-POOL:
childCellType: GPU-NODE
childCellNumber: 2
physicalCells:
- cellType: GPU-NODE-POOL
cellChildren:
- cellAddress: hostname-carol #TODO change to Carol's
- cellAddress: hostname-dan #TODO change to Dan's
virtualClusters:
default:
virtualCells:
- cellType: GPU-NODE-POOL.GPU-NODE
cellNumber: 2
```
然后使用 `paictl` 更新配置文件并重启相应的服务（提示输入的 cluster-id 为 "pai"）：
```sh
python paictl.py service stop -n rest-server hivedscheduler
python paictl.py config push -p /cluster-configuration -m service
python paictl.py service start -n rest-server hivedscheduler
```
6. 群调度 (Gang Scheduling)
（Alice, Bob, Carol, Dan 按顺序依次实验，实验时确保集群中没有其它未结束的任务）
先向 `default` VC 提交一个任务占用一台机器（任务配置文件可参考 `job-config-5.yaml`），在其运行时再向 `default` VC 提交一个有 2 个子任务需要两台机器的任务（任务配置文件可参考 `job-config-6.yaml`），观察任务的运行情况：后提交的任务什么时候开始运行，2 个子任务是否会先后运行？
7. 增量调度 (Incremental Scheduling)
（Alice, Bob, Carol, Dan 按顺序依次实验，实验时确保集群中没有其它未结束的任务）
先向 `default` VC 提交一个任务占用一台机器，在其运行时再向 `default` VC 提交一个有 2 个子任务需要两台机器的任务（任务配置文件可参考 `job-config-7.yaml`），观察任务的运行情况：后提交的任务什么时候开始运行，2 个子任务是否会先后运行？能否在当前只有 2 GPU 的集群中提交一个需要用超过配额（例如用 4 GPU）的任务？
### 7.4.6.5 实验报告
***实验环境***
（Alice/Bob/Carol/Dan 替换为组员姓名）
|||||||
|-------|-|-------|-----|------|------|
| Users | | Alice | Bob | Carol | Dan |
| 硬件环境 | CPU（vCPU数目）||||||
|| GPU(型号，数目) |||||
|| IP |||||
|| HostName |||||
| 软件环境 | OS版本 |||||
|| Docker Engine版本 |||||
|| CUDA版本 |||||
|| OpenPAI版本 |||||
|||||||
***实验结果***
1. 部署 OpenPAI
简述部署中遇到的问题以及相应的解决方案。
2. 使用不同调度策略
||||
| --- | --- | --- |
| 实验名称 | 实验现象（任务运行情况） | 支持文件（任务配置文件, UI 截图等） |
| 虚拟集群（VC） 安全 (VC Safety) | 提交的任务会在哪个机器上运行，当有多个任务在等待并且集群中的 `default` VC 空闲时任务会被怎样调度？其它观察到的现象 | |
| 优先级和抢占 (Priority and Preemption) | 后提交的任务是否在先提交的任务运行完成之后运行，什么时候两个任务都运行结束？其它观察到的现象 | |
| 机会任务 (Opportunistic Job) | 后提交的任务什么时候开始运行，是否会等高优先级的任务运行完？如果在后提交的任务运行时再向 `default` VC 提交优先级为 `test` 的任务会被怎样调度？其它观察到的现象 | |
| 群调度 (Gang Scheduling) | 后提交的任务什么时候开始运行，2 个子任务是否会先后运行？其它观察到的现象 | |
| 增量调度 (Incremental Scheduling) | 后提交的任务什么时候开始运行，2 个子任务是否会先后运行？能否在当前只有 2 GPU 的集群中提交一个需要用超过配额（例如用 4 GPU）的任务？其它观察到的现象 | |
||||
***参考代码***
代码位置：`AI-System/Labs/AdvancedLabs/Lab6/config/`
## 小结与讨论
本章我们主要介绍面向深度学习的异构集群管理系统，这其中会利用深度学习负载的特点，底层硬件的拓扑，从中间系统层去发掘新的问题和优化机会。
请读者思考，当前调度算法有哪些较强的假设，读者面对的环境是否能够提供？
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6. [Juncheng Gu, Mosharaf Chowdhury, Kang G. Shin, Yibo Zhu, Myeongjae Jeon, Junjie Qian, Hongqiang Liu, and Chuanxiong Guo. 2019. Tiresias: a GPU cluster manager for distributed deep learning. In Proceedings of the 16th USENIX Conference on Networked Systems Design and Implementation (NSDI'19). USENIX Association, USA, 485–500.](https://dl.acm.org/doi/10.5555/3323234.3323274)
<div id="hived"></div>
7. [Hanyu Zhao, Zhenhua Han, Zhi Yang, Quanlu Zhang, Fan Yang, Lidong Zhou, Mao Yang, Francis C.M. Lau, Yuqi Wang, Yifan Xiong, and Bin Wang. 2020. HiveD: sharing a GPU cluster for deep learning with guarantees. Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation. USENIX Association, USA, Article 29, 515–532.](https://dl.acm.org/doi/10.5555/3488766.3488795)
<div id="programfail"></div>
8. [Ru Zhang, Wencong Xiao, Hongyu Zhang, Yu Liu, Haoxiang Lin, and Mao Yang. 2020. An empirical study on program failures of deep learning jobs. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (ICSE '20). Association for Computing Machinery, New York, NY, USA, 1159–1170. https://doi.org/10.1145/3377811.3380362](https://dl.acm.org/doi/10.1145/3377811.3380362)
<div id="singularity"></div>
9. [Shukla, Dharma, et al. "Singularity: Planet-Scale, Preemptible, Elastic Scheduling of AI Workloads." arXiv preprint arXiv:2202.07848 (2022).](https://arxiv.org/abs/2202.07848)
|
AI-System/Textbook/第7章-异构计算集群调度与资源管理系统/7.4-面向深度学习的集群管理系统.md/0
|
{
"file_path": "AI-System/Textbook/第7章-异构计算集群调度与资源管理系统/7.4-面向深度学习的集群管理系统.md",
"repo_id": "AI-System",
"token_count": 41828
}
| 17 |
# Starter pipeline
# Start with a minimal pipeline that you can customize to build and deploy your code.
# Add steps that build, run tests, deploy, and more:
# https://aka.ms/yaml
trigger:
batch: true
stages:
- stage: dev
jobs:
- template: steps/deploy.yml
parameters:
deployment_name: MLAKSDeployAMLJob
template: MLAKSDeployAMLJob.yml
azureSubscription: $(devsub)
azure_subscription: $(devsubid)
azureresourcegroup: dcibhprg
workspacename: dcibhpws
aksname: dcibaks
azureregion: $(azureregion)
aksimagename: myimage
project: "e2etestharness"
agent: $(agent)
doCleanup: True
- stage: staging
jobs:
- template: steps/deploy.yml
parameters:
deployment_name: MLAKSDeployAMLJob
template: MLAKSDeployAMLJob.yml
azureSubscription: $(devsub)
azure_subscription: $(devsubid)
azureresourcegroup: staging-pydl
workspacename: stagingpyml
aksname: stagingpymlaks
azureregion: $(azureregion)
aksimagename: myimage
project: "e2etestharness"
agent: $(agent)
# - stage: tridant
# jobs:
# - template: steps/deploy.yml
# parameters:
# deployment_name: DLAKSDeployAMLJob
# template: DLAKSDeployAMLJob.yml
# azureSubscription: $(devsub)
# azure_subscription: $(devsubid)
# azureresourcegroup: tridant-pydl
# workspacename: tridantpydl
# azureregion: $(azureregion)
# aksname: tridant-pydl
# aksimagename: myimage
# doCleanup: False
# project: "e2etestharness"
# expires : "DnD"
# alias: $(Build.RequestedForId)
# agent: "AI-GPU"
# - template: steps/deploy.yml
# parameters:
# deployment_name: MLAKSDeployAMLJob
# template: MLAKSDeployAMLJob.yml
# azureSubscription: $(devsub)
# azure_subscription: $(devsubid)
# azureresourcegroup: tridant-pyml
# workspacename: tridantpy
# azureregion: $(azureregion)
# aksimagename: myimage
# doCleanup: False
# project: "e2etestharness"
# expires : "DnD"
# alias: $(Build.RequestedForId)
# agent: AI-GPU
|
AI/.ci/realtimeserving-release.yml/0
|
{
"file_path": "AI/.ci/realtimeserving-release.yml",
"repo_id": "AI",
"token_count": 994
}
| 18 |
parameters:
Agent: Hosted Ubuntu 1604
jobDisplayName: 'defaultDisplayName'
jobTimeoutInMinutes: 180
TridentWorkloadTypeShort: #
DeployLocation: #
DefaultWorkingDirectory: #
Template: #
azureSubscription: #
azure_subscription: #
ProjectLocation: #
PythonPath: #
doCleanup: False
stages:
- template: ../stage/deploy_notebooks_stage.yml
parameters:
Agent: ${{parameters.Agent}}
stageName: 'stable'
jobDisplayName: ${{parameters.jobDisplayName}}
jobTimeoutInMinutes: ${{parameters.jobTimeoutInMinutes}}
TridentWorkloadTypeShort: ${{parameters.TridentWorkloadTypeShort}}
DeployLocation: ${{parameters.DeployLocation}}
TestPostfix: "-stable"
DefaultWorkingDirectory: ${{parameters.DeployLocation}}
Template: ${{parameters.Template}}
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
ProjectLocation: ${{parameters.ProjectLocation}}
PythonPath: ${{parameters.PythonPath}}
doCleanup: ${{parameters.doCleanup}}
- template: ../stage/deploy_notebooks_stage.yml
parameters:
Agent: ${{parameters.Agent}}
stageName: 'release'
jobDisplayName: ${{parameters.jobDisplayName}}
TridentWorkloadTypeShort: ${{parameters.TridentWorkloadTypeShort}}
DeployLocation: ${{parameters.DeployLocation}}
TestPostfix: "-release"
DefaultWorkingDirectory: ${{parameters.DeployLocation}}
Template: ${{parameters.Template}}
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
ProjectLocation: ${{parameters.ProjectLocation}}
PythonPath: ${{parameters.PythonPath}}
flighting_release: true
doCleanup: ${{parameters.doCleanup}}
- template: ../stage/deploy_notebooks_stage.yml
parameters:
Agent: ${{parameters.Agent}}
stageName: 'preview'
jobDisplayName: ${{parameters.jobDisplayName}}
TridentWorkloadTypeShort: ${{parameters.TridentWorkloadTypeShort}}
DeployLocation: ${{parameters.DeployLocation}}
TestPostfix: "-preview"
DefaultWorkingDirectory: ${{parameters.DeployLocation}}
Template: ${{parameters.Template}}
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
ProjectLocation: ${{parameters.ProjectLocation}}
PythonPath: ${{parameters.PythonPath}}
flighting_preview: true
doCleanup: ${{parameters.doCleanup}}
|
AI/.ci/stages/deploy_notebooks_stages.yml/0
|
{
"file_path": "AI/.ci/stages/deploy_notebooks_stages.yml",
"repo_id": "AI",
"token_count": 825
}
| 19 |
parameters:
location: "x"
azureSubscription: 'x'
conda: MLAKSDeployAML
azure_subscription: 'x'
azureresourcegroup: "dcibrg"
workspacename: "dcibws"
azureregion: "westus"
python_path: "{{cookiecutter.project_name}}"
timeoutInMinutes: 90
steps:
- task: AzureCLI@1
displayName: ${{parameters.notebook}}
inputs:
azureSubscription: ${{parameters.azureSubscription}}
scriptLocation: inlineScript
timeoutInMinutes: ${{parameters.timeoutInMinutes}}
failOnStderr: True
inlineScript: |
source activate ${{parameters.conda}}
export PYTHONPATH=${{parameters.python_path}}:${PYTHONPATH}
cd ${{parameters.location}}
python ${{parameters.python_secret_root}}.ci/scripts/aml_creation.py \
--subscription_id ${{parameters.azure_subscription}} \
--resource_group ${{parameters.azureresourcegroup}} \
--workspace_name ${{parameters.workspacename}} \
--workspace_region ${{parameters.azureregion}}
|
AI/.ci/steps/aml/creation_step.yml/0
|
{
"file_path": "AI/.ci/steps/aml/creation_step.yml",
"repo_id": "AI",
"token_count": 384
}
| 20 |
parameters:
template: ''
azureSubscription: 'x'
azure_subscription: 'x'
azureresourcegroup: 'x'
workspacename: 'x'
azureregion: 'x'
aksimagename: 'x'
aks_name: "mlaks"
location: "" #Root Dir of Project
python_path: "" #Root Dir of Python Env
cluster_name: "-"
flighting_release: false
flighting_preview: false
doCleanup: True
steps:
- template: docker_clean.yml
- template: ${{parameters.template}}
parameters:
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
azureresourcegroup: ${{parameters.azureresourcegroup}}
workspacename: ${{parameters.workspacename}}
azureregion: ${{parameters.azureregion}}
aksimagename: ${{parameters.aksimagename}}
aks_name: ${{parameters.aks_name}}
location: ${{parameters.location}}
python_path: ${{parameters.python_path}}
cluster_name: ${{parameters.cluster_name}}
flighting_release: ${{parameters.flighting_release}}
flighting_preview: ${{parameters.flighting_preview}}
doCleanup: ${{parameters.doCleanup}}
|
AI/.ci/steps/deploy_notebook_steps.yml/0
|
{
"file_path": "AI/.ci/steps/deploy_notebook_steps.yml",
"repo_id": "AI",
"token_count": 408
}
| 21 |
variables:
DeploymentName: ADOTrinDeployJob
TridentWorkloadTypeShort: adomltrain
DeployLocation: eastus2
ProjectLocation: "."
PythonPath: "."
Template: ADOTrainDeployAMLJob.yml
Notebooks: '00_AMLConfiguration.ipynb 01_AutoML_Local.ipynb' # NOT USED YET
|
AI/.ci/vars/ado_ml_batch_train_vars.yml/0
|
{
"file_path": "AI/.ci/vars/ado_ml_batch_train_vars.yml",
"repo_id": "AI",
"token_count": 97
}
| 22 |
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"parameters": {
"name": {
"type": "String"
},
"sqlAdministratorLogin": {
"type": "String"
},
"sqlAdministratorPassword": {
"type": "SecureString"
},
"tagValues": {
"defaultValue": {"Created with":"Synapse Azure Resource Manager deploment template"},
"type": "Object"
}
},
"resources": [
{
"type": "Microsoft.Resources/deployments",
"apiVersion": "2018-05-01",
"name": "storage",
"properties": {
"mode": "Incremental",
"templateLink": {
"uri": "https://raw.githubusercontent.com/Azure-Samples/Synapse/master/Manage/DeployWorkspace/storage/azuredeploy.json",
"contentVersion": "1.0.0.0"
},
"parameters":{
"storageAccount":{"value": "[parameters('name')]"}
}
}
},
{
"type": "Microsoft.Resources/deployments",
"apiVersion": "2018-05-01",
"name": "workspace",
"properties": {
"mode": "Incremental",
"templateLink": {
"uri": "https://raw.githubusercontent.com/Azure-Samples/Synapse/master/Manage/DeployWorkspace/workspace/azuredeploy.json",
"contentVersion": "1.0.0.0"
},
"parameters":{
"name":{"value": "[parameters('name')]"},
"sqlAdministratorLogin":{"value": "[parameters('sqlAdministratorLogin')]"},
"sqlAdministratorPassword":{"value": "[parameters('sqlAdministratorPassword')]"},
"defaultDataLakeStorageAccountName":{"value": "[parameters('name')]"},
"tagValues":{"value": "[parameters('tagValues')]"}
}
},
"dependsOn": [
"storage"
]
}
],
"outputs": {}
}
|
AI/AzureDeployment/DeploySpark/Synapse/azuredeploy.json/0
|
{
"file_path": "AI/AzureDeployment/DeploySpark/Synapse/azuredeploy.json",
"repo_id": "AI",
"token_count": 1108
}
| 23 |
import sys
sys.path += ['../']
import os
import torch
from data.msmarco_data import GetTrainingDataProcessingFn, GetTripletTrainingDataProcessingFn
from utils.util import (
getattr_recursive,
set_seed,
StreamingDataset,
EmbeddingCache,
get_checkpoint_no,
get_latest_ann_data,
is_first_worker
)
import pandas as pd
from transformers import glue_processors as processors
from transformers import (
AdamW,
RobertaConfig,
RobertaForSequenceClassification,
RobertaTokenizer,
get_linear_schedule_with_warmup
)
import transformers
from utils.lamb import Lamb
from model.models import MSMarcoConfigDict, ALL_MODELS
from torch import nn
import torch.distributed as dist
from tqdm import tqdm, trange
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
import numpy as np
from os.path import isfile, join
import argparse
import glob
import json
import logging
import random
torch.multiprocessing.set_sharing_strategy('file_system')
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
logger = logging.getLogger(__name__)
def train(args, model, tokenizer, query_cache, passage_cache):
""" Train the model """
logger.info("Training/evaluation parameters %s", args)
tb_writer = None
if is_first_worker():
tb_writer = SummaryWriter(log_dir=args.log_dir)
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
real_batch_size = args.train_batch_size * args.gradient_accumulation_steps * \
(torch.distributed.get_world_size() if args.local_rank != -1 else 1)
optimizer_grouped_parameters = []
layer_optim_params = set()
for layer_name in [
"roberta.embeddings",
"score_out",
"downsample1",
"downsample2",
"downsample3"]:
layer = getattr_recursive(model, layer_name)
if layer is not None:
optimizer_grouped_parameters.append({"params": layer.parameters()})
for p in layer.parameters():
layer_optim_params.add(p)
if getattr_recursive(model, "roberta.encoder.layer") is not None:
for layer in model.roberta.encoder.layer:
optimizer_grouped_parameters.append({"params": layer.parameters()})
for p in layer.parameters():
layer_optim_params.add(p)
optimizer_grouped_parameters.append(
{"params": [p for p in model.parameters() if p not in layer_optim_params]})
if args.optimizer.lower() == "lamb":
optimizer = Lamb(
optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
elif args.optimizer.lower() == "adamw":
optimizer = AdamW(
optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
else:
raise Exception(
"optimizer {0} not recognized! Can only be lamb or adamW".format(
args.optimizer))
# Check if saved optimizer or scheduler states exist
if os.path.isfile(
os.path.join(
args.model_name_or_path,
"optimizer.pt")) and args.load_optimizer_scheduler:
# Load in optimizer and scheduler states
optimizer.load_state_dict(
torch.load(
os.path.join(
args.model_name_or_path,
"optimizer.pt")))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(
model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[
args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
# Train
logger.info("***** Running training *****")
logger.info(" Max steps = %d", args.max_steps)
logger.info(
" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(
" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
global_step = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to gobal_step of last saved checkpoint from model
# path
if "-" in args.model_name_or_path:
try:
global_step = int(
args.model_name_or_path.split("-")[-1].split("/")[0])
except:
global_step=0
else:
global_step = 0
logger.info(
" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from global step %d", global_step)
tr_loss = 0.0
model.zero_grad()
model.train()
set_seed(args) # Added here for reproductibility
last_ann_no = -1
train_dataloader = None
train_dataloader_iter = None
dev_ndcg = 0
step = 0
if args.single_warmup:
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps)
while global_step < args.max_steps:
if step % args.gradient_accumulation_steps == 0 and global_step % args.logging_steps == 0:
# check if new ann training data is availabe
ann_no, ann_path, ndcg_json = get_latest_ann_data(args.ann_dir)
if ann_path is not None and ann_no != last_ann_no:
logger.info("Training on new add data at %s", ann_path)
with open(ann_path, 'r') as f:
ann_training_data = f.readlines()
dev_ndcg = ndcg_json['ndcg']
ann_checkpoint_path = ndcg_json['checkpoint']
ann_checkpoint_no = get_checkpoint_no(ann_checkpoint_path)
aligned_size = (len(ann_training_data) //
args.world_size) * args.world_size
ann_training_data = ann_training_data[:aligned_size]
logger.info("Total ann queries: %d", len(ann_training_data))
if args.triplet:
train_dataset = StreamingDataset(
ann_training_data, GetTripletTrainingDataProcessingFn(
args, query_cache, passage_cache))
else:
train_dataset = StreamingDataset(
ann_training_data, GetTrainingDataProcessingFn(
args, query_cache, passage_cache))
train_dataloader = DataLoader(
train_dataset, batch_size=args.train_batch_size)
train_dataloader_iter = iter(train_dataloader)
# re-warmup
if not args.single_warmup:
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=len(ann_training_data))
if args.local_rank != -1:
dist.barrier()
if is_first_worker():
# add ndcg at checkpoint step used instead of current step
tb_writer.add_scalar(
"dev_ndcg", dev_ndcg, ann_checkpoint_no)
if last_ann_no != -1:
tb_writer.add_scalar(
"epoch", last_ann_no, global_step - 1)
tb_writer.add_scalar("epoch", ann_no, global_step)
last_ann_no = ann_no
try:
batch = next(train_dataloader_iter)
except StopIteration:
logger.info("Finished iterating current dataset, begin reiterate")
train_dataloader_iter = iter(train_dataloader)
batch = next(train_dataloader_iter)
batch = tuple(t.to(args.device) for t in batch)
step += 1
if args.triplet:
inputs = {
"query_ids": batch[0].long(),
"attention_mask_q": batch[1].long(),
"input_ids_a": batch[3].long(),
"attention_mask_a": batch[4].long(),
"input_ids_b": batch[6].long(),
"attention_mask_b": batch[7].long()}
else:
inputs = {
"input_ids_a": batch[0].long(),
"attention_mask_a": batch[1].long(),
"input_ids_b": batch[3].long(),
"attention_mask_b": batch[4].long(),
"labels": batch[6]}
# sync gradients only at gradient accumulation step
if step % args.gradient_accumulation_steps == 0:
outputs = model(**inputs)
else:
with model.no_sync():
outputs = model(**inputs)
# model outputs are always tuple in transformers (see doc)
loss = outputs[0]
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
if step % args.gradient_accumulation_steps == 0:
loss.backward()
else:
with model.no_sync():
loss.backward()
tr_loss += loss.item()
if step % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(
model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.logging_steps > 0 and global_step % args.logging_steps == 0:
logs = {}
loss_scalar = tr_loss / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
tr_loss = 0
if is_first_worker():
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
logger.info(json.dumps({**logs, **{"step": global_step}}))
if is_first_worker() and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(
args.output_dir, "checkpoint-{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(
optimizer.state_dict(),
os.path.join(
output_dir,
"optimizer.pt"))
torch.save(
scheduler.state_dict(),
os.path.join(
output_dir,
"scheduler.pt"))
logger.info(
"Saving optimizer and scheduler states to %s",
output_dir)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
tb_writer.close()
return global_step
def get_arguments():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the cached passage and query files",
)
parser.add_argument(
"--ann_dir",
default=None,
type=str,
required=True,
help="The ann training data dir. Should contain the output of ann data generation job",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(
MSMarcoConfigDict.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " +
", ".join(ALL_MODELS),
)
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(
processors.keys()),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
# Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--triplet",
default=False,
action="store_true",
help="Whether to run training.",
)
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.",
)
parser.add_argument(
"--log_dir",
default=None,
type=str,
help="Tensorboard log dir",
)
parser.add_argument(
"--optimizer",
default="lamb",
type=str,
help="Optimizer - lamb or adamW",
)
parser.add_argument(
"--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.",
)
parser.add_argument(
"--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.",
)
parser.add_argument(
"--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.",
)
parser.add_argument(
"--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.",
)
parser.add_argument(
"--max_steps",
default=1000000,
type=int,
help="If > 0: set total number of training steps to perform",
)
parser.add_argument(
"--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.",
)
parser.add_argument(
"--logging_steps",
type=int,
default=500,
help="Log every X updates steps.",
)
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save checkpoint every X updates steps.",
)
parser.add_argument(
"--no_cuda",
action="store_true",
help="Avoid using CUDA when available",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="random seed for initialization",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
# ----------------- ANN HyperParam ------------------
parser.add_argument(
"--load_optimizer_scheduler",
default=False,
action="store_true",
help="load scheduler from checkpoint or not",
)
parser.add_argument(
"--single_warmup",
default=False,
action="store_true",
help="use single or re-warmup",
)
# ----------------- End of Doc Ranking HyperParam ------------------
parser.add_argument(
"--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank",
)
parser.add_argument(
"--server_ip",
type=str,
default="",
help="For distant debugging.",
)
parser.add_argument(
"--server_port",
type=str,
default="",
help="For distant debugging.",
)
args = parser.parse_args()
return args
def set_env(args):
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see
# https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(
address=(
args.server_ip,
args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set seed
set_seed(args)
def load_model(args):
# Prepare GLUE task
args.task_name = args.task_name.lower()
args.output_mode = "classification"
label_list = ["0", "1"]
num_labels = len(label_list)
# store args
if args.local_rank != -1:
args.world_size = torch.distributed.get_world_size()
args.rank = dist.get_rank()
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
# Make sure only the first process in distributed training will
# download model & vocab
torch.distributed.barrier()
args.model_type = args.model_type.lower()
configObj = MSMarcoConfigDict[args.model_type]
config = configObj.config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = configObj.tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = configObj.model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
# Make sure only the first process in distributed training will
# download model & vocab
torch.distributed.barrier()
model.to(args.device)
return tokenizer, model
def save_checkpoint(args, model, tokenizer):
# Saving best-practices: if you use defaults names for the model, you can
# reload it using from_pretrained()
if is_first_worker():
# Create output directory if needed
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained
# model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
if args.local_rank != -1:
dist.barrier()
def main():
args = get_arguments()
set_env(args)
tokenizer, model = load_model(args)
query_collection_path = os.path.join(args.data_dir, "train-query")
query_cache = EmbeddingCache(query_collection_path)
passage_collection_path = os.path.join(args.data_dir, "passages")
passage_cache = EmbeddingCache(passage_collection_path)
with query_cache, passage_cache:
global_step = train(args, model, tokenizer, query_cache, passage_cache)
logger.info(" global_step = %s", global_step)
save_checkpoint(args, model, tokenizer)
if __name__ == "__main__":
main()
|
ANCE/drivers/run_ann.py/0
|
{
"file_path": "ANCE/drivers/run_ann.py",
"repo_id": "ANCE",
"token_count": 11185
}
| 24 |
"""
Code for self-training with weak supervision.
Author: Giannis Karamanolakis ([email protected])
"""
import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split
import glob
import joblib
from copy import deepcopy
import shutil
# PreprocessedDataset is used for loading exactly the same dataset splits and features as in our experiments
class PreprocessedDataset:
# Load pre-processed dataset as used in https://github.com/awasthiabhijeet/Learning-From-Rules
def __init__(self, datapath="../../data", orig_train=True, dataset='trec', seed=42):
self.dataset = dataset
self.seed = seed
self.basedatafolder = os.path.join(datapath, self.dataset.upper())
self.datafolder = os.path.join(self.basedatafolder, 'seed{}/'.format(seed))
self.language = 'english'
self.orig_train = orig_train
self.label2ind = self.get_label2ind()
self.num_labels = len(self.label2ind)
# self.lf_names = ["rule_{}".format(i+1) for i in range(15)]
self.lf_names = None
def get_label2ind(self):
if self.dataset == 'trec':
return {"DESC": 0, "ENTY": 1, "HUM": 2, "ABBR": 3, "LOC": 4, "NUM": 5}
elif self.dataset == 'youtube':
return {"ham": 0, "spam": 1}
elif self.dataset == 'sms':
return {"ham": 0, "spam": 1}
elif self.dataset == 'census':
return {"low": 0, "high": 1}
elif self.dataset == 'mitr':
return {'O': 0,
'Location': 1,
'Hours': 2,
'Amenity': 3,
'Price': 4,
'Cuisine': 5,
'Dish': 6,
'Restaurant_Name': 7,
'Rating': 8}
else:
raise(BaseException('Pre-trained dataset not supported: {}'.format(self.dataset)))
def load_data(self, method):
if method == 'train' and not self.orig_train:
method = 'unlabeled'
texts = joblib.load(os.path.join(self.datafolder, "{}_x.pkl".format(method)))
texts = texts.tolist()
labels = joblib.load(os.path.join(self.datafolder, "{}_labels.pkl".format(method)))
labels = labels.squeeze().tolist()
rule_preds = joblib.load(os.path.join(self.datafolder, "{}_rule_preds.pkl".format(method)))
rule_preds[rule_preds == self.num_labels] = -1
rule_preds = rule_preds.tolist()
if method =='train':
exemplars = joblib.load(os.path.join(self.datafolder, 'train_exemplars.pkl'))
return {'texts': texts, 'labels': labels, 'weak_labels': rule_preds, 'exemplar_labels': exemplars}
else:
return {'texts': texts, 'labels': labels, 'weak_labels': rule_preds}
def preprocess(self):
seed = self.seed
self.train_valid_split()
datafolder = os.path.join(self.basedatafolder, 'original_data'.format(seed))
if not os.path.exists(datafolder):
raise(BaseException('You need to download the original dataset for pre-processing, otherwise use our splits'))
savefolder = os.path.join(self.basedatafolder, 'preprocessed_datasets/seed{}/preprocessed/'.format(seed))
self.preprocess_fn(datafolder=datafolder, savefolder=savefolder)
def preprocess_fn(self, datafolder, savefolder):
# Our function used for pre-processing the original datasets and saving them into different splits (for robustness)
# Original datasets can be found here: https://github.com/awasthiabhijeet/Learning-From-Rules
# You can alternatively download the pre-processed versions used in our experiments
# seed0 contains the original dataset. seed<i> (i=1..5) contains 5 random splits of the unlabeled/train/dev data
# All splits consider the same test set, for a fair comparison to previous approaches.
os.makedirs(savefolder, exist_ok=True)
print("\nunlabeled")
data = load_data(os.path.join(datafolder, 'U_processed.p'))
print('x: {}'.format(data.x.shape))
print('rule_preds: {}'.format(data.l.shape))
joblib.dump(data.x, os.path.join(savefolder, 'unlabeled_x.pkl'))
joblib.dump(data.L, os.path.join(savefolder, 'unlabeled_labels.pkl'))
joblib.dump(data.l, os.path.join(savefolder, 'unlabeled_rule_preds.pkl'))
print("\ntrain")
data = load_data(os.path.join(datafolder, 'd_processed.p'))
print('x: {}'.format(data.x.shape))
print('rule_preds: {}'.format(data.l.shape))
joblib.dump(data.x, os.path.join(savefolder, 'train_x.pkl'))
joblib.dump(data.L, os.path.join(savefolder, 'train_labels.pkl'))
joblib.dump(data.l, os.path.join(savefolder, 'train_rule_preds.pkl'))
joblib.dump(data.r, os.path.join(savefolder, 'train_exemplars.pkl'))
print("\nvalidation")
data = load_data(os.path.join(datafolder, 'validation_processed.p'))
print('x: {}'.format(data.x.shape))
print('rule_preds: {}'.format(data.l.shape))
joblib.dump(data.x, os.path.join(savefolder, 'dev_x.pkl'))
joblib.dump(data.L, os.path.join(savefolder, 'dev_labels.pkl'))
joblib.dump(data.l, os.path.join(savefolder, 'dev_rule_preds.pkl'))
print("\ntest")
data = load_data(os.path.join(datafolder, 'test_processed.p'))
print('x: {}'.format(data.x.shape))
print('rule_preds: {}'.format(data.l.shape))
joblib.dump(data.x, os.path.join(savefolder, 'test_x.pkl'))
joblib.dump(data.L, os.path.join(savefolder, 'test_labels.pkl'))
joblib.dump(data.l, os.path.join(savefolder, 'test_rule_preds.pkl'))
print('\nsaved files at {}'.format(savefolder))
def train_valid_split(self):
seed = self.seed
np.random.seed(seed)
datafolder = self.basedatafolder
savefolder = os.path.join(self.basedatafolder, 'preprocessed_datasets/seed{}/p'.format(seed))
os.makedirs(savefolder, exist_ok=True)
print("\nunlabeled")
unlabeled = load_data(os.path.join(datafolder, 'U_processed.p'))
train = load_data(os.path.join(datafolder, 'd_processed.p'))
dev = load_data(os.path.join(datafolder, 'validation_processed.p'))
test = load_data(os.path.join(datafolder, 'test_processed.p'))
# concatenate unlabeled, train, and dev datasets
all = concatenate_data(unlabeled, train)
all = concatenate_data(all, dev)
all_ids = ['unlabeled'] * unlabeled.x.shape[0] + ['train'] * train.x.shape[0] + ['dev'] * dev.x.shape[0]
# split datasets
df = pd.DataFrame()
df['index'] = np.arange(all.x.shape[0])
df['label'] = all.L
df['method'] = all_ids
train_new_df, df = train_test_split(df, train_size=train.x.shape[0], random_state=seed, shuffle=True, stratify=df['label'])
dev_new_df, unlabeled_new_df = train_test_split(df, train_size=dev.x.shape[0], random_state=seed, shuffle=True, stratify=df['label'])
train_new = keep_ind(all, train_new_df['index'].to_numpy())
dev_new = keep_ind(all, dev_new_df['index'].to_numpy())
unlabeled_new = keep_ind(all, unlabeled_new_df['index'].to_numpy())
unlabeled_new = discard_r(unlabeled_new)
assert train_new.x.shape == train.x.shape
assert dev_new.x.shape == dev.x.shape
assert unlabeled_new.x.shape == unlabeled.x.shape
print('Writing new pre-processed (.p) files to {}'.format(savefolder))
dump_data(os.path.join(savefolder, 'd_processed.p'), train_new)
dump_data(os.path.join(savefolder, 'validation_processed.p'), dev_new)
dump_data(os.path.join(savefolder, 'U_processed.p'), unlabeled_new)
dump_data(os.path.join(savefolder, 'test_processed.p'), test)
return
# pre-processing code from https://github.com/awasthiabhijeet/Learning-From-Rules
import pickle
import numpy as np
import collections
f_d = 'f_d'
f_d_U = 'f_d_U'
test_w = 'test_w'
train_modes = [f_d, f_d_U]
F_d_U_Data = collections.namedtuple('GMMDataF_d_U', 'x l m L d r')
def discard_r(data):
r = np.zeros(data.r.shape)
return F_d_U_Data(data.x, data.l, data.m, data.L, data.d, r)
def concatenate_data(d1, d2):
x = np.vstack([d1.x, d2.x])
l = np.vstack([d1.l, d2.l])
m = np.vstack([d1.m, d2.m])
L = np.vstack([d1.L, d2.L])
d = np.vstack([d1.d, d2.d])
r = np.vstack([d1.r, d2.r])
return F_d_U_Data(x, l, m, L, d, r)
def keep_ind(data, inds):
x = data.x[inds]
l = data.l[inds]
m = data.m[inds]
L = data.L[inds]
d = data.d[inds]
r = data.r[inds]
return F_d_U_Data(x, l, m, L, d, r)
def load_data(fname, num_load=None):
print('Loading from ', fname)
with open(fname, 'rb') as f:
x = pickle.load(f)
l = pickle.load(f).astype(np.int32)
m = pickle.load(f).astype(np.int32)
L = pickle.load(f).astype(np.int32)
d = pickle.load(f).astype(np.int32)
r = pickle.load(f).astype(np.int32)
len_x = len(x)
assert len(l) == len_x
assert len(m) == len_x
assert len(L) == len_x
assert len(d) == len_x
assert len(r) == len_x
L = np.reshape(L, (L.shape[0], 1))
d = np.reshape(d, (d.shape[0], 1))
if num_load is not None and num_load < len_x:
x = x[:num_load]
l = l[:num_load]
m = m[:num_load]
L = L[:num_load]
d = d[:num_load]
r = r[:num_load]
return F_d_U_Data(x, l, m, L, d, r)
def dump_data(save_filename, data):
save_file = open(save_filename, 'wb')
pickle.dump(data.x, save_file)
pickle.dump(data.l, save_file)
pickle.dump(data.m, save_file)
pickle.dump(data.L, save_file)
pickle.dump(data.d, save_file)
pickle.dump(data.r, save_file)
save_file.close()
|
ASTRA/astra/dataset/preprocessed_dataset.py/0
|
{
"file_path": "ASTRA/astra/dataset/preprocessed_dataset.py",
"repo_id": "ASTRA",
"token_count": 4723
}
| 25 |
[
{
"tripleset": [
[
"Mars Hill College",
"JOINED",
"1973"
],
[
"Mars Hill College",
"LOCATION",
"Mars Hill, North Carolina"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "A school from Mars Hill, North Carolina, joined in 1973."
}
]
},
{
"tripleset": [
[
"Newberry College",
"NICKNAME",
"Wolves"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Newberry College's nickname is the wolves."
}
]
},
{
"tripleset": [
[
"Presbyterian College",
"TYPE",
"Private"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Presbyterian College is a private school."
}
]
},
{
"tripleset": [
[
"Queens University of Charlotte",
"NICKNAME",
"Royals"
],
[
"Queens University of Charlotte",
"ENROLLMENT",
"2386"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The nickname of the school with an enrollment of 2,386 is the royals."
}
]
},
{
"tripleset": [
[
"Inars Kivlenieks",
"COUNTRY",
"Latvia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The Athlete is Inars Kivlenieks.The country is Latvia"
}
]
},
{
"tripleset": [
[
"New York",
"SWIMSUIT",
"8.713"
],
[
"New York",
"EVENING_GOWN",
"8.400"
],
[
"New York",
"AVERAGE",
"8.525"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "New York\t it 8.713 New York\t 8.525"
}
]
},
{
"tripleset": [
[
"Atlanta",
"OFFICIAL_POPULATION",
"5,457,831"
],
[
"[TABLECONTEXT]",
"METROPOLITAN_AREA",
"Atlanta"
],
[
"5,457,831",
"YEAR",
"2012"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"List of metropolitan areas by population"
],
[
"Atlanta",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Metropolitan areas by population is Atlanta Country United States Official population is 5,457,831 ,2012"
}
]
},
{
"tripleset": [
[
"Bogot\u00e1",
"COUNTRY",
"Colombia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The Bogot\u00e1 metropolitan area is in Colombia."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"METROPOLITAN_AREA",
"Berlin"
],
[
"Berlin",
"COUNTRY",
"Germany"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"List of metropolitan areas by population"
],
[
"Berlin",
"OFFICIAL_POPULATION",
"5,097,712"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Berlin is a metropolitan area in Germany with an official population of 5,097,712."
}
]
},
{
"tripleset": [
[
"1983-1984 season",
"JORNADA_OR_OTHER",
"2"
],
[
"2",
"AWAY_TEAM",
"Am\u00e9rica"
],
[
"2",
"HOME_TEAM",
"Chivas"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Chivas played against Am\u00e9rica in the 1983-1984 season."
}
]
},
{
"tripleset": [
[
"Final Vuelta",
"RESULT",
"3-1"
],
[
"Final Vuelta",
"HOME_TEAM",
"Am\u00e9rica"
],
[
"Final Vuelta",
"DATE",
"10 June 1984"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "America earned a 3-1 victory in the second-leg final in 1984."
}
]
},
{
"tripleset": [
[
"24",
"DATE",
"11 January 1987"
],
[
"24",
"HOME_TEAM",
"Chivas"
],
[
"24",
"AWAY_TEAM",
"Am\u00e9rica"
],
[
"24",
"RESULT",
"2-2"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "On 11 January 1987, Am\u00e9rica and Chivas drew 2-2."
}
]
},
{
"tripleset": [
[
"14",
"STADIUM",
"Estadio Jalisco"
],
[
"14",
"RESULT",
"1-1"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The pair tied the score 1-1 at Estadio Jalisco."
}
]
},
{
"tripleset": [
[
"Apertura 2006",
"JORNADA_OR_OTHER",
"Semifinals Ida"
],
[
"Semifinals Ida",
"AWAY_TEAM",
"Am\u00e9rica"
],
[
"Semifinals Ida",
"HOME_TEAM",
"Chivas"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Chivas and Am\u00e9rica will compete in the semifinals of the Apertura 2006 tournament."
}
]
},
{
"tripleset": [
[
"1983-1984 season",
"JORNADA_OR_OTHER",
"21"
],
[
"21",
"AWAY_TEAM",
"Chivas"
],
[
"21",
"STADIUM",
"Estadio Azteca"
],
[
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],
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"annotations": [
{
"source": "WikiTableQuestions_mturk",
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}
]
},
{
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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}
]
},
{
"tripleset": [
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"[TABLECONTEXT]",
"[TITLE]",
"Eardrum (album)"
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[
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[
"[TABLECONTEXT]",
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]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Talib Kweli Green wrote NY Weather Report, the second song on Eardrum."
}
]
},
{
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[
"Say Something",
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "willi.i.am produced Say Something, performed by Talib Kweli and Jean Grae."
}
]
},
{
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
{
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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}
]
},
{
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"Talib Kweli, KRS-One"
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],
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"annotations": [
{
"source": "WikiTableQuestions_mturk",
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}
]
},
{
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],
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"annotations": [
{
"source": "WikiTableQuestions_mturk",
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}
]
},
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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}
]
},
{
"tripleset": [
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"Arabic numerals",
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Hexadecimal 33 is equivalent to decimal 51."
}
]
},
{
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[
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "0011 0100 in binary is the same as 064 in octal."
}
]
},
{
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
{
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"source": "WikiTableQuestions_lily",
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"source": "WikiTableQuestions_lily",
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]
},
{
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"source": "WikiTableQuestions_lily",
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},
{
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"source": "WikiTableQuestions_mturk",
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}
]
},
{
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[
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"annotations": [
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},
{
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
{
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Tiago Monteiro of the Fittipaldi-Dingman Racing team retired with 1 lap remaining in the 2003 Grand Prix of Monterey."
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]
},
{
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Joel Camathias of Dale Coyne Racing finished 85 laps with 2 more remaining in the 2003 Grand Prix of Monterey."
}
]
},
{
"tripleset": [
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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}
]
},
{
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}
]
},
{
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"Adrian Fern\u00e1ndez",
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Adrian Fern\u00e1ndez (no. 51) of Fern\u00e1ndez Racing team completed 87 laps by +1:01.4."
}
]
},
{
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"annotations": [
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"source": "WikiTableQuestions_mturk",
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}
]
},
{
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{
"source": "WikiTableQuestions_mturk",
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}
]
},
{
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The ecclesiastical Jurisdictions is Nuestra Se\u00f1ora de la Altagracia en Hig\u00fcey. The Higueyensis / a Domina Nostra vulgo de la Altagracia in Hig\u00fcey. The ecclesiastical Province is Santo Domingo. The Established is 1 April 1959 ."
}
]
},
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],
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"annotations": [
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"source": "WikiSQL_decl_sents",
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}
]
},
{
"tripleset": [
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[
"35",
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],
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
},
{
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[
"Trisha",
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"Winner"
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Trisha, a female, is the winner"
}
]
},
{
"tripleset": [
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"London",
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],
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"source": "WikiTableQuestions_lily",
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}
]
},
{
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],
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"source": "WikiTableQuestions_lily",
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}
]
},
{
"tripleset": [
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"Lucas",
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[
"Lucas",
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "A male was the 6th captured (by Kim)"
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]
},
{
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"Andrew",
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],
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"annotations": [
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"source": "WikiTableQuestions_mturk",
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}
]
},
{
"tripleset": [
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"Tracy",
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[
"Tracy",
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],
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"source": "WikiTableQuestions_lily",
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]
},
{
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],
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
},
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],
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]
},
{
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],
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"source": "WikiTableQuestions_lily",
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]
},
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],
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"source": "WikiTableQuestions_lily",
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}
]
},
{
"tripleset": [
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Kansas City Kings was 10 games behind the leading team and finished the season 7-13 playing against teams in the Midwest Division in 1976-77."
}
]
},
{
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"Indiana Pacers",
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Pacers won 36 of their games, which amounts to only .439 for their PCT."
}
]
},
{
"tripleset": [
[
"Milwaukee Bucks",
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Milwaukee performed drastically worse on the road with 6-35 compared to their 24-17 when having home court advantage."
}
]
},
{
"tripleset": [
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],
"subtree_was_extended": false,
"annotations": [
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"source": "WikiSQL_decl_sents",
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}
]
},
{
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"source": "WikiSQL_lily",
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]
},
{
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"-102.602182"
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"Hettinger"
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],
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"annotations": [
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"source": "WikiSQL_lily",
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]
},
{
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],
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"annotations": [
{
"source": "WikiSQL_lily",
"text": "1036399 ANSI code is in Cass county."
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]
},
{
"tripleset": [
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],
"subtree_was_extended": false,
"annotations": [
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"source": "WikiSQL_decl_sents",
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}
]
},
{
"tripleset": [
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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}
]
},
{
"tripleset": [
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"[TITLE]",
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]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "During the 1973 South Australian state election, the Liberal and Country League had a swing of -3.97, 20 seats, and a change of 0."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"PARTY",
"Nationals SA"
],
[
"Nationals SA",
"CHANGE",
"+1"
],
[
"Nationals SA",
"PERCENT",
"3.94"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"South Australian state election, 1973"
],
[
"Nationals SA",
"VOTES",
"24,810"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Nationals SA got 24,810, or 3.94%, in the 1973 South Australian state election, for a change of +1."
}
]
},
{
"tripleset": [
[
"Independent",
"SWING",
"+2.86"
],
[
"[TABLECONTEXT]",
"PARTY",
"Independent"
],
[
"Independent",
"VOTES",
"27,178"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"South Australian state election, 1973"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Independents got 27,178 votes in the 1973 South Australian state election, with a swing of +2.86."
}
]
},
{
"tripleset": [
[
"Other",
"CHANGE",
"0"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"South Australian state election, 1973"
],
[
"Other",
"SEATS",
"0"
],
[
"[TABLECONTEXT]",
"PARTY",
"Other"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Other candidates had 0 seats and a change of zero in the 1973 South Australian state election."
}
]
},
{
"tripleset": [
[
"New York 9",
"INCUMBENT",
"Charles Schumer"
],
[
"New York 9",
"CANDIDATES",
"Anthony Weiner (D) 66% Leslie Telano (R) 24%"
],
[
"Charles Schumer",
"FIRST_ELECTED",
"1980"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The candidates in the district that first elected in 1980 were anthony weiner (d) 66% leslie telano (r) 24%."
}
]
},
{
"tripleset": [
[
"Ed Towns",
"FIRST_ELECTED",
"1982"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The first elected year of the district that Ed Towns is an incumbent of's 1982."
}
]
},
{
"tripleset": [
[
"Jose Serrano",
"PARTY",
"Democratic"
],
[
"Jose Serrano",
"FIRST_ELECTED",
"1990"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The party elected in the district that first elected in 1990's democratic."
}
]
},
{
"tripleset": [
[
"John McHugh",
"PARTY",
"Republican"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "John McHugh is a member of republican."
}
]
},
{
"tripleset": [
[
"LS ( Leading Seaman )",
"MECHANICAL",
"LME"
],
[
"LS ( Leading Seaman )",
"REGULATING",
"LPM"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "LME comes with LPM regulation."
}
]
},
{
"tripleset": [
[
"LS ( Leading Seaman )",
"REGULATING",
"LPM"
],
[
"LS ( Leading Seaman )",
"RADIO_ELECTRICAL",
"LREN"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "LPM regulattopm comes with radio electrical IREN."
}
]
},
{
"tripleset": [
[
"15-01",
"EPISODE",
"183"
],
[
"15-01",
"SEGMENT_D",
"High-Performance Engines"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Segment D for episode 183 was high-performance engines."
}
]
},
{
"tripleset": [
[
"15-06",
"SEGMENT_D",
"Luxury Sports Cars"
],
[
"15-06",
"SEGMENT_A",
"s Pipe"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "When Segment D was luxury sports cars, segment A was s pipe."
}
]
},
{
"tripleset": [
[
"12",
"TEAM",
"charlotte"
],
[
"12",
"SCORE",
"w 88-83 (ot)"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The scores made by Charlotte team are w 88\u201383 (ot)"
}
]
},
{
"tripleset": [
[
"6",
"HIGH_REBOUNDS",
"al horford (17)"
],
[
"6",
"HIGH_ASSISTS",
"joe johnson (8)"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Joe johnson (8) made highest assists when high rebounds was Al Horford (17)"
}
]
},
{
"tripleset": [
[
"Georgian",
"FRIDAY_DAY_SIX",
"\u10de\u10d0\u10e0\u10d0\u10e1\u10d9\u10d4\u10d5\u10d8 p'arask'evi"
],
[
"Georgian",
"THURSDAY_DAY_FIVE",
"\u10ee\u10e3\u10d7\u10e8\u10d0\u10d1\u10d0\u10d7\u10d8 xut\u0161abati"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Thursday day five when friday day six is \u10de\u10d0\u10e0\u10d0\u10e1\u10d9\u10d4\u10d5\u10d8 p'arask'evi is \u10ee\u10e3\u10d7\u10e8\u10d0\u10d1\u10d0\u10d7\u10d8 xut\u0161abati."
}
]
},
{
"tripleset": [
[
"Amharic",
"SATURDAY_DAY_SEVEN",
"\u1245\u12f3\u121c \u1e33\u0259dame (First)"
],
[
"Amharic",
"THURSDAY_DAY_FIVE",
"\u1210\u1219\u1235 hamus"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Saturday day seven when thursday day five is \u1210\u1219\u1235 hamus is \u1245\u12f3\u121c \u1e33\u0259dame (first."
}
]
},
{
"tripleset": [
[
"Malay",
"FRIDAY_DAY_SIX",
"Jumaat [\u26404 ]"
],
[
"Malay",
"MONDAY_DAY_TWO",
"Isnin"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Friday day six when monday day two is isnin is jumaat [\u2640 4."
}
]
},
{
"tripleset": [
[
"Indonesian",
"TUESDAY_DAY_THREE",
"Selasa"
],
[
"Indonesian",
"THURSDAY_DAY_FIVE",
"Kamis"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Tuesday day three when thursday day five is kamis is selasa."
}
]
},
{
"tripleset": [
[
"Khowar",
"THURSDAY_DAY_FIVE",
"\u067e\u0686\u06be\u0645\u0628\u06d2 pachhambey"
],
[
"Khowar",
"FRIDAY_DAY_SIX",
"\u0622\u062f\u06cc\u0646\u06c1 [\u26403 ] adina"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Friday day six when thursday day five is \u067e\u0686\u06be\u0645\u0628\u06d2 pachhambey is \u0622\u062f\u06cc\u0646\u06c1 [\u26403] adina."
}
]
},
{
"tripleset": [
[
"Lamar",
"POVERTY_RATE",
"16.1%"
],
[
"Lamar",
"MARKET_INCOME_PER_CAPITA",
"$16,420"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The poverty rate in the county that has a market income per capita of $16,420 is 16.1%."
}
]
},
{
"tripleset": [
[
"Marshall",
"STATUS",
"Transitional"
],
[
"Marshall",
"POVERTY_RATE",
"14.7%"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The status of the county that has a 14.7% poverty rate is transitional."
}
]
},
{
"tripleset": [
[
"Tallapoosa",
"POVERTY_RATE",
"16.6%"
],
[
"Tallapoosa",
"MARKET_INCOME_PER_CAPITA",
"$20,518"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The povery rate of the county with market income per capita of $20,518 is 16.6%."
}
]
},
{
"tripleset": [
[
"\u0424\u0435\u0440\u043c\u0430\u0442\u0430 Farma",
"MAIN_PRESENTERS",
"TBA (Season 1)"
],
[
"\u0424\u0435\u0440\u043c\u0430\u0442\u0430 Farma",
"REGION/COUNTRY",
"Bulgaria"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The main presenter for Bulgaria is TBA (season 1)."
}
]
},
{
"tripleset": [
[
"La Granja",
"MAIN_PRESENTERS",
"Sergio Lagos (Season 1-3)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The main presenter of La Granja is Sergio Lagos (season 1 - 3)."
}
]
},
{
"tripleset": [
[
"BP6 / DT1",
"OPENING",
"6 November 1999"
],
[
"BP6 / DT1",
"STATION_NAME_TAMIL",
"\u0baa\u0bc1\u0b95\u0bcd\u0b95\u0bbf\u0b9f\u0bcd \u0baa\u0bbe\u0b9e\u0bcd\u0b9a\u0bbe\u0b99\u0bcd"
],
[
"BP6 / DT1",
"STATION_NAME_ENGLISH",
"Bukit Panjang"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Bukit Panjang station, Tamil name \u0baa\u0bc1\u0b95\u0bcd\u0b95\u0bbf\u0b9f\u0bcd \u0baa\u0bbe\u0b9e\u0bcd\u0b9a\u0bbe\u0b99\u0bcd, opened on 6 November 1999."
}
]
},
{
"tripleset": [
[
"BP14",
"STATION_NAME_CHINESE",
"\u5341\u91cc\u5e7f\u573a"
],
[
"BP14",
"STATION_NAME_ENGLISH",
"Ten Mile Junction"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The alpha-numeric code for Ten Mile Junction station (Chinese name: \u5341\u91cc\u5e7f\u573a) is BP14."
}
]
},
{
"tripleset": [
[
"SE2",
"STATION_NAME_ENGLISH",
"Rumbia"
],
[
"SE2",
"OPENING",
"18 January 2003"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Rumbia, station code SE2, opened on Jan 18th 2003."
}
]
},
{
"tripleset": [
[
"PTC / NE17",
"OPENING",
"29 January 2005"
],
[
"PTC / NE17",
"STATION_NAME_ENGLISH",
"Punggol"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Punggol, station code PTC / NE17, started operation on 29 January 2005."
}
]
},
{
"tripleset": [
[
"PW5",
"STATION_NAME_ENGLISH",
"Nibong"
],
[
"[TABLECONTEXT]",
"ALPHA-NUMERIC_CODE",
"PW5"
],
[
"PW5",
"OPENING",
"TBA"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"List of Singapore LRT stations"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Singapore's new LRT station PW5, Nibong, is yet to be opened for operation."
}
]
},
{
"tripleset": [
[
"Ban\u00edk Ostrava (1)",
"THIRD_PLACE",
"Sigma Olomouc"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Sigma olomouc had third place when the champions is ban\u00edk ostrava (1)."
}
]
},
{
"tripleset": [
[
"Louisiana 3",
"CANDIDATES",
"Dave Treen (R) Unopposed"
],
[
"Louisiana 3",
"INCUMBENT",
"Dave Treen"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Dave treen (r) unopposed ran against Dave Treen."
}
]
},
{
"tripleset": [
[
"Louisiana 3",
"INCUMBENT",
"Dave Treen"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Dave Treen served louisiana."
}
]
},
{
"tripleset": [
[
"Jerry Huckaby",
"PARTY",
"Democratic"
],
[
"Jerry Huckaby",
"FIRST_ELECTED",
"1976"
],
[
"Louisiana 5",
"RESULT",
"Re-elected"
],
[
"Louisiana 5",
"INCUMBENT",
"Jerry Huckaby"
],
[
"Louisiana 5",
"CANDIDATES",
"Jerry Huckaby (D) Unopposed"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Jerry Huckaby was first elected in 1976.0."
}
]
},
{
"tripleset": [
[
"Ferruccio Busoni",
"DIED",
"1924"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Ferruccio Busoni died in 1924."
}
]
},
{
"tripleset": [
[
"Baiti",
"FORMER_NAME",
"Beidi"
],
[
"Baiti",
"POPULATION_(2005)",
"572"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "With the population in 2005 of 572, the former name is beidi."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"Charlotte Eagles Year-by-year"
],
[
"[TABLECONTEXT]",
"YEAR",
"1998"
],
[
"1998",
"PLAYOFFS",
"Quarterfinals"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Charlotte Eagles entered quaterfinals in Playoffs in 1998"
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"YEAR",
"2003"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"Charlotte Eagles Year-by-year"
],
[
"2003",
"OPEN_CUP",
"Did not qualify"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Charlotte Eagles didn't qualify for the Open Cup in 2003"
}
]
},
{
"tripleset": [
[
"Fylkir",
"PLAYED",
"18"
],
[
"Fylkir",
"DRAW",
"6"
],
[
"Fylkir",
"LOST",
"3"
],
[
"Fylkir",
"POSITION",
"2"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Team Fylikir gained 33 points in 1988."
}
]
},
{
"tripleset": [
[
"Selfoss",
"LOST",
"7"
],
[
"Selfoss",
"POSITION",
"5"
],
[
"Selfoss",
"WON",
"7"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "In 1988 Dield Karla was part of the Selfoss team in 5th position, with 7 wins and 7 losses."
}
]
},
{
"tripleset": [
[
"\u00cdBV",
"WON",
"6"
],
[
"\u00cdBV",
"LOST",
"10"
],
[
"\u00cdBV",
"POINTS",
"20"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Team \u00cdBV won 6 matches and lose 10 matches their point is 20."
}
]
},
{
"tripleset": [
[
"\u00cdR",
"POSITION",
"4"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The \u00cdR team ranked the 4th position"
}
]
},
{
"tripleset": [
[
"9",
"PROD._CODE",
"10"
],
[
"9",
"DIRECTED_BY",
"terry hughes"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Terry hughes directed the episode with the production code 10."
}
]
},
{
"tripleset": [
[
"5",
"PROD._CODE",
"2"
],
[
"5",
"WRITTEN_BY",
"michelle j. wolff"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Michelle j. wolff wrote the episode with production code 2."
}
]
},
{
"tripleset": [
[
"3",
"WRITTEN_BY",
"craig hoffman"
],
[
"3",
"PROD._CODE",
"5"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Craig hoffman wrote the episode with production code 5."
}
]
},
{
"tripleset": [
[
"MetroStars",
"OVERALL_RECORD",
"11-12-7"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"Goal-Scoring Totals for the 2004 Major League Soccer season"
],
[
"[TABLECONTEXT]",
"CLUB",
"MetroStars"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "The overall recrod of MetroStars is 11-12-7 in the 2004 Major League Soccer season"
}
]
},
{
"tripleset": [
[
"D.C. United",
"GOALS_FOR",
"43"
],
[
"[TABLECONTEXT]",
"CLUB",
"D.C. United"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"Goal-Scoring Totals for the 2004 Major League Soccer season"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "D.C. United scored 43 goals in the 2004 Major League Soccer seaon"
}
]
},
{
"tripleset": [
[
"42",
"GOALS_FOR_AVG.",
"1.40 (3rd)"
],
[
"[TABLECONTEXT]",
"CLUB",
"Los Angeles Galaxy"
],
[
"Los Angeles Galaxy",
"GOALS_FOR",
"42"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"Goal-Scoring Totals for the 2004 Major League Soccer season"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Los Angeles Galaxy scored 1.40 goals per game in 2004 Major League Soccer season"
}
]
},
{
"tripleset": [
[
"New England Revolution",
"GOALS_AGAINST",
"43"
],
[
"[TABLECONTEXT]",
"CLUB",
"New England Revolution"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"Goal-Scoring Totals for the 2004 Major League Soccer season"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "New England Revolution lost 43 goals in the 2004 Major League Soccer season"
}
]
},
{
"tripleset": [
[
"San Jose Earthquakes",
"GOALS_AGAINST",
"35"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"Goal-Scoring Totals for the 2004 Major League Soccer season"
],
[
"35",
"GOALS_AGAINST_AVG.",
"1.17 (4th)"
],
[
"[TABLECONTEXT]",
"CLUB",
"San Jose Earthquakes"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "San Jose Earthquakes lost 1.17 goals per game in the 2004 Major League Soccer season"
}
]
},
{
"tripleset": [
[
"Dallas Burn",
"OVERALL_RECORD",
"10-14-6"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The Dallas Burn club had an overall record of 10-14-6."
}
]
},
{
"tripleset": [
[
"5",
"CABLE_RANK",
"17"
],
[
"5",
"AIRDATE",
"22 April 2010"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "22 april 2010 the episode air that had a ranking of 17 for cable."
}
]
},
{
"tripleset": [
[
"1945-46",
"CONF.",
"0-4"
],
[
"1945-46",
"OVERALL",
"3-7"
],
[
"1945-46",
"COACH",
"Henry Swasey"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The New Hampshire Wildcats Basketball team is the basketball team that represent the University of New Hampshire in Durham"
}
]
},
{
"tripleset": [
[
"1946-47",
"OVERALL",
"6-11"
],
[
"1946-47",
"CONF.",
"0-5"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The team went 0-5 in conference games, and finished 6-11 overall during the 1946-47 season."
}
]
},
{
"tripleset": [
[
"2001(Bulldogs)",
"GOALS",
"1"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Bulldogs scored 1 goal in 2001."
}
]
},
{
"tripleset": [
[
"2003(Bulldogs)",
"POINTS",
"34"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Bulldogs scored 34 points in 2003"
}
]
},
{
"tripleset": [
[
"2006(Roosters)",
"POINTS",
"0"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Roosters scored 0 points in 2006."
}
]
},
{
"tripleset": [
[
"2013(Tigers)",
"POINTS",
"12"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Tigers played in 2013 and scored 12 points."
}
]
},
{
"tripleset": [
[
"2012(Roosters)",
"POINTS",
"140"
],
[
"2012(Roosters)",
"GOALS",
"62"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The Roosters scored 62 goals and 140 points in the 2012 season."
}
]
},
{
"tripleset": [
[
"203",
"FIRST_FLEW",
"31 January 1975"
],
[
"203",
"REGISTRATION",
"F-BTSC"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The registration located on 31 january 1975 where first flew is f - btsc."
}
]
},
{
"tripleset": [
[
"Liam Buchanan",
"TOTAL",
"14"
],
[
"14",
"LEAGUE",
"11"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Liam Buchanan scored 11 goals in the League"
}
]
},
{
"tripleset": [
[
"Mark Roberts",
"TOTAL",
"6"
],
[
"6",
"SCOTTISH_CUP",
"1"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Mark Roberts scored 1 goal in the Scottish Cup"
}
]
},
{
"tripleset": [
[
"Scott Chaplain",
"TOTAL",
"5"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Scott Chaplain scored 5 goals in total"
}
]
},
{
"tripleset": [
[
"Gary Harkins",
"TOTAL",
"3"
],
[
"3",
"LEAGUE_CUP",
"1"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Gary Harkins scored 1 goal in the League Cup"
}
]
},
{
"tripleset": [
[
"Paul Di Giacomo",
"TOTAL",
"2"
],
[
"2",
"CHALLENGE_CUP",
"1"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Paul Di Giacomo scored 1 goal in the Challenge Cup"
}
]
},
{
"tripleset": [
[
"Uladzimir Kazlou",
"POSITION",
"7th"
],
[
"Uladzimir Kazlou",
"COMPETITION",
"European U23 Championships"
],
[
"European U23 Championships",
"YEAR",
"2005"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Uladzimir Kazlou finished 7th in the 2005 European U23 championships."
}
]
},
{
"tripleset": [
[
"Universiade",
"YEAR",
"2009"
],
[
"Universiade",
"NOTES",
"78.29 m"
],
[
"Universiade",
"VENUE",
"Belgrade, Serbia"
],
[
"Uladzimir Kazlou",
"COMPETITION",
"Universiade"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Uladzimir Kazlou got 78.29 m at the 2009 Universiade in Belgrade, Serbia."
}
]
},
{
"tripleset": [
[
"Uladzimir Kazlou",
"COMPETITION",
"Olympic Games"
],
[
"Uladzimir Kazlou",
"POSITION",
"15th (q)"
],
[
"Olympic Games",
"VENUE",
"London, United Kingdom"
],
[
"Olympic Games",
"YEAR",
"2012"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Uladzimir Kazlou finshed in 15th place at the London Olympics."
}
]
},
{
"tripleset": [
[
"49",
"DATE",
"february 6"
],
[
"49",
"HIGH_POINTS",
"dwight howard (21)"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Dwight howard (21) had the high points on February 6."
}
]
},
{
"tripleset": [
[
"hubris",
"DIRECTED_BY",
"timothy van patten"
],
[
"hubris",
"ORIGINAL_AIR_DATE",
"march 27, 1997"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The original air date of the episode directed by Timothy van Patten is March 27, 1997."
}
]
},
{
"tripleset": [
[
"no place like hell",
"ORIGINAL_AIR_DATE",
"may 8, 1997"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The original air date for the episode \"No Place Like Hell\" is May 8, 1997."
}
]
},
{
"tripleset": [
[
"school's out",
"ORIGINAL_AIR_DATE",
"february 6, 1997"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The episode \"School's Out\" was originally aired on February 6, 1997."
}
]
},
{
"tripleset": [
[
"Tamil",
"SUNDAY_SURYA_(THE_SUN)",
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Ny\u0101yitru kizhamai is the word for Sunday in Tamil"
}
]
},
{
"tripleset": [
[
"Tamil",
"SATURDAY_SHANI_(SATURN)",
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],
[
"Tamil",
"SUNDAY_SURYA_(THE_SUN)",
"\u0b9e\u0bbe\u0baf\u0bbf\u0bb1\u0bcd\u0bb1\u0bc1 \u0b95\u0bbf\u0bb4\u0bae\u0bc8 Ny\u0101yitru kizhamai"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "In Tamil, Sunday is \u0b9e\u0bbe\u0baf\u0bbf\u0bb1\u0bcd\u0bb1\u0bc1 \u0b95\u0bbf\u0bb4\u0bae\u0bc8 or ny\u0101yitru kizhamai, while Saturday is \u0b9a\u0ba9\u0bbf\u0b95\u0bcd \u0b95\u0bbf\u0bb4\u0bae\u0bc8 or shani kizhamai."
}
]
},
{
"tripleset": [
[
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"MONDAY_SOMA_(THE_MOON)",
"\u0907\u0928\u094d\u0926\u0941\u0935\u093e\u0938\u0930\u092e\u094d Indu V\u0101saram"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Indu V\u0101saram is the word for Monday in Sanskrit"
}
]
},
{
"tripleset": [
[
"Hindi",
"TUESDAY_MANGALA_(MARS)",
"\u092e\u0902\u0917\u0932\u0935\u093e\u0930 Mangalav\u0101r"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Mangalav\u0101r is the word for Tuesday in Hindi"
}
]
},
{
"tripleset": [
[
"Marathi",
"WEDNESDAY_BUDHA_(MERCURY)",
"\u092c\u0941\u0927\u0935\u093e\u0930 Budhav\u0101r"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Budhav\u0101r is the word for Wednesday in Marathi"
}
]
},
{
"tripleset": [
[
"Bengali",
"THURSDAY_GURU_(JUPITER)",
"\u09ac\u09c3\u09b9\u09b8\u09cd\u09aa\u09a4\u09bf\u09ac\u09be\u09b0 Brih\u00f4shpotibar"
],
[
"Bengali",
"WEDNESDAY_BUDHA_(MERCURY)",
"\u09ac\u09c1\u09a7\u09ac\u09be\u09b0 Budhbar"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "In Bengali, Wednesday is \u09ac\u09c1\u09a7\u09ac\u09be\u09b0 or \"budhbar,\" while Thursday is \u09ac\u09c3\u09b9\u09b8\u09cd\u09aa\u09a4\u09bf\u09ac\u09be\u09b0 or \"brih\u00f4shpotibar.\""
}
]
},
{
"tripleset": [
[
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"SUNDAY_SURYA_(THE_SUN)",
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],
[
"Kashmiri",
"THURSDAY_GURU_(JUPITER)",
"\u0628\u0631\u0633 \u0648\u0627\u0631 Bres'var"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "In language where Thursday is \u0628\u0631\u0633 \u0648\u0627\u0631 bres'var, \u0627\u064e\u062a\u06be \u0648\u0627\u0631 aath'var is Sunday."
}
]
},
{
"tripleset": [
[
"Kashmiri",
"WEDNESDAY_BUDHA_(MERCURY)",
"\u0628\u0631\u06be \u0648\u0627\u0631 Budh'var"
],
[
"Kashmiri",
"SATURDAY_SHANI_(SATURN)",
"\u0628\u0679 \u0648\u0627\u0631 Bat'var"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "In language where Wednesday is \u0628\u0631\u06be \u0648\u0627\u0631 budh'var, \u0628\u0679 \u0648\u0627\u0631 bat'var is Saturday."
}
]
},
{
"tripleset": [
[
"Sinhala",
"SATURDAY_SHANI_(SATURN)",
"\u0dc3\u0dd9\u0db1\u0dc3\u0dd4\u0dbb\u0dcf\u0daf\u0dcf Senasuraadaa"
],
[
"Sinhala",
"TUESDAY_MANGALA_(MARS)",
"\u0d85\u0d9f\u0dc4\u0dbb\u0dd0\u0dc0\u0daf\u0dcf Anngaharuwadaa"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "In Sinhala, Saturday is \u0dc3\u0dd9\u0db1\u0dc3\u0dd4\u0dbb\u0dcf\u0daf\u0dcf or senasuraadaa, while Tuesday is \u0d85\u0d9f\u0dc4\u0dbb\u0dd0\u0dc0\u0daf\u0dcf anngaharuwadaa."
}
]
},
{
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[
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"THURSDAY_GURU_(JUPITER)",
"Respati"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Respati is the word for Thursday inJavanese"
}
]
},
{
"tripleset": [
[
"Austrian Grand Prix",
"CONSTRUCTOR",
"Ferrari"
],
[
"Austrian Grand Prix",
"POLE_POSITION",
"Rubens Barrichello"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The pole position for the ferrari at the austrian grand prix is rubens barrichello."
}
]
},
{
"tripleset": [
[
"Canadian Grand Prix",
"FASTEST_LAP",
"Juan Pablo Montoya"
],
[
"Canadian Grand Prix",
"REPORT",
"Report"
],
[
"Canadian Grand Prix",
"RD.",
"8"
],
[
"Canadian Grand Prix",
"CONSTRUCTOR",
"Ferrari"
],
[
"Canadian Grand Prix",
"WINNING_DRIVER",
"Michael Schumacher"
],
[
"Canadian Grand Prix",
"POLE_POSITION",
"Juan Pablo Montoya"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The rd for the canadian grand prix is 8.0."
}
]
},
{
"tripleset": [
[
"European Grand Prix",
"FASTEST_LAP",
"Michael Schumacher"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The fastest lap for the european grand prix is michael schumacher."
}
]
},
{
"tripleset": [
[
"U.S. Route 30 Bypass",
"SOUTH/WEST_END",
"US 30 in Portland"
],
[
"U.S. Route 30 Bypass",
"FORMED",
"1936"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "US Route 30 in Oregon was formed in 1936"
}
]
},
{
"tripleset": [
[
"CKYB-TV",
"NETWORK",
"CTV"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"List of television stations in Manitoba"
],
[
"CKYB-TV",
"CITY_OF_LICENCE",
"Brandon"
],
[
"[TABLECONTEXT]",
"CALLSIGN",
"CKYB-TV"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "CKYB-TV is a CTV Network television station in Brandon, Manitoba."
}
]
},
{
"tripleset": [
[
"CKYA-TV",
"NOTES",
"satellite of CKY-DT Winnipeg"
],
[
"CKYA-TV",
"ANALOG_CHANNEL",
"8"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Channel 8 station CKYA-TV is a satellite of CKY-DT Winnipeg."
}
]
},
{
"tripleset": [
[
"CHMI-DT",
"DIGITAL_CHANNEL",
"13"
],
[
"CHMI-DT",
"VIRTUAL_CHANNEL",
"13.1"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "On channel 13 and 13.1 airs on CHMI-DT."
}
]
},
{
"tripleset": [
[
"CBWT-DT",
"NETWORK",
"CBC"
],
[
"CBWT-DT",
"DIGITAL_CHANNEL",
"27"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "You can find CBWT-DT on CBC channel 27."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"List of television stations in Manitoba"
],
[
"[TABLECONTEXT]",
"CALLSIGN",
"CKND-DT"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "CKND-DT is a callsign for a television station in Manitoba."
}
]
},
{
"tripleset": [
[
"CKYB-TV",
"NOTES",
"satellite of CKY-DT Winnipeg"
],
[
"CKYB-TV",
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"CTV"
],
[
"CKYB-TV",
"CITY_OF_LICENCE",
"Mccreary"
],
[
"CKYB-TV",
"ANALOG_CHANNEL",
"13"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Manitoba has a station at channel 13 that has a license from Mccreary. Its callsign is CYKB-TV on network CTV which is the satellite of CKY-DT."
}
]
},
{
"tripleset": [
[
"french open",
"OPPONENTS_IN_THE_FINAL",
"nathalie dechy andy ram"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "French open had the final opponents of nathalie dechy andy ram."
}
]
},
{
"tripleset": [
[
"Greg Bryant",
"HOMETOWN",
"Delray Beach, Florida"
],
[
"Greg Bryant",
"SCHOOL",
"American Heritage School"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The hometown of the player who attended American Heritage School is Delray Beach, Florida."
}
]
},
{
"tripleset": [
[
"Manchester Phoenix",
"ARENA",
"Altrincham Ice Dome"
],
[
"Manchester Phoenix",
"COACH",
"Tony Hand"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Tony hand is the head coach for the team that plays at Altrincham Ice Dome."
}
]
},
{
"tripleset": [
[
"frankfurt",
"SCORE_IN_THE_FINAL",
"4-6, 6-3, 7-5, 6-4"
],
[
"1994",
"CHAMPIONSHIP",
"frankfurt"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The score in the final in the year 1994 is 4 \u2013 6, 6 \u2013 3, 7 \u2013 5, 6\u20134."
}
]
},
{
"tripleset": [
[
"1993",
"CHAMPIONSHIP",
"frankfurt"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Frankfurt is the championship for 1993."
}
]
},
{
"tripleset": [
[
"1991",
"CHAMPIONSHIP",
"frankfurt"
],
[
"frankfurt",
"OPPONENT_IN_THE_FINAL",
"jim courier"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Jim courier is the opponent in the final when frankfurt is championship and the year is 1991."
}
]
},
{
"tripleset": [
[
"2002",
"RUNNER-UP",
"UCLA"
],
[
"[TABLECONTEXT]",
"YEAR",
"2002"
],
[
"2002",
"SCORE",
"8-4"
],
[
"2002",
"NATIONAL_CHAMPION",
"Stanford"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"NCAA Women's Water Polo Championship"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Stanford emerged as the 2002 NCAA Women's Water Polo champion after defeating UCLA 8-4."
}
]
},
{
"tripleset": [
[
"2005",
"HOST_OR_SITE",
"University of Michigan, Canham Natatorium, Ann Arbor, Michigan"
],
[
"2005",
"RUNNER-UP",
"Stanford"
],
[
"2005",
"SCORE",
"3-2"
],
[
"2005",
"NATIONAL_CHAMPION",
"UCLA (3)"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "UCLA's team prevailed over Stanford with a close three to two score at the University of Michigan, winning the championship."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"NCAA Women's Water Polo Championship"
],
[
"[TABLECONTEXT]",
"YEAR",
"2009"
],
[
"2009",
"NATIONAL_CHAMPION",
"UCLA (7)"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "UCLA women's water polo team was the 2009 NCAA champion for the sport."
}
]
},
{
"tripleset": [
[
"2010",
"RUNNER-UP",
"Stanford"
],
[
"2010",
"SCORE",
"10-9"
],
[
"[TABLECONTEXT]",
"[TITLE]",
"NCAA Women's Water Polo Championship"
],
[
"[TABLECONTEXT]",
"YEAR",
"2010"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Stanford lost the women's water polo championship 10-9, a close match."
}
]
},
{
"tripleset": [
[
"2011",
"SCORE",
"9-5"
],
[
"2011",
"RUNNER-UP",
"California"
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[
"2011",
"NATIONAL_CHAMPION",
"Stanford (2)"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The NCAA Women's Water Polo NC official home. Get Women's Water Polo rankings, news, schedules and championship brackets."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"YEAR",
"2012"
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[
"2012",
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[
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"[TITLE]",
"NCAA Women's Water Polo Championship"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The 2012 NCAA women's water polo championship was hosted by San Diego State at their Aztec Aquaplex."
}
]
},
{
"tripleset": [
[
"Jozu Rill",
"POPULATION_ROMANIA",
"30,000-35,000"
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[
"[TABLECONTEXT]",
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"1864"
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[
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"[TITLE]",
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[
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[
"1864",
"SOURCE",
"Jozu Rill"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "According to Jozu Rill, there were approximately 30,000-35,000 Banat Bulgarians in Romania and Serbia."
}
]
},
{
"tripleset": [
[
"Hungarian statistics",
"POPULATION_ROMANIA",
"13,536"
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[
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[
"[TABLECONTEXT]",
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"1910"
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[
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"[TITLE]",
"Banat Bulgarians"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Hungarian statistics estimated in 1910 that about 13536 Banat Bulgarians resided in Romania."
}
]
},
{
"tripleset": [
[
"1939",
"SOURCE",
"Romanian census"
],
[
"Romanian census",
"POPULATION_ROMANIA",
"9,951"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The 1939 Romanian census indicated the Romanian Banat Population is 9951."
}
]
},
{
"tripleset": [
[
"Karol Telbizov",
"POPULATION_ROMANIA",
"12,000"
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[
"1940",
"SOURCE",
"Karol Telbizov"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Karol Telbizov's estimation of the Romanian Banat population is around 12000."
}
]
},
{
"tripleset": [
[
"Yugoslav census",
"POPULATION_SERBIA",
"3,745"
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[
"1971",
"SOURCE",
"Yugoslav census"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "In 1971, the census conducted by Yugoslavia indicated there are 3745 Serbian Banat."
}
]
},
{
"tripleset": [
[
"2002",
"SOURCE",
"Serbian census"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The Serbian census was conducted in 2002."
}
]
},
{
"tripleset": [
[
"2002",
"SOURCE",
"Romanian census"
],
[
"Romanian census",
"POPULATION_ROMANIA",
"6,486"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "On 2002 Romanian census was 6,486."
}
]
},
{
"tripleset": [
[
"Class 170/4",
"YEAR_OF_CONSTRUCTION",
"2004"
],
[
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"NO._BUILT",
"7"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Number 7 was built in 2004."
}
]
},
{
"tripleset": [
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"119"
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[
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"1"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Lisa's Mudder Comes for a Visit is Number 119 in the series and number 1 in the season."
}
]
},
{
"tripleset": [
[
"A Tale of a Tail",
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[
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"Jay Sommers and Dick Chevillat"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "A Tale of a Tail is directed by Richard L. Bare and written by Jay Sommers and Dick Chevillat."
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]
},
{
"tripleset": [
[
"The Youth Center",
"ORIGINAL_AIR_DATE",
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[
"The Youth Center",
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"8"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Number 8 in the season and aired on November 15, 1969, was the Green Acre Episode titled The Youth Center"
}
]
},
{
"tripleset": [
[
"Beauty is Skin Deep",
"ORIGINAL_AIR_DATE",
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[
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"125"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Beauty is Skin Deep was aired on December 27, 1969 with the productioon code of 125."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"List of Green Acres episodes, Season 5 (1969-70)"
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"The Ex-Con"
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[
"The Ex-Con",
"SEASON_#",
"18"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "The Ex-Con is number 18 in the season and number 136 in the series."
}
]
},
{
"tripleset": [
[
"the wealthy landowner",
"SEASON_#",
"25"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "\"The Wealthy Landowner\" was the 25th episode in its season."
}
]
},
{
"tripleset": [
[
"trapped",
"PRODUCTION_CODE",
"136"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The episode \"Trapped\" had production code 136."
}
]
},
{
"tripleset": [
[
"johnny williams",
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"slb"
],
[
"johnny williams",
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"sr."
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The class for the SLB position is sr."
}
]
},
{
"tripleset": [
[
"ashton cobb",
"WEIGHT",
"208lb."
],
[
"ashton cobb",
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Number 27 weighs 208 lb."
}
]
},
{
"tripleset": [
[
"ashton cobb",
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Ashton Cobb plays FS."
}
]
},
{
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[
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[
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[
"16 May 1991",
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"Win"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "On 16 May 1991, Jari Litmanen won a game with goals 87' 2-0."
}
]
},
{
"tripleset": [
[
"25 Mar 1992",
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[
"25 Mar 1992",
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"Scotland"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Jari Litmanen played a game in Hampden Park with Scotland as the home team and Finland as visiting."
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]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
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"60' 3-2",
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"13 Oct 1993"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Jari Litmanen played a game on 13 October 1993 where Sweden was home and the score was 3-2."
}
]
},
{
"tripleset": [
[
"16 Nov 1994",
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"Helsinki Olympic Stadium"
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"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "On November 16, 1994, Jari Litmanen played a game at the Helsinki Olympic Stadium for the UEFA Euro 1996 qualifying round."
}
]
},
{
"tripleset": [
[
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"16 Nov 1994",
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Jari Litmanen won a game 5-0 where Finland was the home team."
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]
},
{
"tripleset": [
[
"Raymond van Barneveld",
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[
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[
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"Jelle Klaasen"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Raymond van Barneveld won Jelle Klaasen in round Quarter-Final in 2009 PDC World Darts Championship"
}
]
},
{
"tripleset": [
[
"2nd Round",
"OPPONENT",
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[
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[
"2nd Round",
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]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Raymond van Barnevel won against Brendan Dolan"
}
]
},
{
"tripleset": [
[
"Final",
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[
"[TABLECONTEXT]",
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[
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[
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[
"Adrian Lewis",
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[
"Adrian Lewis",
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]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Adrian Lewis won Gary Anderson in the final round of 2011 PDC World Darts Championship"
}
]
},
{
"tripleset": [
[
"2nd Round",
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[
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[
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[
"Dean Winstanley",
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[
"Dean Winstanley",
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"2013"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Dean Winstanley's opponent is Vincent van der Voort in 2nd Round in 2013 PDC World Darts Championship"
}
]
},
{
"tripleset": [
[
"Semi Final",
"OPPONENT",
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[
"Michael van Gerwen",
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"Semi Final"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Michael van Gerwen entered in a Semi Final\this opponent was James Wade"
}
]
},
{
"tripleset": [
[
"Terry Jenkins",
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"1st Round"
],
[
"Terry Jenkins",
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[
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[
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[
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[
"[TABLECONTEXT]",
"PLAYER",
"Terry Jenkins"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Terry Jenkins lost the game with Per Laursen in the 1st Round of 2014 PDC World Darts Championship"
}
]
},
{
"tripleset": [
[
"Pulteney Grammar School",
"LOCATION",
"Adelaide"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Pulteney grammar school is in adelaide."
}
]
},
{
"tripleset": [
[
"Seymour College",
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"Uniting Church"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The denomination of Seymour College is \"uniting church.\""
}
]
},
{
"tripleset": [
[
"Ahmednagar",
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],
[
"Ahmednagar",
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],
[
"Ahmednagar",
"NUMBER",
"1"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "No 1, Ahmednagar (AH) was formed on May 1st, 1960."
}
]
},
{
"tripleset": [
[
"Amravati",
"ADMINISTRATIVE_DIVISION",
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[
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The headquarters of Amravati is in Amravati is part of the Amravati administrative division."
}
]
},
{
"tripleset": [
[
"Bhandara",
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[
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[
"Bhandara",
"AREA_KM2",
"3,717"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Bhandara has an area of 3,717 km2 and a population of 1.1 million, accounting for 1.17 percent of state population."
}
]
},
{
"tripleset": [
[
"Mumbai City",
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[
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[
"Mumbai City",
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"3,326,837"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Mumbai City has a density of 49,140 km2 and is 100 percent urban population."
}
]
},
{
"tripleset": [
[
"Nashik",
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"927"
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[
"Nashik",
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[
"Nashik",
"LITERACY_(%)",
"74.4"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "According to the district website, Nashik has a litaeracy rate of 74 percent and a 927 sex ratio and 15 tehsils."
}
]
},
{
"tripleset": [
[
"Jalgaon",
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[
"Jalgaon",
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"JG"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Maharashtra is divided into 36 Districts and 6 Administrative Divisions, namely; Mumbai (Konkan), Nashik, Aurangabad, Amravati and Nagpur. The 36 districts are divided into 109 sub-divisions and 358 taluks.Mar 2, 2020"
}
]
},
{
"tripleset": [
[
"Buldhana",
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[
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],
[
"Buldhana",
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[
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Buldhana has 21.2% of its 2,232,480 population in an urban setting, has an area of 9,680 square kilometers, and has 13 Tehsils."
}
]
},
{
"tripleset": [
[
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"ENROLLMENT",
"3488"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The enrollment of Florida Southern College is 3,488."
}
]
},
{
"tripleset": [
[
"Margaret Roggero Category:Articles with hCards",
"LAST_PERFORMANCE",
"04/08/1963"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Margaret roggero category: articles with hcards was the performer that did last performance on 04/08/1963"
}
]
},
{
"tripleset": [
[
"Clarence Whitehill Category:Articles with hCards",
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],
[
"Clarence Whitehill Category:Articles with hCards",
"FIRST_PERFORMANCE",
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "clarence whitehill category:articles with hcards's the first performance is on 11/15/1909 and the last performance 04/09/1932"
}
]
},
{
"tripleset": [
[
"Georgia 2",
"INCUMBENT",
"Frank Park"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Frank parked run georgia in"
}
]
},
{
"tripleset": [
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"INCUMBENT",
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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}
]
},
{
"tripleset": [
[
"Vitaliy Denisov",
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[
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"2004"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Although Denisov had joined CSKA Moscow, he did not make any appearances in the 2004 season."
}
]
},
{
"tripleset": [
[
"Spartak Nizhny Novgorod",
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[
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],
[
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Russian football club Spartak Nizhny Novgorod competed in the Second Division in 2006."
}
]
},
{
"tripleset": [
[
"Vitaliy Denisov",
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[
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[
"Vitaliy Denisov",
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"1"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Denisov scored a goal for Dnipro Dnipropetrovsk in the 2008/09 season."
}
]
},
{
"tripleset": [
[
"Vitaliy Denisov",
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[
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[
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[
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"1"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "He made 10 appearances and 1 goal for the Ukrainian team."
}
]
},
{
"tripleset": [
[
"Lokomotiv Moscow",
"DIVISION",
"1"
],
[
"Vitaliy Denisov",
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"14"
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[
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"Lokomotiv Moscow"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "He scored a goal for Lokomotiv Moscow when they competed in First Division that year."
}
]
},
{
"tripleset": [
[
"1996-97",
"CLASS_AA",
"Marion"
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[
"1996-97",
"CLASS_A",
"Sulphur Bluff"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The class A school when Marion was the class AA school was Sulphur Bluff."
}
]
},
{
"tripleset": [
[
"1997-98",
"CLASS_AAAAA",
"Flower Mound Marcus"
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[
"1997-98",
"CLASS_AAAA",
"San Angelo Lake View"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The class AAAA school when San Angelo Lake View was the class AAAA school was flower mound marcus"
}
]
},
{
"tripleset": [
[
"1998-99",
"CLASS_AAAAA",
"Weslaco"
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[
"1998-99",
"CLASS_AAAA",
"Brownwood"
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[
"1998-99",
"CLASS_A",
"Graford"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The class A school when Weslaco was the class AAAAA and Brownwood the class AAAA school was Graford."
}
]
},
{
"tripleset": [
[
"1998-99",
"CLASS_A",
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[
"1998-99",
"CLASS_AA",
"Lindsay"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The class AA school when Graford was the class A school was Lindsay."
}
]
},
{
"tripleset": [
[
"74",
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"19681014 14.10.1968"
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[
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"Stellbrookmoor"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Stellbrookmoor was established in 19681014 14.10.1968."
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]
},
{
"tripleset": [
[
"130",
"DISTRICT_/_TOWN",
"Herzogtum Lauenburg"
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[
"130",
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"163,62"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Herzogtum lauenburg has the reserve with an area of 163,62."
}
]
},
{
"tripleset": [
[
"Donegal",
"RESULT",
"2-10 : 0-14"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The opponent Donegal result was 2\u201310 : 0\u201314"
}
]
},
{
"tripleset": [
[
"Roscommon",
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"National Football League Round 6"
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[
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"Roscommon",
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"23 March 2003"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The 9 position held in 23 March 2003 the Opponent was Roscommon the Competition is National Football League Round 6"
}
]
},
{
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[
"National Football League Round 2",
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[
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"National Football League Round 2"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Stephen Cluxton faced off against Tyrone on February 17, 2002."
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]
},
{
"tripleset": [
[
"Minnesota 5",
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"Keith Ellison"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Keith Ellison was the incumbent in Minnesota's 5th district."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
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"List of federal judges appointed by Ronald Reagan"
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[
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"COURT",
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "William Marsh Acker Jr. was appointed by Reagan to the N.D. Ala. court, beginning his service on August 18, 1982."
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]
},
{
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[
"[TABLECONTEXT]",
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[
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[
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"[TITLE]",
"List of federal judges appointed by Ronald Reagan"
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Appointed by Ronald Reagan, Raymond L. Acosta served from September 30, 1982 to February 1, 1998."
}
]
},
{
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[
"N.D. Ill.",
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"May 20, 1987"
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[
"James Henry Alesia",
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"July 24, 2003"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Beginning service on May 20, 1987, James Henry Alesia served in the N.D. Ill. court and ended senior status on July 24, 2003."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"List of federal judges appointed by Ronald Reagan"
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[
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[
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"Incumbent"
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Wayne Edward Alley was appointed as federal judge of the W.D. Oklahoma court by Reagan, and has incumbent senior status."
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},
{
"tripleset": [
[
"E.D.N.Y.",
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"[TABLECONTEXT]",
"COURT",
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Ronald Reagan appointed a federal judge to the E.D. New York court who served from December 10, 1982 to December 23, 1985."
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]
},
{
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[
"D. Colo.",
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Lewis Thornton Babcock from Court D. Colo ended his federal service on April4, 2008"
}
]
},
{
"tripleset": [
[
"D.S.C.",
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"Joseph Fletcher Anderson, Jr."
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],
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"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Joseph Fletcher Anderson Jr was appointed to the D.S.C. by Ronald Reagan on October 14, 1986."
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]
},
{
"tripleset": [
[
"Morris S. Arnold",
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"Morris S. Arnold"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "Morris S. Arnold is court W.D. Ark. ended active service on June 1, 1992"
}
]
},
{
"tripleset": [
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"[TABLECONTEXT]",
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[
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[
"[TABLECONTEXT]",
"[TITLE]",
"New South Wales 41 class locomotive"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The New South Wales 41 class locomotive with serial number 1000 entered service on 1 Dec 1953 and withdrawn on 6 Jun 1973"
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
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[
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"ENTERED_SERVICE",
"30 Oct 1953"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The New South Wales 41 class locomotive with serial number 1002 entered service on 30 Oct 1953"
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"New South Wales 41 class locomotive"
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[
"4103",
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"1003"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The New South Wales 41 class locomotive with serial number 1003 withdrawn on 2 Apr 1974"
}
]
},
{
"tripleset": [
[
"4104",
"ENTERED_SERVICE",
"13 Jan 1954"
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[
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[
"4104",
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[
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[
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[
"4104",
"WITHDRAWN",
"12 Oct 1973"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The New South Wales 41 class locomotive with serial number 1004 entered service on 13 Jan 1954 and withdrawn on 12 Oct 1973, travelling 440,810 kilometres"
}
]
},
{
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[
"4106",
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[
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[
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[
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"406,288"
]
],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The New South Wales 41 class locomotive with serial number 1006 traveled 406,288 kilometres while it entered service on 21 Jan 1954 and withdrawn on 4 Aug 1972"
}
]
},
{
"tripleset": [
[
"1998-99",
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"0.79"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The stamp duty reserve tax when the percentage over total tax revenue is 0.79 is n.a.."
}
]
},
{
"tripleset": [
[
"2001-02",
"OVER_GDP_(IN_%)",
"0.28"
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[
"2001-02",
"STANDARD_STAMP_DUTY",
"367"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The standard stamp duty when the percentage over GDP is 0.28 is n.a.."
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]
},
{
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[
"2002-03",
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"0.24"
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[
"2002-03",
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"3,669"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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}
]
},
{
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"2003-04",
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
"tripleset": [
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"Finola Guinnane",
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"Drumbo"
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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},
{
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"Judith Wilson",
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"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
{
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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}
]
},
{
"tripleset": [
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"Lucy Evangelista",
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
{
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"Gayle Williamson",
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],
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"annotations": [
{
"source": "WikiTableQuestions_lily",
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]
},
{
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
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{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"source": "WikiSQL_decl_sents",
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}
]
},
{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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},
{
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]
},
{
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"annotations": [
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"source": "WikiSQL_decl_sents",
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},
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"source": "WikiSQL_decl_sents",
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]
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},
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
},
{
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"Cliff Stearns"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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}
]
},
{
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"Bill McCollum",
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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}
]
},
{
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[
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"Re-elected"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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},
{
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[
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
"tripleset": [
[
"15",
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[
"15",
"RECORD",
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "When they played at the Bankatlantic Center, their record was 5-6-4."
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]
},
{
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[
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"Quakers"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The institution with the nickname of Quakers is guilford college."
}
]
},
{
"tripleset": [
[
"Randolph College *",
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"WildCats"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The institution with the nickname of Wildcats is randolph college."
}
]
},
{
"tripleset": [
[
"Linfield College",
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"Wildcats"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The nickname of Linfield College is wildcats."
}
]
},
{
"tripleset": [
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"1926, 1996 2"
],
[
"University of Puget Sound",
"LOCATION",
"Tacoma, Washington"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The location of the University that joined in 1926, 1996 2 is tacoma, washington."
}
]
},
{
"tripleset": [
[
"Georgia 6",
"RESULT",
"Re-elected"
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[
"Carl Vinson",
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"1914"
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[
"Georgia 6",
"INCUMBENT",
"Carl Vinson"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The result of the election held in 1914 was re- elected."
}
]
},
{
"tripleset": [
[
"Georgia 9",
"CANDIDATES",
"B. Frank Whelchel (D) Unopposed"
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[
"Georgia 9",
"INCUMBENT",
"John S. Wood"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "B. frank whelchel (d) unopposed ran in the election when john s. wood was the incumbent."
}
]
},
{
"tripleset": [
[
"New York 12",
"INCUMBENT",
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]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Shirley chisholm (d) 87.8% charles gibbs (r) 12.2% were the candidates when Shirley Chisholm was the incumbent."
}
]
},
{
"tripleset": [
[
"Stephen J. Solarz",
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"1974"
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"INCUMBENT",
"Stephen J. Solarz"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The Democratic party incumbent Stephen J. Solarz get first elected to new york 13 in 1974."
}
]
},
{
"tripleset": [
[
"New York 18",
"INCUMBENT",
"S. William Green"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "S. William Green belonged to new york 18."
}
]
},
{
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[
"Louisiana 1",
"INCUMBENT",
"Bob Livingston"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Bob livingston's the incumbent of the Louisiana 1 district."
}
]
},
{
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"Louisiana 4",
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"Buddy Roemer (D) Unopposed"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Buddy roemer (d) unopposed were the candidates in the Louisiana 4 district."
}
]
},
{
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[
"Jerry Huckaby",
"FIRST_ELECTED",
"1976"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Jerry huckaby's the incumbent in the district first elected in 1976."
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]
},
{
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"Eddie Vanderdoes",
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"Placer High School"
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[
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"HOMETOWN",
"Placer, California"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Placer High School is located in Placer, California."
}
]
},
{
"tripleset": [
[
"Michael Hutchings",
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"Linebacker"
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[
"Michael Hutchings",
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"Concord, California"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The position of player from Concord, California, is linebacker."
}
]
},
{
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"Michael Hutchings",
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"De La Salle High School"
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[
"Michael Hutchings",
"HOMETOWN",
"Concord, California"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "De La Salle High School is in Concord, California."
}
]
},
{
"tripleset": [
[
"Max Redfield",
"SCHOOL",
"Mission Viejo High School"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Max Redfield went to Mission Viejo High School."
}
]
},
{
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"Leon McQuay III",
"SCHOOL",
"Armwood High School"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Leon McQuay III went to Armwood High School."
}
]
},
{
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[
"Point Park University",
"TYPE",
"Private"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Point Park University is private."
}
]
},
{
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[
"California 30",
"INCUMBENT",
"George E. Danielson"
],
[
"George E. Danielson",
"PARTY",
"Democratic"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The winning one in the elections in the California 30 district was democratic."
}
]
},
{
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"California 31",
"INCUMBENT",
"Charles H. Wilson"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Charles H. Wilson is the incumbent of california 31."
}
]
},
{
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"California 41",
"INCUMBENT",
"Bob Wilson"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The district that has Bob Wilson as an incumbent is california 41."
}
]
},
{
"tripleset": [
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"Charles Floyd Hatcher",
"FIRST_ELECTED",
"1980"
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[
"Georgia 2",
"INCUMBENT",
"Charles Floyd Hatcher"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Georgia shows a first elected of 1980."
}
]
},
{
"tripleset": [
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"Wyche Fowler",
"PARTY",
"Democratic"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The party for the incumbent Wyche Fowler is democratic."
}
]
},
{
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"George Darden"
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[
"Georgia 7",
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"George Darden (D) 55.2% Bill Bronson (R) 44.8%"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "George darden (d) 55.2% bill bronson (r) 44.8% are shown as candidates when George Darden is the incumbent."
}
]
},
{
"tripleset": [
[
"Georgia 9",
"INCUMBENT",
"Ed Jenkins"
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[
"Ed Jenkins",
"PARTY",
"Democratic"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The party for District Georgia 9 is democratic."
}
]
},
{
"tripleset": [
[
"65",
"SCORE",
"3 - 4 OT"
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[
"65",
"RECORD",
"21-31-13"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The score when the record was 21-31-13 was 3 - 4 ot."
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]
},
{
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"Edwin E. Willis",
"PARTY",
"Democratic"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Edwin e. willis affiliate with democratic"
}
]
},
{
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"James H. Morrison",
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"Democratic"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "James h. morrison is part of democratic"
}
]
},
{
"tripleset": [
[
"Georgia 3",
"INCUMBENT",
"Tic Forrester"
],
[
"Georgia 3",
"RESULT",
"Re-elected"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The result of the election when Tic Forrester ran as an incumbent was re- elected."
}
]
},
{
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[
"Su'a Cravens",
"SCHOOL",
"Vista Murrieta High School"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Su'a Cravens went to Vista Murrieta High School."
}
]
},
{
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[
"Jabrill Peppers \u2021",
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"Paramus, New Jersey"
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"POSITION",
"Defensive back"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "A player from Paramus, New Jersey was the defensive back."
}
]
},
{
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"Peter Kalambayi",
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"Linebacker"
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[
"Peter Kalambayi",
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"Butler High School"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The position of the player from Butler High School was linebacker."
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]
},
{
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"Montravius Adams",
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[
"Montravius Adams",
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"Dooly County High School"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "A player from Dooly County High School had a defensive line position."
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]
},
{
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"Dee Liner",
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"Muscle Shoals High School"
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]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Muscle Shoals High School is located in Muscle Shoals, Alabama."
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]
},
{
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"Louisiana 1",
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"F. Edward Hebert (D) Unopposed"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "F. edward hebert (d) unopposed is the candidate wehre district is louisiana 1."
}
]
},
{
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[
"Louisiana 3",
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"Edwin E. Willis (D) Unopposed"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Edwin e. willis (d) unopposed is the candidate where district is louisiana 3."
}
]
},
{
"tripleset": [
[
"Overton Brooks",
"PARTY",
"Democratic"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The party where the incumbent is overton brooks is democratic."
}
]
},
{
"tripleset": [
[
"Ren\u00e9 Louis DeRouen",
"FIRST_ELECTED",
"1927"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Ren\u00e9 louis derouen won in 1927"
}
]
},
{
"tripleset": [
[
"San Marino Grand Prix",
"POLE_POSITION",
"Michael Schumacher"
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[
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[
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[
"San Marino Grand Prix",
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[
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[
"San Marino Grand Prix",
"REPORT",
"Report"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The numbers for the raceways that are constructed by Ferrari, with Michael Schumacher holding the fastest lap and pole position are 4.0 6.0 14.0."
}
]
},
{
"tripleset": [
[
"Monaco Grand Prix",
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"Kimi R\u00e4ikk\u00f6nen"
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[
"Monaco Grand Prix",
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"Report"
],
[
"Monaco Grand Prix",
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"Juan Pablo Montoya"
],
[
"Monaco Grand Prix",
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[
"Monaco Grand Prix",
"CONSTRUCTOR",
"Williams - BMW"
],
[
"Monaco Grand Prix",
"RD.",
"7"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The Monaco Grand Prix is 7.0."
}
]
},
{
"tripleset": [
[
"Canadian Grand Prix",
"REPORT",
"Report"
],
[
"Canadian Grand Prix",
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[
"Canadian Grand Prix",
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"Ferrari"
],
[
"Canadian Grand Prix",
"WINNING_DRIVER",
"Michael Schumacher"
],
[
"Canadian Grand Prix",
"POLE_POSITION",
"Ralf Schumacher"
],
[
"Canadian Grand Prix",
"FASTEST_LAP",
"Fernando Alonso"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The Canadian Grand Prix on the list is 8.0."
}
]
},
{
"tripleset": [
[
"French Grand Prix",
"WINNING_DRIVER",
"Ralf Schumacher"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Ralf schumacher is in the pole position for the French Grand Prix."
}
]
},
{
"tripleset": [
[
"the tale of the jagged sign",
"DIRECTED_BY",
"will dixon"
],
[
"the tale of the jagged sign",
"STORYTELLER",
"kiki"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The name of the episode told by Kiki and directed by Will Dixon is \"The Tale of the Jagged Sign\"."
}
]
},
{
"tripleset": [
[
"the tale of the jagged sign",
"STORYTELLER",
"kiki"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Kiki is the storyteller in the episode called \"The Tale of the Jagged Sign\"."
}
]
},
{
"tripleset": [
[
"Bart Gordon",
"FIRST_ELECTED",
"1984"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Bart gordon was first elected in 1984."
}
]
},
{
"tripleset": [
[
"yaniv green",
"NUMBER",
"14"
],
[
"yaniv green",
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[
"yaniv green",
"POSITION",
"center"
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[
"yaniv green",
"BIRTH_YEAR",
"1980"
],
[
"yaniv green",
"HEIGHT",
"2.06"
],
[
"yaniv green",
"CURRENT_CLUB",
"maccabi tel aviv"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The position of no. 14 player is center."
}
]
},
{
"tripleset": [
[
"lior eliyahu",
"HEIGHT",
"2.07"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The basketball player lior eliyahu is 2.07 meters tall."
}
]
},
{
"tripleset": [
[
"Oak Creek",
"LAND_(_SQMI_)",
"35.445"
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[
"Oak Creek",
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[
"Oak Creek",
"COUNTY",
"Bottineau"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "Oak Creek, Bottineau has a population of 24."
}
]
},
{
"tripleset": [
[
"Oakland",
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"Mountrail"
],
[
"Oakland",
"ANSI_CODE",
"1036997"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "The ANSI code for Oakland town in Mountrail county is 1036997."
}
]
},
{
"tripleset": [
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"Odessa",
"LAND_(_SQMI_)",
"35.766"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "odessa township has 35.766 sqmi"
}
]
},
{
"tripleset": [
[
"Oriska",
"LONGITUDE",
"-97.752733"
],
[
"Oriska",
"LATITUDE",
"46.935397"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "The coordinates of Oriska are 46.935397, -97.752733."
}
]
},
{
"tripleset": [
[
"Orlien",
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"-101.796936"
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[
"Orlien",
"LATITUDE",
"47.985154"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "orlien has the longitude - 101.796936 and a latitude of 47.985154 "
}
]
},
{
"tripleset": [
[
"Osborn",
"COUNTY",
"Mountrail"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The township of Osborn is in mountrail."
}
]
},
{
"tripleset": [
[
"Otis",
"GEO_ID",
"3805560260"
],
[
"Otis",
"ANSI_CODE",
"1759541"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_lily",
"text": "The GEO ID for Otis town is 3805560, while its ANSI code is 1759541."
}
]
},
{
"tripleset": [
[
"Overland",
"COUNTY",
"Ramsey"
],
[
"Overland",
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[
"Overland",
"GEO_ID",
"3807160340"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "overland township in ramsey county has 0.400 water (sqmi) with a geo id 3807159460"
}
]
},
{
"tripleset": [
[
"Bowdoin College",
"NICKNAME",
"Polar Bears"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The Polar Bears is the nickname of bowdoin college."
}
]
},
{
"tripleset": [
[
"18",
"HIGH_REBOUNDS",
"Dydek (8)"
],
[
"18",
"HIGH_POINTS",
"Dydek (17)"
],
[
"18",
"DATE",
"July 17"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "dydek has the high pionts and high rebounds on july 17"
}
]
},
{
"tripleset": [
[
"19",
"DATE",
"July 19"
],
[
"19",
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"Dydek (11)"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "When dydek (11) has the highest rebounds the date is july 19."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"South Australian state election, 1953"
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[
"[TABLECONTEXT]",
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"Australian Labor Party"
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[
"Australian Labor Party",
"VOTES",
"166,106"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Australian Labor Party won 166,106 votes in the 1953 South Australian state election."
}
]
},
{
"tripleset": [
[
"Liberal and Country League",
"CHANGE",
"-3"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "The Liberal and Country League lost 3 seats."
}
]
},
{
"tripleset": [
[
"36,271",
"PERCENT",
"11.10"
],
[
"Independent",
"SWING",
"+1.03"
],
[
"Independent",
"VOTES",
"36,271"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Independents comprised 11.10% of the vote, with a party swing of +1.03"
}
]
},
{
"tripleset": [
[
"Independent",
"SEATS",
"4"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_mturk",
"text": "The Independent party won four seats in the Southern Australian state election of 1953."
}
]
},
{
"tripleset": [
[
"[TABLECONTEXT]",
"[TITLE]",
"South Australian state election, 1953"
],
[
"[TABLECONTEXT]",
"PARTY",
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],
[
"Total",
"VOTES",
"326,721"
]
],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiTableQuestions_lily",
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"annotations": [
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"source": "WikiTableQuestions_lily",
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},
{
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"James R. Domengeaux (D) Unopposed"
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"James R. Domengeaux"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "James r. domengeaux (d) unopposed are all of the candidates in the election featuring james r. domengeaux."
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{
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"Manchester University",
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"1987"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Manchester University joined in 1987."
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]
},
{
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[
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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"North Newton, Kansas"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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},
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"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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[
"1975 (48th)",
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"the enigma of kaspar hauser"
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "A film submitted with the title The Enigma of Kaspar Hauser was in the 1975 (48th) ceremony."
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{
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[
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"July 1"
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The record on the game played on July 1 was 12-5."
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},
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"Blazers"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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},
{
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[
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"annotations": [
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"source": "WikiSQL_decl_sents",
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{
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{
"source": "WikiSQL_decl_sents",
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],
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{
"source": "WikiSQL_decl_sents",
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{
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{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Henderson lovelace lanham (d) unopposed are associated with the Georgia 7 district."
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{
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[
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"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"Prince Hulon Preston, Jr."
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[
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Name all the candidates listed in the race prince hulon preston, jr. (d) 83.7% others the incumbent is Prince Hulon Preston, Jr."
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]
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{
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[
"Carl Vinson",
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"annotations": [
{
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{
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The University of North Carolina at Greensboro is located in Greensboro, North Carolina."
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{
"source": "WikiSQL_decl_sents",
"text": "The University of North Carolina at Greensboro is in Greensboro, North Carolina."
}
]
},
{
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"William Y. Humphreys",
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"Democratic"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"Barton College",
"LOCATION",
"Wilson, North Carolina"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Barton College is located in Wilson, North Carolina."
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]
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{
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"1839"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
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{
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"Bethany, West Virginia"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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[
"Hale Boggs",
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"Democratic"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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[
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{
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{
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"Louisiana 5",
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"Riley Joseph Wilson"
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Louisiana has Riley Joseph Wilson as the incumbent."
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]
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{
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"Jared Y. Sanders, Jr. (D) Unopposed"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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[
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[
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[
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[
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[
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"2002"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"Briar Cliff University",
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"Chargers"
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
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{
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"Bulldogs"
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Game 57 was held on was February 25."
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]
},
{
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[
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[
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"wallace (20)"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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[
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{
"source": "WikiSQL_decl_sents",
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[
"42",
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The team played milwaukee on January 23."
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]
},
{
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"Emmett Marshall Owen (D) Unopposed"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Emmett marshall owen (d) unopposed ran for office at the Georgia 4 district in this election."
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{
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"INCUMBENT",
"John Breaux"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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{
"source": "WikiSQL_decl_sents",
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},
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"annotations": [
{
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"Louisiana 3",
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{
"source": "WikiSQL_decl_sents",
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{
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"annotations": [
{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
},
{
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"annotations": [
{
"source": "WikiSQL_decl_sents",
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]
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{
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"1969"
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Dave obey's the incumbent in the district that first elected in 1969."
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]
},
{
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"8",
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"McWilliams-Franklin (10)"
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"Sales (17)"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Mcwilliams - franklin (10) did the most high rebounds in the game where Sales (17) did the high points."
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]
},
{
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[
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"RECORD",
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The record of the game in which Dydek (10) did the most high rebounds was 8-1."
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]
},
{
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[
"Georgia 2",
"INCUMBENT",
"Sanford Bishop"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Sanford bishop was the incumbent of district Georgia 2."
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]
},
{
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"INCUMBENT",
"Mac Collins"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Mac Collins is an incumbent in georgia."
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]
},
{
"tripleset": [
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"INCUMBENT",
"Charlie Norwood"
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],
"subtree_was_extended": true,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Charlie norwood (r) 52.34% david bell (d) 47.65% were the candidates in the district where Charlie Norwood is the incumbent."
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]
},
{
"tripleset": [
[
"the tale of the forever game",
"VILLAINS",
"nathaniel & the burden beast"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Nathaniel & the burden beast are the villains in the episode titled \"The Tale of the Forever Game\"."
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]
},
{
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[
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[
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"DIRECTED_BY",
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],
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"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "No villains are in the episode where Megan is the storyteller and Lorette LeBlanc is the director."
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]
},
{
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[
"Louisiana 1",
"INCUMBENT",
"James O'Connor"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "James O'Connor is the incumbent in louisiana."
}
]
},
{
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[
"Riley Joseph Wilson",
"PARTY",
"Democratic"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "Riley Joseph Wilson belongs to democratic."
}
]
},
{
"tripleset": [
[
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"RESULT",
"Re-elected"
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[
"John James Flynt, Jr.",
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"1954"
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[
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[
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[
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"INCUMBENT",
"John James Flynt, Jr."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The first elected for georgia 4 is 1954.0."
}
]
},
{
"tripleset": [
[
"Georgia 6",
"INCUMBENT",
"Carl Vinson"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "The district for carl vinson is georgia."
}
]
},
{
"tripleset": [
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"James O'Connor"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiSQL_decl_sents",
"text": "James O'Connor belonged to louisiana."
}
]
},
{
"tripleset": [
[
"Louisiana 2",
"INCUMBENT",
"Henry Garland Dupr\u00e9"
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[
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],
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"annotations": [
{
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"source": "WikiSQL_decl_sents",
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"annotations": [
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"source": "WikiSQL_decl_sents",
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"annotations": [
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"source": "WikiSQL_decl_sents",
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
},
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
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"source": "WikiSQL_decl_sents",
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"annotations": [
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"source": "WikiSQL_decl_sents",
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},
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],
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
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"source": "WikiSQL_decl_sents",
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]
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],
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"source": "WikiSQL_decl_sents",
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}
]
},
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"annotations": [
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"source": "WikiSQL_decl_sents",
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}
]
},
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"Bill Thomas"
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],
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"annotations": [
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"source": "WikiSQL_decl_sents",
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]
},
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"source": "WikiSQL_decl_sents",
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},
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],
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"source": "WikiSQL_decl_sents",
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}
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],
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"source": "WikiTableQuestions_mturk",
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}
]
},
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],
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{
"source": "webnlg",
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]
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"source": "webnlg",
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{
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{
"source": "webnlg",
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},
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]
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],
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"source": "webnlg",
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]
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{
"source": "webnlg",
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]
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{
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},
{
"source": "webnlg",
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]
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"source": "webnlg",
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"source": "webnlg",
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},
{
"source": "webnlg",
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},
{
"source": "webnlg",
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},
{
"source": "webnlg",
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},
{
"source": "webnlg",
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}
]
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"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
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"4349.0"
],
[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
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"Madrid"
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[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
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"610.0"
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[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"OPERATING_ORGANISATION",
"ENAIRE"
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[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"RUNWAY_NAME",
"\"14L/32R\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Adolfo Su\u00e1rez Madrid\u2013Barajas Airport is operated by ENAIRE and can be found in Madrid.The airport is situated 610.0 meters above sea level and has a 4,349 kilometers long runway whose name is 14L/32R."
},
{
"source": "webnlg",
"text": "Adofo Su\u00e1rez Madrid-Barajas Airport, which lies 610 metres above sea level, is located in Madrid and operated by ENAIRE. The airport's runway, named 14L/32R, has a length of 4349.0."
}
]
},
{
"tripleset": [
[
"Agra Airport",
"ICAO_LOCATION_IDENTIFIER",
"\"VIAG\""
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[
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[
"Uttar Pradesh",
"IS_PART_OF",
"Awadh"
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[
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"Ram Naik"
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[
"Uttar Pradesh",
"IS_PART_OF",
"Bundelkhand"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agra Airport is in Uttar Pradesh Bundelkhand, it's ICAO location identifier is VIAG. the leader of Utterar Pradesh is Ram Naik."
},
{
"source": "webnlg",
"text": "Agra Airport is located in Uttar Pradesh. Uttar Pradesh is part of both Awadh ad Bundelkhand. The Airport's ICAO location identifier is \"VIAG\". The leader of Uttar Pradesh is called Ram Naik."
}
]
},
{
"tripleset": [
[
"Agra Airport",
"NATIVE_NAME",
"\"Kheria Air Force Station\""
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[
"Agra Airport",
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[
"Uttar Pradesh",
"IS_PART_OF",
"Awadh"
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[
"Uttar Pradesh",
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[
"Uttar Pradesh",
"IS_PART_OF",
"Bundelkhand"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ram Naik is the leader of Uttar Pradesh which is part of Awadh and Bundelkhand. It is the location of Agra airport which has the native name of Kheria Air Force Station."
},
{
"source": "webnlg",
"text": "Agra Airport, which has the native name of Kheria Air Force Station, is in Uttar Pradesh, part of Awadh and Bundelkhand, where Ram Naik is the leader."
},
{
"source": "webnlg",
"text": "Agra Airport is located in Uttar Pradesh which is part of Awadh and Bundelkhand. Kheria Air Force Station is its native name. Ram Naik is the leader in Uttar Pradesh."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"United States Air Force",
"ATTACK_AIRCRAFT",
"Lockheed AC-130"
],
[
"United States Air Force",
"TRANSPORT_AIRCRAFT",
"Boeing C-17 Globemaster III"
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[
"United States Air Force",
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"General Dynamics F-16 Fighting Falcon"
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[
"United States Air Force",
"BATTLES",
"United States invasion of Panama"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Al Asad airbase is operated by the United States Air Force. Some of its aircraft include the Boeing C-17 Globemaster III (transport aircraft), General Dynamics F-16 Fighting Falcon (aircraft fighter), and the Lockheed AC-130 which can be found on USAF aircraft carriers. The USAF was involved in the Invasion of Panama."
},
{
"source": "webnlg",
"text": "Al Asad airbase is operated by the United States Air Force which was involved in battles at the invasion of Panama. They deploy the Lockheed AC-130 on their aircraft carriers, the Boeing C-17 Globemaster III as transport aircraft and the General Dynamics F-16 Fighting Falcon as fighter aircraft."
},
{
"source": "webnlg",
"text": "The United States Air Force, which was involved in the Invasion of Panama, are the operators of Al Asad airbase. The USAF have aircraft including: the Lockheed AC-130 attack aircraft, the Boeing C-17 Globemaster III transporter and the General Dynamics F-16 Fighting Falcon fighter plane."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
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[
"United States Air Force",
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"Lockheed AC-130"
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[
"United States Air Force",
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"Boeing C-17 Globemaster III"
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[
"United States Air Force",
"AIRCRAFT_FIGHTER",
"McDonnell Douglas F-15 Eagle"
],
[
"United States Air Force",
"BATTLES",
"Korean War"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The United States Air Force has fought in the Korean War. It is the operating organisation for Al Asad airbase. It has a Boeing C-17 Globemaster III transport aircraft and additionally, the Lockheed AC-130 can be found on USAF aircraft carriers. McDonnell Douglas F- Eagle was a Fighter Aircraft of the United States Air Force."
},
{
"source": "webnlg",
"text": "The United States Air Force, which fought battles in the Korean war, is the operating organisation for Al Asad airbase. The US Airforce has a Boeing C-17 Globemaster III transport aircraft, an aircraft fighter called the McDonnell Douglas F-15 Eagle and an attack aircraft named The Lockheed AC-130."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"United States Air Force",
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[
"United States Air Force",
"ATTACK_AIRCRAFT",
"Lockheed AC-130"
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[
"United States Air Force",
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"Korean War"
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[
"United States Air Force",
"TRANSPORT_AIRCRAFT",
"Lockheed C-130 Hercules"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The transport aircraft for the United States Air Force is the Lockheed C-130 Hercules. And Lockheed AC-130 can be found on USAF aircraft carriers. Both the Korean War and the Invasion of Grenda involved the United States Air Force which currently operates the Al Asad Airbase."
},
{
"source": "webnlg",
"text": "The USAF which fought in the Korean War and invaded Grenada, operates the Al Asad airbase. Among the aircraft found on USAF carriers is the Lockheed AC-130 Hercules which is a transport plane."
},
{
"source": "webnlg",
"text": "Al Asad Airbase is operated by the United States Air Force and uses Lockheed AC-130 attack air crafts and Lockheed C-130 Hercules transport aircrafts. The USAF participated in the Korean War and the Invasion of Grenada."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"United States Air Force",
"BATTLES",
"Invasion of Grenada"
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[
"United States Air Force",
"ATTACK_AIRCRAFT",
"Lockheed AC-130"
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[
"United States Air Force",
"TRANSPORT_AIRCRAFT",
"Boeing C-17 Globemaster III"
],
[
"United States Air Force",
"BATTLES",
"United States invasion of Panama"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Invasions of Grenada and Panama in involved the United States Air Force which now operates the Al Asad airbase. The Air Force has several types of aircraft such as the Boeing C-17 Globemaster III transport aircraft and the Lockheed AC-130 which can be found on USAF aircraft carriers."
},
{
"source": "webnlg",
"text": "Al Asad airbase is operated by The United States Air Force. The USAF is equipped with the attack aircraft, Lockheed AC-130 and Boeing C-17 Globemaster III transport aircraft. The USAF has been involved in the Invasion of Grenada and invasion of Panama."
}
]
},
{
"tripleset": [
[
"Allama Iqbal International Airport",
"OPERATING_ORGANISATION",
"Pakistan Civil Aviation Authority"
],
[
"Lahore",
"COUNTRY",
"Pakistan"
],
[
"Allama Iqbal International Airport",
"LOCATION",
"Punjab, Pakistan"
],
[
"Allama Iqbal International Airport",
"CITY_SERVED",
"Lahore"
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[
"Pakistan",
"LEADER_NAME",
"Anwar Zaheer Jamali"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Allama Iqbal International airport is located in Punjab Pakistan and serves Lahore, Pakistan. The Pakistan Civil Aviation Authority governs the Allama Iqbal International Airport. The country is lead by Anwar Zaheer Jamali."
},
{
"source": "webnlg",
"text": "Allama Iqbal International Airport, which serves the city of Lahore, is located in Punjab, Pakistan and is operated by the Pakistan Civil Aviation Authority. Pakistan is led by Anwar Zaheer Jamali."
},
{
"source": "webnlg",
"text": "In the city of Lahore, Punjab, Pakistan, there is an airport called Allama Iqbal International Airport. It is operated by the Pakistan Civil Aviation Authority, in Punjab Pakistan, which is led by Anwar Zaheer Jamali."
}
]
},
{
"tripleset": [
[
"Allama Iqbal International Airport",
"OPERATING_ORGANISATION",
"Pakistan Civil Aviation Authority"
],
[
"Punjab, Pakistan",
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"Shehbaz Sharif"
],
[
"Lahore",
"COUNTRY",
"Pakistan"
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[
"Allama Iqbal International Airport",
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"Punjab, Pakistan"
],
[
"Allama Iqbal International Airport",
"CITY_SERVED",
"Lahore"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Pakistan Civil Aviation Authority is the operating organisation of the Allama Iqbal International Airport. The airport serves the city of Lahore, in Pakistan where Shehbaz Sharif is the leader, It is also where the Allama Iqbal International Airport is found, in Punjab, Pakistan."
},
{
"source": "webnlg",
"text": "The Pakistan Civil Aviation Authority is the operating organisation of the Allama Iqbal International Airport located in Punjab, Pakistan, which is lead by Shehbaz Sharif. The airport serves Lahore in Pakistan."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Andrews County Airport",
"RUNWAY_LENGTH",
"896.0"
],
[
"Andrews County Airport",
"CITY_SERVED",
"Andrews, Texas"
],
[
"Andrews County Airport",
"ELEVATION",
"973.0"
],
[
"Andrews County Airport",
"RUNWAY_NAME",
"\"11/29\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Located in Texas, and serving Andrews, is Andrews County Airport. The airport is 973.0 below sea level, has a runway length of 896, and a runway with the name 11/29."
},
{
"source": "webnlg",
"text": "Andrews County Airport in Texas has a runway length of 896 and serves the city of Andrews. It is located 973 metres below sea level and has the runway name 11/29."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas. It serves the city of Andrews and is 973 metres above sea level. Its runway length is named 11/29 and is 896 long."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"LANGUAGE",
"Spanish language"
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[
"Texas",
"LARGEST_CITY",
"Houston"
],
[
"Texas",
"CAPITAL",
"Austin, Texas"
],
[
"Texas",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews County Airport is in Texas in the U.S.A. where Spanish is one of the languages spoken. Houston is the largest city in the state and its capital is Austin."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas, United States. The largest city in Texas is Houston, the state capital is Austin and Spanish is one of the languages spoken there."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"LANGUAGE",
"Spanish language"
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[
"Texas",
"LARGEST_CITY",
"Houston"
],
[
"Texas",
"CAPITAL",
"Austin, Texas"
],
[
"Texas",
"DEMONYM",
"Texan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Houston is the largest city in Texas, where Austin is the Capital city. Andrews County Airport is located in Texas and Some texans can speak Spanish."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas, a state where Spanish is spoken and the people are called Texans. Houston is the largest city in Texas and Austin is the state's capital."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas where the Spanish language is spoken and the people are known as Texans. The capital city is Austin but the largest city is Houston."
}
]
},
{
"tripleset": [
[
"Angola International Airport",
"LOCATION",
"\u00cdcolo e Bengo"
],
[
"\u00cdcolo e Bengo",
"COUNTRY",
"Angola"
],
[
"Angola International Airport",
"CITY_SERVED",
"Luanda"
],
[
"\u00cdcolo e Bengo",
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"Luanda Province"
],
[
"Angola International Airport",
"ELEVATION",
"159"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Angola International Airport is in \u00cdcolo e Bengo, (Luanda Province), The airport serves Luanda, and is 159 metres above sea level."
},
{
"source": "webnlg",
"text": "Angola International Airport is located at \u00cdcolo e Bengo in Luanda province,Angola.The Airport is situated 159 meters above sea level and serves the city of Luanda."
},
{
"source": "webnlg",
"text": "Angola International Airport, which is located in \u00cdcolo e Bengo in the Luanda Province in Angola, serves Luanda. The airport lies 159 metres above sea level."
}
]
},
{
"tripleset": [
[
"Angola International Airport",
"LOCATION",
"\u00cdcolo e Bengo"
],
[
"\u00cdcolo e Bengo",
"COUNTRY",
"Angola"
],
[
"Angola International Airport",
"RUNWAY_LENGTH",
"4000.0"
],
[
"\u00cdcolo e Bengo",
"IS_PART_OF",
"Luanda Province"
],
[
"Angola International Airport",
"ELEVATION",
"159"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Angola International Airport is located in \u00cdcolo e Bengo, Luanda, Angola. The runway is 4000ft long and is 159m a.s.l."
},
{
"source": "webnlg",
"text": "Angola International airport is located in Icolo e Bengo, which is a part of Luanda Province, located in Angola. The airport is 159 meters above sea level and its runway has a lenght of 4000.0."
},
{
"source": "webnlg",
"text": "Angola International Airport is located in Icolo e Bengo, which is located in the Luanda Province, Angola. The airport is situated 159 meters above sea level and it has a runway that measures a length of 4000.0."
}
]
},
{
"tripleset": [
[
"Antwerp International Airport",
"OWNER",
"Flemish Region"
],
[
"Antwerp International Airport",
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"\"Flemish department of Mobility and Public Works\""
],
[
"Antwerp International Airport",
"CITY_SERVED",
"Antwerp"
],
[
"Antwerp International Airport",
"ELEVATION",
"12.0"
],
[
"Antwerp International Airport",
"RUNWAY_LENGTH",
"600.0"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Flemish department of Mobility and Public Works is the operating organisation of Antwerp International Airport which is owned by the Flemish Region. The airport serves the city of Antwerp, is 12.0 metres above sea level, and has a runway length of 600.0."
},
{
"source": "webnlg",
"text": "The Flemish Region owns Antwerp International Airport which is operated by the Flemish Department of Mobility and Public Works and serves the city of Antwerp. The runway length is 600.0 and the airport is 12 metres above sea level."
},
{
"source": "webnlg",
"text": "Antwerp International Airport is owned by the Flemish Region and located in the city of Antwerp. The airport has a runway length of 600.0 , elevation of 12.0 metres above sea level and the Flemish department of Mobility and Public Works is the operating organisation."
}
]
},
{
"tripleset": [
[
"Appleton International Airport",
"LOCATION",
"Greenville, Wisconsin"
],
[
"Appleton International Airport",
"RUNWAY_LENGTH",
"2439.0"
],
[
"Appleton International Airport",
"CITY_SERVED",
"Appleton, Wisconsin"
],
[
"Appleton International Airport",
"ELEVATION",
"280"
],
[
"Appleton International Airport",
"RUNWAY_NAME",
"\"3/21\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The city of Appleton is served by Appleton International Airport located 280 metres above sea level in Greenville, Wisconsin. The runway at the airport is 3/21 and is 2439 in length."
},
{
"source": "webnlg",
"text": "Appleton International Airport can be found in Greenville, Wisconsin and serves the city of Appleton.It is situated 280 meters above sea level and has a 2,439 kilometers long runway called 3/21."
},
{
"source": "webnlg",
"text": "Appleton International Airport, which serves the city of Appleton is located in Greenville, Wisconsin. It is elevated 2801 meters above sea level. The airport's runway, which is named 3/21 measures a lenght of 2439 units."
}
]
},
{
"tripleset": [
[
"Appleton International Airport",
"LOCATION",
"Greenville, Wisconsin"
],
[
"Greenville, Wisconsin",
"IS_PART_OF",
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],
[
"Greenville, Wisconsin",
"COUNTRY",
"United States"
],
[
"Appleton International Airport",
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"Appleton, Wisconsin"
],
[
"Greenville, Wisconsin",
"IS_PART_OF",
"Clayton, Winnebago County, Wisconsin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The city of Appleton is served by Appleton International Airport in Greenville (part of Menasha), Clayton Winnebago County, Wisconsin, United States."
},
{
"source": "webnlg",
"text": "Appleton, Wisconsin is served by Appleton International Airport which is located in Greenville (part of Menasha town), Clayton Winnebago County, Wisconsin, USA."
}
]
},
{
"tripleset": [
[
"Ardmore Airport (New Zealand)",
"RUNWAY_LENGTH",
"1411.0"
],
[
"Ardmore Airport (New Zealand)",
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"Poaceae"
],
[
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"OPERATING_ORGANISATION",
"Civil Aviation Authority of New Zealand"
],
[
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"ELEVATION",
"34.0"
],
[
"Ardmore Airport (New Zealand)",
"RUNWAY_NAME",
"\"03R/21L\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ardmore Airport in New Zealand is operated by the Civil Aviation Authority and is 34 metres above sea level. It has a runway named 03R/21L which is 1,411 long and its third runway has a poaceae surface."
},
{
"source": "webnlg",
"text": "Ardmore Airport (New Zealand) is operated by the Civil Aviation Authority of New Zealand. Its 3rd runway is made of Poaceae and its name is 03R/21L. It is 1411 m long and the airport is 34 m above sea level."
},
{
"source": "webnlg",
"text": "The Civil Aviation Authority of New Zealand is the operating organisation for Ardmore Airport, New Zealand and is situated 34 meters above sea level.The airport's 3rd runway is surfaced with Poaceae and has a 1,411 meter long runway called \"03R/21L\"."
}
]
},
{
"tripleset": [
[
"Ashgabat International Airport",
"OPERATING_ORGANISATION",
"Turkmenistan Airlines"
],
[
"Ashgabat International Airport",
"LOCATION",
"Ashgabat"
],
[
"Ashgabat International Airport",
"RUNWAY_LENGTH",
"2989.0"
],
[
"Ashgabat International Airport",
"ELEVATION",
"211"
],
[
"Ashgabat International Airport",
"RUNWAY_NAME",
"\"12R/30L\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Turkmenistan Airlines operates Ashgabat International Airport in Ashgabat. The airport is 211 metres above sea level, has the runway length of 2989.0 and 12R/30L is the runway name."
},
{
"source": "webnlg",
"text": "Ashgabat International Airport is operated by Turkmenistan Airlines. The runway 12R/30L is 2989m long and is 211m a.s.l."
},
{
"source": "webnlg",
"text": "Ashgabat International Airport is located in Ashgabat and is operated byTurkmenistan Airlines. It is 211 metres above sea level and has a runway length of 2989.0. It has a runway named 12R/30L."
}
]
},
{
"tripleset": [
[
"Atlantic City International Airport",
"RUNWAY_LENGTH",
"1873.0"
],
[
"Atlantic City International Airport",
"OPERATING_ORGANISATION",
"Port Authority of New York and New Jersey"
],
[
"Atlantic City International Airport",
"ELEVATION",
"23.0"
],
[
"Atlantic City International Airport",
"ICAO_LOCATION_IDENTIFIER",
"\"KACY\""
],
[
"Atlantic City International Airport",
"RUNWAY_NAME",
"\"4/22\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Atlantic City International Airport has a runway length of 1,873 and is operated by the Port Authority of New York and New Jersey. It is situated 23 metres above sea level, has the runway name of 4/22 and is known by the ICAO location identifier KACY."
},
{
"source": "webnlg",
"text": "Runway 4/22 at Atlantic City International Airport has an elevation of 23m above sea level and its length is 1873. It is operated by Port Authority of New York and New Jersey under the location Identifier of KACY."
},
{
"source": "webnlg",
"text": "The Port Authority of New York and New Jersey is the operating organisation of Atlantic City International airport, which lies 23.0 meters above sea level. The airport's ICAO Location Identifier is KACY. The 4/22 is the name of the runway in Atlantic City International Airport and it measures 1873."
}
]
},
{
"tripleset": [
[
"Belgium",
"LEADER_NAME",
"Charles Michel"
],
[
"Antwerp International Airport",
"CITY_SERVED",
"Antwerp"
],
[
"Flemish Region",
"LEADER_NAME",
"Flemish Government"
],
[
"Flemish Region",
"COUNTRY",
"Belgium"
],
[
"Flemish Government",
"JURISDICTION",
"Flemish Region"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Flemish Government have jurisdiction over the Flemish Region of Belgium. The leader of the country is Charles Michel and the city of Antwerp is served by Antwerp International Airport."
},
{
"source": "webnlg",
"text": "Antwerp International Airport serves the city of Antwerp in Belgium, the country where the leader is Charles Michel. The Flemish Region can be found in Belgium and it is led by the Flemish Government who has jurisdiction over it."
},
{
"source": "webnlg",
"text": "Antwerp International Airport serves Antwerp. The Flemish region is in the country of Belgium and it is led by the Flemish Government. The jurisdiction of the Flemish Government is in the Flemish Region and the leader of Belgium is Charles Michel."
}
]
},
{
"tripleset": [
[
"Saranac Lake, New York",
"IS_PART_OF",
"Harrietstown, New York"
],
[
"Saranac Lake, New York",
"IS_PART_OF",
"Essex County, New York"
],
[
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[
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[
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{
"source": "webnlg",
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{
"source": "webnlg",
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},
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{
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{
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{
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{
"source": "webnlg",
"text": "The leader of Albany, Georgia is the Mayor."
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{
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"45.97 (square kilometres)"
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"text": "Albany, Oregon has a total area of 45.97 square kilometres."
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{
"source": "webnlg",
"text": "The total area of Albany Oregon is 45.97 Sq K."
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{
"source": "webnlg",
"text": "The total area of Albany, Oregon is 45.97 sq kms."
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},
{
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{
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},
{
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"Albuquerque City Council"
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"United States"
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{
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{
"source": "webnlg",
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{
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{
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{
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{
"source": "webnlg",
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"657"
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{
"source": "webnlg",
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{
"source": "webnlg",
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},
{
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"\"Adams, Fall Creek, Lafayette, Richland, Union\""
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"source": "webnlg",
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{
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"Angola, Indiana",
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"Pleasant Township, Steuben County, Indiana"
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"source": "webnlg",
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{
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]
},
{
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"source": "webnlg",
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{
"source": "webnlg",
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]
},
{
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{
"source": "webnlg",
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{
"source": "webnlg",
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{
"source": "webnlg",
"text": "The city of Ann Arbor, Michigan, has a population of 1580.7 as per square kilometre."
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{
"source": "webnlg",
"text": "AnnArbor, Michigan has a population of 1580.7 peer square kilometer."
},
{
"source": "webnlg",
"text": "The population density of Ann Arbor, Michigan is 1580.7 inhabitants per sq km."
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]
},
{
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"Antioch, California",
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"13.0"
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"source": "webnlg",
"text": "Antioch, CA is 13 above sea level."
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{
"source": "webnlg",
"text": "Antioch, California is 13.0 metres above sea level."
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{
"source": "webnlg",
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"California's 11th State Assembly district"
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"source": "webnlg",
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"source": "webnlg",
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"source": "webnlg",
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},
{
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{
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{
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"source": "webnlg",
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{
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"source": "webnlg",
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{
"source": "webnlg",
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"source": "webnlg",
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{
"source": "webnlg",
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},
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"source": "webnlg",
"text": "The state of Georgia is in the U.S."
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{
"source": "webnlg",
"text": "Georgia is in the country of United States."
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{
"source": "webnlg",
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{
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"Indiana",
"CAPITAL",
"Indianapolis"
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{
"source": "webnlg",
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"Lee County, Alabama",
"STATE",
"Alabama"
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"source": "webnlg",
"text": "Lee County is in Alabama."
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{
"source": "webnlg",
"text": "Lee County is in the state of Alabama."
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{
"source": "webnlg",
"text": "Lee County is situated within the state of Alabama."
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]
},
{
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[
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"CAPITAL",
"Trenton, New Jersey"
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"source": "webnlg",
"text": "Trenton is the capital of New Jersey."
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{
"source": "webnlg",
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"Potter County, Texas",
"STATE",
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"source": "webnlg",
"text": "Potter County is in Texas."
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{
"source": "webnlg",
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"source": "webnlg",
"text": "Tarrant County, Texas is situated in the United States."
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{
"source": "webnlg",
"text": "Tarrant County in Texas is in the U.S."
},
{
"source": "webnlg",
"text": "Tarrant County, Texas is located within the United States."
}
]
},
{
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"Washtenaw County, Michigan",
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"source": "webnlg",
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{
"source": "webnlg",
"text": "The largest town in Washtenaw county Michigan is Ann Arbor."
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{
"source": "webnlg",
"text": "The largest city in Washtenaw County, Michigan is Ann Arbor."
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]
},
{
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"Auron (comicsCharacter)",
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"Auron (comicsCharacter)",
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"text": "The comic character Auron was created by Walt Simonson and the American, Karl Kesel."
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{
"source": "webnlg",
"text": "Karl Kesel, an American along with Walt Simonson created the comic book character Auron."
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{
"source": "webnlg",
"text": "The comic character, Auron, was created by Walt Simonson and the American Karl Kesel."
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]
},
{
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"BBC",
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[
"BBC",
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"London"
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"source": "webnlg",
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{
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"Bananaman",
"STARRING",
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[
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[
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"BROADCASTED_BY",
"\"STV\""
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{
"source": "webnlg",
"text": "Bill Oddie starred in Bananaman which first aired on 3 October 1983 and was broadcast by STV."
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{
"source": "webnlg",
"text": "Bill Oddie starred in Bananaman which was first aired on 3 October 1983 and broadcast by STV."
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{
"source": "webnlg",
"text": "Bananaman which first aired on October 3rd, 1983 stars Billie Oddie and is broadcasted by STV."
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},
{
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"Baymax",
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[
"Baymax",
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"annotations": [
{
"source": "webnlg",
"text": "Baymax first appeared in Big Hero 6 and was created by Duncan Rouleau and Steven T. Seagle."
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{
"source": "webnlg",
"text": "Baymax's first film appearance was in Big Hero 6. He was created by Duncan Rouleau and Steven T Seagle."
},
{
"source": "webnlg",
"text": "Duncan Rouleau and Steven T. Seagle created Baymax who had his first movie appearance in Big Hero 6."
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]
},
{
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"Bibbo Bibbowski",
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[
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"\"Bo Bibbowski\""
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"source": "webnlg",
"text": "The creator of Bibbo Bibbowski, who is often called Bibbo, are Jerry Ordway and Marv Wolfman."
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"Blockbuster (comicsCharacter)",
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[
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[
"Blockbuster (comicsCharacter)",
"CREATOR",
"Tom Lyle"
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],
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"annotations": [
{
"source": "webnlg",
"text": "The comic character Blockbuster, also known as Roland Desmond, was created by Roger Stern and Tom Lyle."
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{
"source": "webnlg",
"text": "The comic book character Blockbuster's alter ego is Roland Desmond and he was created by Roger Stern and Tom Lyle."
}
]
},
{
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"Bolt (comicsCharacter)",
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"Bolt (comicsCharacter)",
"CREATOR",
"Dan Mishkin"
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],
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"annotations": [
{
"source": "webnlg",
"text": "The comic character Bolt has the alternative name of Larry Bolatinsky and was created by Ernie Colon and Dan Mishkin."
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{
"source": "webnlg",
"text": "Bolt, a comic character AKA Larry Bolatinsky was created by Dan Mishkin."
},
{
"source": "webnlg",
"text": "The comic character Bolt whose alternative name is Larry Bolatinsky was created by Ernie Colon and Dan Mishkin."
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"Bolt (comicsCharacter)",
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"Dan Mishkin"
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"annotations": [
{
"source": "webnlg",
"text": "An alternative name for the comic character Bolt is Larry Bolatinsky and he was created by Paris Cullins and Dan Mishkin."
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{
"source": "webnlg",
"text": "The comic character Bolt, aka Larry Bolatinsky, was created by Paris Cullins and Dan Mishkin."
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]
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"Aarhus",
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"source": "webnlg",
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"source": "webnlg",
"text": "Aarhus Airport's runway length is 2702.0."
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{
"source": "webnlg",
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]
},
{
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"Adirondack Regional Airport",
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"507"
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"source": "webnlg",
"text": "Adirondack Regional Airport is 507 metres above sea level."
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]
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"source": "webnlg",
"text": "Adirondack Regional airport is located at Harrietstown, New York."
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{
"source": "webnlg",
"text": "Adirondack Regional Airport is located in Harrietstown, New York."
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]
},
{
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"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
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"annotations": [
{
"source": "webnlg",
"text": "Adolfo Su\u00e1rez Madrid\u2013Barajas Airport is found in San Sebasti\u00e1n de los Reyes."
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{
"source": "webnlg",
"text": "Adolfo Suarez Madrid- Barajas airport is located at San Sebastian de los Reyes."
},
{
"source": "webnlg",
"text": "The Adolfo Su\u00e1rez Madrid\u2013Barajas Airport is in San Sebasti\u00e1n de los Reyes."
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]
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"annotations": [
{
"source": "webnlg",
"text": "Adolfo Su\u00e1rez Madrid-Barajas Airport runway name is 14L/32R."
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{
"source": "webnlg",
"text": "The Adolfo Su\u00e1rez Madrid\u2013Barajas Airport runway name is 14L/32R."
},
{
"source": "webnlg",
"text": "14L/32R is the runway name of Adofo Su\u00e1rez Madrid-Barajas Airport."
}
]
},
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"annotations": [
{
"source": "webnlg",
"text": "14R/32L is the name of the runway for Adolfo Su\u00e1rez Madrid\u2013Barajas Airport."
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{
"source": "webnlg",
"text": "14R/32L is the runway name of Adolfo Su\u00e1rez Madrid-Barajas Airport."
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]
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{
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"source": "webnlg",
"text": "The operating organization for Afonso Pena International Airport is Infraero."
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{
"source": "webnlg",
"text": "Afonso Pena International Airport is operated by Infraero."
}
]
},
{
"tripleset": [
[
"Agra Airport",
"LOCATION",
"Agra"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agra Airport is in Agra."
},
{
"source": "webnlg",
"text": "Agra airport is located in Agra."
}
]
},
{
"tripleset": [
[
"Al-Taqaddum Air Base",
"ELEVATION",
"84.0"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Al-Taqaddum Air Base is 84 metres above sea level."
},
{
"source": "webnlg",
"text": "Al Taqaddum Air Base is 84 metres above sea level."
},
{
"source": "webnlg",
"text": "The elevation above the sea level (in metres) of Al-Taqaddum Air Base is 84.0."
}
]
},
{
"tripleset": [
[
"Alderney Airport",
"CITY_SERVED",
"Alderney"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Alderney Airport serves Alderney."
},
{
"source": "webnlg",
"text": "Alderney Airport serves the city of Alderney."
}
]
},
{
"tripleset": [
[
"Allama Iqbal International Airport",
"RUNWAY_NAME",
"\"18R/36L\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "18R/36L is the runway name at Allama Iqbal International airport."
},
{
"source": "webnlg",
"text": "Allama Iqbal International Airport has a runway named 18R/36L."
},
{
"source": "webnlg",
"text": "Allama Iqbal International Airport's runway name is \"18R/36L\"."
}
]
},
{
"tripleset": [
[
"Alpena County Regional Airport",
"RUNWAY_LENGTH",
"1533.0"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The runway length of Alpena County Regional Airport is 1,533."
},
{
"source": "webnlg",
"text": "The runway length of Alpena County Regional airport is 1533.0."
}
]
},
{
"tripleset": [
[
"Alpena County Regional Airport",
"RUNWAY_NAME",
"\"1/19\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alpena Country Regional Airport runway name is 1/19/."
},
{
"source": "webnlg",
"text": "1/19 is the runway name of Alpena County Regional Airport."
},
{
"source": "webnlg",
"text": "The runway name of Alpena County Regional Airport is 1/19."
}
]
},
{
"tripleset": [
[
"Amsterdam Airport Schiphol",
"CITY_SERVED",
"Amsterdam"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Amsterdam Airport Schiphol serves the city of Amsterdam."
},
{
"source": "webnlg",
"text": "Amsterdam airport, Schipol serves the city of Amsterdam."
},
{
"source": "webnlg",
"text": "Amsterdam Airport Schuphol serves Amsterdam."
}
]
},
{
"tripleset": [
[
"Andrews County, Texas",
"COUNTY_SEAT",
"Andrews, Texas"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews County, Texas is country seat to Andrews, Texas."
},
{
"source": "webnlg",
"text": "Andrews, Texas is the country seat of Andrews County, Texas."
},
{
"source": "webnlg",
"text": "Andrews County, Texas has its county seat in Andrews, Texas."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"CITY_SERVED",
"Andrews, Texas"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews Country Airport's city is served by Andrews, Texas."
},
{
"source": "webnlg",
"text": "Andrews County Airport serves Andrews, Texas."
},
{
"source": "webnlg",
"text": "Andrews County Airport serves the city of Andrews in Texas."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"RUNWAY_NAME",
"\"11/29\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews Country Airport runway name is 11/29."
},
{
"source": "webnlg",
"text": "11/29 is the runway name of Andrews County Airport."
},
{
"source": "webnlg",
"text": "The runway name of Andrews County Airport is 11/29."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"RUNWAY_NAME",
"\"Helipad\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The name of the runway at Andrews County Airport is Helipad."
},
{
"source": "webnlg",
"text": "Helipad is the runway name of Andrews County Airport."
},
{
"source": "webnlg",
"text": "The runway at Andrews County Airport is called Helipad."
}
]
},
{
"tripleset": [
[
"Angola International Airport",
"CITY_SERVED",
"Luanda"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Angola International Airport city served is Luanda."
},
{
"source": "webnlg",
"text": "Angola International Airport serves the city of Luanda."
},
{
"source": "webnlg",
"text": "Angola International Airport serves Luanda."
}
]
},
{
"tripleset": [
[
"Ardmore Airport (New Zealand)",
"LOCATION",
"Auckland"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Ardmore Airport, New Zealand is in Auckland."
},
{
"source": "webnlg",
"text": "Ardmore Airport is located in Auckland, New Zealand."
}
]
},
{
"tripleset": [
[
"Athens",
"MAYOR",
"Giorgos Kaminis"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Athens mayor is Giorgos Kaminis."
},
{
"source": "webnlg",
"text": "Giorgos Kaminis is the mayor of Athens."
},
{
"source": "webnlg",
"text": "The mayor of Athens is Giorgos Kaminis."
}
]
},
{
"tripleset": [
[
"Athens International Airport",
"LOCATION",
"Spata"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Athens International Airport is in Spata."
},
{
"source": "webnlg",
"text": "Athens International Airport is located in Spata."
}
]
},
{
"tripleset": [
[
"Belgium",
"LANGUAGE",
"German language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Belgium language is German language."
},
{
"source": "webnlg",
"text": "German is the language of Belgium."
},
{
"source": "webnlg",
"text": "The language spoken is Belgium is German."
}
]
},
{
"tripleset": [
[
"Belgium",
"LEADER_NAME",
"Philippe of Belgium"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Philippe of Belgium is the leader of Belgium."
},
{
"source": "webnlg",
"text": "The leader of Belgium is Philippe of Belgium."
},
{
"source": "webnlg",
"text": "The leader of Belgium is Phillipe of Belgium."
}
]
},
{
"tripleset": [
[
"Denmark",
"CAPITAL",
"Copenhagen"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The capital of Denmark is Copenhagen."
},
{
"source": "webnlg",
"text": "Copenhagen is the capital of Denmark."
}
]
},
{
"tripleset": [
[
"Denmark",
"LEADER_NAME",
"Lars L\u00f8kke Rasmussen"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Denmark's leader name is Lars L\u00f8kke Rasmussen."
},
{
"source": "webnlg",
"text": "Lars L\u00f8kke Rasmussen leads Denmark."
},
{
"source": "webnlg",
"text": "Lars L\u00f8kke Rasmussen is the leader of Denmark."
}
]
},
{
"tripleset": [
[
"Flemish Region",
"LEADER_NAME",
"Flemish Government"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Flemish region is led by the Flemish government."
},
{
"source": "webnlg",
"text": "The Flemish Government leads the Flemish Region."
}
]
},
{
"tripleset": [
[
"Greece",
"LANGUAGE",
"Greek language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Greek is the language spoken in Greece."
},
{
"source": "webnlg",
"text": "The language spoken in Greece is Greek."
}
]
},
{
"tripleset": [
[
"Greece",
"LEADER_NAME",
"Nikos Voutsis"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The leader of Greece is Nikos Voutsis."
}
]
},
{
"tripleset": [
[
"Harrietstown, New York",
"IS_PART_OF",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Harrietstown, New York is part of the United States."
},
{
"source": "webnlg",
"text": "Harrietstown, N.Y. is part of the U.S."
}
]
},
{
"tripleset": [
[
"Pakistan",
"LEADER_NAME",
"Anwar Zaheer Jamali"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The leader of Pakistan is Anwar Zaheer Jamali."
},
{
"source": "webnlg",
"text": "Pakistan has a leader called Anwar Zaheer Jamali."
}
]
},
{
"tripleset": [
[
"Paracuellos de Jarama",
"IS_PART_OF",
"Community of Madrid"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Paracuellos de Jarama is part of the community of Madrid."
}
]
},
{
"tripleset": [
[
"Port Authority of New York and New Jersey",
"HEADQUARTER",
"Four World Trade Center"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Port Authority of New York and New Jersey headquarter is Four World Trade Center."
},
{
"source": "webnlg",
"text": "The Four World Trade Center is the headquarters of the Port Authority of New York and New Jersey."
}
]
},
{
"tripleset": [
[
"San Sebasti\u00e1n de los Reyes",
"COUNTRY",
"Spain"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "San Sebasti\u00e1n de los Reyes is in Spain."
},
{
"source": "webnlg",
"text": "San Sebastian de los Reyes is located in Spain."
}
]
},
{
"tripleset": [
[
"Saranac Lake, New York",
"IS_PART_OF",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Saranac Lake, New York is part of the United States."
},
{
"source": "webnlg",
"text": "Saranac Lake, New York, is part of the United States."
}
]
},
{
"tripleset": [
[
"South Jersey Transportation Authority",
"CHIEF",
"Stephen Dilts"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "South Jersey Transportation Authority chief is Stephen Dilts."
},
{
"source": "webnlg",
"text": "Stephen Dilts is the chief of the South Jersey Transportation Authority."
},
{
"source": "webnlg",
"text": "Stephen Dilts is the chief of South Jersey Transportation Authority."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Ajoblanco",
"INGREDIENT",
"Bread"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bread is an ingredient of Ajoblanco which originates from Spain."
},
{
"source": "webnlg",
"text": "One of the ingredients of Ajoblanco is bread and it originates from Spain."
},
{
"source": "webnlg",
"text": "Bread is an ingredient of Ajoblanco which comes from Spain."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"INGREDIENT",
"Almond"
],
[
"Ajoblanco",
"REGION",
"Andalusia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ajoblanco, from Andalusia, has almond as one of its ingredients."
},
{
"source": "webnlg",
"text": "Almond is an ingredient in ajoblanco, from Andalusia."
},
{
"source": "webnlg",
"text": "Ajoblanco is from the Andalusia region and has almond as one of its ingredients."
}
]
},
{
"tripleset": [
[
"Amatriciana sauce",
"REGION",
"Lazio"
],
[
"Amatriciana sauce",
"INGREDIENT",
"Tomato"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Tomato is an ingredient in Amatriciana sauce which comes from Lazio."
},
{
"source": "webnlg",
"text": "Amatriciana sauce is from the Lazio region and tomato is one of the ingredients."
},
{
"source": "webnlg",
"text": "Tomato is an ingredient in Amatriciana sauce which comes from the region of Lazio."
}
]
},
{
"tripleset": [
[
"Antioquia Department",
"COUNTRY",
"Colombia"
],
[
"Bandeja paisa",
"REGION",
"Antioquia Department"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bandeja paisa comes from the Antioquia Department of Colombia."
},
{
"source": "webnlg",
"text": "Bandeja paisa is a dish found in the Antioquia Department of Colombia."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"REGION",
"Indonesia"
],
[
"Arem-arem",
"INGREDIENT",
"Banana leaf"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem-arem uses the ingredient banana leaf, it is a common dish in Indonesia."
},
{
"source": "webnlg",
"text": "Banana leaf is an ingredient in arem arem, which comes from Indonesia."
},
{
"source": "webnlg",
"text": "Arem arem which includes banana leaf, comes from Indonesia."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Arrabbiata sauce",
"INGREDIENT",
"Olive oil"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Olive oil is an ingredient used in the preparation of Arrabbiata sauce, found in Italy."
},
{
"source": "webnlg",
"text": "Olive Oil is used to create Italian Arrabbiata sauce."
},
{
"source": "webnlg",
"text": "Olive oil is an ingredient in Arrabbiata sauce, which can be found in Italy."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Italy",
"CAPITAL",
"Rome"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arrabbiata sauce is found in Italy where the capital city is Rome."
},
{
"source": "webnlg",
"text": "Rome is the capital of Italy which is where Arrabbiata sauce comes from."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce is from Italy which has the capital city of Rome."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"INGREDIENT",
"Tomato"
],
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Tomatoes are found in Arrabbiata sauce which comes from Italy."
},
{
"source": "webnlg",
"text": "Tomatoes are an ingredient of Arrabbiata sauce from Italy."
},
{
"source": "webnlg",
"text": "Tomatoes are found in Arrabbiata sauce, which originated in Italy."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Squid"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arr\u00f2s negre is from Spain, it includes squid."
},
{
"source": "webnlg",
"text": "Arr\u00f2s negre is a traditional dish from Spain, it includes squid."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"REGION",
"Valencian Community"
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Cuttlefish"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Cuttlefish is an ingredient in Arros negre which comes from the region of Valencian Community."
},
{
"source": "webnlg",
"text": "An ingredient of arros negre is cuttlefish, the dish comes from the Valencian community region."
}
]
},
{
"tripleset": [
[
"Ayam penyet",
"COUNTRY",
"Java"
],
[
"Ayam penyet",
"MAIN_INGREDIENTS",
"\"Squeezed\" or \"smashed\" fried chicken served with sambal"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet is a food found in Java and the main ingredients are \"squeezed\" or \"smashed\" chicken served with sambal."
},
{
"source": "webnlg",
"text": "Ayam penyet (Javanese) includes squeezed/smashed fried chicken and is served with sambal."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"Bacon Explosion",
"INGREDIENT",
"Sausage"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A sausage is used when making a Bacon Explosion which has The United states to thank for inventing it."
},
{
"source": "webnlg",
"text": "Bacon Explosion is a dish from the United States that contains sausage."
},
{
"source": "webnlg",
"text": "Bacon Explosion comes from the United States and includes sausage among its ingredients."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"United States",
"LEADER_NAME",
"John Roberts"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The United States, where John Roberts is a leader, is the country of the Bacon Explosion."
},
{
"source": "webnlg",
"text": "The Bacon Explosion comes from the United States where John Roberts is a leader."
},
{
"source": "webnlg",
"text": "The United States (with a leader, John Roberts) boasts the Bacon Explosion."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"INGREDIENT",
"Bacon"
],
[
"Bacon Explosion",
"COURSE",
"\"Main course\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon is an ingredient in the main course dish 'Bacon Explosion'."
},
{
"source": "webnlg",
"text": "Bacon is an ingredient in a main course called Bacon Explosion."
},
{
"source": "webnlg",
"text": "Bacon Explosion is an entree that includes bacon, the main ingredient."
}
]
},
{
"tripleset": [
[
"Bacon sandwich",
"ALTERNATIVE_NAME",
"\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\""
],
[
"Bacon sandwich",
"INGREDIENT",
"Bread"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A bacon sandwich on bread is also known as bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin."
},
{
"source": "webnlg",
"text": "The bacon sandwich, which one of two ingedients is bread, has different names including: Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COUNTRY",
"\"France, United States or China\""
],
[
"Baked Alaska",
"INGREDIENT",
"Christmas pudding"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska is thought to have originated in the United States, France or China, and contains Christmas pudding as an ingredient."
},
{
"source": "webnlg",
"text": "France, United States and China all claim to have invented Baked Alaska which can have Christmas pudding as an ingredient."
},
{
"source": "webnlg",
"text": "Christmas pudding is an ingredient in Baked Alaska which is from France, the United States and China."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COUNTRY",
"China"
],
[
"China",
"LANGUAGE",
"Standard Chinese"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska comes from China, where Standard Chinese is spoken."
},
{
"source": "webnlg",
"text": "Standard Chinese is spoken in China where baked Alaska is a dish."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COURSE",
"Dessert"
],
[
"Dessert",
"DISH_VARIATION",
"Cookie"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska and cookies are types of dessert."
},
{
"source": "webnlg",
"text": "Baked Alaska and a cookie are both desserts."
},
{
"source": "webnlg",
"text": "A Baked Alaska is a dessert along with cookies which you can also eat as a dessert."
}
]
},
{
"tripleset": [
[
"Bakewell pudding",
"CREATOR",
"\"Rutland Arms, Bakewell, in 1820\""
],
[
"Bakewell pudding",
"MAIN_INGREDIENTS",
"\"Ground almond, jam, butter, eggs\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakewell pudding was created in 1820 at the Rutland Arms, Bakewell and has ground almond, jam, butter and eggs as main ingredients."
},
{
"source": "webnlg",
"text": "The main ingredients of Bakewell pudding, which was invented in Rutland Arms, Bakewell, in 1820, are ground almond, jam, butter and eggs."
}
]
},
{
"tripleset": [
[
"Bakewell pudding",
"DISH_VARIATION",
"Bakewell tart"
],
[
"Bakewell tart",
"INGREDIENT",
"Shortcrust pastry"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakewell tart, made with shortcrust pastry, is a variation of Bakewell pudding."
},
{
"source": "webnlg",
"text": "A variant of bakewell pudding is bakewell tart, made with shortcrust pastry."
},
{
"source": "webnlg",
"text": "A variant of bakewell pudding is bakewell tart which uses shortcrust pastry."
}
]
},
{
"tripleset": [
[
"Bandeja paisa",
"INGREDIENT",
"Lemon"
],
[
"Lemon",
"GENUS",
"Citrus"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Lemon (part of genus Citrus) is an ingredient in Bandeja paisa."
},
{
"source": "webnlg",
"text": "Lemon, from the genus citrus, is an ingredient in Bandeja paisa."
},
{
"source": "webnlg",
"text": "Lemon (genus; citrus) is an ingredient found in Bandeja paisa."
}
]
},
{
"tripleset": [
[
"Batchoy",
"COUNTRY",
"Philippines"
],
[
"Batchoy",
"INGREDIENT",
"Shrimp"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Batchoy is a food in the Philippines and one of its ingredient is shrimp."
},
{
"source": "webnlg",
"text": "The Philippines is the country of origin for the dish Batchoy, of which shrimp is one of the main ingredients."
},
{
"source": "webnlg",
"text": "The Philippines is the country that Batchoy, a dish containing shrimp, comes from."
}
]
},
{
"tripleset": [
[
"Batchoy",
"COUNTRY",
"Philippines"
],
[
"Philippines",
"ETHNIC_GROUP",
"Chinese Filipino"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Chinese Filipino is an ethnic group in the Philippines, where batchoy is a traditional dish."
},
{
"source": "webnlg",
"text": "Chinese Filipino are from the Philippines, where batchoy is eaten."
},
{
"source": "webnlg",
"text": "Batchoy is a food from the Philippines, the country where there is an ethnic group called the Chinese Filipino."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"REGION",
"Singapore"
],
[
"Singapore",
"LANGUAGE",
"Standard Chinese"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow originates from Singapore, where Chinese is spoken."
},
{
"source": "webnlg",
"text": "Standard chinese speaking Singapore is the origin of beef kway teow."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"REGION",
"Singapore"
],
[
"Singapore",
"LEADER_NAME",
"Tony Tan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow is a dish of Singapore where Tony Tan is a leader."
},
{
"source": "webnlg",
"text": "Tony Tan is the leader of Singapore, from where Beef kway teow originates."
},
{
"source": "webnlg",
"text": "Beef kway teow originates from Singapore and it's leader is Tony Tan."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji originates from India and Sumitra Mahajan is it's leader."
},
{
"source": "webnlg",
"text": "Bhajji comes from India where the leader is Sumitra Mahajan."
},
{
"source": "webnlg",
"text": "Bhajji originates from India where the leader is Sumitra Mahajan."
}
]
},
{
"tripleset": [
[
"Bhajji",
"SIMILAR_DISH",
"\"Pakora and other fritters made from wheat or corn flour\""
],
[
"Bhajji",
"INGREDIENT",
"Gram flour"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji, which uses gram flour, is a dish similar to pakora and other fritters made from wheat or corn flour."
},
{
"source": "webnlg",
"text": "Gram flour is an ingredient of a Bhajji, which is a similar dish to pakora and other fritters made from wheat or corn flour."
}
]
},
{
"tripleset": [
[
"Binignit",
"INGREDIENT",
"Sweet potato"
],
[
"Binignit",
"MAIN_INGREDIENTS",
"Banana"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "An ingredient of Binignit is sweet potato, and one of the main ingredients is the banana."
},
{
"source": "webnlg",
"text": "Banana is a main ingredient of Binignit and sweet potato is also an ingredient."
},
{
"source": "webnlg",
"text": "The main ingredient of Binignit is banana, and also important is the inclusion of sweet potatoes."
}
]
},
{
"tripleset": [
[
"Binignit",
"INGREDIENT",
"Sweet potato"
],
[
"Binignit",
"MAIN_INGREDIENTS",
"Sago"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sago is the main ingredient in binignit but sweet potatoes are also used in it."
},
{
"source": "webnlg",
"text": "Binignit has the ingredients sweet potato and sago."
},
{
"source": "webnlg",
"text": "Sweet potato and sago are main ingredients in Binignit."
}
]
},
{
"tripleset": [
[
"Binignit",
"INGREDIENT",
"Sweet potato"
],
[
"Sweet potato",
"DIVISION",
"Flowering plant"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sweet potatoes (a kind of flowering plant) are used in binignit recipes."
},
{
"source": "webnlg",
"text": "Sweet potato belongs to flowering plants and is an ingredient of Binignit."
}
]
},
{
"tripleset": [
[
"Binignit",
"MAIN_INGREDIENTS",
"Sweet potato"
],
[
"Sweet potato",
"ORDER",
"Solanales"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sweet potatoes are the main ingredient of binignit and the potatoes are part of the order of Solanales."
},
{
"source": "webnlg",
"text": "Sweet potatoes are members of the Solanales order of plants. They are the main ingredient of binignit."
},
{
"source": "webnlg",
"text": "Sweet potato is of the order Solanales and are a main ingredient of binignit."
}
]
},
{
"tripleset": [
[
"Bionico",
"COURSE",
"Dessert"
],
[
"Bionico",
"INGREDIENT",
"Granola"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bionico is a dessert and it requires granola as one of it's ingredients."
},
{
"source": "webnlg",
"text": "Bionico is a dessert, requiring granola as one of its ingredients."
},
{
"source": "webnlg",
"text": "Bionico is a dessert that includes the ingredient granola."
}
]
},
{
"tripleset": [
[
"Bionico",
"DISH_VARIATION",
"Honey"
],
[
"Bionico",
"COURSE",
"Dessert"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A popular dessert is Bionico and a variation of this dish can be honey."
},
{
"source": "webnlg",
"text": "Honey can be used as a variaton in Bionico which is a dessert."
},
{
"source": "webnlg",
"text": "Honey can be added to bionico, a dessert dish."
}
]
},
{
"tripleset": [
[
"Dessert",
"DISH_VARIATION",
"Ice cream"
],
[
"Bionico",
"COURSE",
"Dessert"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bionico and ice cream are both served as a dessert course."
},
{
"source": "webnlg",
"text": "Bionico and ice cream are types of dessert."
},
{
"source": "webnlg",
"text": "Bionico and Ice Cream are served at the dessert course."
}
]
},
{
"tripleset": [
[
"Siomay",
"INGREDIENT",
"Peanut sauce"
],
[
"Batagor",
"DISH_VARIATION",
"Siomay"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Batagor and Siomay (which has peanut sauce as an ingredient) are variations of the same dish."
},
{
"source": "webnlg",
"text": "Peanut sauce is an ingredient in Siomay and Batagor is a variation of this dish."
},
{
"source": "webnlg",
"text": "Batagor is a variation of the dish Siomay, of which peanut sauce is an ingredient."
}
]
},
{
"tripleset": [
[
"Abilene Regional Airport",
"CITY_SERVED",
"Abilene, Texas"
],
[
"Abilene, Texas",
"IS_PART_OF",
"Taylor County, Texas"
],
[
"Abilene, Texas",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Abilene Regional Airport serves the city of Abilene which is part of Taylor County,Texas, in the United States."
},
{
"source": "webnlg",
"text": "Abilene Regional Airport serves the city of Abilene which is part of Taylor County, Texas, in the United States."
},
{
"source": "webnlg",
"text": "The Abilene regional airport serves Abilene, Texas, which is a part of Taylor County, Texas in the United States."
}
]
},
{
"tripleset": [
[
"Adirondack Regional Airport",
"RUNWAY_LENGTH",
"1219.0"
],
[
"Adirondack Regional Airport",
"CITY_SERVED",
"Lake Placid, New York"
],
[
"Adirondack Regional Airport",
"CITY_SERVED",
"Saranac Lake, New York"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Adirondack Regional Airport has a runway length of 1219.0 and serves the cities of Saranac Lake and Lake Placid, New York."
},
{
"source": "webnlg",
"text": "Adirondack Regional Airport serves the city of Saranac Lake, New York and the residents of Lake Placid and has a runway that's 1,219 long."
}
]
},
{
"tripleset": [
[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"RUNWAY_LENGTH",
"4100.0"
],
[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"LOCATION",
"Madrid"
],
[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"RUNWAY_NAME",
"\"14L/32R\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The 14L/32R runway of the Adolfo Suarez Madrid - Barajas Airport in Madrid has a length of 4100.0."
},
{
"source": "webnlg",
"text": "Adolfo Suarez Madrid-Barajas Airport is in Madrid; its runway name is 14L/32R and its runway length is 4100."
},
{
"source": "webnlg",
"text": "The Adolfo Suarez Madrid-Barajas Airport is in Madrid. Its runway, called 14L/32R has length of 4100."
}
]
},
{
"tripleset": [
[
"Agra Airport",
"LOCATION",
"India"
],
[
"Agra Airport",
"OPERATING_ORGANISATION",
"Indian Air Force"
],
[
"Agra Airport",
"ICAO_LOCATION_IDENTIFIER",
"\"VIAG\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Indian Air Force in India, operates Agra Airport which uses VIAG as its ICAO location identifier."
},
{
"source": "webnlg",
"text": "Agra Airport, located in India, is operated by the Indian Air Force, and has an ICAO identifier is VIAG."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"Al Asad Airbase",
"LOCATION",
"Iraq"
],
[
"Al Asad Airbase",
"RUNWAY_LENGTH",
"3078.48"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Operated by the United States Air Force, Al Asad Airbase, in Iraq, has a runway length of 3078.48 metres."
},
{
"source": "webnlg",
"text": "The USAF operated the Al Asad Airbase, located in iraq, with a length of 3078.48."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"United States Air Force",
"BATTLES",
"Invasion of Grenada"
],
[
"United States Air Force",
"BATTLES",
"Korean War"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Al Asad airbase is operated by the United States Air Force which was involved in the Invasion of Grenada and the Korean war."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"United States Air Force",
"BATTLES",
"Invasion of Grenada"
],
[
"United States Air Force",
"BATTLES",
"Operation Enduring Freedom"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Al Asad Airbase is operated by the United States Air Force which has the Invasion of Grenada as one of its noted battles as well as The Operation Enduring Freedom."
},
{
"source": "webnlg",
"text": "The USAF operates Al Asad Airbase. They fought in the Invasion of Grenada and The Operation Enduring Freedom."
},
{
"source": "webnlg",
"text": "The US Air Force is the operating organisation for Al Asad Airbase, used in battles such as Invasion of Grenada and the operation enduring freedom."
}
]
},
{
"tripleset": [
[
"Al Asad Airbase",
"OPERATING_ORGANISATION",
"United States Air Force"
],
[
"United States Air Force",
"BATTLES",
"Korean War"
],
[
"United States Air Force",
"BATTLES",
"Operation Enduring Freedom"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The United States Air Force who is the operating organisation for Al Asad airbase fought battles in the Korean war and also in Operation Enduring Freedom."
},
{
"source": "webnlg",
"text": "The US Air Force, veteran of the Korean war and Operation Enduring Freedom, operated Al Asad Airbase,."
}
]
},
{
"tripleset": [
[
"Alderney Airport",
"1ST_RUNWAY_SURFACE_TYPE",
"Poaceae"
],
[
"Alderney Airport",
"RUNWAY_LENGTH",
"733.0"
],
[
"Alderney Airport",
"CITY_SERVED",
"Alderney"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The 1st runway at Alderney Airport is made from Poaceae and has a length of 733.0."
}
]
},
{
"tripleset": [
[
"Alpena County Regional Airport",
"LOCATION",
"Wilson Township, Alpena County, Michigan"
],
[
"Alpena County Regional Airport",
"RUNWAY_LENGTH",
"1533.0"
],
[
"Alpena County Regional Airport",
"ELEVATION",
"210"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Located in Wilson Township, Alpena County, Michigan, Alpena County Regional Airport has an elevation of 210 metres above sea level and a runway length of 1,533 metres."
},
{
"source": "webnlg",
"text": "Aplena County Regional Airport; is elevated 210 metres above sea level, has a runway length of 1,533 metres and is located in Wilson Township, Alpena County, Michigan."
},
{
"source": "webnlg",
"text": "Alpena County Regional Airport is located in the Wilson Township, Alpena County, Michigan. It is 210 m above sea level and its runway length is 1533 feet."
}
]
},
{
"tripleset": [
[
"Amsterdam Airport Schiphol",
"CITY_SERVED",
"Amsterdam"
],
[
"Amsterdam Airport Schiphol",
"RUNWAY_LENGTH",
"3500.0"
],
[
"Amsterdam Airport Schiphol",
"ELEVATION",
"-3.3528"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Amsterdam airport, Schipol which serves the city of Amsterdam is located at -3.3528 metres above sea level and has a runway length of 3500."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"LARGEST_CITY",
"Houston"
],
[
"Texas",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas (United States), where Houston is the largest city."
},
{
"source": "webnlg",
"text": "Andrews County Airport is in Texas, United States. Houston is the largest city in Texas."
},
{
"source": "webnlg",
"text": "The United States Andrews County Airport is located in Texas, whose largest city is Houston."
}
]
},
{
"tripleset": [
[
"Angola International Airport",
"LOCATION",
"\u00cdcolo e Bengo"
],
[
"\u00cdcolo e Bengo",
"COUNTRY",
"Angola"
],
[
"Angola International Airport",
"RUNWAY_LENGTH",
"4000.0"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Angola International airport is located in Icolo e Bengo Angola, it has a runway length of 4000ft."
},
{
"source": "webnlg",
"text": "Angola International Airport is located at \u00cdcolo e Bengo, Angola and is 4000 in length."
},
{
"source": "webnlg",
"text": "Angola International Airport, located in Icolo e Bengo, Angola has a runway length of 4000."
}
]
},
{
"tripleset": [
[
"Angola International Airport",
"LOCATION",
"\u00cdcolo e Bengo"
],
[
"\u00cdcolo e Bengo",
"COUNTRY",
"Angola"
],
[
"Angola International Airport",
"RUNWAY_NAME",
"\"South Runway\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Angola International Airport is located at \u00cdcolo e Bengo in Angola and the runway is named \"south runway\"."
},
{
"source": "webnlg",
"text": "Angola International Airport is located at \u00cdcolo e Bengo, Angola and the runway is known as \"South Runway\"."
}
]
},
{
"tripleset": [
[
"Antwerp International Airport",
"OPERATING_ORGANISATION",
"Flemish Government"
],
[
"Antwerp International Airport",
"OWNER",
"Flemish Region"
],
[
"Antwerp International Airport",
"ELEVATION",
"12.0"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Antwerp International Airport which has an elevation of 12.0 metres above sea level is owned and operated by the Flemish government."
},
{
"source": "webnlg",
"text": "Antwerp International Airport which is 12 meters above sea level is owned by Flemish Region and operated by the Flemish government."
}
]
},
{
"tripleset": [
[
"Antwerp International Airport",
"OWNER",
"Flemish Region"
],
[
"Antwerp International Airport",
"OPERATING_ORGANISATION",
"\"Flemish department of Mobility and Public Works\""
],
[
"Antwerp International Airport",
"CITY_SERVED",
"Antwerp"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Flemish department of Mobility and Public Works is the operating organisation and owner of Antwerp International Airport that serves the city of Antwerp."
},
{
"source": "webnlg",
"text": "Antwerp International Airport; serves the city of Antwerp, is in the Flemish region and is operated by the Flemish department of mobility and public works."
},
{
"source": "webnlg",
"text": "Antwerp International Airport serves Antwerp. It is owned by the Flemish Region and operated by the Flemish department of Mobility and Public Works."
}
]
},
{
"tripleset": [
[
"Appleton, Wisconsin",
"IS_PART_OF",
"Kimberly, Wisconsin"
],
[
"Appleton, Wisconsin",
"IS_PART_OF",
"Little Chute, Wisconsin"
],
[
"Appleton International Airport",
"CITY_SERVED",
"Appleton, Wisconsin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Appleton International airport serves the city of Appleton which is part of Kimberly, Wisconsin, as is Little Chute."
},
{
"source": "webnlg",
"text": "Appletone International Airport served the city of Appleton, which is part of a little chute in Kimberly, Wisconsin."
}
]
},
{
"tripleset": [
[
"Appleton International Airport",
"LOCATION",
"Greenville, Wisconsin"
],
[
"Greenville, Wisconsin",
"COUNTRY",
"United States"
],
[
"Greenville, Wisconsin",
"IS_PART_OF",
"Clayton, Winnebago County, Wisconsin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Appleton International Airport is in Greenville which is part of Clayton Winnebago County, Wisconsin, in the United States."
},
{
"source": "webnlg",
"text": "Appleton International Airport is located in Greenville, Wisconsin, USA, which is part of Clayton, Winnebago county."
},
{
"source": "webnlg",
"text": "The Appleton International Airport is in Greenville, Clayton, Winnebago County, Wisconsin, United States."
}
]
},
{
"tripleset": [
[
"Ardmore Airport (New Zealand)",
"3RD_RUNWAY_SURFACE_TYPE",
"Poaceae"
],
[
"Poaceae",
"DIVISION",
"Flowering plant"
],
[
"Poaceae",
"ORDER",
"Poales"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The 3rd runway at Ardmore Airport (New Zealand) is made of Poaceae which is of the order Poales and belongs to the division of flowering plants."
},
{
"source": "webnlg",
"text": "The 3rd runway at Ardmore Airport (New Zealand) is made of Poaceae which is part of the Poales order and is in the division of flowering plants."
}
]
},
{
"tripleset": [
[
"Ashgabat International Airport",
"LOCATION",
"Ashgabat"
],
[
"Ashgabat International Airport",
"RUNWAY_LENGTH",
"3800.0"
],
[
"Ashgabat International Airport",
"ELEVATION",
"211"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ashgabat International Airport (located in Ashgabat) is elevated 211 metres above sea level and has a runway length of 3800."
},
{
"source": "webnlg",
"text": "Ashgabat is the location of Ashgabat International Airport which has a runway length of 3800.0 and is 211 metres above sea level."
},
{
"source": "webnlg",
"text": "Ashgabat International airport is located in Ashgabat at 211 metres above sea level and has a runway length of 3800.0."
}
]
},
{
"tripleset": [
[
"Athens International Airport",
"CITY_SERVED",
"Athens"
],
[
"Athens",
"COUNTRY",
"Greece"
],
[
"Greece",
"LEADER_NAME",
"Alexis Tsipras"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Athens International Airport serves the city of Athens, in Greece where Alexis Tsipras is the leader."
},
{
"source": "webnlg",
"text": "Athens International Airport serves the city of Athens in Greece where Alexis Tsipras is the leader."
},
{
"source": "webnlg",
"text": "Athens International Airport serves the city of Athens in Greece, a country led by Alexis Tsipras."
}
]
},
{
"tripleset": [
[
"Atlantic City International Airport",
"RUNWAY_NAME",
"\"4/22\""
],
[
"Atlantic City International Airport",
"LOCATION",
"Egg Harbor Township, New Jersey"
],
[
"Egg Harbor Township, New Jersey",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Atlantic City International Airport, which has a runway with the name is 4/22, is located in Egg Harbor Township, in New Jersey, United States."
},
{
"source": "webnlg",
"text": "Atlantic City International Airport is in Egg Harbor Township, New Jersey, United States and has the runway name 4/22."
}
]
},
{
"tripleset": [
[
"Belgium",
"LEADER_NAME",
"Philippe of Belgium"
],
[
"Antwerp International Airport",
"CITY_SERVED",
"Antwerp"
],
[
"Antwerp",
"COUNTRY",
"Belgium"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Antwerp International Airport serves the city of Antwerp in Belgium where the leader is Phillipe of Belgium."
},
{
"source": "webnlg",
"text": "Antwerp, Belgium, is led by Philippe of Belgium and served by Antwerp International Airport."
}
]
},
{
"tripleset": [
[
"Poaceae",
"CLASS",
"Monocotyledon"
],
[
"Poaceae",
"ORDER",
"Commelinids"
],
[
"Ardmore Airport (New Zealand)",
"2ND_RUNWAY_SURFACE_TYPE",
"Poaceae"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Poaceae is in the class Monocotyledon and the order of Commelinids. It is also the surface type of the second runway of Ardmore Airport, New Zealand."
},
{
"source": "webnlg",
"text": "The 2nd runway at Ardmore Airport (New Zealand) is made of Poaceae which belongs to the order of Commelinids and the class of Monocotyledon."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"ALTERNATIVE_NAME",
"\"Ajo blanco\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The alternative name for Ajoblanco is \"Ajo blanco\"."
},
{
"source": "webnlg",
"text": "Ajo blanco is an alternative name to Ajoblanco."
},
{
"source": "webnlg",
"text": "Ajo blanco is an alternative name of Ajoblanco."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"MAIN_INGREDIENTS",
"\"Bread, almonds, garlic, water, olive oil\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The main ingredients in ajoblanco are bread, almonds, garlic, water and olive oil."
},
{
"source": "webnlg",
"text": "Bread, almonds, garlic, water, and olive oil are the main ingredients in Ajoblanco."
},
{
"source": "webnlg",
"text": "The main ingredients of Ajoblanco are bread, almonds, garlic, water, olive oil."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"REGION",
"\"Nationwide in Indonesia, but more specific to Java\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem-arem is found nationwide in Indonesia, but is more specific to Java."
},
{
"source": "webnlg",
"text": "Arem-arem is nationwide in Indonesia, but more specific to Java."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"REGION",
"Indonesia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem arem comes from Indonesia."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"MAIN_INGREDIENTS",
"\"Tomatoes, red chili, garlic, olive oil\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arrabbiata sauce is made with tomatoes, red chili, garlic and olive oil."
},
{
"source": "webnlg",
"text": "the main ingredients in Arrabbiata sauce are Tomatoes, red chili, garlic and olive oil."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"INGREDIENT",
"Cuttlefish"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Cuttlefish is an ingredient in Arros negre."
},
{
"source": "webnlg",
"text": "Cuttlefish is an ingredient of the dish Arr\u00f2s negre."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"INGREDIENT",
"Squid"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Squid is an ingredient of Arros negre."
}
]
},
{
"tripleset": [
[
"Asam pedas",
"COUNTRY",
"Malaysia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asam pedas is a food found in Malaysia."
}
]
},
{
"tripleset": [
[
"Avocado",
"GENUS",
"Persea"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The genus of the avocado is persea."
},
{
"source": "webnlg",
"text": "The genus of Avocado is Persea."
}
]
},
{
"tripleset": [
[
"Ayam penyet",
"SERVING_TEMPERATURE",
"\"Hot\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet should be served hot."
},
{
"source": "webnlg",
"text": "Ayam penyet is a dish that should be served hot."
},
{
"source": "webnlg",
"text": "ayam penyet is served hot."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COURSE",
"Dessert"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska is a dessert."
}
]
},
{
"tripleset": [
[
"Bakewell tart",
"INGREDIENT",
"Frangipane"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "An ingredient of bakewell tart is frangipane."
},
{
"source": "webnlg",
"text": "Frangipane is an ingredient in a Bakewell tart."
},
{
"source": "webnlg",
"text": "Frangipane is an ingredient of Bakewell tart."
}
]
},
{
"tripleset": [
[
"Bakso",
"INGREDIENT",
"Noodle"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The dish Bakso contains noodles."
},
{
"source": "webnlg",
"text": "Noodle is an ingredient in Bakso."
},
{
"source": "webnlg",
"text": "bakso contains noodles."
}
]
},
{
"tripleset": [
[
"Bakso",
"INGREDIENT",
"Vermicelli"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Vermicelli is an ingredient in Bakso."
},
{
"source": "webnlg",
"text": "Vermicelli is an ingredient of the dish Bakso."
},
{
"source": "webnlg",
"text": "Vermicelli is included in bakso."
}
]
},
{
"tripleset": [
[
"Bandeja paisa",
"INGREDIENT",
"Chorizo"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Chorizo is an ingredient in the dish Bandeja paisa."
},
{
"source": "webnlg",
"text": "Chorizo is an ingredient in Bandeja paisa."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"CREATOR",
"Mondelez International"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Mondelez International is the creator of Barny cakes."
},
{
"source": "webnlg",
"text": "Barny cakes were created by Mondelez International."
},
{
"source": "webnlg",
"text": "Barny cakes is created by Mondelez International."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"DISH_VARIATION",
"Chocolate"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Barny Cakes can be chocolate flavoured."
},
{
"source": "webnlg",
"text": "Chocolate is one variation of the Barny Cakes dish."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"PROTEIN",
"1.8 g"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Barny cakes contain 1.8 g of protein."
},
{
"source": "webnlg",
"text": "Barny cakes contain 1.8g of protein."
}
]
},
{
"tripleset": [
[
"Batchoy",
"COUNTRY",
"Philippines"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Batchoy is eaten in the Philippines."
},
{
"source": "webnlg",
"text": "Philippines is the country Batchoy comes from."
}
]
},
{
"tripleset": [
[
"Batchoy",
"INGREDIENT",
"Chicken"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Chicken is an ingredient in Batchoy."
},
{
"source": "webnlg",
"text": "Batchoy includes chicken."
}
]
},
{
"tripleset": [
[
"Batchoy",
"INGREDIENT",
"Vegetable"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Vegetable are used in the preparation of Batchoy."
},
{
"source": "webnlg",
"text": "Vegetables are an ingredient in Batchoy."
}
]
},
{
"tripleset": [
[
"Binignit",
"REGION",
"Visayas"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Binignit is a dish from the region of Visayas."
}
]
},
{
"tripleset": [
[
"Derbyshire Dales",
"LEADER_NAME",
"Patrick McLoughlin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Patrick McLoughlin is a leader in the Derbyshire Dales."
},
{
"source": "webnlg",
"text": "Patrick McLoughlin is a leader in Derbyshire Dales."
},
{
"source": "webnlg",
"text": "Patrick McLoughlin is the leader of Derbyshire Dales."
}
]
},
{
"tripleset": [
[
"Dessert",
"DISH_VARIATION",
"Cake"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A variation on dessert is cake."
}
]
},
{
"tripleset": [
[
"Dessert",
"DISH_VARIATION",
"Ice cream"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ice cream is a type of dessert."
},
{
"source": "webnlg",
"text": "A type of dessert is an ice cream."
},
{
"source": "webnlg",
"text": "Ice cream is a dessert."
}
]
},
{
"tripleset": [
[
"Indonesia",
"LEADER_NAME",
"Jusuf Kalla"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Jusuf Kalla is a leader in Indonesia."
},
{
"source": "webnlg",
"text": "Jusuf Kalla is the leader of Indonesia."
}
]
},
{
"tripleset": [
[
"Italy",
"DEMONYM",
"Italians"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Italians are from Italy."
},
{
"source": "webnlg",
"text": "Italians is the name given to people from Italy."
}
]
},
{
"tripleset": [
[
"Java",
"ETHNIC_GROUP",
"Baduy"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baduy is the ethnic group of Java."
},
{
"source": "webnlg",
"text": "The Baduy is an ethnic group in Java."
},
{
"source": "webnlg",
"text": "Baduy is an ethnic group of Java."
}
]
},
{
"tripleset": [
[
"Lemon",
"ORDER",
"Rosids"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Lemon is under the order of Rosids."
},
{
"source": "webnlg",
"text": "the order of lemons is Rosids."
}
]
},
{
"tripleset": [
[
"Mexico",
"DEMONYM",
"Mexicans"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Mexico is inhabited by Mexicans."
},
{
"source": "webnlg",
"text": "Mexicans are people from Mexico."
}
]
},
{
"tripleset": [
[
"Mexico",
"LANGUAGE",
"Spanish language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "In Mexico, the spoken language is Spanish."
},
{
"source": "webnlg",
"text": "Spanish is the language spoken in Mexico."
},
{
"source": "webnlg",
"text": "The language of Mexico is Spanish."
}
]
},
{
"tripleset": [
[
"Philippines",
"LANGUAGE",
"Arabic"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "One of the languages used in the Philippines is Arabic."
},
{
"source": "webnlg",
"text": "Arabic is a language spoken in the Philippines."
},
{
"source": "webnlg",
"text": "One of the languages in Philippines is Arabic."
},
{
"source": "webnlg",
"text": "Arabic is one of the languages spoken in the Philippines."
}
]
},
{
"tripleset": [
[
"Siomay",
"DISH_VARIATION",
"Shumai"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Shumai is a variation of the dish Siomay."
},
{
"source": "webnlg",
"text": "Siomay and Shumai are variations of the same dish."
}
]
},
{
"tripleset": [
[
"United States",
"ETHNIC_GROUP",
"Native Americans in the United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Native Americans in the United States are one of the ethnic groups of the country."
}
]
},
{
"tripleset": [
[
"1634: The Bavarian Crisis",
"ISBN_NUMBER",
"\"978-1-4165-4253-7\""
],
[
"1634: The Bavarian Crisis",
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"\"Virginia DeMarce and Eric Flint\""
],
[
"1634: The Bavarian Crisis",
"MEDIA_TYPE",
"\"Print\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "1634: The Bavarian Crisis can be located by it's ISBN number \"978-1-4165-4253-7\". It is written by Virginia DeMarce and Eric Flint and was put in print."
},
{
"source": "webnlg",
"text": "1634: The Bavarian Crisis was authored by Virginia DeMarce and Eric Flint and has a ISBN number of 978-1-4165-4253-7."
},
{
"source": "webnlg",
"text": "The ISBN number of 1634: The Bavarian Crisis is 978-1-4165-4253-7, it is written by Virginia DeMarce and Eric Flint and is available in print form."
}
]
},
{
"tripleset": [
[
"1634: The Bavarian Crisis",
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],
[
"1634: The Bavarian Crisis",
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"1634: The Baltic War"
],
[
"Eric Flint",
"BIRTH_PLACE",
"Burbank, California"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Born in Burbank, California, Eric Flint wrote 1634: The Bavarian Crisis which was preceded by 1634: The Baltic War."
},
{
"source": "webnlg",
"text": "1634: The Bavarian Crisis and it's predecessor, 1634: The Baltic War, was written by Eric Flint who was born in Burbank California."
}
]
},
{
"tripleset": [
[
"1634: The Ram Rebellion",
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[
"1634: The Ram Rebellion",
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],
[
"1634: The Ram Rebellion",
"NUMBER_OF_PAGES",
"\"512\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Eric Flint is the author of 1634: The Ram Rebellion which has 512 pages and is available as an E-Book."
},
{
"source": "webnlg",
"text": "During 1634, Eric Flint wrote the RAM Rebellion which is 512 pages long and is of the media type E book."
}
]
},
{
"tripleset": [
[
"1634: The Ram Rebellion",
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],
[
"1634: The Ram Rebellion",
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"Hardcover"
],
[
"1634: The Ram Rebellion",
"ISBN_NUMBER",
"\"1-4165-2060-0\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The hardcover book 1634: The Ram Rebellion was written by Eric Flint and has the ISBN number 1-4165-2060-0."
},
{
"source": "webnlg",
"text": "1634: The Ram Rebellion, ISBN number 1-4165-2060-0, was written by Eric Flint and is available in hardcover."
},
{
"source": "webnlg",
"text": "Eric Flint wrote 1634: The Ram Rebellion found in hardcover with an ISBN of 1-4165-2060-0."
}
]
},
{
"tripleset": [
[
"1634: The Ram Rebellion",
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[
"1634: The Ram Rebellion",
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],
[
"1634: The Ram Rebellion",
"AUTHOR",
"\"Eric Flint, Virginia DeMarce, et al.\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "1634: The Ram Rebellion, written by Eric Flint, Virginia DeMarce. et al., is 512 pages long and can be found as an E book."
},
{
"source": "webnlg",
"text": "1634: The Ram Rebellion was written by Eric Flint, Virginia DeMarce, et al. It has 512 pages and is available as an E-Book."
},
{
"source": "webnlg",
"text": "1634: The Ram Rebellion has 512 pages and was written by Eric Flint, Virginia DeMarce, et al. . It can be found as an E book."
}
]
},
{
"tripleset": [
[
"ACM Transactions on Information Systems",
"ABBREVIATION",
"\"ACM Trans. Inf. Syst.\""
],
[
"ACM Transactions on Information Systems",
"ACADEMIC_DISCIPLINE",
"Computer science"
],
[
"ACM Transactions on Information Systems",
"ISSN_NUMBER",
"\"1046-8188\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "ACM Transactions on Information Systems, or ACM Trans. Inf. Syst., is part of the academic Discipline of Computer Science and has an ISSN number of 1046-8188."
},
{
"source": "webnlg",
"text": "ACM Transactions on Information Systems is part of the academic Discipline of Computer Science and has the abbreviation of ACM Trans. Inf. Syst. it has the ISSN number 1046-8188."
},
{
"source": "webnlg",
"text": "The abbreviation \"ACM Trans. Inf. Syst.\" is for ACM Transactions on Information Systems which is part of the academic Discipline of Computer Science and has an ISSN number of 1046-8188."
}
]
},
{
"tripleset": [
[
"ACM Transactions on Information Systems",
"ACADEMIC_DISCIPLINE",
"Computer science"
],
[
"ACM Transactions on Information Systems",
"ABBREVIATION",
"\"ACM Trans. Inf. Syst.\""
],
[
"ACM Transactions on Information Systems",
"ISSN_NUMBER",
"\"1558-2868\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The ACM Transactions on Information Systems ISSN number is 1558-2868. It is part of the academic Discipline of Computer Science and abbreviated to ACM Trans. Inf. Syst."
},
{
"source": "webnlg",
"text": "ACM Transactions on Information Systems is part of the academic Discipline of Computer Science and has the abbreviation \"ACM Trans. Inf. Syst.\" and the ISSN number 1558-2868."
},
{
"source": "webnlg",
"text": "ACM Transactions on Information Systems, or ACM Trans. Inf. Syst., is part of the academic Discipline of Computer Science and has a ISSN number of 1558-2868."
}
]
},
{
"tripleset": [
[
"AIDS (journal)",
"COUNTRY",
"United Kingdom"
],
[
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"source": "webnlg",
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{
"source": "webnlg",
"text": "The Book Alcatraz Versus the Evil Librarians originated in the U.S. where Native Americans make up many of the countries ethnic groups. English is the national language of both Great Britain and the United States."
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{
"source": "webnlg",
"text": "English is spoken in Great Britain and is also the language of the United States, of which Native Americans are one of its ethnic groups and from where Alcatraz Versus the Evil Librarians has come."
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{
"source": "webnlg",
"text": "AIDS Journal, published by Lippincott Williams and Wilkins, is from the United Kingdom. The leader of the UK was David Cameron and Elizabeth II is the Queen."
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{
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{
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{
"source": "webnlg",
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{
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{
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{
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{
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{
"source": "webnlg",
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{
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{
"source": "webnlg",
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"source": "webnlg",
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"source": "webnlg",
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{
"source": "webnlg",
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"source": "webnlg",
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{
"source": "webnlg",
"text": "The United States Air Force is the operating organisation for Al Asad airbase and has the Lockhead AC-130 and General Dynamics F-16 Fighting Falcon as aircraft. One of their battles was Operation Enduring Freedom.."
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{
"source": "webnlg",
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{
"source": "webnlg",
"text": "Operation Enduring Freedom was a battle involving the United States Air Force who operate Al Asad Airbase. They deploy the Lockheed AC-130 on their aircraft carriers and the McDonnell Douglas F-15 Eagle as a fighter aircraft."
},
{
"source": "webnlg",
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"source": "webnlg",
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},
{
"source": "webnlg",
"text": "The United States Air Force, which is the operating organisation for Al Asad airbase was involved in battles at the Invasion of Grenada. They deploy the Lockheed AC-130 attack aircraft and the General Dynamics F-16 Fighting Falcon."
},
{
"source": "webnlg",
"text": "The United States Air Force, who fought in the Invasion of Grenada operates the Al Asad Airbase. The USAF fly Lockheed AC-130 aircraft from aircraft carriers, in addition to the General Dynamics F-16 Fighting Falcon."
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{
"source": "webnlg",
"text": "The Allama Iqbal International Airport is located in Punjab, Pakistan which is led by the Provincial Assembly of the Punjab. The airport is governed by the Pakistan Civil Aviation Authority headquartered in Jinnah International Airport."
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{
"source": "webnlg",
"text": "Allama Iqbal International Airport is located in Punjab,Pakistan which is led by the Provincial Assembly of Punjab.The airport is operated by the Pakistan Civil Aviation Authority whose headquarters is in the Jinnah International Airport."
}
]
},
{
"tripleset": [
[
"Alpena County Regional Airport",
"LOCATION",
"Maple Ridge Township, Alpena County, Michigan"
],
[
"Alpena County Regional Airport",
"RUNWAY_LENGTH",
"1533.0"
],
[
"Alpena County Regional Airport",
"CITY_SERVED",
"Alpena, Michigan"
],
[
"Alpena County Regional Airport",
"ELEVATION",
"210"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alpena County Regional Airport serves Alpena, Michigan and is located at Maple Ridge Township, Alpena County, Michigan. It has a runway length of 1533.0 and is 210 metres above sea level."
},
{
"source": "webnlg",
"text": "Alpena County Regional Airport is located in Maple Ridge Township, Alpena County and serves the city of Alpena, Michigan. It is located 210 metres above sea level and has a runway length of 1533.0."
},
{
"source": "webnlg",
"text": "Alpena, Michigan, is served by the Alpena County Regional Airport in Maple Ridge Township. The runway length is 1533.0 and the elevation is 210 metres above sea level."
}
]
},
{
"tripleset": [
[
"Amsterdam Airport Schiphol",
"CITY_SERVED",
"Amsterdam"
],
[
"Amsterdam Airport Schiphol",
"RUNWAY_NAME",
"\"06/24 'Kaagbaan'\""
],
[
"Amsterdam Airport Schiphol",
"RUNWAY_LENGTH",
"2014.0"
],
[
"Amsterdam Airport Schiphol",
"ELEVATION",
"-3.3528"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The city of Amsterdam is served by Amsterdam Airport Schiphol which is -3.3528 metres above sea level. It has a runway length of 2014.0 meters and 06/24, Kaagbaan, is the runway name."
},
{
"source": "webnlg",
"text": "Serving the city of Amsterdam, Amsterdam airport Schipol is -3.3528 above sea level. Amsterdam Airport Schiphol's runway length is 2014.0 and it has the runway name, 06/24 Kaagbaan."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"CAPITAL",
"Austin, Texas"
],
[
"Texas",
"LANGUAGE",
"English language"
],
[
"Texas",
"DEMONYM",
"Tejano"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The demonym for the people of Texas is Tejano and the spoken language is English. The state is the location of Andrews County airport and has the capital city of Austin."
},
{
"source": "webnlg",
"text": "Andrews County airport is in Texas where the capital city is Austin, the local people are known as Tejano and the spoken language is English."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas whose capital city is Austin.Tejano are the inhabitants of the state and English language is spoken there."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"LANGUAGE",
"English language"
],
[
"Texas",
"DEMONYM",
"Texan"
],
[
"Texas",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews County Airport, Texas is in the United States. The inhabitants of Texas, Texans , speak English."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas within the United States, the people of Texas are referred to as Texans and the local language is English."
},
{
"source": "webnlg",
"text": "Texas is the location of Andrews County airport and is in the United States. The inhabitants are known as Texans and they speak the English language."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"LANGUAGE",
"Spanish language"
],
[
"Texas",
"DEMONYM",
"Texan"
],
[
"Texas",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews County airport is in Texas, United States, where the people are known as Texans and Spanish is spoken."
},
{
"source": "webnlg",
"text": "Texans are the people from Texas in the United States where Spanish is spoken and Andrews County airport is located."
},
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texts,United States. The people of Texas are called Texans and Spanish language is spoken there."
}
]
},
{
"tripleset": [
[
"Andrews County Airport",
"LOCATION",
"Texas"
],
[
"Texas",
"LARGEST_CITY",
"Houston"
],
[
"Texas",
"CAPITAL",
"Austin, Texas"
],
[
"Texas",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Andrews County Airport is located in Texas in the United States. In Texas, the capital city is Austin and the largest city is Houston."
},
{
"source": "webnlg",
"text": "Andrews County airport in the US is located in Texas where the capital is Austin and the largest city is Houston."
},
{
"source": "webnlg",
"text": "Andrews County airport is located in Texas, United States, where Houston is the largest city and Austin is the capital."
}
]
},
{
"tripleset": [
[
"Angola International Airport",
"LOCATION",
"\u00cdcolo e Bengo"
],
[
"\u00cdcolo e Bengo",
"COUNTRY",
"Angola"
],
[
"\u00cdcolo e Bengo",
"IS_PART_OF",
"Luanda Province"
],
[
"Angola International Airport",
"ELEVATION",
"159"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "At 159 meters above sea level, Angola International Airport is located at \u00cdcolo e Bengo, part of Luanda Province, in Angola."
},
{
"source": "webnlg",
"text": "Angola International airport, situated 159 metres above sea level, is located at \u00cdcolo e Bengo, part of Luanda Province in Angola."
},
{
"source": "webnlg",
"text": "Angola International Airport is located in \u00cdcolo e Bengo in Luanda Province, Angola. It is 159 metres above sea level.."
}
]
},
{
"tripleset": [
[
"Antwerp International Airport",
"CITY_SERVED",
"Antwerp"
],
[
"Flemish Region",
"LEADER_NAME",
"Flemish Government"
],
[
"Flemish Region",
"COUNTRY",
"Belgium"
],
[
"Flemish Government",
"JURISDICTION",
"Flemish Region"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Flemish Government runs the flemish region in Belgium where Antwerp is served by Antwerp International airport."
},
{
"source": "webnlg",
"text": "Antwerp International Airport serves the city of Antwerp.The Flemish region is in the country of Belgium and is under the jurisdiction of the Flemish government."
},
{
"source": "webnlg",
"text": "The Flemish region, which is led by the Flemish government, is part of Belgium. Antwerp International airport serves Antwerp."
}
]
},
{
"tripleset": [
[
"Appleton International Airport",
"LOCATION",
"Greenville, Wisconsin"
],
[
"Appleton International Airport",
"RUNWAY_LENGTH",
"2439.0"
],
[
"Appleton International Airport",
"CITY_SERVED",
"Appleton, Wisconsin"
],
[
"Appleton International Airport",
"ELEVATION",
"280"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Appleton International Airport (in Greenville, Wisconsin) has an elevation of 280 metres above sea level, serves the city of Appleton, and has a runway length of 2,439."
},
{
"source": "webnlg",
"text": "Appleton International Airport is located in Greenville, Wisconsin and serves the nearby city of Appleton. The airport is 280 metres above sea level and has a runway which is 2439 metres long."
},
{
"source": "webnlg",
"text": "Appleton International Airport can be found in Greenville, Wisconsin it serves Appleton, it's runway length is 2439 units and it is 280 metres above sea level."
}
]
},
{
"tripleset": [
[
"Appleton International Airport",
"LOCATION",
"Greenville, Wisconsin"
],
[
"Greenville, Wisconsin",
"IS_PART_OF",
"Menasha (town), Wisconsin"
],
[
"Greenville, Wisconsin",
"IS_PART_OF",
"Dale, Wisconsin"
],
[
"Appleton International Airport",
"CITY_SERVED",
"Appleton, Wisconsin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Appleton International airport, which serves Appleton, Wisconsin, is located in Greenville, Wisconsin which is part of both Menasha and Dale."
},
{
"source": "webnlg",
"text": "Appletone International airport serves the city of Appleton in Greenville, Wisconsin. Both Menasha and Dale are parts of Greenville."
},
{
"source": "webnlg",
"text": "Appleton International airport serves Appleton in Greenville, Wisconsin. Both Menasha (town) and Dale are parts of Greenville."
}
]
},
{
"tripleset": [
[
"Ardmore Airport (New Zealand)",
"3RD_RUNWAY_SURFACE_TYPE",
"Poaceae"
],
[
"Poaceae",
"ORDER",
"Poales"
],
[
"Poaceae",
"CLASS",
"Monocotyledon"
],
[
"Poaceae",
"ORDER",
"Commelinids"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Poaceae is of the order poales, the class of Monocotyledon and belongs to the order of Commelinids. It constitutes the surface of the 3rd runway at Ardmore Airport in New Zealand."
},
{
"source": "webnlg",
"text": "Ardmore Airport (New Zealand)'s 3rd runway surface type is Poaceae which is a member of the order of Poales, the class is Monocotyledon, and belongs to the order of Commelinids."
},
{
"source": "webnlg",
"text": "Ardmore Airport (New Zealand)'s 3rd runway is surfaced with Poaceae.Poaceae belongs to the Poales and Commelinids order and classified as Monocotyledon."
}
]
},
{
"tripleset": [
[
"Ardmore Airport (New Zealand)",
"RUNWAY_LENGTH",
"597.0"
],
[
"Ardmore Airport (New Zealand)",
"3RD_RUNWAY_SURFACE_TYPE",
"Poaceae"
],
[
"Ardmore Airport (New Zealand)",
"ELEVATION",
"34.0"
],
[
"Ardmore Airport (New Zealand)",
"RUNWAY_NAME",
"\"03R/21L\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Ardmore Airport in New Zealand is 34 meters above sea level and has a runway length of 597.0. It has the runway name 03R/21L and the 3rd runway is made of Poaceae."
},
{
"source": "webnlg",
"text": "Ardmore airport in New Zealand is 34 metres above sea level and has a runway length of 597.0. The 3rd runway is made of poaceae and one of the runways is called 03R/21L."
},
{
"source": "webnlg",
"text": "Ardmore airport in New Zealand has a 3rd runway made of poaceae and is 34 meters above sea level. It has a runway name of 03R/21L with a length of 597.0."
}
]
},
{
"tripleset": [
[
"Atlantic City International Airport",
"RUNWAY_LENGTH",
"1873.0"
],
[
"Atlantic City International Airport",
"LOCATION",
"Egg Harbor Township, New Jersey"
],
[
"Egg Harbor Township, New Jersey",
"COUNTRY",
"United States"
],
[
"Egg Harbor Township, New Jersey",
"IS_PART_OF",
"Atlantic County, New Jersey"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "With a runway length of 1,873, Atlantic City International Airport is located in Egg Harbor Township, New Jerse y(part of Atlantic County) in the United States."
},
{
"source": "webnlg",
"text": "Egg Harbor Township, is a township in Atlantic County, New Jersey, United States. With a runway length of 1873 metres, Atlantic City International Airport is located in Egg Harbor Township."
}
]
},
{
"tripleset": [
[
"ENAIRE",
"LOCATION_CITY",
"Madrid"
],
[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"LOCATION",
"\"Madrid, Paracuellos de Jarama, San Sebasti\u00e1n de los Reyes and Alcobendas\""
],
[
"Madrid",
"COUNTRY",
"Spain"
],
[
"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
"OPERATING_ORGANISATION",
"ENAIRE"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Adolfo Suarez Madrid-Barajas Airport is operated by ENAIRE, which is located in the city of Madrid, Spain. The Adolfo Suarez Madrid-Barajas Airport is located in Madrid, Paracuellos de Jarama, San Sebastian de los Reyes and Alcobendas."
},
{
"source": "webnlg",
"text": "Adolfo Su\u00e1rez Madrid\u2013Barajas Airport can be found in Madrid, Paracuellos de Jarama, San Sebasti\u00e1n de los Reyes and Alcobendas and is operated by ENAIRE from the city of Madrid, Spain."
},
{
"source": "webnlg",
"text": "Adolfo Su\u00e1rez Madrid\u2013Barajas Airport, operated by ENAIRE (Madrid), is found in Madrid, Paracuellos de Jarama, San Sebasti\u00e1n de los Reyes and Alcobendas; Spain."
}
]
},
{
"tripleset": [
[
"Egg Harbor Township, New Jersey",
"IS_PART_OF",
"New Jersey"
],
[
"Atlantic City International Airport",
"RUNWAY_LENGTH",
"3048.0"
],
[
"Atlantic City International Airport",
"LOCATION",
"Egg Harbor Township, New Jersey"
],
[
"Egg Harbor Township, New Jersey",
"COUNTRY",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Egg Harbor Township is in New Jersey, in the United States and is where Atlantic City International Airport is located. This airport has a runway length of 3048.0."
},
{
"source": "webnlg",
"text": "Egg Harbor Township, New Jersey is part of New Jersey in the United States. It is where Atlantic City International Airport(with a runway length of 3048.0) is located."
},
{
"source": "webnlg",
"text": "Atlantic City Airport, (runway length 3048.0) is located in Egg Harbor Township, which is part of New Jersey in the United States."
}
]
},
{
"tripleset": [
[
"Poaceae",
"CLASS",
"Monocotyledon"
],
[
"Poaceae",
"DIVISION",
"Flowering plant"
],
[
"Poaceae",
"ORDER",
"Commelinids"
],
[
"Ardmore Airport (New Zealand)",
"2ND_RUNWAY_SURFACE_TYPE",
"Poaceae"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Poaceae, of the class Monocotyledon and the order of commelinids belongs to the division of flowering plants and is used as the surface of the second runway of Ardmore airport, New Zealand."
},
{
"source": "webnlg",
"text": "The surface type of the second runway of Ardmore Airport, New Zealand is Poaceae. Poaceae is a flowering plant of the order of Commelinids and the class of Monocotyledon."
},
{
"source": "webnlg",
"text": "The 2nd runway at Ardmore Airport (New Zealand) is made of Poaceae. Poaceae is a flowering plant in the class of Monocotyledon in the order of Commelinids."
}
]
},
{
"tripleset": [
[
"Atat\u00fcrk Monument (\u0130zmir)",
"DESIGNER",
"Pietro Canonica"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Atat\u00fcrk Monument in Izmir was designed by Pietro Canonica."
},
{
"source": "webnlg",
"text": "Pietro Canonica designed the Atat\u00fcrk Monument in Izmir."
},
{
"source": "webnlg",
"text": "Pietro Canonica is the designer of the Ataturk Monument (Izmir)."
},
{
"source": "webnlg",
"text": "The Ataturk Monument was designed by Pietro Canonica."
},
{
"source": "webnlg",
"text": "Ataturk Monument was designed by Pietro Canonica."
},
{
"source": "webnlg",
"text": "The Atat\u00fcrk Monument (\u0130zmir) was designed by Pietro Canonica."
},
{
"source": "webnlg",
"text": "The designer of the Atat\u00fcrk_Monument (\u0130zmir) is Pietro Canonica."
},
{
"source": "webnlg",
"text": "Pietro Canonica is the designer of the Ataturk Monument in Izmir."
}
]
},
{
"tripleset": [
[
"Atat\u00fcrk Monument (\u0130zmir)",
"MATERIAL",
"\"Bronze\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Atat\u00fcrk Monument in Izmir is made of bronze."
},
{
"source": "webnlg",
"text": "The material of the Ataturk Monument (Izmir) is bronze."
},
{
"source": "webnlg",
"text": "The Ataturk Monument is made from Bronze."
},
{
"source": "webnlg",
"text": "The Atat\u00fcrk Monument (\u0130zmir) is made of Bronze."
},
{
"source": "webnlg",
"text": "Ataturk Monument is made of Bronze."
}
]
},
{
"tripleset": [
[
"Azerbaijan",
"LEADER_NAME",
"Artur Rasizade"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Azerbaijan leader's name is Artur Rasizade."
},
{
"source": "webnlg",
"text": "The name of the leader of Azerbaijan is Artur Rasizade."
}
]
},
{
"tripleset": [
[
"Baku Turkish Martyrs' Memorial",
"LOCATION",
"Azerbaijan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is located in Azerbaijan."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyr's Memorial is in Azerbaijan."
},
{
"source": "webnlg",
"text": "The Turkish martyrs memorial is located in Baku, Azerbaijan."
},
{
"source": "webnlg",
"text": "Baku Turkish Martyrs' Memorial is located in Azerbaijan."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial can be found is Azerbaijan."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is in Azerbaijan."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is found in Azerbaijan."
}
]
},
{
"tripleset": [
[
"Baku Turkish Martyrs' Memorial",
"MATERIAL",
"\"Red granite and white marble\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The materials used for the Baku Turkish Martyrs' Memorial is red granite and white marble."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is created in red granite and white marble."
},
{
"source": "webnlg",
"text": "The material of the Baku Turkish Martyrs' Memorial is Red granite and white marble."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is made from red granite and white marble."
},
{
"source": "webnlg",
"text": "Baku Turkish Martyrs' Memorial is made of red granite and white marble."
},
{
"source": "webnlg",
"text": "Baku Turkish Martyrs' Memorial is made from red granite and white marble."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is made with red granite and white marble."
}
]
},
{
"tripleset": [
[
"A.C. Chievo Verona",
"FULL_NAME",
"\"Associazione Calcio ChievoVerona S.r.l.\""
],
[
"A.C. Chievo Verona",
"GROUND",
"\"Verona, Italy\""
],
[
"A.C. Chievo Verona",
"SEASON",
"2014\u201315 Serie A"
],
[
"A.C. Chievo Verona",
"NUMBER_OF_MEMBERS",
"39371"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The full name of A.C. Chievo Verona is Associazione Calcio ChievoVerona S.r.l. and their home ground in Verona, Italy holds 39371 fans. The club played the 2014-15 season in Serie A."
},
{
"source": "webnlg",
"text": "A.C. Chievo Verona played the 2014-15 season in Serie A and has 39371 members. Their full name is \"Associazione Calcio ChievoVerona S.r.l.\" and their home ground is in Verona, Italy."
}
]
},
{
"tripleset": [
[
"A.C. Chievo Verona",
"MANAGER",
"Rolando Maran"
],
[
"Rolando Maran",
"PLACE_OF_BIRTH",
"Italy"
],
[
"Rolando Maran",
"CLUB",
"Unione Triestina 2012 S.S.D."
],
[
"Rolando Maran",
"CLUB",
"Carrarese Calcio"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Italian born Rolando Maran manages Associazione Calcio Chievo Verona. He is also in the Unione Triestina 2012 S.S.D. club as well as playing for Carrarese Calcio."
},
{
"source": "webnlg",
"text": "Italian born Rolando Maran plays at the Carrarese Calcio and is in the Unione Triestina 2012 S.S.D. club. He is currently the manager of AC Chievo Verona."
},
{
"source": "webnlg",
"text": "Born in Italy, Rolando Maran is the manager of AC Chievo Verona. He also plays for Carrarese Calcio and is in the Unione Triestina 2012 S.S.D. club."
}
]
},
{
"tripleset": [
[
"A.C. Lumezzane",
"MANAGER",
"Michele Marcolini"
],
[
"Michele Marcolini",
"PLACE_OF_BIRTH",
"Italy"
],
[
"Michele Marcolini",
"CLUB",
"Torino F.C."
],
[
"Michele Marcolini",
"CLUB",
"F.C. Bari 1908"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Michele Marcolini, manager of A.C. Lumezzane, was born in Italy, owns Torino F.C. And has been associated with F.C. Bari 1908."
},
{
"source": "webnlg",
"text": "Italian born Michele Marcolini, previously at FC Bari 1908 and manager of AC Lumezzane, is the owner of Torino F.C."
},
{
"source": "webnlg",
"text": "Italian born Michele Marcolini, previously of FC Bari 1908, is now the manager of A.C. Lumezzane, whist also the owner of Torino F.C."
}
]
},
{
"tripleset": [
[
"A.E Dimitra Efxeinoupolis",
"LOCATION",
"Greece"
],
[
"Greece",
"LEADER",
"Alexis Tsipras"
],
[
"Greece",
"LEADER",
"Nikos Voutsis"
],
[
"Greece",
"LANGUAGE",
"Greek language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Greece, led by Alexis Tsipras and Nikos Voutsis is the location of A.E. Dimitra Efxeinoupolis, where the language is greek."
},
{
"source": "webnlg",
"text": "Greece is the location of the A.E Dimitra Efxeinoupolis club. The language of the country is Greek and two of the leaders are Alexis Tsipras and Nikos Voutsis."
},
{
"source": "webnlg",
"text": "The A.E Dimitra Efxeinoupolis club is located in Greece where Alexis Tsipras heads Greece and Nikos Voutsis is the leader."
}
]
},
{
"tripleset": [
[
"A.S. Gubbio 1910",
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"2014"
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[
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"Italy"
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[
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"NUMBER_OF_MEMBERS",
"5300"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The full name of the Italian club AS Gubbio 1910 is \"Associazione Sportiva Gubbio 1910 Srl\". They played in the 2014 season and they have 5300 members."
},
{
"source": "webnlg",
"text": "The full name of A.S. Gubbio 1910 is Associazione Sportiva Gubbio 1910 Srl. The club has 5300 members and has a ground in Italy. It competed in the 2014 season."
},
{
"source": "webnlg",
"text": "Associazione Sportiva Gubbio 1910 Srl (abbreviated to A.S.Gubbio 1910) has 5300 members, has its grounds in Italy and played in the 2014 season."
}
]
},
{
"tripleset": [
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[
"A.S. Gubbio 1910",
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"Stadio Pietro Barbetti"
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[
"A.S. Gubbio 1910",
"NUMBER_OF_MEMBERS",
"5300"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The fullname of A.S. Gubbio 1910 is Associazione Sportiva Gubbio 1910 Srl and they competed in the 2014 season. Their ground is called the Stadio Pietro Barbetti and they have 5300 members."
},
{
"source": "webnlg",
"text": "Associazione Sportive Gubbio 1910 Srl (abbreviated to AS Gubbio 1910) has 5300 members, played in the 2014 season and its ground is Stadio Pietro Barbetti."
},
{
"source": "webnlg",
"text": "The full name of AS Gubbio 1910 is \"Associazione Sportiva Gubbio 1910 Srl\". Its ground is called Stadio Pietro Barbetti, it competed in the 2014 season and has 5300 members."
}
]
},
{
"tripleset": [
[
"A.S. Livorno Calcio",
"GROUND",
"Stadio Armando Picchi"
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"A.S. Livorno Calcio",
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"19238"
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"A.S. Livorno Calcio",
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"2014\u201315 Serie B"
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[
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"FULL_NAME",
"\"Livorno Calcio S.p.A.\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The fullname of A.S. Livorno Calcio is Livorno Calcio S.p.A. and their home ground is the Stadio Armando Picchi. They competed in Serie B during the 2014-15 season and has 19238 members."
},
{
"source": "webnlg",
"text": "19238 members strong Livorno Calcio S.p.A., AKA A.S. Livorno Calcio, played season 2014-15 in Serie B from their home stadium Stadio Armando Picchi."
},
{
"source": "webnlg",
"text": "Stadio Armando Picchi's is home to A S Livorno Calcio (Livorno Calcio S.p.A.).They have 19238 members and played the 2014-15 Serie B season."
}
]
},
{
"tripleset": [
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"SEASON",
"2014\u201315 Serie A"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.S. Roma's fullname is Associazione Sportiva Roma S.p.A. and they have 70634 members. Their ground is in Rome and the club played in Serie A in 2014-15."
},
{
"source": "webnlg",
"text": "The fullname of A.S. Roma is Associazione Sportiva Roma S.p.A. and they have 70634 members. The club have a ground in Rome and competed in Serie A in 2014-15."
},
{
"source": "webnlg",
"text": "A.S Roma were in Serie A in 2014-15. They have a ground in Rome and have 70634 members, there full name is Associazione Sportiva Roma S.p.A."
}
]
},
{
"tripleset": [
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"AZAL PFK",
"LEAGUE",
"Azerbaijan Premier League"
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"Azerbaijan Premier League",
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"Qaraba\u011f FK"
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"AZAL Arena"
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[
"AZAL Arena",
"LOCATION",
"Azerbaijan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AZAL PFK play their home matches at the AZAL Arena in Azerbaijan and they compete in the Azerbaijan Premier League where Qarabag FK are champions."
},
{
"source": "webnlg",
"text": "AZAL PFK plays at AZAL Arena in Azerbaijan and competes in their Premier League. The champions of the league are Qarabag FK."
},
{
"source": "webnlg",
"text": "AZAL PFK play in the Azerbaijan Premier League, which champions are Qarabag FK, its ground is AZAL Arena, that is located in Azerbaijan."
}
]
},
{
"tripleset": [
[
"AZAL PFK",
"LEAGUE",
"Azerbaijan Premier League"
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[
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[
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"3500"
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[
"AZAL PFK",
"GROUND",
"AZAL Arena"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AZAL PRK, known as the Qarabag FK in the Azerbaijan Premier League is located in AZAL Arena and has 3500 members."
},
{
"source": "webnlg",
"text": "The AZAL PFK, with 3500 members, competes in The Azerbaijan Premier League, with championship team being Qarabag FK. AZAL Arena is the ground of AZAL PFK."
}
]
},
{
"tripleset": [
[
"AZ Alkmaar",
"MANAGER",
"John van den Brom"
],
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"John van den Brom",
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"AFC Ajax"
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"John van den Brom",
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"\u0130stanbulspor A.\u015e."
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[
"AZ Alkmaar",
"OWNER",
"Robert Eenhoorn"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "John van den Brom plays for AFC Ajax and Istanbulspor A.S. He manages AZ Alkmaair which is owned by Robert Eenhoorn."
}
]
},
{
"tripleset": [
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"LEAGUE",
"Campeonato Brasileiro S\u00e9rie C"
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[
"Campeonato Brasileiro S\u00e9rie C",
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"Brazil"
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[
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"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense, which has the nickname Asa Gigante, play in the Campeonato Brasileiro S\u00e9rie C league from Brazil. The league was previously won by Vila Nova Futebol Clube."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense, nicknamed Asa Gigante, play in the Campeonato Brasileiro S\u00e9rie C league in Brazil. Vila Nova Futebol Clube are the champions of Campeonato Brasileiro Serie C."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league based in Brazil. Their nickname is Asa Gigante. The champions of the league are Vila Nova Futebol Clube."
}
]
},
{
"tripleset": [
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"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
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"Campeonato Brasileiro S\u00e9rie C"
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[
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"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Brazilian football team Agremia\u00e7\u00e3o Sportiva Arapiraquense play their football in the Campeonato Brasileiro S\u00e9rie C league. Campeonato Brasileiro Serie C is a Brazilian league. Agremia\u00e7\u00e3o Sportiva Arapiraquense competed in the 2015 Campeonato Brasileiro S\u00e9rie C. The Vila Nova Futebol Clube were the winners at the Campeonato Brasileiro S\u00e9rie C."
},
{
"source": "webnlg",
"text": "Agremiacao Sportiva Arapiraquense play in the Campeonato Brasileiro Serie C League, as they did in 2015, which is based in Brazil. The current champions of this league are Villa Nova Futebol Clube."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in Brazil's Campeonato Brasileiro S\u00e9rie C league and competed in the 2015 event. Vila Nova Futebol Clube have been champions of S\u00e9rie C."
}
]
},
{
"tripleset": [
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"Akron Summit Assault",
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"\"Akron Metro Futbol Club Summit Assault\""
],
[
"Akron Summit Assault",
"NUMBER_OF_MEMBERS",
"3000"
],
[
"Akron Summit Assault",
"MANAGER",
"Denzil Antonio"
],
[
"Akron Summit Assault",
"SEASON",
"2011 PDL season"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "3000 member strong Akron Summit Assault, fullname \"Akron Metro Futbol Club Summit Assault\", play in the 2011 PDL season. Denzil Antonio was previously their manager."
}
]
},
{
"tripleset": [
[
"Akron Summit Assault",
"GROUND",
"Akron, Ohio"
],
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"Akron, Ohio",
"COUNTRY",
"United States"
],
[
"Akron Summit Assault",
"LEAGUE",
"Premier Development League"
],
[
"Premier Development League",
"CHAMPIONS",
"K-W United FC"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Akron Summit Assault team is based in Akron, Ohio, United States. They play in the Premier Development League, which has been won by K-W United FC."
},
{
"source": "webnlg",
"text": "The Akron Summit Assault team is based in Akron, Ohio, USA. They play in the Premier Development League which was previously won by K-W United FC."
},
{
"source": "webnlg",
"text": "The Akron Summit Assault team of Akron, Ohio, U.S currently play in the Premier Development League, of which K-W United FC have previously been champions."
}
]
},
{
"tripleset": [
[
"Italy",
"CAPITAL",
"Rome"
],
[
"Italy",
"LEADER",
"Sergio Mattarella"
],
[
"A.S. Gubbio 1910",
"GROUND",
"Italy"
],
[
"Italy",
"LANGUAGE",
"Italian language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The ground of A.S. Gubbio 1910 is located in Italy, where the capital is Rome, the leader is Sergio Mattarella and the language spoken is Italian."
},
{
"source": "webnlg",
"text": "Italian is spoken in Italy where the capital city is Rome. The country, which is lead by Sergio Mattarella is the location of the AS Gubbio 1910's ground."
}
]
},
{
"tripleset": [
[
"Italy",
"LEADER",
"Pietro Grasso"
],
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"OFFICIAL_LANGUAGE",
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"Italy",
"CAPITAL",
"Rome"
],
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"A.S. Gubbio 1910",
"GROUND",
"Italy"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The capital of Italy is Rome and the leader is Pietro Grasso. The Italian language is spoken in the country which is the location of the home ground of AS Gubbio 1910."
},
{
"source": "webnlg",
"text": "The ground of A.S. Gubbio 1910 is located in Italy where the capital is Rome, Italian is the official language and Pietro Grasso is the leader."
}
]
},
{
"tripleset": [
[
"Serie A",
"CHAMPIONS",
"Juventus F.C."
],
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"FULL_NAME",
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],
[
"A.S. Roma",
"GROUND",
"Stadio Olimpico"
],
[
"A.S. Roma",
"LEAGUE",
"Serie A"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The \"Associazione Sportiva Roma S.p.A.\" is the non-abbreviated name of A.S. Roma with their ground in Stadio Olimpico. A.S. Roma play in Serie A whose current champions are Juventus F.C.."
},
{
"source": "webnlg",
"text": "The \"Associazione Sportiva Roma S.p.A.\" is the full name name of A.S. Roma who have their home ground at Stadio Olimpico. The club plays in Serie A alongside Juventus FC who have been previous champions."
},
{
"source": "webnlg",
"text": "A.S. Roma play in Serie A where Juventus F.C are among there former champions. There full name is Associazione Sportiva Roma S.p.A. and they play at the Stadio Olimpico."
}
]
},
{
"tripleset": [
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"Acharya Institute of Technology",
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"\"Dr. G. P. Prabhukumar\""
],
[
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"\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090 and the director is Dr. G. P. Prabhukumar."
},
{
"source": "webnlg",
"text": "The director of the Acharya Institute of Technology is Dr G P Prabhukumar. The campus is located at In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore - 560090."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"OFFICIAL_SCHOOL_COLOUR",
"\"Blue, White and Orange\""
],
[
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"WAS_GIVEN_THE_'TECHNICAL_CAMPUS'_STATUS_BY",
"All India Council for Technical Education"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The All India Council for Technical Education gave the Acharya Institute of Technology the status of \"Technical Campus\". The official school colours are blue, white and orange."
},
{
"source": "webnlg",
"text": "The official school colours for Acharya Institute of Technology are blue, white and orange and the school got its Technical Campus status from the All India Council."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology (official colour blue, white and orange) has been given the 'Technical Campus' status by All India Council for Technical Education."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"SPORTS_OFFERED",
"Tennis"
],
[
"Tennis",
"SPORTS_GOVERNING_BODY",
"International Tennis Federation"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The International Tennis Federation, which is the sport's governing body, has agreed to let Acharya Institute of Technology offer tennis at the school."
},
{
"source": "webnlg",
"text": "Tennis, as governed by the International Tennis Federation, is a sport offered at the Acharya Institute of Technology."
}
]
},
{
"tripleset": [
[
"European University Association",
"HEADQUARTERS",
"Brussels"
],
[
"School of Business and Social Sciences at the Aarhus University",
"AFFILIATION",
"European University Association"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University is affiliated to the European University Association which has its headquarters in Brussels."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at Aarhus University is affiliated with the European University Association in Brussels."
},
{
"source": "webnlg",
"text": "School of Business and Social Sciences at the Aarhus University is affiliated with the European University Association and is located in Brussels."
}
]
},
{
"tripleset": [
[
"Karnataka",
"HAS_TO_ITS_WEST",
"Arabian Sea"
],
[
"Acharya Institute of Technology",
"STATE",
"Karnataka"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institue of Technology is located in the state of Karnataka which has the Arabian Sea to the west."
},
{
"source": "webnlg",
"text": "Acharya Institute of Technology is in the state of Karnataka which has the Arabian Sea to its west."
},
{
"source": "webnlg",
"text": "Acharya Institute of Technology is located in the state of Karnataka, which has to its west the Arabian Sea."
}
]
},
{
"tripleset": [
[
"Accademia di Architettura di Mendrisio",
"COUNTRY",
"Switzerland"
],
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"Accademia di Architettura di Mendrisio",
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"Mario Botta"
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"Accademia di Architettura di Mendrisio",
"ACADEMIC_STAFF_SIZE",
"100"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Accademia di Architettura di Mendrisio in Mendrisio, Switzerland had 100 academic staff and 600 students. Its dean is Mario Botta."
},
{
"source": "webnlg",
"text": "The Dean of the Accademia di Architettura di Mendrisio in the city of Mendrisio, Switzerland is Mario Botta. There are 600 students and 100 academic staff at the Accademia."
},
{
"source": "webnlg",
"text": "The Accademia di Architettura di Mendrisio in Switzerland has a staff of 100 and a student population of 600. The dean is Mario Botta."
}
]
},
{
"tripleset": [
[
"Accademia di Architettura di Mendrisio",
"COUNTRY",
"Switzerland"
],
[
"Accademia di Architettura di Mendrisio",
"NUMBER_OF_STUDENTS",
"600"
],
[
"Accademia di Architettura di Mendrisio",
"ESTABLISHED",
"1996"
],
[
"Switzerland",
"LEADER_NAME",
"Johann Schneider-Ammann"
],
[
"Accademia di Architettura di Mendrisio",
"LOCATION",
"Ticino"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Accademia di Architettura di Mendrisio is located in Ticino, Switzerland. It was established in 1996 and it has 600 students. The leader of Switzerland is Johann Schneider-Ammann."
},
{
"source": "webnlg",
"text": "The leader of Switzerland is Johann Schneider-Ammann. The country is the location of the Accademia di Architettura di Mendrisio in Ticino which was established in 1996 and has 600 students."
},
{
"source": "webnlg",
"text": "The Accademia di Architettura di Mendrisio , which has 600 students, was founded in 1996 in Ticino, Switzerland. The leader of Switzerland is Johann Schneider - Amman."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
],
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"Karnataka",
"HAS_TO_ITS_NORTHEAST",
"Telangana"
],
[
"Acharya Institute of Technology",
"CITY",
"Bangalore"
],
[
"Karnataka",
"HAS_TO_ITS_WEST",
"Arabian Sea"
],
[
"Acharya Institute of Technology",
"STATE",
"Karnataka"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Acharya Institute of Technology is affiliated with the Visvesvaraya Technological University and is located in Bangalore, Karnataka. To its northeast is Telangana and to its west is the Arabian Sea."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology in Balgalore, Karnataka is east of the Arabian Sea and southwest of Telangana. It is affiliated with the Visvesvaraya Technological University."
},
{
"source": "webnlg",
"text": "Karnataka state which has the Arabian Sea to it's west and Telangana to it' s northwest is also home to the Acharya Institute of Technology. The Institute is located in the city of Bangalore and is affiliated with the Visvesvaraya Technological University."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"CITY",
"Bangalore"
],
[
"Acharya Institute of Technology",
"ESTABLISHED",
"2000"
],
[
"Acharya Institute of Technology",
"COUNTRY",
"\"India\""
],
[
"Acharya Institute of Technology",
"NUMBER_OF_POSTGRADUATE_STUDENTS",
"700"
],
[
"Acharya Institute of Technology",
"CAMPUS",
"\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Acharya Institute of Technology is located at In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090, India. The School was established in 2000 and has 700 Postgraduate Students."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology was established in 2000 and is based in the city of Bangalore in India. It currently has 700 Postgraduate Students and it's exact location is ' In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore - 560090."
}
]
},
{
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"Bangalore",
"FOUNDER",
"Kempe Gowda I"
],
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"Karnataka",
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"Arabian Sea"
],
[
"Acharya Institute of Technology",
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"Karnataka"
],
[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
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"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology was founded by Kempe Gowda I in Bangalore in the state of Karnataka. It has the Arabian Sea to its west and is affiliated with Visvesvaraya Technological University."
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{
"source": "webnlg",
"text": "Acharya Institute of Technology is located in Bangalore, Karnataka, east of the Arabian Sea. The Institute is affiliated with Visvesvaraya Technological University. Bangalore was founded by Kempe Gowda."
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{
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"source": "webnlg",
"text": "The 1 Decembrie 1918 University is in Alba Iulia, Romania. The country's capital is Bucharest and the prime minister is Klaus Iohannis."
},
{
"source": "webnlg",
"text": "1 Decembrie 1918 University is in Alba Iulia, Romania. Bucharest is the capital of Romania and Klaus Iohannis is the country's Prime Minister."
},
{
"source": "webnlg",
"text": "1 Decembrie 1918 University is located in the city of Alba lulia in Romania. The capital of Romania is Bucharest and the Prime Minister is Klaus lohannis."
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{
"tripleset": [
[
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"source": "webnlg",
"text": "The School of Business and Social Sciences at Aarhus University in Denmark is affiliated with the European University Association. It has 16,000 students, 737 employees. and was established in 1928."
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{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University in Denmark was established in 1928. It is affiliated with the European University Association and has a staff compliment of 737. 16000 students attend the university currently."
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{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University was established in Denmark in 1928. The School is affiliated with the European University Association and has 16,000 students and 737 academic staff."
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{
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[
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"source": "webnlg",
"text": "American Alan Bean was a member of NASA's Apollo 12 which was commanded by David Scott. Bean, who was born in Wheeler, Texas, is now retired."
},
{
"source": "webnlg",
"text": "American Alan Bean was born in Wheeler, Texas and is retired. He was on the crew of Apollo 12, whose operator was NASA and David Scott was the commander of Apollo 12."
},
{
"source": "webnlg",
"text": "Alan Bean is originally from Wheeler, Texas and joined NASA where he became a crew member of Apollo 12. David Scott was his commander and Mr Bean is now retired."
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]
},
{
"tripleset": [
[
"Alan Shepard",
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"source": "webnlg",
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{
"source": "webnlg",
"text": "Alan Shepard was an American who went to school at NWC and graduated with an MA in 1957. He was selected by NASA in 1959. He was born in New Hampshire on November 18, 1923 and died in California."
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"annotations": [
{
"source": "webnlg",
"text": "Alan Shepard was born on the 18th of November, 1923 in New Hampshire. He graduated from NWC with a M.A. in 1957 and was selected by NASA in 1959. His death place was California."
},
{
"source": "webnlg",
"text": "Alan Shepard was born November 18th, 1923 in New Hampshire. He graduated from NWC with a M.A. in 1957 and was selected by NASA in 1959. He passed away in California."
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{
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"annotations": [
{
"source": "webnlg",
"text": "Alan Shepard was born on November 18, 1923, in New Hampshire. He served as a crew member on NASA's Apollo 14 mission before retiring on August 1, 1974. Shepard passed away in California."
},
{
"source": "webnlg",
"text": "Alan Shepard was part of NASA's Apollo 14 crew. He was born in New Hampshire on November 18th, 1923, he retired August 1st, 1974 and died in California."
},
{
"source": "webnlg",
"text": "Alan Shepard was born in 1923 in New Hampshire . He worked for NASA and became a crew member for Apollo 14 before he retired in August 1974. He later died in California."
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]
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{
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[
"Alan Shepard",
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"AWARD",
"Distinguished Service Medal (United States Navy)"
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"annotations": [
{
"source": "webnlg",
"text": "Alan Shephard was born in New Hampshire. He was won the American Defence Service Ribbon. The United States Navy also awarded him the Distinguished Service Medal, which is higher than the Department of Commerce Gold Medal. He died on July 21st, 1998."
},
{
"source": "webnlg",
"text": "Alan Shepard (who died on the 21st of July, 1998 in California) was born in New Hampshire. He was awarded American Defense Service ribbon, as well as the Distinguished Service Medal by the United States Navy. The Distinguished Service Medal of the United States Navy is ranked higher than the Department of Commerce Gold Medal."
},
{
"source": "webnlg",
"text": "The U.S Navy awarded Alan Shepard the Distinguished Service Medal, an award that ranks higher than the Department of Commerce Gold Medal. Shepard who was born in New Hampshire and died July 21st, 1998 in California, was also the recipient of the American Defense Service Ribbon."
}
]
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"annotations": [
{
"source": "webnlg",
"text": "Buzz Aldrin was a fighter pilot who was born in Glen Ridge, New Jersey om January 20th, 1930. His real name is Edwin E. Aldrin Jr. and after graduating from MIT with a doctorate in Science in 1963, he became a member of Apollo 11."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was born in Glen Ridge, NJ on January 20th 1930. His real name was Edwin E. Aldrin, Jr. He graduated from MIT with a Sc.D in 1963. He was a fighter pilot and crew member of Apollo 11."
},
{
"source": "webnlg",
"text": "Edwin E Aldrin Jr, more commonly known as Buzz Aldrin was born in Glen Ridge New Jersey in 1930 and graduated from MIT with a Doctorate in Science in 1963. He was a fighter pilot and a crew member of Apollo 11."
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]
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{
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"annotations": [
{
"source": "webnlg",
"text": "Buzz Aldrin was born in Glen Ridge New Jersey in 1930. He graduated from MIT in 1963 and was hired by NASA in the same year. He was a member of the Apollo 11 crew and is now retired."
},
{
"source": "webnlg",
"text": "Retired Apollo 11 crew member Buzz Aldrin was born January 20 1930 in Glen Ridge, New Jersey. He was selected by NASA in 1963 when he graduated from MIT with a Sc. D."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was originally from Glen Ridge, New Jersey and graduated in 1963 from Massachusetts Institute of Technology. He then went on to join NASA in 1963 and became a member of the Apollo 11 crew before he retired."
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"annotations": [
{
"source": "webnlg",
"text": "Buzz Aldrin graduated from Massachusetts Institute of Technology with a Sc.D in 1963 and became a fighter pilot. He was selected to work for NASA in 1963, and was chosen as a crew member (serving as backup pilot) of the NASA operated Apollo 11 mission."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was a fighter pilot. In 1963, he graduated from Massachusetts Institute of Technology with a doctorate in Science and was selected to be part of NASA's Apollo 11 crew. William Anders was the backup pilot on Apollo 11."
}
]
},
{
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"source": "webnlg",
"text": "Elliot See was born in Dallas as US national and graduated from the University of Texas at Austin. Elliot See has died on the 28th of February 1966 in St Louis."
},
{
"source": "webnlg",
"text": "United States national Elliot See was born in Dallas, and studied at University of Texas at Austin. On the 28th of February, 1996 he passed away in St. Louis."
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]
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"source": "webnlg",
"text": "Elliot See was born on 07/23/1927 in Dallas and died in St. Louis. He attended the University of Texas at Austin which is a part of the university of Texas system."
},
{
"source": "webnlg",
"text": "Elliot See was born on 07/23/1927 in Dallas has graduated from the University of Texas which affiliated to the university of Texas System has died and deceased in St Louis."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas on July 23rd 1927. He graduated from the University of Texas at Austin which is affiliated to the university of texas system. Elliot See died in St.Louis."
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]
},
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"tripleset": [
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"annotations": [
{
"source": "webnlg",
"text": "William Anders was born on October 17, 1933, in British Hong Kong. He graduated from AFIT in 1962 with an M.S., and would go on to serve as a fighter pilot, and as a crew member on Apollo 8. Anders retired on September 1, 1969."
},
{
"source": "webnlg",
"text": "William Anders was born on the 17th of October 1933 in British Hong Kong. He graduated in 1962 from AFIT with a M.S. and went on to become a fighter pilot and a crew member on Apollo 8 before he retired in 1969."
},
{
"source": "webnlg",
"text": "William Anders was born in 1933 in British Hong Kong and graduated in 1962 from AFIT with a M.S. He then went on to become a test pilot and joined the Apollo 8 crew before he retired in 1969."
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]
},
{
"tripleset": [
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"BROADCASTED_BY",
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"annotations": [
{
"source": "webnlg",
"text": "Tim Brooke-Taylor was the star of Bananaman, an STV series first aired on 10/03/1983 and created by Steve Bright."
},
{
"source": "webnlg",
"text": "Bananaman was created by Steve Bright and starred Tim Brooke-Taylor. The show was first aired on 3rd October 1983 by STV."
},
{
"source": "webnlg",
"text": "Tim Brooke-Taylor starred in Bananaman that was created by Steve Bright. It was broadcast by STV and first aired on 3 October 1983."
}
]
},
{
"tripleset": [
[
"Bolt (comicsCharacter)",
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"Bolt (comicsCharacter)",
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"Dan Mishkin"
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"annotations": [
{
"source": "webnlg",
"text": "Dan Mishkin and the Puerto Rican national, Ernie Colon created the comic character of Bolt who has the alternative name of Larry Bolatinsky."
},
{
"source": "webnlg",
"text": "Dan Mishkin and Ernie Col\u00f3n (Puerto Rican) created the comic character Bolt, the alternative name of which is Larry Bolatinsky."
},
{
"source": "webnlg",
"text": "The comic book character of Bolt has the alternative name of Larry Bolatinsky. It was created by Dan Mishkin and the Puerto Rican national, Ernie Colon."
}
]
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{
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[
"Duncan Rouleau",
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"Damon Wayans, Jr."
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"subtree_was_extended": false,
"annotations": [
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"source": "webnlg",
"text": "Baymax is a character in Big Hero 6 that stars Damon Wayans Jr. The character was created by the American, Duncan Rouleau."
},
{
"source": "webnlg",
"text": "Damon Wayans Jr starred in the film Big Hero 6 which also included the character of Baymax who was created by the American, Duncan Rouleau."
},
{
"source": "webnlg",
"text": "Baymax is a character in the film Big Hero 6 which starred Damon Wayans, Jr. The character was created by the American national Duncan Rouleau."
}
]
},
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"Duncan Rouleau",
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"Jamie Chung"
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"subtree_was_extended": false,
"annotations": [
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"source": "webnlg",
"text": "Baymax, a character in Big Hero 6, starring Jamie Chung, was created by Duncan Rouleau, an American."
},
{
"source": "webnlg",
"text": "Jamie Chung starred in the film Big Hero 6 in which the character of Baymax appeared. Baymax was created by the American national, Duncan Rouleau."
},
{
"source": "webnlg",
"text": "Duncan Rouleau, who is American, created the character Baymax, which appeared in the movie Big Hero 6 starring Jamie Chung."
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]
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"Big Hero 6 (film)",
"STARRING",
"Maya Rudolph"
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"annotations": [
{
"source": "webnlg",
"text": "Big Hero 6 starred May Rudolph and the character of Baymax who was created by the American, Duncan Rouleau."
},
{
"source": "webnlg",
"text": "Baymax is a character in the film Big Hero 6 which starred Maya Rudolph. The creator of this character was American born Duncan Rouleau."
},
{
"source": "webnlg",
"text": "Maya Rudolph stars in Big Hero 6 in which the character Baymax also appears. Baymax was created by the American, Duncan Rouleau."
}
]
},
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"Abilene Regional Airport",
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"source": "webnlg",
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{
"source": "webnlg",
"text": "Abilene, part of Texas, is served by the Abilene regional airport."
},
{
"source": "webnlg",
"text": "Abilene regional airport serves the city of Abilene which is in Texas."
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]
},
{
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"Adolfo Su\u00e1rez Madrid\u2013Barajas Airport",
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"source": "webnlg",
"text": "Adolfo Su\u00e1rez Madrid\u2013Barajas Airport is in Alcobendas and has a runway length of 3500."
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{
"source": "webnlg",
"text": "Located in Alcobendas, Adolfo Suarez Madrid-Barajas Airport has a runway with the length of 3500.0 metres."
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"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Adolfo Su\u00e1rez Madrid\u2013Barajas Airport located at San Sebastian de los Reye has a runway length of 3500."
},
{
"source": "webnlg",
"text": "The Adolfo Su\u00e1rez Madrid\u2013Barajas Airport is in San Sebasti\u00e1n de los Reyes and has a runway length of 3500.0 metres."
},
{
"source": "webnlg",
"text": "Adolfo Suarez Madrid-Barajas airport is located at San Sebastian de los Reyes and has a runway length of 3500."
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]
},
{
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"source": "webnlg",
"text": "Agra Airport is in India where one of its leaders is T.S. Thakur."
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{
"source": "webnlg",
"text": "Agra Airport is in India, where the leader is TS Thakur."
},
{
"source": "webnlg",
"text": "Agra Airport is located in India, where the leader is T S Thakur."
}
]
},
{
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"Al Asad Airbase",
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"618"
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"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Al Asad Airbase is 618 ft above sea level and is located in Al Anbar Province, Iraq."
},
{
"source": "webnlg",
"text": "The Al Asad Airbase which is elevated 618 feet above sea level, is located in the Al Anbar Province in Iraq."
},
{
"source": "webnlg",
"text": "The Al Asad Airbase, which is 618 ft above sea level, is located in Al Anbar Province, Iraq."
}
]
},
{
"tripleset": [
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"Al Asad Airbase",
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{
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{
"source": "webnlg",
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},
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}
]
},
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"source": "webnlg",
"text": "Athens International Airport serves the city of Athens and has a runway length of 3.800 metres."
}
]
},
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{
"source": "webnlg",
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"source": "webnlg",
"text": "Lead by Cyril Ramaphosa, South Africa is location of 11 Diagonal Street, has the capital of Cape Town and has an ethnic group of Asian South Africans."
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{
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"text": "With the capital of Cape Town, South Africa is the location of 11 Diagonal Street, has the ethnic group of Asian South African and one of its leaders is Cyril Ramaphosa."
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"source": "webnlg",
"text": "11 Diagonal Street is located in South Africa, of which Cape Town is the capital. Two ethnic groups within South Africa are the Asian South Africans, and white South Africans."
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{
"source": "webnlg",
"text": "11 Diagonal Street is located in South Africa, where the capital is Cape Town. White South Africans and Asian South Africans are ethnic groups in South Africa."
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"source": "webnlg",
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"source": "webnlg",
"text": "Construction of the 12 floor Postmodern style building , 250 Delaware Avenue, which has a floor area of 30843.8 square metres began in January 2014."
},
{
"source": "webnlg",
"text": "First begun to be built in January 2014, the postmodernist art 250 Delaware Avenue has 12 floors and a total floor area of 30843.8 square metres."
}
]
},
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"300 North LaSalle",
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"source": "webnlg",
"text": "300 North Lasalle is located in Chicago, DuPage County, Illinois. The leader or Chicago is Susana Mendoza."
},
{
"source": "webnlg",
"text": "300 North LaSalle is situated in Chicago which is part of the Dupage County in Illinois located in the U.S. Susana Mendoza is currently the leader of Chicago."
}
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},
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},
{
"source": "webnlg",
"text": "The Lord Mayor of Dublin (Republic of Ireland) is Criona Ni Dhalaigh. Based in Dublin is 3Arena which is owned by Live Nation Entretainment."
},
{
"source": "webnlg",
"text": "the 3Arena, Dublin, Republic of Ireland is owned by Live Nation Entertainment. Cr\u00edona N\u00ed Dh\u00e1laigh was Lord Mayor of Dublin."
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]
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},
{
"source": "webnlg",
"text": "Owned by Live Nation Entertainment, 3Arena can be found in Dublin in the Republic of Ireland, a Republic run by Lord Mayor Cr\u00edona N\u00ed Dh\u00e1laigh."
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]
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{
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{
"source": "webnlg",
"text": "There are 23 floors in the AC Hotel Bella Sky Copenhagen, Denmark. The tenant is the Marriott International Hotel and the owner is Bella Center."
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{
"source": "webnlg",
"text": "Located in Denmark and with tenant Marriott International, AC Hotel Bella Sky Copenhagen is owned by Bella Center and has 23 floors."
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"source": "webnlg",
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},
{
"source": "webnlg",
"text": "3XN was the architect of the AC Hotel Bella Sky Copenhagen which has 23 floors and is owned by the Bella Center and currently tenanted by Marriott International."
},
{
"source": "webnlg",
"text": "Bella Center owns AC Hotel Bella Sky Copenhagen and Marriott International is a tenant. The hotel has 23 floors and was designed by 3XN."
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]
},
{
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"Adisham Hall",
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"source": "webnlg",
"text": "Construction began in 1927 and finished in 1931 of Adisham Hall in Sri Lanka and was in the Tudor and Jacobean style."
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{
"source": "webnlg",
"text": "The Tudor and Jacobean styled Adisham Hall was built in Sri Lanka between 1927 and 1931."
},
{
"source": "webnlg",
"text": "Adisham Hall began in 1927 and completed in 1931. It has a Tudor and Jacabian style and is located in Sri Lanka."
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]
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"source": "webnlg",
"text": "Adisham Hall is located in Haputale, Sri Lanka which has the capital city of Sri Jayawardenepura Kotte . Tamil is the language of the country."
},
{
"source": "webnlg",
"text": "Adisham Hall can be found in Haputale, Sri Lanka, a country with the capital Sri Jayawardenepura Kotte and language of the Tamil language."
},
{
"source": "webnlg",
"text": "Adisham Hall can be found in Haputale, Sri Lanka, where Sri Lanka's capital is Sri Jayawardenepura Kotte and the language is the Tamil Language."
}
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},
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"source": "webnlg",
"text": "Construction of Adisham Hall in Sri Lanka began in 1927 and ended in 1931. It was built in the Tudor Revival architectural style."
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{
"source": "webnlg",
"text": "Adisham Hall , Sri Lanka, was started in 1927 and completed in 1931. It has the Tudor Revival style."
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{
"source": "webnlg",
"text": "The architectural styled \"Tudor Revival\" Adisham Hall is located in Sri Lanka and was built in 1927 and completed in 1931."
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]
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{
"source": "webnlg",
"text": "Akita Museum of Art is located in Akita, Akita, Akita Prefecture, Japan, where one of the ethnic groups is the Filipinos."
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{
"source": "webnlg",
"text": "The Akita Prefecture, where the Akita Museum of Art is located, is part of the country of Japan a country where one of the ethnic groups is Filipinos."
}
]
},
{
"tripleset": [
[
"Alan B. Miller Hall",
"LOCATION",
"Virginia"
],
[
"Alan B. Miller Hall",
"ARCHITECT",
"Robert A. M. Stern"
],
[
"Mason School of Business",
"COUNTRY",
"United States"
],
[
"Alan B. Miller Hall",
"CURRENT_TENANTS",
"Mason School of Business"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Mason School of Business is located in the US and they are the current tenants of Alan B Miller Hall. Robert A.M. Stern is the architect for Alan B. Miller Hall, which is situated in Virginia."
},
{
"source": "webnlg",
"text": "The US based Mason School of Business are the current tenants of Alan B Miller Hall in Virginia which was designed by the architect Robert A M Stern."
},
{
"source": "webnlg",
"text": "The Mason School of Business are the current tenants of Alan B Miller Hall, which is located in Virginia, United States and was designed by the architect Robert A M Stern."
}
]
},
{
"tripleset": [
[
"Alan B. Miller Hall",
"LOCATION",
"Virginia"
],
[
"Alan B. Miller Hall",
"COMPLETION_DATE",
"\"1 June 2009\""
],
[
"Mason School of Business",
"COUNTRY",
"United States"
],
[
"Alan B. Miller Hall",
"CURRENT_TENANTS",
"Mason School of Business"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Situated in Virginia, United States,, Alan B. Miller Hall was completed on 1st June 2009. The current tenants are the Mason School of Business."
},
{
"source": "webnlg",
"text": "Located in Virginia, United States and with the current tenants of the Mason School of Business, the Alan B. Miller Hall was completed in June 1st, 2009."
}
]
},
{
"tripleset": [
[
"Amdavad ni Gufa",
"LOCATION",
"Ahmedabad"
],
[
"Amdavad ni Gufa",
"COUNTRY",
"India"
],
[
"India",
"LEADER_NAME",
"Narendra Modi"
],
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Narendra Modi and Sumitra Mahajan are leaders in India where Amdavad ni Gufa is located in Ahmedabad."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Ahmedabad, India. Narendra Modi is the Prime Minister of India whilst Sumitra Mahajan is another leader of India."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa can be found in Ahmedabad, India, a country lead by Narendra Modi and Sumitra Mahajan."
}
]
},
{
"tripleset": [
[
"Amdavad ni Gufa",
"LOCATION",
"Gujarat"
],
[
"Amdavad ni Gufa",
"ADDRESS",
"\"Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road\""
],
[
"Amdavad ni Gufa",
"COUNTRY",
"India"
],
[
"Amdavad ni Gufa",
"LOCATION",
"Ahmedabad"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The address for Amdavad ni Gufa is Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road, Gujarat, Ahmedabad, India."
},
{
"source": "webnlg",
"text": "The address of Amdavad ni Gufa is Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road, Ahmedabad, Gujarat, India."
}
]
},
{
"tripleset": [
[
"Amdavad ni Gufa",
"LOCATION",
"Gujarat"
],
[
"India",
"LEADER_NAME",
"T. S. Thakur"
],
[
"Amdavad ni Gufa",
"LOCATION",
"Ahmedabad"
],
[
"Amdavad ni Gufa",
"COUNTRY",
"India"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Ahmedabad, Gujarat, India. T S Thakur was an Indian leader."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Ahmedabad, Gujurat, India. T S Thakur is a leader in India."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Gujarat, Ahmedabad, India, where the leader was T. S. Thakur."
}
]
},
{
"tripleset": [
[
"Ampara Hospital",
"COUNTRY",
"Sri Lanka"
],
[
"Sri Lanka",
"LEADER_NAME",
"Ranil Wickremesinghe"
],
[
"Sri Lanka",
"CURRENCY",
"Sri Lankan rupee"
],
[
"Ampara Hospital",
"STATE",
"Eastern Province, Sri Lanka"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ampara Hospital is located in the Eastern Province of Sri Lanka, where the currency is the Ski Lankan rupee. One of the leaders of Sri Lanka is Ranil Wickremesinghe."
},
{
"source": "webnlg",
"text": "Ampara Hospital is located in Eastern Province, Sri Lanka. The currency of Sri Lanka is the Sri Lankan rupee and the leader of the country is Ranil Wickremesinghe."
},
{
"source": "webnlg",
"text": "Ampara Hospital is in the Eastern Province, Sri Lanka. The leader of Sri Lanka is Ranil Wickremesinghe and its currency is the Sri Lankan rupee."
}
]
},
{
"tripleset": [
[
"Asilomar Conference Grounds",
"ARCHITECT",
"Julia Morgan"
],
[
"Julia Morgan",
"BIRTH_PLACE",
"San Francisco"
],
[
"Julia Morgan",
"SIGNIFICANT_BUILDING",
"Los Angeles Herald-Examiner"
],
[
"Julia Morgan",
"SIGNIFICANT_BUILDING",
"Asilomar State Beach"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "San Francisco born architect Julia Morgan designed many significant buildings including the Asilomar Conference Grounds, the Los Angeles Herald Examiner Building and the Asilomar State Beach."
},
{
"source": "webnlg",
"text": "Julia Morgan, born in San Francisco and designer of many significant buildings, including the Los Angeles Herald Examiner building and the Asilomar State Beach, was also the architect of the grounds of Asilomar Conference."
},
{
"source": "webnlg",
"text": "Julia Morgan was born in San Fransisco. She designed the Asilomar Conference Grounds, the Asilomar State Beach and the Los Angeles Herald examiner, a landmark in California."
}
]
},
{
"tripleset": [
[
"Asilomar Conference Grounds",
"ARCHITECT",
"Julia Morgan"
],
[
"Julia Morgan",
"SIGNIFICANT_BUILDING",
"Los Angeles Herald-Examiner"
],
[
"Julia Morgan",
"SIGNIFICANT_BUILDING",
"Riverside Art Museum"
],
[
"Julia Morgan",
"BIRTH_PLACE",
"California"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Born in California, Julia Morgan has designed buildings such as Los Angeles Herald Examiner building and The Riverside Art Museum, and was also the architect of the grounds of Asilomar Conference."
},
{
"source": "webnlg",
"text": "Julia Morgan from California, designed many important buildings including the Los Angeles Herald building and the Asilomer Conference Grounds."
}
]
},
{
"tripleset": [
[
"Asser Levy Public Baths",
"LOCATION",
"New York City"
],
[
"New York City",
"COUNTRY",
"United States"
],
[
"New York City",
"IS_PART_OF",
"Manhattan"
],
[
"New York City",
"IS_PART_OF",
"New Netherland"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asser Levy Public Baths are in New York City, U.S. which is part of Manhattan. New York City was part of New Netherland."
},
{
"source": "webnlg",
"text": "Asser Levy Public Baths are located in New York City, Manhattan, New Netherland in the United States."
},
{
"source": "webnlg",
"text": "Asser Levy Public Baths is located in New York City, on Manhattan Island, New York, in the United States. New York City was part of what was New Netherland."
}
]
},
{
"tripleset": [
[
"Birmingham",
"POSTAL_CODE",
"B postcode area"
],
[
"103 Colmore Row",
"ARCHITECT",
"John Madin"
],
[
"John Madin",
"HOMETOWN",
"Birmingham"
],
[
"Birmingham",
"LEADER_NAME",
"John Clancy (Labour politician)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The architect John Madin was the designer of 103 Colmore Row, which is located in his home city of Birmingham. Labour politician, John Clancy is the leader of Birmingham and the city has the postal code 'B'."
},
{
"source": "webnlg",
"text": "103 Colmore Row was designed by the architect, John Madin, Birmingham native. Birmingham has the postcode area 'B' and is lead by Labour politician, John Clancy."
},
{
"source": "webnlg",
"text": "Labour politician, John Clancy is the leader of Birmingham which has the postcode area 'B' and is home town of John Madin the architect who designed 103 Colmore Row."
}
]
},
{
"tripleset": [
[
"Ethiopia",
"LEADER_NAME",
"Mulatu Teshome"
],
[
"Addis Ababa",
"IS_PART_OF",
"Addis Ababa Stadium"
],
[
"Addis Ababa City Hall",
"COUNTRY",
"Ethiopia"
],
[
"Addis Ababa City Hall",
"LOCATION",
"Addis Ababa"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Addis Ababa, Ethiopia is home to Addis Ababa Stadium and the Addis Ababa City Hall. The name of the leader in Ethiopia is Malatu Teshome."
},
{
"source": "webnlg",
"text": "Mulatu Teshome is a leader in Ethiopia, a country where Addis Ababa is located and within which is Addis Ababa City Hall and Addis Ababa Stadium."
},
{
"source": "webnlg",
"text": "Mulata Teshome is an Ethiopian leader. Addis Ababa City Hall is located in Addis Ababa, Ethopia. Addis Ababa Stadium is part of Addid Ababa."
}
]
},
{
"tripleset": [
[
"United States",
"CAPITAL",
"Washington, D.C."
],
[
"United States",
"LEADER_TITLE",
"President of the United States"
],
[
"United States",
"LEADER_NAME",
"Paul Ryan"
],
[
"250 Delaware Avenue",
"LOCATION",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The leader of the US is known as the President and the capital city is Washington DC. One of the country's leaders if Paul Ryan and it is the location of 250 Delaware Avenue."
},
{
"source": "webnlg",
"text": "Paul Ryan is president of the United States and both 250 Delaware Avenue and Washington DC (the capital) are located there."
},
{
"source": "webnlg",
"text": "Paul Ryan is the leader, or President of the United States, of which Washington D.C. is the capital. 250 Delaware Avenue is part of the United States."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Ajoblanco",
"MAIN_INGREDIENTS",
"\"Bread, almonds, garlic, water, olive oil\""
],
[
"Ajoblanco",
"REGION",
"Andalusia"
],
[
"Ajoblanco",
"INGREDIENT",
"Bread"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bread, almonds, garlic, water, and olive oil are the main ingredients in Ajoblanco, a food which originates from Andalusia, in Spain."
},
{
"source": "webnlg",
"text": "Ajoblanco is from Andalusia Spain and ingredients include bread, almonds, garlic, water, and olive oil."
},
{
"source": "webnlg",
"text": "Ajoblanco, a dish from the Andalusia region of Spain, has the main ingredients bread, almonds, garlic, water and olive oil."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Ajoblanco",
"REGION",
"Andalusia"
],
[
"Ajoblanco",
"ALTERNATIVE_NAME",
"\"Ajo blanco\""
],
[
"Ajoblanco",
"INGREDIENT",
"Water"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Water is an ingredient in Ajoblanco, also known as Ajo blanco, a dish that comes from Andalusia Spain."
},
{
"source": "webnlg",
"text": "Ajoblanco is a food originates from the Andalusian region of Spain and contains water.An other alternative name of the dish is Ajo blanco."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"DISH_VARIATION",
"Lemper"
],
[
"Arem-arem",
"INGREDIENT",
"Banana leaf"
],
[
"Arem-arem",
"MAIN_INGREDIENTS",
"\"compressed rice cooked in banana leaf with vegetables or minced meat fillings\""
],
[
"Arem-arem",
"REGION",
"Javanese cuisine"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Lemper, a dish variation of Arem-Arem contains Banana leaf in which is cooked compressed rice with vegetables or minced meat fillings and is a form of Javanese cuisine."
},
{
"source": "webnlg",
"text": "The main ingredients of Arem-arem are compressed rice cooked in banana leaf with vegetables or minced meat fillings. The dish is of Javanese cuisine and has a variation known as Lemper."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"INGREDIENT",
"Banana leaf"
],
[
"Arem-arem",
"COURSE",
"\"Main course\""
],
[
"Arem-arem",
"MAIN_INGREDIENTS",
"\"compressed rice cooked in banana leaf with vegetables or minced meat fillings\""
],
[
"Arem-arem",
"REGION",
"\"Nationwide in Indonesia, but more specific to Java\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem-arem is a food that is found nationwide in Indonesia, but more specific to Java, it can be served as a main course and has a banana leaf in it. It's main ingredients are compressed rice cooked in banana leaf with vegetables or minced meat fillings."
},
{
"source": "webnlg",
"text": "The main ingredients of Arem-arem are compressed rice cooked in banana leaf with vegetables or minced meat fillings. It is served as a main course and is found nationwide in Indonesia and more specifically in Java."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"INGREDIENT",
"Chili pepper"
],
[
"Arrabbiata sauce",
"MAIN_INGREDIENTS",
"\"Tomatoes, red chili, garlic, olive oil\""
],
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Arrabbiata sauce",
"REGION",
"Rome"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arrabbiata sauce originates from the region of Rome in Italy. It is made with tomatoes, red chili, garlic and olive oil."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce is a traditional dish from Rome,Italy.The main ingredients of it are tomatoes,red chili pepper,garlic and olive oil."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce is a traditional dish from Rome in Italy. An important ingredient is chili pepper and it also contains tomatoes, red chili, garlic and olive oil."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Arr\u00f2s negre",
"MAIN_INGREDIENTS",
"\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\""
],
[
"Arr\u00f2s negre",
"REGION",
"Catalonia"
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Cephalopod ink"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arros Negre is a traditional dish from the Catalonia region of Spain. The main ingredients of Arros negre are white rice, cuttlefish or squid, cephalopod ink, and cubanelle peppers."
},
{
"source": "webnlg",
"text": "Arros negre is from the region of Catalonia in Spain. The main ingredients of Arros negre are white rice, cuttlefish or squid, cephalopod ink, and cubanelle peppers."
},
{
"source": "webnlg",
"text": "Arros negre, from the Catalonia region of Spain, includes Cephalopod ink, white rice, cuttlefish/squid and cubanelle peppers."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Arr\u00f2s negre",
"MAIN_INGREDIENTS",
"\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\""
],
[
"Arr\u00f2s negre",
"REGION",
"Catalonia"
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Cubanelle"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The main ingredients of Arr\u00f2s negre, which is from Spain, are white rice, cuttlefish or squid, cephalopod ink, cubanelle and cubanelle peppers. Arr\u00f2s negre is from the Catalonia region."
},
{
"source": "webnlg",
"text": "The main ingredients in arr\u00f2s negre are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers. It is a traditional dish from the Catalonia region of Spain."
},
{
"source": "webnlg",
"text": "The main ingredients in arr\u00f2s negre are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers. It is a traditional Spanish dish from the Catalonia region."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"REGION",
"Valencian Community"
],
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Arr\u00f2s negre",
"MAIN_INGREDIENTS",
"\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\""
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Cubanelle"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arros negre is a traditonal dish from the Valencian Community, Spain. It has the main ingredients of white rice, cuttlefish or squid, cephalopod ink and cubnelle peppers."
},
{
"source": "webnlg",
"text": "Arros negre comes from the region of the Valencian Community, Spain. The main ingredients are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers."
},
{
"source": "webnlg",
"text": "Arros negre comes from the region of the Valencian Community in Spain. The main ingredients are white rice, cuttlefish or squid, cephalopod ink, cubanelle peppers."
}
]
},
{
"tripleset": [
[
"Asam pedas",
"COUNTRY",
"Malaysia"
],
[
"Asam pedas",
"REGION",
"\"Sumatra and Malay Peninsula\""
],
[
"Asam pedas",
"MAIN_INGREDIENTS",
"\"Fish cooked in sour and hot sauce\""
],
[
"Asam pedas",
"ALTERNATIVE_NAME",
"\"Asam padeh\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asam pedas is found in the region of Sumatra and Malay peninsula in Malaysia. It can also be called Asam padeh and it's ingredients are fish cooked in sour and hot sauce."
},
{
"source": "webnlg",
"text": "Asam pedas (aka asam padeh) is from the Sumatra and Malay Peninsula regions of Malaysia. The main ingredients are fish cooked in sour and hot sauce."
},
{
"source": "webnlg",
"text": "Asam padeh is also known as asam pedas and is a food found in Malaysia originating from the Sunatra and Malay Peninsula regions. The main ingredients is fish cooked in a sour and hot sauce."
}
]
},
{
"tripleset": [
[
"Asam pedas",
"COUNTRY",
"Malaysia"
],
[
"Malaysia",
"ETHNIC_GROUP",
"Malaysian Malay"
],
[
"Sumatra",
"ETHNIC_GROUP",
"Minangkabau people"
],
[
"Asam pedas",
"REGION",
"Sumatra"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asam pedas is a food found in Malaysia and Sumatra. Malaysian Malay is an ethnic group in Malaysia and Sumatra has an ethnic group called the Minangkabau people."
},
{
"source": "webnlg",
"text": "Asam pedas is a food found in Malaysia and Sumatra. Malay is the ethnic group of Malaysia and the Minangkabau people are an ethnic group of Sumatra."
},
{
"source": "webnlg",
"text": "Asam pedas is found in Malaysia, originating from Sumatra. Sumatra is home to the Minangkabau people and Malaysia, the Malay people."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"Bacon Explosion",
"INGREDIENT",
"Sausage"
],
[
"Bacon Explosion",
"REGION",
"Kansas City metropolitan area"
],
[
"Bacon Explosion",
"MAIN_INGREDIENTS",
"Bacon"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Bacon Explosion comes from the Kansas city metro area in the U.S. The main ingredient in it is bacon and also includes sausage."
},
{
"source": "webnlg",
"text": "Bacon Explosion comes from the Kansas City metropolitan area of the United States and includes the main ingredient of sausage and bacon."
},
{
"source": "webnlg",
"text": "Bacon Explosion is from the Kansas City metropolitan area of the United States. The main ingredient is bacon along with sausage."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"United States",
"LEADER_NAME",
"Paul Ryan"
],
[
"United States",
"ETHNIC_GROUP",
"Asian Americans"
],
[
"United States",
"CAPITAL",
"Washington, D.C."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon Explosion comes from the United States, where one of the leaders is Paul Ryan. It is also where Asian Americans are one of the ethnic groups and Washington, D.C. is the capital."
},
{
"source": "webnlg",
"text": "The United States is the country of the Bacon Explosion and the leader is Paul Ryan. Asian Americans are an ethnic group in the United States and the capital is Washington, D.C."
},
{
"source": "webnlg",
"text": "The Bacon Explosion originates from the United States. The U.S. boasts leader Paul Ryan, is home to Asian Americans and has its capital in Washington D.C."
}
]
},
{
"tripleset": [
[
"Bacon sandwich",
"DISH_VARIATION",
"BLT"
],
[
"Bacon sandwich",
"COUNTRY",
"United Kingdom"
],
[
"Bacon sandwich",
"ALTERNATIVE_NAME",
"\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\""
],
[
"Bacon sandwich",
"INGREDIENT",
"Bacon"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon sandwiches are popular in the UK, have a variant called a BLT, include the ingredient bacon and sometimes go by the name of: bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin."
},
{
"source": "webnlg",
"text": "The bacon sandwich, of which an ingredient is bacon, has a variation called the BLT and has different names including: bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin."
},
{
"source": "webnlg",
"text": "A bacon sandwich, which is a variation of the BLT, can also be known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin. It is a dish containing bacon and comes from the United Kingdom."
}
]
},
{
"tripleset": [
[
"Bacon sandwich",
"DISH_VARIATION",
"BLT"
],
[
"Bacon sandwich",
"COUNTRY",
"United Kingdom"
],
[
"Bacon sandwich",
"ALTERNATIVE_NAME",
"\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\""
],
[
"Bacon sandwich",
"INGREDIENT",
"Brown sauce"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon sandwich is a dish from the United Kingdom and a variation is the BLT. An ingredient used is brown sauce and it's different names include Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin."
},
{
"source": "webnlg",
"text": "The country that bacon sandwich comes from is the United Kingdom, it is also known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin. Brown sauce can be served with it and a variation is the BLT."
},
{
"source": "webnlg",
"text": "Bacon sandwiches are popular in the UK and can contain the ingredient brown sauce. A variation of the sandwich is a BLT and it can also be known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin."
}
]
},
{
"tripleset": [
[
"Bacon sandwich",
"MAIN_INGREDIENTS",
"\"Bread and bacon, with a condiment, often ketchup or brown sauce\""
],
[
"Bacon sandwich",
"COUNTRY",
"United Kingdom"
],
[
"Bacon sandwich",
"INGREDIENT",
"Ketchup"
],
[
"Bacon sandwich",
"ALTERNATIVE_NAME",
"\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "From the United Kingdom, the bacon sandwich's main ingredients are bread and bacon and a condiment, often ketchup or brown sauce. It can also be known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin."
},
{
"source": "webnlg",
"text": "Bacon sandwich originates from the United Kingdom and its main ingredients are bread,bacon with ketchup or brown sauce.It has different names including:Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin."
},
{
"source": "webnlg",
"text": "The main ingredients of a bacon sandwich, (popular in the UK), are bread, bacon and ketchup or brown sauce as a condiment. The bacon sandwich has several alternative names including: bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"MAIN_INGREDIENTS",
"\"Meringue, ice cream, sponge cake or Christmas pudding\""
],
[
"Baked Alaska",
"COUNTRY",
"\"France, United States or China\""
],
[
"Baked Alaska",
"REGION",
"\"Paris, New York or Hong Kong\""
],
[
"Baked Alaska",
"INGREDIENT",
"Meringue"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska has the main ingredients of meringue, ice cream and sponge cake (or Christmas pudding). It is found in France, the US, China, Hong Kong, New York and Paris."
},
{
"source": "webnlg",
"text": "Baked Alaska contains: ice cream, sponge cake (Christmas pudding) and meringue. It is found in Hong Kong, New York and Paris. The origins are either France, Canada or the United States."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"MAIN_INGREDIENTS",
"\"Meringue, ice cream, sponge cake or Christmas pudding\""
],
[
"Baked Alaska",
"REGION",
"\"Paris, New York or Hong Kong\""
],
[
"Baked Alaska",
"COUNTRY",
"United States"
],
[
"Baked Alaska",
"INGREDIENT",
"Ice cream"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska comes from either Paris, New York USA or Hong Kong. Meringue, ice cream, sponge cake or Christmas pudding are main ingredients in baked Alaska."
},
{
"source": "webnlg",
"text": "Baked Alaska is found in Hong Kong, New York and Paris. Meringue, ice cream, sponge cake or Christmas pudding are its main ingredients."
}
]
},
{
"tripleset": [
[
"Bakewell pudding",
"DISH_VARIATION",
"Bakewell tart"
],
[
"Bakewell tart",
"REGION",
"Derbyshire Dales"
],
[
"Derbyshire Dales",
"IS_PART_OF",
"Derbyshire"
],
[
"Bakewell tart",
"INGREDIENT",
"Frangipane"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The bakewell tart is popular in the Derbyshire Dales area which is part of Derbyshire. It's a variant of bakewell pudding and uses frangipane."
},
{
"source": "webnlg",
"text": "Bakewell tart, a variation of Bakewell pudding, includes the ingredient frangipane and originates from the Derbyshire Dales region of Derbyshire."
},
{
"source": "webnlg",
"text": "Bakewell tart is a variant of bakewell pudding and comes from the Derbyshire Dales. It contains frangipane."
}
]
},
{
"tripleset": [
[
"Bakewell pudding",
"REGION",
"Derbyshire Dales"
],
[
"Bakewell pudding",
"DISH_VARIATION",
"Bakewell tart"
],
[
"Bakewell pudding",
"COURSE",
"\"Dessert\""
],
[
"Bakewell pudding",
"MAIN_INGREDIENTS",
"\"Ground almond, jam, butter, eggs\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakewell tart is a variation of Bakewell pudding that originates from the Derbyshire Dales. It is a dessert that has the main ingredients of ground almond, jam, butter and eggs."
},
{
"source": "webnlg",
"text": "Bakewell tart is a variation of Bakewell pudding which is a dessert that comes from the Derbyshire Dales area and has the main ingredients ground almonds, jam, butter and eggs."
},
{
"source": "webnlg",
"text": "Bakewell pudding is a dessert from the Derbyshire Dales region and contains the ingredients ground almonds, jam, butter and eggs. It has a variation called Bakewell tart."
}
]
},
{
"tripleset": [
[
"Bakewell pudding",
"REGION",
"Derbyshire Dales"
],
[
"Bakewell pudding",
"DISH_VARIATION",
"Bakewell tart"
],
[
"Derbyshire Dales",
"IS_PART_OF",
"Derbyshire"
],
[
"Bakewell tart",
"INGREDIENT",
"Shortcrust pastry"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "From the Derbyshire Dales region (in Derbyshire), isBakewell pudding. A variation of which, is Bakewell tart, its main ingredient is shortcrust pastry."
},
{
"source": "webnlg",
"text": "Bakewell tart is a variation of Bakewell pudding which uses shortcut pastry and comes from the Derbyshire Dales in Derbyshire."
},
{
"source": "webnlg",
"text": "Bakewell tart is a variant of bakewell pudding from the Derbyshire Dales in Derbyshire. The main ingredient is shortcrust pastry."
}
]
},
{
"tripleset": [
[
"Bandeja paisa",
"INGREDIENT",
"Rice"
],
[
"Bandeja paisa",
"MAIN_INGREDIENTS",
"\"red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon\""
],
[
"Bandeja paisa",
"REGION",
"Paisa Region"
],
[
"Bandeja paisa",
"COUNTRY",
"Colombian cuisine"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bandeja paisa is a Colombian dish from the Paisa region. Its main ingredients include red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon."
},
{
"source": "webnlg",
"text": "Bandeja paisa originates from the Paisa region, Columbia. The main ingredients are: red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon."
},
{
"source": "webnlg",
"text": "Bandeja paisa is a typical Colombian dish from the Paisa region and contains the ingredients: red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon."
}
]
},
{
"tripleset": [
[
"Batagor",
"COUNTRY",
"Indonesia"
],
[
"Batagor",
"INGREDIENT",
"Peanut sauce"
],
[
"Batagor",
"DISH_VARIATION",
"Siomay"
],
[
"Batagor",
"MAIN_INGREDIENTS",
"\"fried fish dumpling with tofu and vegetables in peanut sauce\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Batagor is an Indonesian food and a variation of the Siomay dish.It contains a fried fish dumpling with tofu and vegetables in peanut suace."
},
{
"source": "webnlg",
"text": "Batagor, (a variation of Siomay), is a dish found in Indonesia and its main ingredients are fried fish dumpling with tofu and vegetables in peanut sauce."
},
{
"source": "webnlg",
"text": "Batagor, from Indonesia, is a dish consisting of fried fish dumplings, tofu and vegetables in peanut sauce. Siomay is a variation of batagor."
}
]
},
{
"tripleset": [
[
"Batagor",
"COUNTRY",
"Indonesia"
],
[
"Shumai",
"DISH_VARIATION",
"Siomay"
],
[
"Shumai",
"INGREDIENT",
"Shiitake"
],
[
"Batagor",
"DISH_VARIATION",
"Shumai"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Batagor is found in the country of Indonesia. It is a variation on Siomay or Shumai, the latter of which includes Shiitake."
},
{
"source": "webnlg",
"text": "Shumai, which has shiitake as a component and is similar to Siomay, is a variation of the Indonesian dish batagor."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"COUNTRY",
"Singapore"
],
[
"Beef kway teow",
"REGION",
"Indonesia"
],
[
"Singapore",
"LANGUAGE",
"Standard Chinese"
],
[
"Singapore",
"LEADER_NAME",
"Halimah Yacob"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow is a dish from the country of Singapore, where Halimah Yacob is leader and Standard Chinese is spoken. The dish originated from the region of Indonesia."
},
{
"source": "webnlg",
"text": "Beef kway teow comes from the region of Indonesia and also Singapore. The leader of Singapore, where Standard Chinese is the spoken language, is Halimah Yacob."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"MAIN_INGREDIENTS",
"\"Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil\""
],
[
"Beef kway teow",
"INGREDIENT",
"Oyster sauce"
],
[
"Beef kway teow",
"REGION",
"Indonesia"
],
[
"Beef kway teow",
"COUNTRY",
"\"Singapore and Indonesia\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow is a dish found in Singapore and Indonesia. Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil are main ingredients in beef kway teow."
},
{
"source": "webnlg",
"text": "Beef kway teow is served in the region of Indonesia and is also found in Singapore. The main ingredients are Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil."
},
{
"source": "webnlg",
"text": "Beek kway teos includes: kway teow, beef tenderloin, gula Melaka, black beans, garlic, oyster, soya and soy sauce, lengkuas, chili and sesame oil. It is found in Singapore."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"REGION",
"Singapore"
],
[
"Beef kway teow",
"MAIN_INGREDIENTS",
"\"Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil\""
],
[
"Beef kway teow",
"COUNTRY",
"Indonesia"
],
[
"Beef kway teow",
"INGREDIENT",
"Oyster sauce"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow is known to come from the Singapore region and also Indonesia. It's main ingredients are Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil."
},
{
"source": "webnlg",
"text": "Beef kway teow, originating in Indonesia, is a dish found in Singapore. Its main ingredients are Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil."
},
{
"source": "webnlg",
"text": "Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil are the main ingredients of Beef kway teow. The dish originated in Singapore and is found in Indonesia."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"REGION",
"Singapore"
],
[
"Singapore",
"LANGUAGE",
"English language"
],
[
"Beef kway teow",
"COUNTRY",
"\"Singapore and Indonesia\""
],
[
"Singapore",
"LEADER_NAME",
"Tony Tan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow comes from the Singapore region where The English language is spoken. The dish is found in the countries of Indonesia and Singapore. The leader of Singapore is Tony Tan."
},
{
"source": "webnlg",
"text": "Beef kway teow is a dish of Singapore where Tony Tan is the leader and English is spoken. The dish is also popular in Indonesia."
},
{
"source": "webnlg",
"text": "Singapore's leader is Tony Tan and one of the country's spoken language is English.Beef kway teow is made there and Indonesia too."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"CURRENCY",
"Indian rupee"
],
[
"India",
"LEADER_NAME",
"T. S. Thakur"
],
[
"India",
"LEADER_NAME",
"Narendra Modi"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji comes from the country India, where two of the leaders are T.S. Thakur and Narendra Modi. It is also where the Indian rupee is the currency."
},
{
"source": "webnlg",
"text": "Bhajji comes from the country India, where the currency is the rupee. The leader of India is either T. S. Thakur and/or Narendra Modi."
},
{
"source": "webnlg",
"text": "Bhajji comes from the country India, where the currency is the rupee and the leader is either T. S. Thakur and/or Narendra Modi."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"DEMONYM",
"Indian people"
],
[
"India",
"LEADER_NAME",
"T. S. Thakur"
],
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji originate from India where the Indian people come from. The leaders of the country are T S Thakur and Sumitra Mahajan."
},
{
"source": "webnlg",
"text": "Bhajji originate from India where the Indian people are lead by T S Thakur and Sumitra Mahajan."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"LEADER_NAME",
"Narendra Modi"
],
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
],
[
"Bhajji",
"REGION",
"Karnataka"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji come from the Karnataka region of India. Narendra Modi is the name of the leader of India, another leader is Sumitra Mahajan."
},
{
"source": "webnlg",
"text": "Bhajji originates from the Karnataka region of India.The country's leaders are Narendra Modi and Sumitra Mahajan."
},
{
"source": "webnlg",
"text": "Bhajji originate from the Karnataka region in India. The country's leaders are Narendra Modi and Sumitra Mahajan."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"LEADER_NAME",
"T. S. Thakur"
],
[
"Bhajji",
"REGION",
"Karnataka"
],
[
"Karnataka",
"LEADER_NAME",
"Vajubhai Vala"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji comes from Karnataka, India. One leader in India is T.S. Thakur and another Vajubhai Vala."
},
{
"source": "webnlg",
"text": "Bhajji are found in the region of Karnataka, India. The leader of India is called T. S. Thakur and Vajubhai Vala is the leader of Karnataka."
},
{
"source": "webnlg",
"text": "Bhajji comes from the Karnataka region or India. Vajubhai Vala is a leader of Karnatka and T.S. Thakur is the leader of India."
}
]
},
{
"tripleset": [
[
"Binignit",
"INGREDIENT",
"Sweet potato"
],
[
"Binignit",
"COURSE",
"Dessert"
],
[
"Dessert",
"DISH_VARIATION",
"Cookie"
],
[
"Sweet potato",
"ORDER",
"Solanales"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Cookies and binignit are types of dessert. One of the ingrediens in binignit is sweet potato which belongs to the order Solanales."
}
]
},
{
"tripleset": [
[
"Bionico",
"COUNTRY",
"Mexico"
],
[
"Bionico",
"MAIN_INGREDIENTS",
"\"Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins\""
],
[
"Bionico",
"REGION",
"Guadalajara"
],
[
"Bionico",
"INGREDIENT",
"Condensed milk"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut are the main ingredients in Bionico, a food found in Guadalajara, in Mexico."
},
{
"source": "webnlg",
"text": "Bionico has the ingredients chopped fruits, sour cream, condensed milk, granola, shredded coconut and raisins and can be found in Guadalajara, Mexico."
},
{
"source": "webnlg",
"text": "The main ingredients of Bionico, a dish from the Guadalaraja region of Mexico, are chopped fruits, sour cream, condensed milk, granola, shredded coconut and raisins."
}
]
},
{
"tripleset": [
[
"Bionico",
"COUNTRY",
"Mexico"
],
[
"Bionico",
"MAIN_INGREDIENTS",
"\"Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins\""
],
[
"Bionico",
"REGION",
"Jalisco"
],
[
"Bionico",
"INGREDIENT",
"Sour cream"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bionico is a food found in the Jalisco region of Mexico;its ingredients contain the following: sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut."
},
{
"source": "webnlg",
"text": "Bionico comes from the Jalisco region of Mexico.The main ingredients of the food are:chopped Fruits, sour cream, condensed milk, granola, shredded coconut and raisins."
},
{
"source": "webnlg",
"text": "Bionico is found in the region of Jalisco, Mexico and consists of sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut."
}
]
},
{
"tripleset": [
[
"Indonesia",
"CAPITAL",
"Jakarta"
],
[
"Indonesia",
"LEADER_NAME",
"Jusuf Kalla"
],
[
"Bakso",
"REGION",
"Indonesia"
],
[
"Bakso",
"COUNTRY",
"Chinese cuisine"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakso is a Chinese dish found in Indonesia, the leader of which is Jusuf Kalla. Jakarta is the capital of Indonesia."
},
{
"source": "webnlg",
"text": "Bakso is from the Chinese cuisine and is found in Indonesia where the capital city is Jakarta and Jusuf Kalla is a leader."
}
]
},
{
"tripleset": [
[
"Indonesia",
"LEADER_NAME",
"Jusuf Kalla"
],
[
"Bakso",
"REGION",
"Indonesia"
],
[
"Bakso",
"COUNTRY",
"Indonesia"
],
[
"Bakso",
"INGREDIENT",
"Celery"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakso comes from Indonesia, and celery is a main ingredient. Jasuf Kalla is a leader in Indonesia."
},
{
"source": "webnlg",
"text": "Celery is an ingredient of Bakso, a dish from the country of Indonesia, where Jusuf Kalla is a leader."
},
{
"source": "webnlg",
"text": "Bakso ( which contains celery) is a dish found in Indonesia where it originated. One of the country's leaders is Jusuf Kalla."
}
]
},
{
"tripleset": [
[
"Italy",
"CAPITAL",
"Rome"
],
[
"Italy",
"LEADER_NAME",
"Matteo Renzi"
],
[
"Amatriciana sauce",
"COUNTRY",
"Italy"
],
[
"Italy",
"LEADER_NAME",
"Sergio Mattarella"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Two leaders of Italy, where Amatriciana sauce is found, are Matteo Renzi and Sergio Mattarella. Its capital is Rome."
},
{
"source": "webnlg",
"text": "Italy is the country Amatriciana sauce comes from. The capital is Rome and political leaders include Matteo Renzi and Sergio Mattarella."
},
{
"source": "webnlg",
"text": "Rome is the capital of Italy, where Sergio Mattarell and Matteo Renzi are leaders and where amatriciana sauce is a traditional dish."
}
]
},
{
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[
"Java",
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[
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[
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"Singapore"
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[
"Ayam penyet",
"COUNTRY",
"Java"
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"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet comes from the Singaporean region and Halimah Yacob is the country's leader.It can be found in Java as well where Baduy people is an ethnic group."
},
{
"source": "webnlg",
"text": "Ayam penyet is a dish from the region of Singapore which is lead by Halimah Yacob. It is also found in Java where the Baduy are an ethnic group."
},
{
"source": "webnlg",
"text": "Ayem penyet is a dish from Java where the Baduy are an ethnic group. It also originates from the region of Singapore where the leader is Halimah Yacob."
}
]
},
{
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[
"Java",
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[
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[
"Ayam penyet",
"COUNTRY",
"Java"
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"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet is a dish from Java and Singapore. The leader of the latter is Tony Tan and the Banyumasan people are an ethnic group from Java."
},
{
"source": "webnlg",
"text": "Ayam penyet is a dish from Singapore and Java. Banyumasan people is one of the ethnic groups in Java and Tony Tan is a leader in Singapore."
},
{
"source": "webnlg",
"text": "The Banyumasan people are an ethnic group in Java, where the dish ayam penyet is found. The dish originates in Singapore, where Tony Tan is the leader."
}
]
},
{
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[
"Philippines",
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[
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[
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"Philippines"
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[
"Philippines",
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"Moro people"
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"annotations": [
{
"source": "webnlg",
"text": "The language spoken in the Philippines is Philippine Spanish and batchoy is eaten there. Ethnic groups include the llocano people and the Moro people."
},
{
"source": "webnlg",
"text": "Batchoy is eaten in the Philippines where Phillippine Spanish is spoken and ethnic groups include the Ilocano and Moro."
},
{
"source": "webnlg",
"text": "The Moro and the Ilocano people are ethnic groups within the Philippines where batchoy is eaten and the language used is Philippine Spanish."
}
]
},
{
"tripleset": [
[
"Spain",
"CURRENCY",
"Euro"
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[
"Arr\u00f2s negre",
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[
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"Spaniards"
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[
"Spain",
"LEADER_NAME",
"Felipe VI of Spain"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arr\u00f2s negre is a traditional dish from Spain whose leader is Felipe VI and Euro is its currency. Spaniards are the ethnic group of Spain."
},
{
"source": "webnlg",
"text": "The Euro is the currency in Spain, where the leader is Felipe VI and Spaniards are an ethnic group. Spain is also the home of the traditional dish Arr\u00f2s negre."
},
{
"source": "webnlg",
"text": "Arr\u00f2s negre is a dish from Spain where the leader is Felipe VI. Spaniards are an ethnic group of Spain and the currency used is the euro."
}
]
},
{
"tripleset": [
[
"11 Diagonal Street",
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"South Africa"
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[
"South Africa",
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"Coloured"
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{
"source": "webnlg",
"text": "11 Diagonal Street is in South Africa. One South African group is the Coloured."
},
{
"source": "webnlg",
"text": "The address, 11 Diagonal Street is located in South Africa which is home to some coloured people."
}
]
},
{
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[
"200 Public Square",
"LOCATION",
"Cleveland"
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[
"200 Public Square",
"FLOOR_COUNT",
"45"
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"annotations": [
{
"source": "webnlg",
"text": "There are 45 floors at 200 Public Square in Cleveland."
},
{
"source": "webnlg",
"text": "200 Public square, Cleveland, has a floor count of 45."
}
]
},
{
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[
"20 Fenchurch Street",
"LOCATION",
"London"
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[
"London",
"LEADER_TITLE",
"European Parliament"
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"annotations": [
{
"source": "webnlg",
"text": "20 Fenchurch Street is located in London, which is currently led by the European Parliament."
},
{
"source": "webnlg",
"text": "20 Fenchurch Street is located in London, which is led via the European Parliament."
},
{
"source": "webnlg",
"text": "20 Fenchurch Street is located in London which is lead via the European Parliament."
}
]
},
{
"tripleset": [
[
"250 Delaware Avenue",
"LOCATION",
"United States"
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[
"United States",
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"Barack Obama"
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"annotations": [
{
"source": "webnlg",
"text": "250 Delaware Avenue is located in the United States, the leader of which was Barack Obama."
},
{
"source": "webnlg",
"text": "250 Delaware Avenue is located in the United States - which is lead by Barack Obama."
}
]
},
{
"tripleset": [
[
"300 North LaSalle",
"LOCATION",
"Illinois"
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[
"300 North LaSalle",
"FLOOR_COUNT",
"60"
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"annotations": [
{
"source": "webnlg",
"text": "There are 60 floors at 300 North LaSalle in Illinois."
},
{
"source": "webnlg",
"text": "300 North LaSalle in Illinois has 60 floors."
}
]
},
{
"tripleset": [
[
"3Arena",
"LOCATION",
"North Wall, Dublin"
],
[
"3Arena",
"ARCHITECT",
"\"HOK SVE\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "HOK SVE was the architect of the 3Arena at North Wall, Dublin."
},
{
"source": "webnlg",
"text": "3Arena is located at North Wall, Dublin, and was built by HOK SVE."
},
{
"source": "webnlg",
"text": "The 3Arena is located at North Wall, Dublin and was designed by HOK SVE."
}
]
},
{
"tripleset": [
[
"3Arena",
"OWNER",
"Live Nation Entertainment"
],
[
"3Arena",
"LOCATION",
"\"East Link Bridge\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "3Arena at East Link Bridge is owned by Live Nation Entertainment."
},
{
"source": "webnlg",
"text": "3 Arena is located at East Link Bridge and is owned by Live Nation Entertainment."
}
]
},
{
"tripleset": [
[
"3Arena",
"OWNER",
"Live Nation Entertainment"
],
[
"Live Nation Entertainment",
"LOCATION",
"Beverly Hills, California"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "3Arena, owned by Live Nation Entertainment, is located in Beverly Hills, California."
},
{
"source": "webnlg",
"text": "Live Nation Entertainment from Beverly Hills, California, are the owners of 3Arena."
}
]
},
{
"tripleset": [
[
"AC Hotel Bella Sky Copenhagen",
"TENANT",
"Marriott International"
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[
"AC Hotel Bella Sky Copenhagen",
"FLOOR_COUNT",
"23"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Marriott International is the tenant of AC Hotel Bella Sky Copenhagen and it has 23 floors."
},
{
"source": "webnlg",
"text": "The tenant of the AC Hotel Bella Sky Copenhagen, which has 23 floors, is the Marriott International Hotel."
}
]
},
{
"tripleset": [
[
"Addis Ababa City Hall",
"CURRENT_TENANTS",
"\"Government of Addis Ababa\""
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[
"Addis Ababa City Hall",
"HEIGHT",
"\"42 m\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Addis Ababa City Hall is 42 m high and houses the Government of Addis Ababa."
},
{
"source": "webnlg",
"text": "The \" Government of Addis Ababa\" are the current tenants of Addis Ababa City Hall which is 42 metres high."
},
{
"source": "webnlg",
"text": "Addis Ababa City Hall is 42 m high and is occupied by the Government of Addis Adaba."
}
]
},
{
"tripleset": [
[
"Adisham Hall",
"ARCHITECTURAL_STYLE",
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[
"Adisham Hall",
"LOCATION",
"\"Haputale, Sri Lanka\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Admisham Hall's architectural style is Tudor and Jacabian and the building is located in Haputale, Sri Lanka."
},
{
"source": "webnlg",
"text": "Adisham Hall, Haputale, Sri Lanka, is Tudor and Jacabian in style."
}
]
},
{
"tripleset": [
[
"Adisham Hall",
"COUNTRY",
"Sri Lanka"
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[
"Sri Lanka",
"LANGUAGE",
"Tamil language"
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"annotations": [
{
"source": "webnlg",
"text": "Adisham Hall is located in Sri Lanka, where they speak the Tamil language."
},
{
"source": "webnlg",
"text": "Adisham Hall is located in the country of Sri Lanka where they speak Tamil."
}
]
},
{
"tripleset": [
[
"Alan B. Miller Hall",
"BUILDING_START_DATE",
"\"30 March 2007\""
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[
"Alan B. Miller Hall",
"ADDRESS",
"\"101 Ukrop Way\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The construction of Alan B Miller Hall, 101 Ukrop Way, began on 30th March 2007."
},
{
"source": "webnlg",
"text": "Alan B. Miller Hall's building start date was in 30th March 2007 and is located at 101 Ukrop Way."
},
{
"source": "webnlg",
"text": "Alan B. Miller Hall at 101 Ukrop Way opened on March 30, 2007."
}
]
},
{
"tripleset": [
[
"Alan B. Miller Hall",
"OWNER",
"College of William & Mary"
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[
"Alan B. Miller Hall",
"LOCATION",
"Williamsburg, Virginia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alan B Miller Hall is owned by The College of William and Mary and located in Williamsburg, Virginia."
},
{
"source": "webnlg",
"text": "Alan B. Miller Hall in Williamsburg, Virginia is owned by The College of William and Mary."
},
{
"source": "webnlg",
"text": "The College of William and Mary is the owner of the Alan B. Miller Hall in Williamsburg, Virginia."
}
]
},
{
"tripleset": [
[
"Alan B. Miller Hall",
"TENANT",
"Mason School of Business"
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[
"Alan B. Miller Hall",
"ARCHITECT",
"Robert A. M. Stern"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Robert A. M. Stern is the architect of the Alan B. Miller Hall where the Mason School of Business is located."
},
{
"source": "webnlg",
"text": "Robert A M Stern designed Alan B Miller Hall and the Mason School of Business is a tenant there."
},
{
"source": "webnlg",
"text": "Mason School of Business is a tenant of the Alan B. Miller Hall, which was designed by Robert A.M. Stern."
}
]
},
{
"tripleset": [
[
"Ampara Hospital",
"COUNTRY",
"Sri Lanka"
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[
"Ampara Hospital",
"REGION",
"Ampara District"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ampara Hospital is located in the Ampara District in Sri Lanka."
},
{
"source": "webnlg",
"text": "Ampara Hospital is located in the Ampara District of Sri Lanka."
}
]
},
{
"tripleset": [
[
"Ampara Hospital",
"COUNTRY",
"Sri Lanka"
],
[
"Sri Lanka",
"CAPITAL",
"Sri Jayawardenepura Kotte"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sri Jayawardenepura Kotte is the capital of Sri Lanka, where Ampara Hospital is located."
},
{
"source": "webnlg",
"text": "Ampara Hospital is located in Sri Lanka whose capital is Sri Jayawardenepura Kotte."
},
{
"source": "webnlg",
"text": "Ampara Hospital is located in Sri Lanka whose capital is Sri Jayawardenepura."
}
]
},
{
"tripleset": [
[
"Asher and Mary Isabelle Richardson House",
"LOCATION",
"U.S. Route 83"
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"\"88002539\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Asher and Mary Isabelle Richardson House is located on U.S. Route 83 and has the reference number 88002539 in the National Register of Historic Places."
},
{
"source": "webnlg",
"text": "The National Register of Historic Places has the Asher and Mary Isabelle Richardson House, located at U.S. Route 83, referenced at 88002539."
},
{
"source": "webnlg",
"text": "Asher and Mary Isabelle Richardson House has the reference number 88002539 in the National Register of Historic Places, and is located at U.S. Route 83."
}
]
},
{
"tripleset": [
[
"Asilomar Conference Grounds",
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"\"87000823\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asilomar Conference Grounds, Asilomar Blvd., Pacific Grove, California has a reference number in the National Register of Historic Places of: 87000823."
},
{
"source": "webnlg",
"text": "Asilomar Conference Grounds is located at Asilomar Blvd., Pacific Grove, California and it's reference number is 87000823 in the National Register of Historic Places."
},
{
"source": "webnlg",
"text": "\"87000823\" is Asilomar Conference Grounds' reference number in the National Register of Historic Places, and it is located on Asilomar Blvd, Pacific Grove, California."
}
]
},
{
"tripleset": [
[
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[
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"\"1987-02-27\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asilomar Conference Grounds at Pacific Grove, California was added to the National Register of Historic Places on February the 27nd 1987."
},
{
"source": "webnlg",
"text": "Asilomar Conference Grounds are located in Pacific Grove, California and was added to the National Register of Historic Places on February the 27nd 1987."
},
{
"source": "webnlg",
"text": "Asilomar Conference Grounds, Pacific Grove, California was added to the National Register of Historic Places on February 27, 1987."
}
]
},
{
"tripleset": [
[
"Asser Levy Public Baths",
"LOCATION",
"23rd Street (Manhattan)"
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[
"Asser Levy Public Baths",
"YEAR_OF_CONSTRUCTION",
"1904"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asser Levy Public Baths is located on 23rd Street, Manhattan, and was constructed in 1904."
},
{
"source": "webnlg",
"text": "Asser Levy Baths which was built in 1904 is located at 23rd Street in Manhattan."
},
{
"source": "webnlg",
"text": "The Asser Levy Public Baths, constructed in 1904, is located at 23rd Street (Manhattan)."
}
]
},
{
"tripleset": [
[
"Gujarat",
"LEADER_NAME",
"Anandiben Patel"
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[
"Amdavad ni Gufa",
"LOCATION",
"Gujarat"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Anandiben Patel was the leader of Gujurat where Amdavad ni Gufa is located."
},
{
"source": "webnlg",
"text": "Amdavad Gufa is located in Gujarat and it's leader is Anandiben Patel."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Gujarat and it's leader is Anandiben Patel."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"AFFILIATION",
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[
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[
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[
"Acharya Institute of Technology",
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[
"Acharya Institute of Technology",
"STATE",
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[
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, Karnataka, India \u2013 560090. The Institute (president B M Reddy) was established in 2000 and is affiliated with Visvesvaraya Technological University."
},
{
"source": "webnlg",
"text": "President B M Reddy is head of the Acharya Institute of Technology located at \"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\" The Institute was established in the state of Karnataka in 2000 and is affiliated with the Visvesvaraya Technological University."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, Karnataka, India, 560090. It was established in 2000; its president is B.M. Reddy and it is affiliated with the Visvesvaraya Technological University."
}
]
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[
"Acharya Institute of Technology",
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[
"Acharya Institute of Technology",
"COUNTRY",
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[
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[
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[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090 is the location of the Acharya Institute of Technology in India which is affiliated with the Visvesvaraya Technological University. The motto of the Institute which was established in the year 2000 is \"Nurturing Excellence\" and there are 700 postgraduate students."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, India, 560090. It was established in 2000 and its motto is \"Nurturing Excellence\". It has 700 postgraduate students and it is affiliated to the Visvesvaraya Technological University."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology in Bangalore, India was created in 2000, has 700 post graduate students and uses as its motto Nurturing Excellence. It is affiliated with Visvesvaraya Technological University and its full address is In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090."
}
]
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{
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[
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[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology in Bangalore, India was established in 2000 and is affiliated with Visvesvaraya Technological University. The school president is B. M. Reddy and its director is Dr. G. P. Prabhukumar. The school's full address is In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology is in Bangalore, India was established in 2000. The president is B.M. Reddy and the director is Dr. G. P. Prabhukumar. The institute is affiliated with the Visvesvaraya Technological University and the campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\"."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology, affiliated with the Visvesvaraya Technological University, is located in Soldevanahalli, on Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, India (560090). It was established in the year 2000, and its current president is B.M. Reddy, while its director is Dr. G.P. Prabhukumar."
}
]
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{
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[
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[
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"\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\""
],
[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology (president B M Reddy) is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, India \u2013 560090. The Institute was established in 2000, has 700 postgraduate students and is affiliated with Visvesvaraya Technological University of Belgaum."
},
{
"source": "webnlg",
"text": "In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090 is the location of the Acharya Institute of Technology in India. The President of the Institute which was established in the year 2000 is B M Reddy and there are 700 postgraduate students. The Institute is affiliated with the Visvesvaraya Technological University."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology was established in the year 2000 in India. Its campus is in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090 and its president is B. M. Reddy. It has 700 postgraduate students and is affiliated with the Visvesvaraya Technological University in Belgaum."
}
]
},
{
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[
"India",
"LARGEST_CITY",
"Mumbai"
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[
"AWH Engineering College",
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"India"
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[
"AWH Engineering College",
"ACADEMIC_STAFF_SIZE",
"250"
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[
"AWH Engineering College",
"STATE",
"Kerala"
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[
"Kerala",
"HAS_TO_ITS_NORTHWEST",
"Mah\u00e9, India"
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[
"AWH Engineering College",
"CITY",
"\"Kuttikkattoor\""
],
[
"India",
"RIVER",
"Ganges"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AWH Engineering College in Kuttikkattoor, India is in the state of Kerala which has Mahe to its northwest. The school has 250 employees. India is home to the Ganges River and its largest city is Mumbai."
},
{
"source": "webnlg",
"text": "India is known for the river Ganges and the largest city being Mumbai is also home to the state of Kerala which is positioned with Mahe to it's northwest. Kerala is also where AWH Engineering College is located within the city of Kuttikkattoor. The college currently has 250 members of staff."
},
{
"source": "webnlg",
"text": "The AWH Engineering College in Kuttikkattoor, Kerala, India has an academic staff of 250. Kerala has Mah\u00e9, Indiato its northwest. Mumbai is the largest city in India. The river Ganges runs through India."
}
]
},
{
"tripleset": [
[
"Romania",
"ETHNIC_GROUP",
"Germans of Romania"
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[
"Romania",
"LEADER_TITLE",
"Prime Minister of Romania"
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[
"Romania",
"LEADER_NAME",
"Klaus Iohannis"
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[
"Romania",
"CAPITAL",
"Bucharest"
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[
"1 Decembrie 1918 University",
"CITY",
"Alba Iulia"
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[
"1 Decembrie 1918 University",
"COUNTRY",
"Romania"
],
[
"Romania",
"ANTHEM",
"De\u0219teapt\u0103-te, rom\u00e2ne!"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "1 Decembrie 1918 University is located in the city of Alba Iulia, Romania. The country's capital is Bucharest and the leader (title Prime Minister of Romania) is Klaus Iohannis. An ethnic group of the country is The Germans of Romania and the country's national anthem is De\u0219teapt\u0103-te, rom\u00e2ne!"
},
{
"source": "webnlg",
"text": "The national anthem of Romania is \"Desteapta-te, romane!\" and the capital city is Bucharest. The country is lead by Prime Minister Klaus Iohannis and is the location of the 1 Decembrie 1918 University in the city of Alba Iulia. One of the ethnic groups of the country is the Germans of Romania."
},
{
"source": "webnlg",
"text": "The 1 Decembrie 1918 University is located in Alba Iulia, Romania. Romania's capital is Bucharest; its leader is Prime Minister Klaus Iohannis and its ethnic group is the Germans of Romania. Romania's capital is Bucharest and its anthem is Desteapta-te, Romane!"
}
]
},
{
"tripleset": [
[
"Visvesvaraya Technological University",
"CITY",
"Belgaum"
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[
"Acharya Institute of Technology",
"DIRECTED_BY",
"\"Dr. G. P. Prabhukumar\""
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[
"Acharya Institute of Technology",
"ESTABLISHED",
"2000"
],
[
"Acharya Institute of Technology",
"STATE",
"Karnataka"
],
[
"Acharya Institute of Technology",
"COUNTRY",
"\"India\""
],
[
"Acharya Institute of Technology",
"CAMPUS",
"\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\""
],
[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "In the year 2000 the Acharya Institute of Technology was established in the state of Karnataka, India. The current location is \"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\" The Director is Dr G P Prabhukumar and it is affiliated with the Visvesvaraya Technological University in the city of Belgaum."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, Karnatka, 560090. It was established in 2000 and its director is Dr G P Prabhukumar. It is affiliated to the Visvesvaraya Technological University in Belgaum."
},
{
"source": "webnlg",
"text": "Acharya Institute of Technology is affiliated with Visvesvaraya Technological University which is in Belgium. The institute itself is in India's Karnataka state and its full address is In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090. It was created in 2000 and its director is Dr. G. P. Prabhukumar."
}
]
},
{
"tripleset": [
[
"Alan Bean",
"WAS_A_CREW_MEMBER_OF",
"Apollo 12"
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[
"Alan Bean",
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"United States"
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[
"Apollo 12",
"OPERATOR",
"NASA"
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[
"Alan Bean",
"OCCUPATION",
"Test pilot"
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[
"Alan Bean",
"BIRTH_PLACE",
"Wheeler, Texas"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alan Bean was an American born in Wheeler, Texas. He served as a test pilot and became a crew member of Apollo 12, which was operated by NASA."
},
{
"source": "webnlg",
"text": "Apollo 12 was operated by NASA and its crew members included American national Alan Bean. He was born in Wheeler, Texas and served as a test pilot."
},
{
"source": "webnlg",
"text": "Alan Bean, a US national, was born in Wheeler, Texas. He served as a test pilot before becoming part of the NASA operated Apollo 12 mission as a crew member."
}
]
},
{
"tripleset": [
[
"Alan Shepard",
"ALMA_MATER",
"\"NWC, M.A. 1957\""
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[
"Alan Shepard",
"DEATH_PLACE",
"California"
],
[
"Alan Shepard",
"BIRTH_PLACE",
"New Hampshire"
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[
"Alan Shepard",
"DATE_OF_DEATH",
"\"1998-07-21\""
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[
"Alan Shepard",
"DATE_OF_BIRTH",
"\"1923-11-18\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alan Shepard, born in New Hampshire on November 18, 1923, graduated from NWC in 1957 with a M.A. He died in California on July 21, 1998."
},
{
"source": "webnlg",
"text": "Alan Shepard was born in New Hampshire on the 18th of November, 1923. He graduated from NWC in 1957 with a M.A. On the 21st of July, 1998, he passed away in California."
},
{
"source": "webnlg",
"text": "Alan Shepard was born on the 18th of November, 1923 in New Hampshire and passed away on the 21st of July, 1998 in California. He graduated with a M.A. from NWC in 1957."
}
]
},
{
"tripleset": [
[
"Alan Shepard",
"STATUS",
"\"Deceased\""
],
[
"Alan Shepard",
"ALMA_MATER",
"\"NWC, M.A. 1957\""
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[
"Alan Shepard",
"DEATH_PLACE",
"California"
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[
"Alan Shepard",
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"Test pilot"
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[
"Alan Shepard",
"BIRTH_PLACE",
"New Hampshire"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alan Shepard, born in New Hampshire, has died in California. After graduating from NWC, MA in 1957 he served as a test pilot."
},
{
"source": "webnlg",
"text": "Alan Shepard has died in California. He was born in New Hampshire and went to school at NWC, graduating with an MA in 1957, He served as a test pilot."
},
{
"source": "webnlg",
"text": "Alan Shepard has died in California. He was born in New Hampshire and graduated from NWC MA in 1957. He served as a test pilot."
}
]
},
{
"tripleset": [
[
"Alan Shepard",
"WAS_A_CREW_MEMBER_OF",
"Apollo 14"
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[
"Alan Shepard",
"STATUS",
"\"Deceased\""
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[
"Alan Shepard",
"WAS_SELECTED_BY_NASA",
"1959"
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[
"Alan Shepard",
"BIRTH_PLACE",
"New Hampshire"
],
[
"Apollo 14",
"OPERATOR",
"NASA"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The late Alan Shepard was born in New Hampshire, and chosen by NASA in 1959 a a crew member of Apollo 14."
},
{
"source": "webnlg",
"text": "The late Alan Shepard was born in New Hampshire. He was selected by NASA in 1959 and served as a crew member of NASA operated Apollo 14."
},
{
"source": "webnlg",
"text": "Alan Shepard was originally from New Hampshire and joined NASA in 1959 where he became a member of the Apollo 14 crew. Mr Shepard passed away."
}
]
},
{
"tripleset": [
[
"Apollo 12",
"BACKUP_PILOT",
"Alfred Worden"
],
[
"Alan Bean",
"WAS_A_CREW_MEMBER_OF",
"Apollo 12"
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[
"Apollo 12",
"OPERATOR",
"NASA"
],
[
"Alan Bean",
"TIME_IN_SPACE",
"\"100305.0\"(minutes)"
],
[
"Apollo 12",
"COMMANDER",
"David Scott"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alan Bean (born in Wheeler, Texas) was a crew member of Apollo 12 operated by NASA and was in space 100305 minutes. Apollo 12 was commanded by David Scott and Alfred Worden was a backup pilot."
},
{
"source": "webnlg",
"text": "Alfred Worden was a backup pilot on the NASA operated Apollo 12 on which David Scott the commander and Alan Bean, who spent a total of 100305 minutes in space, was a crewman."
}
]
},
{
"tripleset": [
[
"Distinguished Service Medal (United States Navy)",
"HIGHER",
"Department of Commerce Gold Medal"
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[
"Alan Shepard",
"DEATH_PLACE",
"California"
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[
"Alan Shepard",
"BIRTH_PLACE",
"New Hampshire"
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[
"Alan Shepard",
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"\"1923-11-18\""
],
[
"Alan Shepard",
"AWARD",
"Distinguished Service Medal (United States Navy)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alan Shepard (born November 18th, 1923, New Hampshire) was awarded the United States Navy Distinguished Service Medal which is higher than the department of commerce gold medal has died in California."
},
{
"source": "webnlg",
"text": "Alan Shepard was born on 18 November 1923 in New Hampshire. He was awarded the Distinguished Service Medal, which is higher than the Department of Commerce Gold Medal, by the US Navy. He died in California."
},
{
"source": "webnlg",
"text": "Alan Shepard was born in New Hampshire on 18 November 1923. Before his death in California he had been awarded the Distinguished Service Medal by the US Navy an award higher than the Department of Commerce Gold Medal."
}
]
},
{
"tripleset": [
[
"Elliot See",
"ALMA_MATER",
"University of Texas at Austin"
],
[
"Elliot See",
"BIRTH_PLACE",
"Dallas"
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"University of Texas at Austin",
"COMPETE_IN",
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"University of Texas at Austin",
"AFFILIATION",
"University of Texas System"
],
[
"University of Texas at Austin",
"PRESIDENT",
"Gregory L. Fenves"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Elliot See was born in Dallas and later attended the University of Texas at Austin. The University of Texas at Austin is affiliated with University of Texas System and is competing in the Big 12 Conference. Nowadays, Gregory L. Fenves is the President of the University."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas and attended the University of Texas at Austin which is affiliated with the University of Texas System and competed in the Big 12 Conference in Austin. Gregory L. Fenves is the appointed president of the University of Texas at Austin."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas and was a student at University of Texas at Austin. This University, whose president is Gregory L. Fenves, is affiliated with the University of Texas System. The University of Texas at Austin competes in the Big 12 Conference."
}
]
},
{
"tripleset": [
[
"Elliot See",
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"University of Texas at Austin"
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[
"Elliot See",
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[
"Elliot See",
"DEATH_PLACE",
"St. Louis"
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[
"Elliot See",
"BIRTH_PLACE",
"Dallas"
],
[
"Elliot See",
"DATE_OF_DEATH",
"\"1966-02-28\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Elliot See, a graduate of the University of Texas at Austin, passed away on 28th February 1966 in St. Louis. He was born in Dallas."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas and attended the University of Texas at Austin. He died in St Louis on 28 February 1966."
},
{
"source": "webnlg",
"text": "Elliot See, who was born on the 28th February 1966 in Dallas, has died in St Louis. He will be remembered as a great student at University of Texas at Austin."
}
]
},
{
"tripleset": [
[
"Elliot See",
"ALMA_MATER",
"University of Texas at Austin"
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[
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[
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"PARTS_TYPE",
"List of counties in Texas"
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[
"Elliot See",
"BIRTH_PLACE",
"Dallas"
],
[
"University of Texas at Austin",
"COMPETE_IN",
"Big 12 Conference"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Elliot See was born in Dallas, which is a county in Texas. He attended the University of Texas at Austin, which is affiliated to the University of Texas system. The University of Texas at Austin will be part of the Big 12 Conference competition."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas, Texas and attended the University of Texas (affiliated to the University of Texas system) which competed in the Big 12 conference."
},
{
"source": "webnlg",
"text": "Elliot See, who was born in Dallas, Texas, attended the University of Texas at Austin (affiliated to the University of Texas system) which will compete in the big 12 conference."
}
]
},
{
"tripleset": [
[
"Elliot See",
"ALMA_MATER",
"University of Texas at Austin"
],
[
"University of Texas at Austin",
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[
"Elliot See",
"BIRTH_PLACE",
"Dallas"
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[
"Elliot See",
"DEATH_PLACE",
"St. Louis"
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[
"Elliot See",
"STATUS",
"\"Deceased\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Elliot See was born in Dallas and studied at the University of Texas in Austin (which is affiliated to the university of Texas system). He died in St. Louis."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas and studied at Austin University (part of the university of Texas system). He died in St. Louis."
},
{
"source": "webnlg",
"text": "Elliot See , who was born in Dallas , has died in St Louis. He graduated from the University of Texas at Austin which is affiliated to the University of Texas system."
}
]
},
{
"tripleset": [
[
"William Anders",
"DATE_OF_RETIREMENT",
"\"1969-09-01\""
],
[
"Apollo 8",
"COMMANDER",
"Frank Borman"
],
[
"William Anders",
"WAS_A_CREW_MEMBER_OF",
"Apollo 8"
],
[
"Apollo 8",
"BACKUP_PILOT",
"Buzz Aldrin"
],
[
"Apollo 8",
"OPERATOR",
"NASA"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "William Anders was a member of the Apollo 8 crew (operated by NASA) and he retired on September 1st, 1969. Frank Borman was the commander of Apollo 8 and Buzz Aldrin was a back up pilot."
},
{
"source": "webnlg",
"text": "William Anders, who retired on September 1st, 1969, was a crew member on Apollo 8 and served under commander Frank Borman. Apollo 8 was operated by NASA with Buzz Aldrin as backup pilot."
},
{
"source": "webnlg",
"text": "William Anders joined NASA and was a member of Apollo 8 along with Frank Borman as Commander and Buzz Aldrin as back up pilot. William Anders retired on September 1st 1969."
}
]
},
{
"tripleset": [
[
"A.C. Cesena",
"GROUND",
"Cesena"
],
[
"A.C. Cesena",
"LEAGUE",
"Serie B"
],
[
"A.C. Cesena",
"NUMBER_OF_MEMBERS",
"23900"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.C. Cesena, in the Serie B League, has 23900 members and is located is Cesena."
},
{
"source": "webnlg",
"text": "AC Cesena's ground is in Cesena and they have 23900 members. The club plays in Serie B."
},
{
"source": "webnlg",
"text": "A.C. Cesena, with 23900 members, is in the Serie B league and has a ground in Cesena."
}
]
},
{
"tripleset": [
[
"A.C. Cesena",
"MANAGER",
"Massimo Drago"
],
[
"Massimo Drago",
"CLUB",
"S.S.D. Potenza Calcio"
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[
"Massimo Drago",
"CLUB",
"Calcio Catania"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Massimo Drago played for the club SSD Potenza Calcio and his own club was Calcio Catania. He is currently managing AC Cesena."
},
{
"source": "webnlg",
"text": "Massimo Drago manages A.C. Cesena and played for the club SSD Potenza Calcio. He is also in club Calcio Catania."
},
{
"source": "webnlg",
"text": "Massimo Drago played for S.S.D. Potenza Calcio and now plays for Calcio Catania while managing A.C. Cesena."
}
]
},
{
"tripleset": [
[
"A.C. Lumezzane",
"LEAGUE",
"\"Lega Pro/A\""
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[
"A.C. Lumezzane",
"SEASON",
"2014"
],
[
"A.C. Lumezzane",
"NUMBER_OF_MEMBERS",
"4150"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AC Lumezzane has 4150 members and is in the Lega Pro/A league where they played in the 2014 season."
},
{
"source": "webnlg",
"text": "I am interested in the 2014 A.C. Lumezzane season. It plays in Lega Pro/A. and has 4150 members."
},
{
"source": "webnlg",
"text": "A.C Lumezzane, with 4150 members, play in the Lega Pro/A, participating in the 2014 season."
}
]
},
{
"tripleset": [
[
"A.D. Isidro Metap\u00e1n",
"GROUND",
"Estadio Jorge Calero Su\u00e1rez"
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[
"A.D. Isidro Metap\u00e1n",
"FULL_NAME",
"\"Isidro Metap\u00e1n\""
],
[
"A.D. Isidro Metap\u00e1n",
"NUMBER_OF_MEMBERS",
"10000"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.D. Isidro Metap\u00e1n are at Estadio Jorge Calero Su\u00e1rez whose full name is Isidro Metapan and has 10000 members."
},
{
"source": "webnlg",
"text": "A.D. Isidro Metapan, (full name, Isidro Metapan), has 10000 members and play their home games at the Estadio Jorge Caler0 Suarez stadium."
},
{
"source": "webnlg",
"text": "Isidro Metapan (A.D. Isidro Metapan) has 10000 members and are at Estadio Jorge Calero Suarez."
}
]
},
{
"tripleset": [
[
"A.E Dimitra Efxeinoupolis",
"LOCATION",
"Greece"
],
[
"Greece",
"LEADER",
"Nikos Voutsis"
],
[
"Greece",
"LEADER",
"Prokopis Pavlopoulos"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The A.E Dimitra Efxeinoupolis club is located in Greece, where there are leaders called Nikos Voutsis and Prokopis Pavlopoulos."
},
{
"source": "webnlg",
"text": "There are two prominent leaders in Greece: Nikos Voutsis and Prokopis Pavlopoulos as well the A.E. Dimitra Efxeinoupolis is located there."
},
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis is located in Greece, the leader of the country is Nikos Voutsis and/or Prokopis Pavloopoulos."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"MANAGER",
"Stuart Parker (footballer)"
],
[
"Stuart Parker (footballer)",
"CLUB",
"Drogheda United F.C."
],
[
"Stuart Parker (footballer)",
"CLUB",
"Runcorn F.C. Halton"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The manager of A.F.C. Blackpool is Stuart Parker who is with Drogheda United F.C and plays for Runcorn F.C. Halton."
},
{
"source": "webnlg",
"text": "Footballer Stuart Parker, the manager of AFC Blackpool, was at Drogheda United FC and plays for Runcorn F.C. Halton."
},
{
"source": "webnlg",
"text": "Stuart Parker is a footballer with the Drogheda United F.C., also plays for Runcorn F.C. Halton and has managed AFC Blackpool."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"MANAGER",
"Stuart Parker (footballer)"
],
[
"Stuart Parker (footballer)",
"CLUB",
"Drogheda United F.C."
],
[
"Stuart Parker (footballer)",
"CLUB",
"Stockport County F.C."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The manager of A.F.C. Blackpool is Stuart Parker who was at Drogheda United FC and once played for Stockport County FC."
},
{
"source": "webnlg",
"text": "AFC Blackpool have had Stuart Parker as their manager. He plays for Drogheda United F.C. and Stockport County F.C."
},
{
"source": "webnlg",
"text": "Stuart Parker (football player of Drogheda United F.C.) managed AFC Blackpool. He was a member of Stockport County F.C."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"MANAGER",
"Stuart Parker (footballer)"
],
[
"Stuart Parker (footballer)",
"CLUB",
"KV Mechelen"
],
[
"Stuart Parker (footballer)",
"CLUB",
"Irlam Town F.C."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Stuart Parker is a member of the Iriam Town F.C. as well as the manager of A.F.C. Blackpool and has also represented the KV Mechelen club."
},
{
"source": "webnlg",
"text": "AFC Blackpool have had Stuart Parker as their manager. He has represented the club KV Mechelen and is a member of the Irlam Town F.C."
},
{
"source": "webnlg",
"text": "AFC Blackpool have had Stuart Parker as their manager. He is attached to Irlam Town Football Club and is part of the KV Mechelen club."
}
]
},
{
"tripleset": [
[
"A.F.C. Fylde",
"GROUND",
"Warton, Fylde"
],
[
"A.F.C. Fylde",
"NUMBER_OF_MEMBERS",
"3180"
],
[
"A.F.C. Fylde",
"FULL_NAME",
"\"Association Football Club Fylde\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AFC Fylde has the full name \"Association Football Club Fylde,\" they have 3180 members and their ground is located in Warton, Fylde."
},
{
"source": "webnlg",
"text": "Association Football Club Fylde, abbreviated to A.F.C Fylde, has 3180 members and its ground is Warton Fylde."
}
]
},
{
"tripleset": [
[
"A.S. Livorno Calcio",
"MANAGER",
"Christian Panucci"
],
[
"Christian Panucci",
"CLUB",
"Genoa C.F.C."
],
[
"Christian Panucci",
"CLUB",
"Real Madrid C.F."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Christian Panucci, who has been attached to the Genoa CFC and Real Madrid CF clubs, manages A.S. Livorno Calcio."
},
{
"source": "webnlg",
"text": "A.S. Livorno Calcio is managed by Christian Panucci. is was attached to the Real Madrid CF club. He is currently attached to the club Genoa CFC."
},
{
"source": "webnlg",
"text": "Christian Panucci was attached to the Real Madrid CF club, he managed A.S. Livorno Calcio and played football for Genoa C.F.C."
}
]
},
{
"tripleset": [
[
"A.S. Roma",
"FULL_NAME",
"\"Associazione Sportiva Roma S.p.A.\""
],
[
"A.S. Roma",
"GROUND",
"\"Rome, Italy\""
],
[
"A.S. Roma",
"NUMBER_OF_MEMBERS",
"70634"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The \"Associazione Sportiva Roma S.p.A.\" is the non-abbreviated name of A.S. Roma which has 70634 members and a home ground in Rome, Italy."
},
{
"source": "webnlg",
"text": "A.S.Roma have 70634 members, play their home games in Rome and their full title is Associazione Roma S.p.A."
},
{
"source": "webnlg",
"text": "A.S. Roma, or Associazione Sportiva Roma S.p.A., has 70634 members and its ground is in Rome, Italy."
}
]
},
{
"tripleset": [
[
"AEK Athens F.C.",
"LEAGUE",
"Superleague Greece"
],
[
"AEK Athens F.C.",
"GROUND",
"Athens"
],
[
"AEK Athens F.C.",
"NUMBER_OF_MEMBERS",
"69618"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AEK Athens F.C. ground is in Athens, they have 69618 members and they compete in the Superleague Greece."
},
{
"source": "webnlg",
"text": "AEK Athens F.C., with 69618 members, play in the Superleague Greece. Their ground is in Athens."
},
{
"source": "webnlg",
"text": "AEK Athens F.C. has 69618 members, their ground is in Athens and they play in the Superleague of Greece."
}
]
},
{
"tripleset": [
[
"AFC Ajax (amateurs)",
"NUMBER_OF_MEMBERS",
"5000"
],
[
"AFC Ajax (amateurs)",
"LEAGUE",
"Hoofdklasse"
],
[
"AFC Ajax (amateurs)",
"SEASON",
"2014"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AFC Ajax (amateurs) has 5000 members play in the Hoofdklasse league and competed in the 2014 season."
},
{
"source": "webnlg",
"text": "AFC Ajax (Amateurs) competed in the 2014 season, they play in the Hoofdklasse league and the main team have 5000 members."
},
{
"source": "webnlg",
"text": "AFC Ajax, who played in the 2014 season, have 5000 members and play in the Hoofdklasse league."
}
]
},
{
"tripleset": [
[
"AZAL PFK",
"LEAGUE",
"Azerbaijan Premier League"
],
[
"Azerbaijan Premier League",
"CHAMPIONS",
"Qaraba\u011f FK"
],
[
"AZAL PFK",
"GROUND",
"AZAL Arena"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AZAL PFK play their home matches at the AZAL Arena, and are in the Azerbaijan Premier League, who's championships name is the Qarabag FK."
},
{
"source": "webnlg",
"text": "AZAL Arena is the ground of AZAL PFK and competes in The Azerbaijan Premier League, which champions are Qarabag FK."
}
]
},
{
"tripleset": [
[
"AZ Alkmaar",
"FULL_NAME",
"\"Alkmaar Zaanstreek\""
],
[
"AZ Alkmaar",
"SEASON",
"2014"
],
[
"AZ Alkmaar",
"NUMBER_OF_MEMBERS",
"17023"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alkmaar Zaanstreek is the full name of AZ Alkmaar who played in the 2014 season and has 17023 members."
},
{
"source": "webnlg",
"text": "\"Alkmaar Zaanstreek\" is the full name of AZ Alkmaar who played in the 2014 season and has 17023 members."
},
{
"source": "webnlg",
"text": "Alkmaar Zaanstreek (AZ Alkmaar) has 170723 members and played in the 2014 season."
}
]
},
{
"tripleset": [
[
"AZ Alkmaar",
"MANAGER",
"John van den Brom"
],
[
"John van den Brom",
"CLUB",
"Vitesse Arnhem"
],
[
"John van den Brom",
"CLUB",
"De Graafschap"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "John van den Brom manages the AZ Alkmaair and plays for Vitesse Arnhem and De Graafschap."
},
{
"source": "webnlg",
"text": "John van den Brom, the manager of AZ Alkmaar, is in Vitesse Arnhem and plays for De Graafschap."
},
{
"source": "webnlg",
"text": "John van den Brom has been manager of AZ Alkmaar, he is in Vitesse Arnhem and plays for De Graafschap."
}
]
},
{
"tripleset": [
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"LEAGUE",
"Campeonato Brasileiro S\u00e9rie C"
],
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"GROUND",
"Est\u00e1dio Municipal Coaracy da Mata Fonseca"
],
[
"Campeonato Brasileiro S\u00e9rie C",
"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play at their ground Estadio Minicipal Coaracy da Mata Fonseca in the Campeonato Brasileiro S\u00e9rie C league. The champions of this league are Vila Nova Futebol Clube."
},
{
"source": "webnlg",
"text": "Agremiacao Sportiva Arapiraquense's ground is the Estadio Minicipal Coaracy da Mata Fonseca and they play in the Campeonato Brasileiro S\u00e9rie C league. The Serie C champions have been Vila Nova Futebol Clube."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense plays at Est\u00e1dio Municipal Coaracy da Mata Fonseca. The club play in the Campeonato Brasileiro S\u00e9rie C league and the champions of the league were the Vila Nova Futebol Clube."
}
]
},
{
"tripleset": [
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"LEAGUE",
"Campeonato Brasileiro S\u00e9rie C"
],
[
"Campeonato Brasileiro S\u00e9rie C",
"COUNTRY",
"Brazil"
],
[
"Campeonato Brasileiro S\u00e9rie C",
"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league in Brazil. Vila Nova Futebol Clube are the champions of this league."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league in brazil. The Vila Nova Futebol Clube were champions at the Campeonato Brasileiro S\u00e9rie C."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league, that is based in Brazil. The champions are Vila Nova Futebol Clube."
}
]
},
{
"tripleset": [
[
"Akron Summit Assault",
"GROUND",
"St. Vincent\u2013St. Mary High School"
],
[
"Akron Summit Assault",
"LEAGUE",
"Premier Development League"
],
[
"Premier Development League",
"CHAMPIONS",
"K-W United FC"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "St Vincent-St Mary High School is the ground of Akron Summit Assault who play in the Premier Development League, of which K-W United FC have been champions."
},
{
"source": "webnlg",
"text": "Akron Summit Assault who play in the Premier Development League, won by K-W United FC, have their home ground at St. Vincent-St. Mary High School."
},
{
"source": "webnlg",
"text": "St Vincent-St Mary High School is the ground of Akron Summit Assault who play in the Premier Development League which K-W United FC have been champions of."
}
]
},
{
"tripleset": [
[
"Akron Summit Assault",
"NUMBER_OF_MEMBERS",
"3000"
],
[
"Akron Summit Assault",
"LEAGUE",
"Premier Development League"
],
[
"Premier Development League",
"CHAMPIONS",
"K-W United FC"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "K-W United FC were champions of the Premier Development League where Akron Summit Assault (which has 3000 members) also play."
},
{
"source": "webnlg",
"text": "3000 member club Akron Summit Assault play in the Premier Development League alongside former champions K-W United FC."
},
{
"source": "webnlg",
"text": "Akron Summit Assault has 3000 members and plays in the Premier Development League of which K-W United FC were champions."
}
]
},
{
"tripleset": [
[
"Italy",
"DEMONYM",
"Italians"
],
[
"Italy",
"LEADER",
"Sergio Mattarella"
],
[
"A.S. Gubbio 1910",
"GROUND",
"Italy"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The ground of A.S. Gubbio 1910 is located in Italy where Sergio Mattarella is a leader of the Italian people."
},
{
"source": "webnlg",
"text": "The ground of A.S. Gubbio 1910 is located in Italy which is lead by Sergio Mattarella and inhabited by Italians."
},
{
"source": "webnlg",
"text": "The ground of A.S. Gubbio 1910 is located in Italy, country of Italians where the leader is Sergio Mattarella."
}
]
},
{
"tripleset": [
[
"Olympic Stadium (Athens)",
"LOCATION",
"Athens"
],
[
"Athens",
"MAYOR",
"Giorgos Kaminis"
],
[
"AEK Athens F.C.",
"GROUND",
"Olympic Stadium (Athens)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The ground for AEK Athens FC is the Olympic Stadium (Athens) in Athens whose mayor is Giorgos Kaminis."
},
{
"source": "webnlg",
"text": "Home ground of AEK Athens FC, the Olympic Stadium is located in Athens, who's mayor is Giorgos Kaminis."
},
{
"source": "webnlg",
"text": "The Olympic Stadium in Athens is the home ground of AEK Athens FC. The mayor of Athens is Giorgos Kaminis."
}
]
},
{
"tripleset": [
[
"11th Mississippi Infantry Monument",
"COUNTRY",
"\"United States\""
],
[
"Adams County, Pennsylvania",
"HAS_TO_ITS_SOUTHEAST",
"Carroll County, Maryland"
],
[
"11th Mississippi Infantry Monument",
"ESTABLISHED",
"2000"
],
[
"Adams County, Pennsylvania",
"HAS_TO_ITS_NORTH",
"Cumberland County, Pennsylvania"
],
[
"11th Mississippi Infantry Monument",
"CATEGORY",
"Contributing property"
],
[
"11th Mississippi Infantry Monument",
"MUNICIPALITY",
"Gettysburg, Pennsylvania"
],
[
"11th Mississippi Infantry Monument",
"LOCATION",
"Adams County, Pennsylvania"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Municipality of Gettysburg is located in Adams County, Pennsylvania which lies to the south of Cumberland County and to the northwest of Carroll County, Maryland. It is the location of the 11th Mississippi Infantry monument which was established in 2000 and categorised as a contributing property within the United States."
},
{
"source": "webnlg",
"text": "The 11th Mississippi Infantry monument which was erected in the municipality of Gettysburg, Adams County in 2000 is categorised as a contributing property. Adams County is located south of Cumberland County in the same state, and Carrol County, Maryland is situated to the southeast."
}
]
},
{
"tripleset": [
[
"Atat\u00fcrk Monument (\u0130zmir)",
"DESIGNER",
"Pietro Canonica"
],
[
"Turkey",
"LEADER_NAME",
"Ahmet Davuto\u011flu"
],
[
"Turkey",
"CAPITAL",
"Ankara"
],
[
"Atat\u00fcrk Monument (\u0130zmir)",
"MATERIAL",
"\"Bronze\""
],
[
"Turkey",
"CURRENCY",
"Turkish lira"
],
[
"Atat\u00fcrk Monument (\u0130zmir)",
"INAUGURATION_DATE",
"\"1932-07-27\""
],
[
"Atat\u00fcrk Monument (\u0130zmir)",
"LOCATION",
"Turkey"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ankara is the capital of Turkey where the currency is the Turkish lira and the leader is Ahmet Davutoglu. Izmir is located in the country and is the site of the bronze Ataturk Monument which was inaugurated in Izmir on 27 July 1932, having been designed by Pietro Canonica."
},
{
"source": "webnlg",
"text": "The Atat\u00fcrk Monument is located in \u0130zmir, in the country of Turkey, whose leader is Ahmet Davuto\u011flu. The monument is made of bronze, was designed by Pietro Canonica and was inaugurated on the 27th of July, 1932. Turkey's capital city is Ankara and the currency is the Turkish lira."
},
{
"source": "webnlg",
"text": "The Turkish lira is the currency of Turkey where Ahmet Davutoglu is the leader and Ankara is the capital. The Ataturk Monument designed in bronze by Pietro Canonica is located in Izmir and was inaugurated on 27 July 1932."
}
]
},
{
"tripleset": [
[
"Azerbaijan",
"CAPITAL",
"Baku"
],
[
"Baku Turkish Martyrs' Memorial",
"MATERIAL",
"\"Red granite and white marble\""
],
[
"Azerbaijan",
"LEADER_TITLE",
"Prime Minister of Azerbaijan"
],
[
"Baku Turkish Martyrs' Memorial",
"DEDICATED_TO",
"\"Ottoman Army soldiers killed in the Battle of Baku\""
],
[
"Baku Turkish Martyrs' Memorial",
"LOCATION",
"Azerbaijan"
],
[
"Baku Turkish Martyrs' Memorial",
"DESIGNER",
"\"H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner\""
],
[
"Azerbaijan",
"LEGISLATURE",
"National Assembly (Azerbaijan)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The National Assembly is the official legislature of Azerbaijan which is lead by a Prime Minister. The capital city is Baku which is the location of the memorial designed in red granite and white marble by Huseyin Butuner and Hilmi Guner. The memorial is dedicated to the Ottoman army soldiers who were killed in the Battle of Baku."
},
{
"source": "webnlg",
"text": "Baku is the capital of Azerbaijan where the leader's title is Prime Minister and the legislature is the National Assembly. The city is the location of the Baku Turkish Martyrs memorial which was designed by Huseyin Butuner and Hilmi Guner in red granite and white marble. The memorial is dedicated to the soldiers of the Ottoman army who died in the Battle of Baku."
},
{
"source": "webnlg",
"text": "Huyseyin Butuner and Hilmi Guner designed the Baku Turkish Martyrs' Memorial. It is located in Baku, Azerbaijan, which has legislature of National Assembly, and led by the Prime Minster. The memorial is made from red granite and white marble, and is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku."
}
]
},
{
"tripleset": [
[
"A.C. Cesena",
"LEAGUE",
"Serie B"
],
[
"Serie B",
"CHAMPIONS",
"Carpi F.C. 1909"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AC Cesena are in the Serie B league, the previous champions of which are Carpi FC 1909."
},
{
"source": "webnlg",
"text": "A.C. Cesana play in Serie B, in which Carpi FC 1909 are the champions."
},
{
"source": "webnlg",
"text": "AC Cesena are in the Serie B league, previously won by Carpi F.C. 1909."
}
]
},
{
"tripleset": [
[
"A.C. Chievo Verona",
"GROUND",
"Stadio Marc'Antonio Bentegodi"
],
[
"A.C. Chievo Verona",
"LEAGUE",
"Serie A"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AC Chievo Verona play in the Serie A league and their home ground is Stadio Marc Antonio Bentegodi."
},
{
"source": "webnlg",
"text": "The home ground of A.C. Chievo Verona is Stadio Marc'Antonio Bentegodi and the club is in the league, Serie A."
},
{
"source": "webnlg",
"text": "The home ground of A.C. Chievo Verona is Stadio Marc'Antonio Bentegodi, he played in Serie A."
}
]
},
{
"tripleset": [
[
"A.C. Lumezzane",
"GROUND",
"Italy"
],
[
"Italy",
"LEADER",
"Pietro Grasso"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.C. Lumezzane's ground is in Italy where the leader is Pietro Grasso."
},
{
"source": "webnlg",
"text": "A.C. Lumezzane play in italy where the leader is Pietro Grasso."
},
{
"source": "webnlg",
"text": "A.C. Lumezzane play in Italy, where the leader is Pietro Grasso."
}
]
},
{
"tripleset": [
[
"A.D. Isidro Metap\u00e1n",
"MANAGER",
"Jorge Humberto Rodr\u00edguez"
],
[
"Jorge Humberto Rodr\u00edguez",
"CLUB",
"C.D. FAS"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Jorge Humberto Rodr\u00edguez manages the A.D. Isidro Metapan, he played for the C.D. FAS."
},
{
"source": "webnlg",
"text": "Jorge Humberto Rodriguez manages A.D. Isidro Metapan. He is in the C.D. FAS."
},
{
"source": "webnlg",
"text": "Jorge Humberto Rodr\u00edguez manages the A.D. Isidro Metapan and plays for C.D. FAS."
}
]
},
{
"tripleset": [
[
"A.E Dimitra Efxeinoupolis",
"NUMBER_OF_MEMBERS",
"1500"
],
[
"A.E Dimitra Efxeinoupolis",
"LEAGUE",
"A EPSTH 2nd GROUP"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.E. Dimitra Efeinoupolis has 1500 members and is in the EPSTH 2nd GROUP league."
},
{
"source": "webnlg",
"text": "With 1500 members, A.E Dimitra Efxeinoupolis, play in the A EPSTH 2nd GROUP."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"MANAGER",
"Stuart Parker (footballer)"
],
[
"Stuart Parker (footballer)",
"CLUB",
"Runcorn F.C. Halton"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The manager of A.F.C. Blackpool is Stuart Parker (footballer) who plays for Runcorn F.C. Halton."
},
{
"source": "webnlg",
"text": "AFC Blackpool have had Stuart Parker as their manager who was a football player for Runcorn FC Halton."
},
{
"source": "webnlg",
"text": "The manager of A.F.C. Blackpool is Stuart Parker (footballer), he played for Runcorn FC Halton."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"MANAGER",
"Stuart Parker (footballer)"
],
[
"Stuart Parker (footballer)",
"CLUB",
"Sparta Rotterdam"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The manager of A.F.C. Blackpool is Stuart Parker (footballer), who plays at the Sparta Rotterdam club."
},
{
"source": "webnlg",
"text": "Stuart Parker, who plays at the Sparta Rotterdam Club, has also managed AFC Blackpool."
},
{
"source": "webnlg",
"text": "AFC Blackpool have had the manager Stuart Parker who currently plays for Sparta Rotterdam."
}
]
},
{
"tripleset": [
[
"A.F.C. Fylde",
"GROUND",
"The Fylde"
],
[
"A.F.C. Fylde",
"NUMBER_OF_MEMBERS",
"3180"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AFC Fylde has the home ground called The Fylde and has 3180 members."
},
{
"source": "webnlg",
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"source": "webnlg",
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{
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{
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{
"source": "webnlg",
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{
"source": "webnlg",
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{
"source": "webnlg",
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"source": "webnlg",
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{
"source": "webnlg",
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{
"source": "webnlg",
"text": "Test pilot Elliot See, born in Dallas on July 23, 1927, graduated from the University of Texas in Austin. He died in St. Louis on February 28, 1966."
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{
"source": "webnlg",
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{
"source": "webnlg",
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{
"source": "webnlg",
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"source": "webnlg",
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{
"source": "webnlg",
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{
"source": "webnlg",
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"source": "webnlg",
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{
"source": "webnlg",
"text": "The Baku Turkish Martyr's Memorial in Azerbaijan is dedicated to the Ottoman Army soldiers killed in the Battle of Baku. The native name of the memorial is \"T\u00fcrk Sehitleri Aniti\" and was designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner. The leader of Turkey is Artur Rasizade."
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"source": "webnlg",
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},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is located in Azerbaijan, where it is known as \"T\u00fcrk Sehitleri Aniti\". The memorial is dedicated to Ottoman Army soldiers who were killed in the Battle of Baku. It was designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner, and constructed out of red granite and white marble."
},
{
"source": "webnlg",
"text": "Baku Turkish Martyrs' Memorial, made of red granite and white marble, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku and was designed by Huseyin Butuner and Hilmi Guner. The memorial is known in Turkish as Turk Sehitleri Aniti and can be found in Azerbaijan."
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{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial, designed by Huseyin Butuner and Hilmi Guner and made of granite and white marble, is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku. The memorial is found in Azerbaijan whose leader is the Prime Minister."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial in Azerbaijan is dedicated to the Ottoman Army soldiers killed in the Battle of Baku. It was designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner and is built using Red granite and white marble. Azerbaijan's leader is the Prime Minister of Azerbaijan."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyr's Memorial, designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner, was constructed using red granite and white marble. It is located in Baku, Azerbaijan, which is led by a Prime Minister. This Memorial is dedicated to the Ottoman Army soldiers killed in the Battle of Baku."
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{
"source": "webnlg",
"text": "Adams County, Pennsylvania has Carrol County, Maryland to its southeast and Pennsylvania's Cumberland County is to the north of the Adams County. The 11th Mississippi Infantry Monument is in Adams County of the municipality of Gettysburg Pennsylvania. ."
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{
"source": "webnlg",
"text": "The 11th Mississippi Infantry Monument is located at Gettysburg, Adams County, Pennsylvania, which has Cumberland County, Pennsylvania to its north and Carrol County, Maryland to its southeast."
},
{
"source": "webnlg",
"text": "The municipality of Gettysburg, Adams County, Pennsylvania is the location of the 11th Mississippi Infantry monument. Adams County is south of Cumberland County and to the northwest of Carroll County, Maryland."
}
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{
"source": "webnlg",
"text": "The 11th Mississippi Infantry Monument is a Contributing Property located in Adams County, Pennsylvania, with Franklin County to its west and Carrol County, Maryland to its southeast."
},
{
"source": "webnlg",
"text": "Pennsylvania's Franklin County is found to the west of Adams County while Carrol County Maryland is southeast of Adams County Pennsylvania. The location of the 11th Mississippi Infantry Monument is Adams County, Pennsylvania and falls under the category of Contributing property."
}
]
},
{
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"annotations": [
{
"source": "webnlg",
"text": "Baku, the capital of Azerbaijan is the location of the Turkish Martyrs memorial dedicated to the soldiers of the Ottoman army killed in the Battle of Baku. The memorial was designed by Huseyin Butuner and Hilmi Guner."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs Memorial has been dedicated to Ottoman Army soldiers killed in the Battle of Baku and is located in Azerbaijan's capital Baku. The Memorial was designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner."
},
{
"source": "webnlg",
"text": "Baku is the capital of Azerbaijan and the location of the Turkish Martyrs memorial dedicated to the soldiers of the Ottoman army who died in the battle of Baku. The memorial was designed by Huseyin Butuner and Hilmi Guner."
}
]
},
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"annotations": [
{
"source": "webnlg",
"text": "Baku is the capital of Azerbaijan and the leader of the legislature (the National Assembly) is Artur Rasizade. Azerbaijan is the location of the Baku Turkish Martyrs memorial."
},
{
"source": "webnlg",
"text": "The National Assembly is the legislative branch of government in Azerbaijan where the leader is Artur Rasizade. The Turkish martyrs memorial is located in the capital, Baku."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is located in Baku, the capital of Azerbaijan, where leader is Artur Rasizade and the legislature is known as the National Assembly."
}
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{
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"annotations": [
{
"source": "webnlg",
"text": "The red granite and white marble Baku Turkish Martyrs' Memorial is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku, and is located in Baku, Azerbaijan, where the leader is Artur Rasizade."
},
{
"source": "webnlg",
"text": "Artur Rasizade was an Azerbaijan leader. Baku Turkish Martyrs' Memorial is made of red granite and white marble, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku and located in Azerbaijan."
},
{
"source": "webnlg",
"text": "Artur Rasizade is an Azerbaijan leader. The Baku Turkish Martyrs' Memorial located in Baku, Azerbaijan is made with red granite and white marble and has been dedicated to Ottoman Army soldiers killed in the Battle of Baku."
}
]
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{
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"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial, which is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku, is located in Azerbaijan, where the official leader (Prime Minister of Azerbaijan) is called Artur Rasizade."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyr's Memorial is dedicated to the Ottoman Army soldiers killed in the Battle of Baku, and is located in Azerbaijan, which is led by the Prime Minister of Azerbaijan, Artur Rasizade."
},
{
"source": "webnlg",
"text": "Artur Rasizade is the Prime Minister of Azerbaijan. Azerbaijan is the location of the Baku Turkish Martyrs memorial which is dedicated to the soldiers of the Ottoman army killed in the Battle of Baku."
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]
},
{
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"text": "Alan Bean was under commander David Scott on Nasa's Apollo 12 mission."
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{
"source": "webnlg",
"text": "Alan Bean was a member of the NASA operated Apollo 12 crew commanded by David Scott."
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{
"source": "webnlg",
"text": "David Scott was the commander of the NASA operated Apollo 12 flight mission on which the crew included Alan Bean."
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"annotations": [
{
"source": "webnlg",
"text": "Dianne Feinstein is a senator in California where Alan Shepard died on July 21 1998."
},
{
"source": "webnlg",
"text": "Alan Shephard died on the 21st of July, 1998 in California (whose Senator was Dianne Feinstein)."
}
]
},
{
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[
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"annotations": [
{
"source": "webnlg",
"text": "Buzz Aldrin, also called Edwin E. Aldrin Jr., graduated in 1963 from MIT with a Sc.D. and was a crew member on Apollo 11."
},
{
"source": "webnlg",
"text": "Buzz Aldrin, whose real name was Edwin E. Aldrin, Jr., graduated from Massachusetts Institute of Technology in 1963 with a doctorate in Science and later became a crew member of Apollo 11."
},
{
"source": "webnlg",
"text": "Buzz Aldrin (AKA Edwin E Aldrin Jr) graduated in 1963 from MIT with a Sc.D. was a crew member of Apollo 11."
},
{
"source": "webnlg",
"text": "Buzz Aldrin, whose real name is Edwin E. Aldrin Jr., was a member of Apollo 11 who in 1963 graduated with a Sc. D. from MIT."
}
]
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{
"source": "webnlg",
"text": "Buzz Aldrin, who was a member of the Apollo 11 crew and spent 52 minutes in space, was born in Glen Ridge New Jersey."
},
{
"source": "webnlg",
"text": "Born in Glen Ridge New Jersey, Apollo 11 crewman Buzz Aldrin spent 52 minutes in space."
},
{
"source": "webnlg",
"text": "Born in Glen Ridge, NJ, Buzz Aldrin spent 52 minutes in space as part of Apollo 11's crew."
}
]
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"annotations": [
{
"source": "webnlg",
"text": "Alan Shepard, who was awarded the US Navy Distinguished Service Medal died in California, incidentally a Smilodon is a fossil from California."
},
{
"source": "webnlg",
"text": "Alan Shephard was awarded the Distinguished Service Medal by United States Navy and died in California (where a Smilodon fossil was found)."
},
{
"source": "webnlg",
"text": "Alan Shepard, who was awarded the Distinguished Service Medal by the US Navy, died in California where the Smilodon fossil was found."
}
]
},
{
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"annotations": [
{
"source": "webnlg",
"text": "Elliot See who was selected by NASA in 1962 died on 28 February 1966 in St Louis."
},
{
"source": "webnlg",
"text": "Elliot See, ex NASA worker since 1962, died in St Louis on the 28th of February 1966."
},
{
"source": "webnlg",
"text": "Elliot See was selected by NASA in 1962. He died in St. Louis on 1966-02-28."
}
]
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"source": "webnlg",
"text": "Elliot See, born July 23 1927 in Dallas, has died."
},
{
"source": "webnlg",
"text": "Elliot See is deceased, was born on 23rd July 1927 in Dallas."
},
{
"source": "webnlg",
"text": "Elliot See (deceased) was born in Dallas 1927-07-23."
},
{
"source": "webnlg",
"text": "Elliot See was born in Dallas on July 23, 1927. He is now deceased."
}
]
},
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{
"source": "webnlg",
"text": "Born on October 17th, 1933 in British Hong Kong, William Anders would serve as a crew member on Apollo 8."
},
{
"source": "webnlg",
"text": "William Anders who was born on 17 October 1933 in British Hong Kong later served as a crew member of Apollo 8."
},
{
"source": "webnlg",
"text": "William Anders, who was born in British Hong Kong on the 17th of October 1933, crewed Apollo 8."
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]
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"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Adams County, Pennsylvania is the location of the 11th Mississippi Infantry monument. Franklin County Pennsylvania is to the west and Frederick County to the south west."
},
{
"source": "webnlg",
"text": "The 11th Mississippi Infantry Monument is located in Adams County Pennsylvania, between Franklin County to the west and Frederick County to the southwest."
}
]
},
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],
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"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyr's memorial which is dedicated to the soldiers of the Ottoman army who were killed in the Battle of Baku is located in Azerbaijan. At present Artur Rasizade is the leader of the country."
}
]
},
{
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"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs Memorial in Azerbaijan was designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner and is dedicated to Ottoman Army soldiers killed in the Battle of Baku."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial in Azerbaijan was designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner and is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyr's Memorial in Azerbaijan, designed by H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku."
}
]
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],
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"annotations": [
{
"source": "webnlg",
"text": "Azerbaijan, where the leader is Artur Rasizade, is the location of the Baku Turkish Martyrs Memorial. The memorial was designed by Huseyin Butuner and Hilmi Guner."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial, designed by Huseyin Butuner and Hilmi Guner, is located in Azerbaijan, where the leader is Artur Rasizade."
}
]
},
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]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial was designed by Huseyin Butuner and Hilmi Guner, and is located in Azerbaijan, where it is called Turk Sehitleri Aniti."
}
]
},
{
"tripleset": [
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],
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"annotations": [
{
"source": "webnlg",
"text": "Apollo 14 crew member Alan Shepard retired on the first of August, 1974."
},
{
"source": "webnlg",
"text": "Alan Shepard, who served as a crew member of Apollo 14, retired 1974-08-01."
}
]
},
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[
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"annotations": [
{
"source": "webnlg",
"text": "Buzz Aldrin is a US national because he was born in Glen Ridge, New Jersey."
},
{
"source": "webnlg",
"text": "American Buzz Aldrin hails from Glen Ridge, New Jersey."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was born in Glen Ridge, New Jersey in the United States."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was a US citizen born in Glen Ridge, New Jersey."
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]
},
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[
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"annotations": [
{
"source": "webnlg",
"text": "Retiree Buzz Aldrin is a former fighter pilot."
},
{
"source": "webnlg",
"text": "Buzz Aldrin is now retired but he was once a fighter pilot."
},
{
"source": "webnlg",
"text": "Buzz Aldrin has since retired but he once served as a fighter pilot."
},
{
"source": "webnlg",
"text": "Buzz Aldrin served as a fighter pilot and is now retired."
}
]
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"tripleset": [
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"Buzz Aldrin",
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"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Apollo 11 program was organized by NASA and included Buzz Aldrin as one of its crew members."
},
{
"source": "webnlg",
"text": "NASA operated the Apollo 11 program of which Buzz Aldrin was a member."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was on Apollo 11 which NASA operated."
},
{
"source": "webnlg",
"text": "Buzz Aldrin served as a crew member of Apollo 11 which is operated by NASA."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was a crew member of Apollo 11 - whose operator was NASA."
},
{
"source": "webnlg",
"text": "buzz aldrin was a crew member of apollo 11, which was operated by NASA."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was a crew member of NASA's Apollo 11."
}
]
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{
"tripleset": [
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"Buzz Aldrin",
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"Fighter pilot"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Buzz Aldrin performed as a fighter pilot and was a crew member on Apollo 11."
},
{
"source": "webnlg",
"text": "Buzz Aldrin, who was a fighter pilot, was a crew member on Apollo 11."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was a fighter pilot who was also part of the Apollo 11 crew."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was both a fighter pilot and member of the Apollo 11 flight crew."
},
{
"source": "webnlg",
"text": "Buzz Aldrin was a fighter pilot and crew member of Apollo 11."
}
]
},
{
"tripleset": [
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],
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"annotations": [
{
"source": "webnlg",
"text": "The academic staff size of Accademia di Architettura di Mendrisio is 100."
},
{
"source": "webnlg",
"text": "The academic staff number 100 at the Accademia di Architettura di Mendrisio."
},
{
"source": "webnlg",
"text": "The Accademia di Architettura di Mendrisio has an academic staff of 100."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"CAMPUS",
"\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The campus address for the Acharya Institue of Technology is: Soldenvanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore - 560090."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090."
},
{
"source": "webnlg",
"text": "The campus of the Acharya Institute of Technology is located at Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090."
}
]
},
{
"tripleset": [
[
"Alba Iulia",
"COUNTRY",
"Romania"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alba Iulia is located in Romania."
},
{
"source": "webnlg",
"text": "Alba Iulia is in the country of Romania."
},
{
"source": "webnlg",
"text": "Alba Iulia is in Romania."
}
]
},
{
"tripleset": [
[
"Alba Iulia",
"IS_PART_OF",
"Alba County"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Alba Iulia os part of the county of Alba."
},
{
"source": "webnlg",
"text": "Alba lulia is part of Alba County."
},
{
"source": "webnlg",
"text": "Alba Iulia is part of Alba County."
}
]
},
{
"tripleset": [
[
"Romania",
"PATRON_SAINT",
"Andrew the Apostle"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The patron Saint of Romania is Andrew the Apostle."
},
{
"source": "webnlg",
"text": "Andrew the Apostle is the patron saint of Romania."
}
]
},
{
"tripleset": [
[
"School of Business and Social Sciences at the Aarhus University",
"ACADEMIC_STAFF_SIZE",
"737"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The academic staff of the School of Business and Social Sciences at the Aarhus University number 737."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University has academic staff size of 737."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University has an academic staff of 737."
}
]
},
{
"tripleset": [
[
"School of Business and Social Sciences at the Aarhus University",
"COUNTRY",
"Denmark"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Aarhus University School of Business and Social Sciences is in the country of Denmark."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University is situated in Denmark."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University is located in Denmark."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University is in Denmark."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Ajoblanco",
"MAIN_INGREDIENTS",
"\"Bread, almonds, garlic, water, olive oil\""
],
[
"Ajoblanco",
"REGION",
"Andalusia"
],
[
"Ajoblanco",
"ALTERNATIVE_NAME",
"\"Ajo blanco\""
],
[
"Ajoblanco",
"INGREDIENT",
"Bread"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "From Andalusia, Spain, Ajoblanco is made with bread, almonds, garlic, water and olive oil. Ajo blanco is an alternative name to Ajoblanco."
},
{
"source": "webnlg",
"text": "Ajoblanco, also known as Ajo blanco is a dish from the Andalusian region of Spain and includes the main ingredients of bread, almonds, garlic, water and olive oil."
},
{
"source": "webnlg",
"text": "A dish found in the Andalusian region of Spain is called Ajoblanco (or Ajo blanco). It contains bread, almonds, garlic, water and olive oil."
}
]
},
{
"tripleset": [
[
"Amatriciana sauce",
"REGION",
"Lazio"
],
[
"Amatriciana sauce",
"COUNTRY",
"Italy"
],
[
"Amatriciana sauce",
"INGREDIENT",
"Guanciale"
],
[
"Amatriciana sauce",
"COURSE",
"Italian meal structure"
],
[
"Amatriciana sauce",
"MAIN_INGREDIENTS",
"\"Tomatoes, guanciale, cheese, olive oil\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Tomatoes, guanciale, cheese, and olive oil are main ingredients in the traditional Amatriciana sauce, which comes from the region of Lazio and can be found in an Italian meal."
},
{
"source": "webnlg",
"text": "Amatriciana is a traditional sauce from Lazio, Italy. It includes cheese along with tomatoes, guanciale and olive oil."
},
{
"source": "webnlg",
"text": "Amatriciana sauce can be found in Italian food and comes from the country's Lazio region.The main ingredients of the sauce are guanciale,tomatoes,cheese, and olive oil."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"COUNTRY",
"Indonesia"
],
[
"Arem-arem",
"REGION",
"Javanese cuisine"
],
[
"Indonesia",
"LEADER_NAME",
"Joko Widodo"
],
[
"Indonesia",
"CURRENCY",
"Indonesian rupiah"
],
[
"Indonesia",
"LANGUAGE",
"Indonesian language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem-arem, a Javanese dish, is commonly served in Indonesia where the leader is Joko Widodo. It is also where the currency is the Indonesian rupiah and where Indonesian language is the language spoken."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Italy",
"DEMONYM",
"Italians"
],
[
"Italy",
"CAPITAL",
"Rome"
],
[
"Italy",
"LANGUAGE",
"Italian language"
],
[
"Italy",
"LEADER_NAME",
"Sergio Mattarella"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sergio Mattarella is a leader in Italy, a country where Rome is the capital,and both the people and the language spoken are Italian. Arrabbiata sauce can be found in Italy."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce is from Italy where the capital is Rome, Italian is the language spoken and Sergio Mattarella is a leader."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"REGION",
"Valencian Community"
],
[
"Valencian Community",
"LEADER_NAME",
"Ximo Puig"
],
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Spain",
"DEMONYM",
"Spaniards"
],
[
"Spain",
"LEADER_NAME",
"Felipe VI of Spain"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ximo Puig is a leader of the Valencian Community which is where Arros negre comes from. It is a a traditional dish from Spain, where the people are called Spaniards and the leader is Felipe VI of Spain."
},
{
"source": "webnlg",
"text": "Arros negre is from the Valencian Community of Spain, home to the Spaniards, led by Felipe VI and Ximo Puig."
}
]
},
{
"tripleset": [
[
"Asam pedas",
"COUNTRY",
"Malaysia"
],
[
"Malaysia",
"ETHNIC_GROUP",
"Malaysian Chinese"
],
[
"Malaysia",
"CAPITAL",
"Kuala Lumpur"
],
[
"Malaysia",
"ETHNIC_GROUP",
"Malaysian Indian"
],
[
"Asam pedas",
"REGION",
"Malay Peninsula"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asam pedas is a food found in the Malay Peninsula of Malaysia, whose capital is Kuala Lumpur, and includes the Malaysian Chinese and Malaysian Indians."
},
{
"source": "webnlg",
"text": "Asam pedas is a food found in the Malay Peninsula in Malaysia, whose capital is Kuala Lumpur, where Malaysian Indians and Malaysian Chinese are ethnic groups."
},
{
"source": "webnlg",
"text": "Malaysia and the Malay Peninsula are home to the dish Asam pedas. The capital of Malaysian Chinese and Indian inhabited Malaysia is Kuala Lumpur."
}
]
},
{
"tripleset": [
[
"Asam pedas",
"COUNTRY",
"Malaysia"
],
[
"Malaysia",
"ETHNIC_GROUP",
"Malaysian Chinese"
],
[
"Malaysia",
"LEADER_NAME",
"Arifin Zakaria"
],
[
"Malaysia",
"ETHNIC_GROUP",
"Malaysian Indian"
],
[
"Asam pedas",
"REGION",
"Malay Peninsula"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Malaysian Indian and Malaysian chinese are two of the ethnic groups in Malaysia where Arifin Zakaria is the leader. Asam pedas is found in Malaysia and is from the Malay Peninsula region."
},
{
"source": "webnlg",
"text": "Asam pedas is a food found in the Malay Peninsula region, Malaysia, where ethnic groups include Malaysian Indian and Chinese. Arifin Zakaria is the country's leader."
},
{
"source": "webnlg",
"text": "Malaysia, led by Arifin Zakaria, is home to the Malaysian Indian and Chinese, as well as the asam pedas of the Malay Peninsula region."
}
]
},
{
"tripleset": [
[
"Ayam penyet",
"REGION",
"\"Nationwide, also can be found in Malaysia and Singapore\""
],
[
"Ayam penyet",
"COUNTRY",
"Java"
],
[
"Ayam penyet",
"INGREDIENT",
"Fried chicken"
],
[
"Ayam penyet",
"MAIN_INGREDIENTS",
"\"Squeezed\" or \"smashed\" fried chicken served with sambal"
],
[
"Ayam penyet",
"SERVING_TEMPERATURE",
"\"Hot\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet is a Javanese dish made nationwide, and can also be found in Malaysia and Singapore. It should be served hot and contains the main ingredients of squeezed or smashed fried chicken served with sambal."
},
{
"source": "webnlg",
"text": "Ayam penyet is a hot food dish found in Java, Malaysia and Singapore and consists of fried chicken and sambal sauce."
},
{
"source": "webnlg",
"text": "Ayam penyet is a food made in Java regions of Malaysia and Singapore. It contains fried chicken that is smashed and served hot with sambal."
}
]
},
{
"tripleset": [
[
"Ayam penyet",
"REGION",
"Malaysia"
],
[
"Malaysia",
"ETHNIC_GROUP",
"Malaysian Chinese"
],
[
"Ayam penyet",
"INGREDIENT",
"Fried chicken"
],
[
"Ayam penyet",
"COUNTRY",
"Indonesia"
],
[
"Ayam penyet",
"MAIN_INGREDIENTS",
"\"Squeezed\" or \"smashed\" fried chicken served with sambal"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Malaysian Chinese is an ethnic group from Malaysia and Ayam Penyet is from there. Ayam Penyet is also from Indonesia and it contains fried chicken as well as \"squeezed\" or \"smashed\" chicken served with sambal."
},
{
"source": "webnlg",
"text": "Ayam penyet is a dish comprising \"squeezed\" or \"smashed\" fried chicken served with sambal. It can be found in Indonesia and in Malaysia, where there is an ethnic group called Malaysian Chinese."
},
{
"source": "webnlg",
"text": "Ayam penyet (origins: Indonesia) contains squeezed/smashed fried chicken with sambel and is popular in Malaysia. Malasysia is home to the Malaysian chinese."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"United States",
"LEADER_NAME",
"Barack Obama"
],
[
"United States",
"ETHNIC_GROUP",
"Native Americans in the United States"
],
[
"United States",
"CAPITAL",
"Washington, D.C."
],
[
"United States",
"LANGUAGE",
"English language"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon Explosion is a dish that originates in the United States, where English is the language spoken and the capital is Washington DC. Barack Obama is leader of the US and Native Americans are one of the ethnic groups of the country."
},
{
"source": "webnlg",
"text": "Barack Obama is leader of United States, the home of the dish Bacon Explosion. Native Americans are one of the ethnic groups of the country, where Washington DC is the capital and English is the language spoken."
},
{
"source": "webnlg",
"text": "Bacon Explosion comes from the United States whose leader is Barack Obama. The capital of the United States is Washington, D.C., the spoken language is English and Native Americans are one of the ethnic groups of the country."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COUNTRY",
"France"
],
[
"Hong Kong",
"LEADER_NAME",
"Carrie Lam (politician)"
],
[
"France",
"LEADER_NAME",
"G\u00e9rard Larcher"
],
[
"France",
"LANGUAGE",
"French language"
],
[
"Baked Alaska",
"REGION",
"Hong Kong"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baked Alaska originates from France where the national language is French and one of the leader is Gerard Larcher. The dessert is served in Hong Kong as well where the leader is Carrie Lam."
},
{
"source": "webnlg",
"text": "Baked Alaska comes from France where the spoken language is French,and the country's leader is Gerard Larcher. This dessert is also served in Hong Kong."
},
{
"source": "webnlg",
"text": "Baked Alaska, served in Hong Kong, who's political leader is Carrie Lam, but is actually from France where the national language is French and the leader is Gerard Larcher."
}
]
},
{
"tripleset": [
[
"Bakewell pudding",
"REGION",
"Derbyshire Dales"
],
[
"Derbyshire Dales",
"LEADER_NAME",
"Patrick McLoughlin"
],
[
"Bakewell pudding",
"DISH_VARIATION",
"Bakewell tart"
],
[
"Derbyshire Dales",
"IS_PART_OF",
"Derbyshire"
],
[
"Bakewell tart",
"INGREDIENT",
"Shortcrust pastry"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakewell Tart (or pudding) is from the Derbyshire Dales area of Derbyshire where Patrick McLoughlin is a leader. The tart is made with shortcrust pastry."
},
{
"source": "webnlg",
"text": "The shortcrust pastry-based dessert Bakewell Tart (or pudding) hails from the Derbyshire Dales area of Derbyshire where Patrick McLoughlin is a leader."
},
{
"source": "webnlg",
"text": "A variant of bakewell pudding is bakewell tart, which key ingredient is Shortcrust pastry. Bakewell pudding originates from the Derbyshire Dales, which leader is Patrick McLoughlin."
}
]
},
{
"tripleset": [
[
"Bandeja paisa",
"INGREDIENT",
"Lemon"
],
[
"Lemon",
"FAMILY",
"Rutaceae"
],
[
"Bandeja paisa",
"COUNTRY",
"Colombian cuisine"
],
[
"Bandeja paisa",
"REGION",
"Paisa Region"
],
[
"Lemon",
"GENUS",
"Citrus"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bandeja paisa is a dish from Colombian cuisine and originates from the Paisa region. One of the ingredients in Bandeja paisa is lemon which is a member of the family Rutaceae and is from the genus citrus."
},
{
"source": "webnlg",
"text": "Bandeja paisa is a traditional dish from the Paisa region, Colombia. Lemon is one of its ingredients and is a member of the family Rutaceae and is from the genus citrus."
}
]
},
{
"tripleset": [
[
"Bandeja paisa",
"INGREDIENT",
"Lemon"
],
[
"Lemon",
"ORDER",
"Rosids"
],
[
"Lemon",
"FAMILY",
"Rutaceae"
],
[
"Bandeja paisa",
"COUNTRY",
"Colombian cuisine"
],
[
"Bandeja paisa",
"REGION",
"Paisa Region"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bandeja paisa is typical Colombian cuisine that originates from the Paisa region and contains lemon. The lemon belongs to the rutaceae family and the order of Rosids."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"COUNTRY",
"France"
],
[
"France",
"LANGUAGE",
"French language"
],
[
"France",
"LEADER_NAME",
"G\u00e9rard Larcher"
],
[
"France",
"LEADER_NAME",
"Claude Bartolone"
],
[
"Barny Cakes",
"INGREDIENT",
"Sponge cake"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Barny Cakes, which are made from sponge cake, are from France, where Gerard Larcher and Claude Bartolone are leaders and French is the language spoken."
},
{
"source": "webnlg",
"text": "Barny cakes, made with sponge cake, are found in France, where French is spoken and whose leaders are Claude Bartolone and Gerard Larcher."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"COUNTRY",
"France"
],
[
"France",
"LEADER_NAME",
"G\u00e9rard Larcher"
],
[
"France",
"LANGUAGE",
"French language"
],
[
"Mondelez International",
"FOUNDATION_PLACE",
"Chicago"
],
[
"Barny Cakes",
"CREATOR",
"Mondelez International"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Created by Mondelez International (founded in Chicago), Barny Cakes come from France where Gerard Larcher is leader and they speak French."
},
{
"source": "webnlg",
"text": "Barny cakes is created by Mondelez International, which was founded in Chicago. It comes from France, which leader is G\u00e9rard Larcher and the language is French."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"REGION",
"Singapore"
],
[
"Singapore",
"LANGUAGE",
"English language"
],
[
"Beef kway teow",
"COUNTRY",
"\"Singapore and Indonesia\""
],
[
"Singapore",
"LEADER_NAME",
"Tony Tan"
],
[
"Singapore",
"LEADER_NAME",
"Halimah Yacob"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "English speaking Singapore, led by Tony Tan and Halimah Yacob, offers the dish Beef kway teow, also found in Indonesia."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"Bhajji",
"REGION",
"Karnataka"
],
[
"Bhajji",
"MAIN_INGREDIENTS",
"\"Gram flour, vegetables\""
],
[
"Bhajji",
"ALTERNATIVE_NAME",
"\"Bhaji, bajji\""
],
[
"Bhajji",
"INGREDIENT",
"Vegetable"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji is also known as Bhaji or bajj, originates from the Karnataka region of India. It is usually made up of vegetables and gram flour."
},
{
"source": "webnlg",
"text": "Bhajji, also known as Bhaji or bajji, originates from India in the Karnataka region and the main ingredients are gram flour and vegetables."
},
{
"source": "webnlg",
"text": "Bhajji (Bhaji), or bajji, contains vegetables and gram flour; it originates from the Karnataka region of India."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"DEMONYM",
"Indian people"
],
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
],
[
"Bhajji",
"REGION",
"Karnataka"
],
[
"Karnataka",
"LEADER_NAME",
"Vajubhai Vala"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Indians is the name given to people from India and the leaders are Sumitra Mahajan and Vajubhai Vala. Bhajji is from the Karnataka region of India."
},
{
"source": "webnlg",
"text": "Bhajji is from the Karantake region of India. India is led by Vajubhai Vala and home to the Indian people."
},
{
"source": "webnlg",
"text": "Bhajji originates from the Karnataka region (led by Vajubhai Vala) of India. The Indians of India are led by Sumitra Mahajan."
}
]
},
{
"tripleset": [
[
"Bionico",
"COUNTRY",
"Mexico"
],
[
"Bionico",
"REGION",
"Jalisco"
],
[
"Jalisco",
"LEADER_NAME",
"Jes\u00fas Casillas Romero"
],
[
"Dessert",
"DISH_VARIATION",
"Cake"
],
[
"Bionico",
"COURSE",
"Dessert"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bionico is a dessert from Jalisco, Mexio. The leader in Jalisco is Jesus Casillas Romero. Another dessert is a cake."
},
{
"source": "webnlg",
"text": "Bionico is a dessert found in the Jalisco region of Mexico. The name of the leader in Jalisco is Jesus Casillas Romero. Another dessert is a cake."
}
]
},
{
"tripleset": [
[
"Bionico",
"COUNTRY",
"Mexico"
],
[
"Dessert",
"DISH_VARIATION",
"Cake"
],
[
"Mexico",
"LEADER_NAME",
"Enrique Pe\u00f1a Nieto"
],
[
"Bionico",
"COURSE",
"Dessert"
],
[
"Bionico",
"REGION",
"Guadalajara"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A type of dessert is a cake. However, bionico is not a cake, and instead a dessert from the Guadalajara region, in Mexico. Mexico is where Enrique Pena Nieto is the leader."
},
{
"source": "webnlg",
"text": "Bionico is a dessert from Guadalajara, Mexico where the leader is Enrique Pena Nieto. Another dessert is a cake."
}
]
},
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[
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[
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[
"Bakewell tart",
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"Frangipane"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Patrick McLoughlin is the leader of Derbyshire Dales which is in the County of Derbyshire. Bakewell tart, a variation of Bakewell pudding, comes from the Derbyshire Dales and contains frangipane."
},
{
"source": "webnlg",
"text": "Patrick McLoughlin is a leader in the Derbyshire Dales (in the county of Derbyshire) where the Bakewell tart is a popular dish. This dish contains frangipane and has a variant called Bakewell pudding."
},
{
"source": "webnlg",
"text": "Patrick McLoughlin is a leader in Derbyshire Dales in the County of Derbyshire The frangipane Bakewell tart, a variation of Bakewell pudding, is popular in the Derbyshire Dales area."
}
]
},
{
"tripleset": [
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[
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[
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[
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"Derbyshire"
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[
"Bakewell tart",
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"Frangipane"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Patrick McLoughlin is the leader of Derbyshire Dales which is part of Derbyshire. Coming from the Derbyshire Dales region is Bakewell tart (a variation of Bakewell pudding) which has Frangipane as an ingredient."
},
{
"source": "webnlg",
"text": "The Derbyshire Dales, whose leader is Patrick McLoughlin, is part of Derbyshire and is where Bakewell tarts come from. Bakewell tart is a variation of Bakewell pudding and one of the ingredients is frangipane."
},
{
"source": "webnlg",
"text": "The Frangipane -based dessert Bakewell Tart (or pudding) is popular in the Derbyshire Dales area of Derbyshire where Patrick McLoughlin is a leader."
}
]
},
{
"tripleset": [
[
"Indonesia",
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"Jakarta"
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[
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"LANGUAGE",
"Indonesian language"
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[
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"LEADER_NAME",
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[
"Bakso",
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"Indonesia"
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[
"Bakso",
"COUNTRY",
"Indonesia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Jakarta is the capital of Indonesia where the leader is Jusuf Kalla, they speak Indonesian and eat bakso."
},
{
"source": "webnlg",
"text": "Bakso is a dish from the country of Indonesia, which leader is Jusuf Kalla, the capital is Jakarta and the language is Indonesian."
},
{
"source": "webnlg",
"text": "Bakso is a food and a dish found in Indonesia, where the capital is Jakarta, the language is Indonesia and Jusuf Kalla is the leader."
}
]
},
{
"tripleset": [
[
"Italy",
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"Italians"
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[
"Italy",
"CAPITAL",
"Rome"
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[
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"LEADER_NAME",
"Matteo Renzi"
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[
"Amatriciana sauce",
"COUNTRY",
"Italy"
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[
"Italy",
"LEADER_NAME",
"Sergio Mattarella"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Italy is the country Amatriciana sauce comes from and people there are called Italians. The capital is Rome and the leaders are Matteo Renzi and Sergio Mattarella."
},
{
"source": "webnlg",
"text": "Rome is the capital of Italy where the leaders are Matteo Renzi and Sergio Mattarella. Amatriciana sauce can be found there and people are called Italians."
},
{
"source": "webnlg",
"text": "Amatriciana sauce is from Italy, where Rome is the capital and where Italians live. Leaders of the country include Sergio Mattarella and Matteo Renzi."
}
]
},
{
"tripleset": [
[
"Java",
"ETHNIC_GROUP",
"Baduy"
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[
"Singapore",
"LANGUAGE",
"English language"
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[
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"LEADER_NAME",
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[
"Ayam penyet",
"REGION",
"Singapore"
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[
"Ayam penyet",
"COUNTRY",
"Java"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Halimah Yacob is the leader in Singapore where English is spoken and the dish Ayam penyet is from. It is also from Java, a country where one of the ethnic groups are the Baduy."
},
{
"source": "webnlg",
"text": "The Baduy is an ethnic group in Java where Ayam penyet is from. This dish is also from Singapore where English is spoken and Halimah Yacob is the leader."
},
{
"source": "webnlg",
"text": "Ayam penyet originates from Java where Baduy people are one of the ethnic groups.The food can also be found in Singapore where English is mainly spoken and Halimah Yacob is the president."
}
]
},
{
"tripleset": [
[
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"Ilocano people"
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[
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"Arabic"
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[
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"Philippine English"
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[
"Batchoy",
"COUNTRY",
"Philippines"
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[
"Philippines",
"ETHNIC_GROUP",
"Igorot people"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Philippines is the country the dish Batchoy comes from. The llocano and Igorot peoples are ethnic groups from the Philippines, where languages spoken include Arabic and Phillippine English."
},
{
"source": "webnlg",
"text": "Batchoy is eaten in the Philippines, languages spoken there include English (official language) and also Arabic. Ethnic groups there include the Ilocano and Igorot peoples."
}
]
},
{
"tripleset": [
[
"Singapore",
"LANGUAGE",
"English language"
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[
"Beef kway teow",
"COUNTRY",
"Singapore"
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[
"Beef kway teow",
"REGION",
"\"Nationwide in Singapore and Indonesia\""
],
[
"Singapore",
"LEADER_NAME",
"Tony Tan"
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[
"Singapore",
"LEADER_NAME",
"Halimah Yacob"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Available nationwide in Indonesia and Singapore, Beef kway teow is a dish which comes from Singapore, where English is spoken. It is also where two of the leaders are Halimah Yacob and Tony Tan."
},
{
"source": "webnlg",
"text": "Just like in Indonesia, Beef kway teow is popular nationwide is Singapore (where it comes from). In this country, English is spoken and two of the leaders are Halimah Yacob and Tony Tan."
},
{
"source": "webnlg",
"text": "Beef kway teow is a popular dish in Singapore and Indonesia. Tony Tan and Halimah Yacob are leaders in Singapore where English is spoken."
}
]
},
{
"tripleset": [
[
"Spain",
"LEADER_NAME",
"Felipe VI of Spain"
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[
"Spain",
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"Spanish language"
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[
"Spain",
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"Euro"
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[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Spain",
"DEMONYM",
"Spaniards"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ajoblanco is a dish from Spain, where the currency is the euro, the leader is Felipe VI, the language spoken is Spanish and the people that live there are called Spaniards."
},
{
"source": "webnlg",
"text": "Ajoblanco is a food found in Spanish speaking and Felipe VI led Spain. It is also home to the Spaniards and can be purchased with the Euro."
}
]
},
{
"tripleset": [
[
"Asterix (comicsCharacter)",
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"Ren\u00e9 Goscinny"
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[
"Asterix (comicsCharacter)",
"CREATOR",
"Albert Uderzo"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The comic strip character Asterix was created by Albert Uderzo and Rene Goscinny."
},
{
"source": "webnlg",
"text": "The comic book character Asterix was created by Ren\u00e9 Goscinny and Albert Uderzo."
},
{
"source": "webnlg",
"text": "The character Asterix was created by Rene\u00e9 Goscinny and Albert Uderzo."
}
]
},
{
"tripleset": [
[
"Aurakles",
"CREATOR",
"Len Wein"
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[
"Aurakles",
"ALTERNATIVE_NAME",
"\"Aurakles\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Created by Len Wein, Aurakles's alternative name is also Aurakles."
},
{
"source": "webnlg",
"text": "The character Aurakles, also known as Aurakles, was created by Len Wein."
}
]
},
{
"tripleset": [
[
"Ballistic (comicsCharacter)",
"CREATOR",
"Doug Moench"
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[
"Ballistic (comicsCharacter)",
"ALTERNATIVE_NAME",
"\"Kelvin Mao\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Doug Moench was the creator of the comic character Ballistic, who has the alternative name Kelvin Mao."
},
{
"source": "webnlg",
"text": "Ballistic, ( also known as Kelvin Mao ), is a fictional comic superhero created by Doug Moench."
},
{
"source": "webnlg",
"text": "Doug Moench created the character Ballistic, the fictional superhero whose alter ego is Kelvin Mao."
}
]
},
{
"tripleset": [
[
"Bananaman",
"BROADCASTED_BY",
"BBC"
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[
"Bananaman",
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"Graeme Garden"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Starring Graeme Garden, Bananaman, the TV series was shown on the BBC."
},
{
"source": "webnlg",
"text": "Graeme Garden starred in the TV series Bananman, which was shown on the BBC."
},
{
"source": "webnlg",
"text": "Bananaman , which starred Graeme Garden, was broadcasted by the BBC."
}
]
},
{
"tripleset": [
[
"Bananaman",
"STARRING",
"Bill Oddie"
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[
"Bill Oddie",
"BIRTH_PLACE",
"Lancashire"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bill Oddie, who stars in Bananaman, was born in Lancashire."
},
{
"source": "webnlg",
"text": "Bananaman starred Bill Oddie who was born in Lancashire."
},
{
"source": "webnlg",
"text": "Bill Oddie, who was born in Lancashire, starred in Bananaman."
}
]
},
{
"tripleset": [
[
"Bibbo Bibbowski",
"CREATOR",
"Jerry Ordway"
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[
"Jerry Ordway",
"NATIONALITY",
"Americans"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bibbo Bibbowski was created by American, Jerry Ordway."
},
{
"source": "webnlg",
"text": "The creator of Bibbo Bibbowski is Jerry Ordway, an American."
},
{
"source": "webnlg",
"text": "Jerry Ordway, an American, is the creator of Bibbo Bibbowski."
}
]
},
{
"tripleset": [
[
"Black Pirate",
"CREATOR",
"Sheldon Moldoff"
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[
"Black Pirate",
"ALTERNATIVE_NAME",
"\"Jon Valor\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Black Pirate, also known as Jon Valor, was created by Sheldon Moldoff."
},
{
"source": "webnlg",
"text": "The Black Pirate ( Jon Valor ), was created by Sheldon Moldoff."
},
{
"source": "webnlg",
"text": "Sheldon Moldoff is the creator of Black Pirate, who is also known as Jon Valor."
}
]
},
{
"tripleset": [
[
"Blockbuster (comicsCharacter)",
"CREATOR",
"Roger Stern"
],
[
"Blockbuster (comicsCharacter)",
"CREATOR",
"Tom Lyle"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The comic character Blockbuster was created by Roger Stern and Tom Lyle."
},
{
"source": "webnlg",
"text": "Blockbuster is a comic book character which was created by both Roger Stern and Tom Lyle."
},
{
"source": "webnlg",
"text": "The comic book character Blockbuster was created by Roger Stern and Tom Lyle."
}
]
},
{
"tripleset": [
[
"Bolt (comicsCharacter)",
"CREATOR",
"Paris Cullins"
],
[
"Bolt (comicsCharacter)",
"CREATOR",
"Gary Cohn (comics)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Paris Culins and Gary Cohn are the creators of the comics character Bolt."
},
{
"source": "webnlg",
"text": "Both Paris Cullins and Gary Cohn were the creator's of the comic character Bolt."
},
{
"source": "webnlg",
"text": "The comic character Bolt, was created by Paris Cullins and Gary Cohn."
}
]
},
{
"tripleset": [
[
"1. FC K\u00f6ln",
"MANAGER",
"Peter St\u00f6ger"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Peter Stoger is the manager of FC Kolin."
},
{
"source": "webnlg",
"text": "Peter St\u00f6ger is manager of 1. FC K\u00f6ln."
},
{
"source": "webnlg",
"text": "The manager of 1. FC Koln is Peter Stoger."
}
]
},
{
"tripleset": [
[
"1. FC Magdeburg",
"SEASON",
"2014"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "1 FC Magdeburg played in the 2014 season."
}
]
},
{
"tripleset": [
[
"A.C. Lumezzane",
"SEASON",
"2014\u201315 Lega Pro"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A C Lumezzane played the 2014-2015 season in Lega Pro."
},
{
"source": "webnlg",
"text": "Lumezzane played in the Lega Pro during the 2014 season."
}
]
},
{
"tripleset": [
[
"A.D. Isidro Metap\u00e1n",
"FULL_NAME",
"\"Asociaci\u00f3n Deportiva\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.D. Isidro Metapan's full name is \"Asociaci\u00f3n Deportiva\"."
},
{
"source": "webnlg",
"text": "A.D. Isidro Metap\u00e1n's full name is Asociaci\u00f3n Deportiva."
},
{
"source": "webnlg",
"text": "The fullname of A.D. Isidro Metapan is Asociacion Deportiva."
}
]
},
{
"tripleset": [
[
"A.E Dimitra Efxeinoupolis",
"FULL_NAME",
"\"A.E Dimitra Efxeinoupolis\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis's full name is A.E Dimitra Efxeinoupolis."
},
{
"source": "webnlg",
"text": "\"A.E Dimitra Efxeinoupolis\" is the full name of A.E Dimitra Efxeinoupolis."
},
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis has the fullname A.E Dimitra Efxeinoupolis."
}
]
},
{
"tripleset": [
[
"A.E Dimitra Efxeinoupolis",
"SEASON",
"2014"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis played in the 2014 season."
},
{
"source": "webnlg",
"text": "A.E Dimitia Efxeinoupolis were in the 2014 season."
},
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis played in season 2014."
}
]
},
{
"tripleset": [
[
"A.E Dimitra Efxeinoupolis",
"SEASON",
"2014\u201315 A EPSTH, Greece"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis were in the 2014\u201315 A EPSTH. Greece season."
},
{
"source": "webnlg",
"text": "A.E Dimitra Efxeinoupolis were in the 2014-15 A EPSTH, Greece."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"NUMBER_OF_MEMBERS",
"1500"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.F.C. Blackpool has 1500 members."
}
]
},
{
"tripleset": [
[
"A.F.C. Fylde",
"GROUND",
"The Fylde"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Fylde is the home ground of AFC Fylde."
},
{
"source": "webnlg",
"text": "AFC Fylde has the home ground called The Fylde."
}
]
},
{
"tripleset": [
[
"A.S. Gubbio 1910",
"GROUND",
"Italy"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The ground of A.S. Gubbio 1910 is located in Italy."
}
]
},
{
"tripleset": [
[
"A.S. Livorno Calcio",
"NUMBER_OF_MEMBERS",
"19238"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.S. Livorno Calcio has 19238 members."
},
{
"source": "webnlg",
"text": "The AS Livorno Calcio has 19238 members."
},
{
"source": "webnlg",
"text": "The A.S. Livorno Calcio has 19238 members."
}
]
},
{
"tripleset": [
[
"A.S. Roma",
"SEASON",
"2014\u201315 Serie A"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A.S. Roma were in Serie A in 2014-15."
}
]
},
{
"tripleset": [
[
"AFC Ajax",
"MANAGER",
"Frank de Boer"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Frank de Boer manages the AFC Ajax team."
},
{
"source": "webnlg",
"text": "AFC Ajax's manager is Frank de Boer."
},
{
"source": "webnlg",
"text": "The manager of AFC Ajax is Frank de Boer."
}
]
},
{
"tripleset": [
[
"AFC Ajax (amateurs)",
"NICKNAME",
"\"Joden , Godenzonen\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AFC Ajax (amateurs) have the nickname \"Joden , Godenzonen\"."
},
{
"source": "webnlg",
"text": "The AFC Ajax team's nickname is Joden, Godenzonen."
},
{
"source": "webnlg",
"text": "AFC Ajax (amateurs) nickname is Joden, Godenzonen."
}
]
},
{
"tripleset": [
[
"AFC Ajax (amateurs)",
"NUMBER_OF_MEMBERS",
"5000"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AFC Ajax has 5000 Members."
},
{
"source": "webnlg",
"text": "AFC Ajax (amateurs) has 5000 members."
}
]
},
{
"tripleset": [
[
"AZAL PFK",
"LOCATION",
"\"Shuvalan, Baku, Azerbaijan\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AZAL PFK is located at Shuvalan, Baku, Azerbaijan."
},
{
"source": "webnlg",
"text": "AZAL PFK is located in Shuvalan, Baku, Azerbaijan."
}
]
},
{
"tripleset": [
[
"AZ Alkmaar",
"NUMBER_OF_MEMBERS",
"17023"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "AZ Alkmaar has 17023 members."
}
]
},
{
"tripleset": [
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"MANAGER",
"Vica"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agremiacao Sportiva Arapiraquense are managed by Vica."
}
]
},
{
"tripleset": [
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
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"2015 Campeonato Brasileiro S\u00e9rie C"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense competed in the 2015 Campeonato Brasileiro S\u00e9rie C."
},
{
"source": "webnlg",
"text": "Agremiacao Sportiva Arapiraquense were in Campeonato Brasileiro Serie C in 2015."
}
]
},
{
"tripleset": [
[
"Akron Summit Assault",
"GROUND",
"St. Vincent\u2013St. Mary High School"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Akron Summit Assault's ground is St. Vincent-St. Mary High School."
},
{
"source": "webnlg",
"text": "St Vincent-St Mary High School is the ground of Akron Summit Assault."
}
]
},
{
"tripleset": [
[
"Akron Summit Assault",
"NUMBER_OF_MEMBERS",
"3000"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Akron Summit Assault has 3000 members."
},
{
"source": "webnlg",
"text": "Akron Summit Assault has got 3000 members."
}
]
},
{
"tripleset": [
[
"Amsterdam",
"PART",
"Amsterdam-Centrum"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Amsterdam-Centrum is part of Amsterdam."
}
]
},
{
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{
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"Delfino Pescara 1936"
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{
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"11 Diagonal Street",
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[
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"3Arena",
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"Live Nation Entertainment"
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{
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"AC Hotel Bella Sky Copenhagen",
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"AC Hotel Bella Sky Copenhagen",
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"3XN"
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"Adare Manor",
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"J. P. McManus"
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]
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"source": "webnlg",
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]
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"Akita Museum of Art",
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"3746.66 (square metres)"
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"Japan"
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"Alan B. Miller Hall",
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"Virginia"
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"Robert Gates"
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]
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"source": "webnlg",
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]
},
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"source": "webnlg",
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{
"source": "webnlg",
"text": "Alan B. Miller Hall's building, started on 30th March 2007, is located in Williamsburg, Virginia, and owned by the College of William and Mary."
},
{
"source": "webnlg",
"text": "The Alan B. Miller Hall, owned by The College of William and Mary, is in Williamsburg, Virginia and was opened on the 30th March 2007."
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]
},
{
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"Ampara Hospital",
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"BED_COUNT",
"476"
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"source": "webnlg",
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"source": "webnlg",
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]
},
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"Sri Lanka"
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"Eastern Province, Sri Lanka"
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"source": "webnlg",
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}
]
},
{
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"Asher and Mary Isabelle Richardson House",
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"1911"
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"text": "The Asher and Mary Isabelle Richardson House located on US Route 83 and built in 1911 was added to the National Register of Historic Places, on 22nd November 1988."
},
{
"source": "webnlg",
"text": "The Asher and Mary Isabelle Robinson House on U.S. Route 83 was constructed in 1911 and added to the National Register of Historic Places on 22 November 1988."
},
{
"source": "webnlg",
"text": "The Asher and Mary Isabelle Richardson House on U.S. Route 83 was constructed in 1911 and added to the National Register of Historic Places on 22 November 1988."
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]
},
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"Asilomar Conference Grounds",
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"source": "webnlg",
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},
{
"source": "webnlg",
"text": "The Asilomar Conference Grounds of Pacific Grove in California was added to the National Register of Historic Places on 27th of February in 1987, where the reference number for the National register was 87000823."
},
{
"source": "webnlg",
"text": "Asilomar Conference Grounds, Pacific Grove, California, was added to the National Register of Historic Places on February the 27nd 1987 with a reference number of 87000823."
}
]
},
{
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[
"Asser Levy Public Baths",
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"\"1980-04-23\""
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"source": "webnlg",
"text": "The Asser Levy Public Baths are located in Asser Levy Place , East 23rd Street and were added to the National Register of Historic Places on 1980-04-23, with the number 80002709."
},
{
"source": "webnlg",
"text": "The Asser Levy Public Baths are located in Asser Levy Place , East 23rd Street and were added to the National Register of Historic Places on 23rd April 1980 with the reference number 80002709 ."
}
]
},
{
"tripleset": [
[
"Asser Levy Public Baths",
"LOCATION",
"New York City"
],
[
"New York City",
"COUNTRY",
"United States"
],
[
"New York City",
"IS_PART_OF",
"Manhattan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Asser Levy Public Baths are located in New York City, U.S. Manhattan is a borough in New York City."
},
{
"source": "webnlg",
"text": "Asser Levy Public Baths are located in Manhattan, New York City, United States."
},
{
"source": "webnlg",
"text": "The location of Asser Levy Public Baths is New York City, Manhattan in the U.S."
}
]
},
{
"tripleset": [
[
"India",
"LEADER_NAME",
"T. S. Thakur"
],
[
"Amdavad ni Gufa",
"COUNTRY",
"India"
],
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "T S Thakur and Sumitra Mahajan are leaders in India where Amdavad ni Gufa is located."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in India where T S Thakur and Sumitra Mahajan are leaders."
}
]
},
{
"tripleset": [
[
"Atat\u00fcrk Monument (\u0130zmir)",
"DESIGNER",
"Pietro Canonica"
],
[
"Turkey",
"LEADER_NAME",
"Ahmet Davuto\u011flu"
],
[
"Turkey",
"CAPITAL",
"Ankara"
],
[
"Atat\u00fcrk Monument (\u0130zmir)",
"MATERIAL",
"\"Bronze\""
],
[
"Atat\u00fcrk Monument (\u0130zmir)",
"INAUGURATION_DATE",
"\"1932-07-27\""
],
[
"Atat\u00fcrk Monument (\u0130zmir)",
"LOCATION",
"Turkey"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ankara is the Turkish capital and the country's leader's name is Ahmet Davutoglu. The Atat\u00fcrk Monument (\u0130zmir) can be found in Turkey. Pietro Canonica designed the Monument and it is made of bronze. The Ataturk Monument was inaugurated on 27th July, 1932."
},
{
"source": "webnlg",
"text": "The Ataturk Monument in Izmir was inaugurated on the 27th July, 1932. It is made of bronze and it was designed by Pietro Canonica. Turkey's leader is called Ahmet Davutoglu and its capital is Ankara."
},
{
"source": "webnlg",
"text": "The capital city of Turkey is Ankara and the country's leader is Ahmet Davutoglu. The country is the location of Izmir where the bronze Ataturk Monument designed by Pietro Canonica was inaugurated on 27 July 1932."
}
]
},
{
"tripleset": [
[
"Azerbaijan",
"LEADER_TITLE",
"Prime Minister of Azerbaijan"
],
[
"Baku Turkish Martyrs' Memorial",
"DEDICATED_TO",
"\"Ottoman Army soldiers killed in the Battle of Baku\""
],
[
"Baku Turkish Martyrs' Memorial",
"LOCATION",
"Azerbaijan"
],
[
"Baku Turkish Martyrs' Memorial",
"NATIVE_NAME",
"\"T\u00fcrk \u015eehitleri An\u0131t\u0131\""
],
[
"Azerbaijan",
"LEADER_NAME",
"Artur Rasizade"
],
[
"Baku Turkish Martyrs' Memorial",
"DESIGNER",
"\"H\u00fcseyin B\u00fct\u00fcner and Hilmi G\u00fcner\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial was designed by Huseyin Butuner and Hilmi Guner and is located in Azerbaijan. It is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku. The leader, the Prime Minister of Azerbaijan is Artur Rasizade."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs' Memorial is located in Azerbaijan and the country's leader is Prime Minister Artur Rasizade. The Memorial is dedicated to the Ottoman Army soldiers killed in the Battle of Baku and its designers are Huseyin Butuner and Hilmi Guner. The native name for the Baku Turkish Martyrs' Memorial is T\u00fcrk \u015eehitleri An\u0131t\u0131."
},
{
"source": "webnlg",
"text": "Huseyin Butuner and Hilmi Guner are the designers of the Baku Turkish Martyrs memorial, also known as Turk Sehitleri Aniti. The memorial is located in Azerbaijan and dedicated to the Ottoman army soldiers who died in the battle of Baku. Prime Minister Artur Rasizade is the leader of the country."
}
]
},
{
"tripleset": [
[
"Monocacy National Battlefield",
"LOCATION",
"Frederick County, Maryland"
],
[
"14th New Jersey Volunteer Infantry Monument",
"ESTABLISHED",
"\"1907-07-11\""
],
[
"14th New Jersey Volunteer Infantry Monument",
"CATEGORY",
"Historic districts in the United States"
],
[
"14th New Jersey Volunteer Infantry Monument",
"DISTRICT",
"Monocacy National Battlefield"
],
[
"Monocacy National Battlefield",
"NEAREST_CITY",
"Frederick, Maryland"
],
[
"14th New Jersey Volunteer Infantry Monument",
"OWNING_ORGANISATION",
"National Park Service"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The 14th New Jersey Volunteer Infantry Monument is located at the Monocacy National Battlefield near Frederick, Maryland. The monument was established 1907-07-11 and belongs to the category of Historic districts in the United States. The monument has been provided by the National Park Service."
},
{
"source": "webnlg",
"text": "The 14th New Jersey Volunteer Infantry Monument was established on July 11, 1907, in the Monocacy National Battlefield, in Frederick County, Maryland. The monument is categorized as a United States historic district and is under the responsibility of the National Park Service. The closest city to the battlefield is Frederick, Maryland."
},
{
"source": "webnlg",
"text": "The 14th New Jersey Volunteer Infantry Monument (established on 11th July 1907) belongs to the category of Historic districts in the United States and is located on Monocacy National Battlefield. The nearest city is Frederick, Frederick County, Maryland. The 14th New Jersey Volunteer Infantry Monument is owned by the National Park Service."
}
]
},
{
"tripleset": [
[
"Monocacy National Battlefield",
"LOCATION",
"Frederick County, Maryland"
],
[
"14th New Jersey Volunteer Infantry Monument",
"ESTABLISHED",
"\"1907-07-11\""
],
[
"14th New Jersey Volunteer Infantry Monument",
"COUNTRY",
"\"United States\""
],
[
"14th New Jersey Volunteer Infantry Monument",
"CATEGORY",
"Historic districts in the United States"
],
[
"14th New Jersey Volunteer Infantry Monument",
"DISTRICT",
"Monocacy National Battlefield"
],
[
"14th New Jersey Volunteer Infantry Monument",
"OWNING_ORGANISATION",
"National Park Service"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The 14th New Jersey Volunteer Infantry Monument is located in the Monocacy National Battlefield, in Frederick County, Maryland, United States. The monument was established on July 11th, 1907. It is now categorized as a United States historic district and is owned by the National Park Service."
},
{
"source": "webnlg",
"text": "The 14th New Jersey Volunteer Infantry Monument was established on 11 July 1907 in the district of the Monocacy National Battlefield, Frederick County, Maryland, United States. It belongs to the category of historic US districts and is owned by the National Park service."
},
{
"source": "webnlg",
"text": "The 14th New Jersey Volunteer Infantry Monument is located in the Monocacy National Battlefield, in Frederick County, Maryland, USA. Established on July 11, 1907, the monument is categorized as a historic district and is owned by the National Park Service."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Ajoblanco",
"ALTERNATIVE_NAME",
"\"Ajo blanco\""
],
[
"Ajoblanco",
"INGREDIENT",
"Bread"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "From Spain, Ajoblanco (alternatively known as Ajo blanco) has bread as an ingredient."
},
{
"source": "webnlg",
"text": "Ajoblanco, also known as \"Ajo blanco\", comes from Spain. Bread is one of the ingredients."
},
{
"source": "webnlg",
"text": "Bread is an ingredient of Ajoblanco, which is from Spain and an alternative name to Ajo blanco."
}
]
},
{
"tripleset": [
[
"Ajoblanco",
"COUNTRY",
"Spain"
],
[
"Ajoblanco",
"REGION",
"Andalusia"
],
[
"Ajoblanco",
"INGREDIENT",
"Water"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ajoblanco is from Andalusia, in Spain. Ajoblanco contains water."
},
{
"source": "webnlg",
"text": "Water is an ingredient in Ajoblanco, which is a food found in Andalusia, in Spain."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"COUNTRY",
"Indonesia"
],
[
"Indonesia",
"LEADER_NAME",
"Joko Widodo"
],
[
"Indonesia",
"LEADER_NAME",
"Jusuf Kalla"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem arem originates from the country of Indonesia, where two of the leaders are, Joko Widodo and Jusuf Kalla."
},
{
"source": "webnlg",
"text": "Arem arem originates from Indonesia where two of the leaders are Joko Widodo and Jusef Kalla."
}
]
},
{
"tripleset": [
[
"Arem-arem",
"COUNTRY",
"Indonesia"
],
[
"Indonesia",
"LEADER_NAME",
"Jusuf Kalla"
],
[
"Arem-arem",
"REGION",
"\"Nationwide in Indonesia, but more specific to Java\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arem-arem is a food found nationwide in Indonesia, but is more specific to Java. Jusuf Kalla is the leader of Indonesia."
},
{
"source": "webnlg",
"text": "Jusuf Kalla is the leader of Indonesia, the country which serves Arem-arem. While Arem-arem is nationwide in Indonesia, it is more specific to Java."
},
{
"source": "webnlg",
"text": "Jusuf Kalla is a leader in Indonesia, where the dish arem-arem is found nationwide, though it is more specific to Java."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Italy",
"LANGUAGE",
"Italian language"
],
[
"Italy",
"LEADER_NAME",
"Sergio Mattarella"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The leader of Italy is Sergio Mattarella, it is also where Italian is spoken and the arrabbiata sauce is from."
},
{
"source": "webnlg",
"text": "Sergio Mattarella is a leader of Italy where the Italian language is spoken and Arrabbiata sauce can be found."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce is from Italy, where the spoken language is Italian and a leader is Sergio Mattarella."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"INGREDIENT",
"Chili pepper"
],
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Arrabbiata sauce",
"REGION",
"Rome"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arrabbiata sauce, a traditional dish from Rome in Italy, has chili pepper as one of its important ingredients."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce from Rome in Italy contains chili pepper."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce is from the region of Rome in Italy and one of the main ingredients is chili pepper."
}
]
},
{
"tripleset": [
[
"Arrabbiata sauce",
"INGREDIENT",
"Garlic"
],
[
"Arrabbiata sauce",
"COUNTRY",
"Italy"
],
[
"Arrabbiata sauce",
"REGION",
"Rome"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Garlic is an ingredient in arrabbiata sauce which originates from the region of Rome, in Italy."
},
{
"source": "webnlg",
"text": "Arrabbiata sauce contains garlic and comes from the Rome region of italy."
},
{
"source": "webnlg",
"text": "Garlic is an ingredient in arrabbiata sauce, which is a traditional dish from Rome, Italy."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Arr\u00f2s negre",
"REGION",
"Catalonia"
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Squid"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Arros negre contains Squid and comes from the Catalonia region of Spain."
},
{
"source": "webnlg",
"text": "Squid is an ingredient of arros negre which is from the region of Catalonia in Spain."
},
{
"source": "webnlg",
"text": "Arros negre is from the region of Catalonia, Spain, and one of its ingredients is Squid."
}
]
},
{
"tripleset": [
[
"Arr\u00f2s negre",
"REGION",
"Valencian Community"
],
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Arr\u00f2s negre",
"INGREDIENT",
"Cuttlefish"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Cuttlefish is an ingredient in Arr\u00f2s negre which comes from the region of the Valencian Community, in Spain."
},
{
"source": "webnlg",
"text": "Cuttlefish is an ingredient of the dish Arr\u00f2s negre, which is a dish that comes from the Valencian community in Spain."
},
{
"source": "webnlg",
"text": "Cuttlefish is an ingredient in Arr\u00f2s negre, which is a traditional dish from the Valencian community in Spain."
}
]
},
{
"tripleset": [
[
"Asam pedas",
"COUNTRY",
"Malaysia"
],
[
"Asam pedas",
"REGION",
"\"Sumatra and Malay Peninsula\""
],
[
"Asam pedas",
"MAIN_INGREDIENTS",
"\"Fish cooked in sour and hot sauce\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The main ingredients of asam pedas , a food from the Sumatra and Malay Peninsula regions of Malaysia, consist of fish cooked in a sour and hot sauce."
},
{
"source": "webnlg",
"text": "Asam pedas is made from fish cooked in a sour and hot sauce and comes from the Sumatra and Malay Peninsula regions in Malaysia."
},
{
"source": "webnlg",
"text": "The dish Asam pedas comes from the region of Sumatra and the Malay Peninsula. Malaysia and the main ingredients are fish cooked in sour and hot sauce."
}
]
},
{
"tripleset": [
[
"Ayam penyet",
"REGION",
"Malaysia"
],
[
"Ayam penyet",
"COUNTRY",
"Java"
],
[
"Ayam penyet",
"MAIN_INGREDIENTS",
"\"Squeezed\" or \"smashed\" fried chicken served with sambal"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet is made from squeezed or squashed chicken served with sambla and is a popular dish in Java, Malaysia."
},
{
"source": "webnlg",
"text": "Ayam penyet's main ingredients are squeezed or smashed fried chicken served with sambal. It is a popular dish in Malaysia and also found in Java."
},
{
"source": "webnlg",
"text": "Ayam penyet originates from Malaysia and Java. The main ingredients are squeezed or smashed fried chicken served with sambal."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"Bacon Explosion",
"INGREDIENT",
"Bacon"
],
[
"Bacon Explosion",
"MAIN_INGREDIENTS",
"Sausage"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon and sausage are the main ingredients in a Bacon Explosion, which comes from the United States."
},
{
"source": "webnlg",
"text": "Bacon Explosion, whose name comes from the United States, has bacon and sausage."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"United States",
"ETHNIC_GROUP",
"Native Americans in the United States"
],
[
"United States",
"CAPITAL",
"Washington, D.C."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "One of the ethnic groups in the United States are native Americans, the country is also the home of the dish bacon explosion and has Wasington D.C. as it's capitol city."
},
{
"source": "webnlg",
"text": "Bacon Explosion originated in the United States, where the capital is Washington DC and Native Americans are an ethnic group."
},
{
"source": "webnlg",
"text": "Bacon Explosion comes from the United States where Washington D.C. is the capital and Native Americans are an ethnic group."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"United States",
"LEADER_NAME",
"Barack Obama"
],
[
"United States",
"CAPITAL",
"Washington, D.C."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The United States is the country of Bacon Explosion. Barack Obama is the leader and Washington D.C. is the capital."
},
{
"source": "webnlg",
"text": "The Bacon Explosion comes from the United States where Barack Obama is a leader and the capital city is Washington DC."
},
{
"source": "webnlg",
"text": "The Bacon Explosion originates from the United States where the leader is Barack Obama and the capital is Washington DC."
}
]
},
{
"tripleset": [
[
"Bacon Explosion",
"COUNTRY",
"United States"
],
[
"United States",
"LEADER_NAME",
"John Roberts"
],
[
"United States",
"ETHNIC_GROUP",
"African Americans"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Bacon Explosion originated in the United States, where African Americans are an ethnic group and John Roberts is a leader."
},
{
"source": "webnlg",
"text": "Bacon Explosion comes from the United States where African Americans are an ethnic group and John Roberts is a leader."
}
]
},
{
"tripleset": [
[
"Bacon sandwich",
"DISH_VARIATION",
"BLT"
],
[
"Bacon sandwich",
"COUNTRY",
"United Kingdom"
],
[
"Bacon sandwich",
"INGREDIENT",
"Condiment"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bacon sandwiches are variations of BLT and come from the U.K. Condiments are found in these sandwiches."
},
{
"source": "webnlg",
"text": "The UK dish, the bacon sandwich uses condiments as an ingredient and has the variation BLT."
},
{
"source": "webnlg",
"text": "The bacon sandwich, a variation of the BLT, comes from the UK and contains condiments."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COUNTRY",
"France"
],
[
"Baked Alaska",
"REGION",
"New York"
],
[
"Baked Alaska",
"INGREDIENT",
"Meringue"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Meringue is an ingredient of a Baked Alaska, which is from the New York region and France."
},
{
"source": "webnlg",
"text": "Meringue is an ingredient of a Baked Alaska, which is reputed to come from both France and New York."
},
{
"source": "webnlg",
"text": "An ingredient of baked Alaska is meringue which originates in France and the New York regions."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COUNTRY",
"United States"
],
[
"Baked Alaska",
"REGION",
"New York"
],
[
"Baked Alaska",
"INGREDIENT",
"Sponge cake"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sponge cake is one of the ingredients in Baked Alaska, a dish from the New York region and found in the United States."
},
{
"source": "webnlg",
"text": "Sponge cake is an ingredient of Baked Alaska, which comes from the region of New York and can be found in the U.S."
},
{
"source": "webnlg",
"text": "An ingredient of baked Alaska is sponge cake, the dish originates from New York, USA."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COURSE",
"Dessert"
],
[
"Dessert",
"DISH_VARIATION",
"Cookie"
],
[
"Baked Alaska",
"INGREDIENT",
"Christmas pudding"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Christmas pudding is an ingredient in the dessert Baked Alaska. Cookie is also a dessert."
},
{
"source": "webnlg",
"text": "A cookie can be a dessert, as can baked alaska which has christmas pudding as an ingredient."
},
{
"source": "webnlg",
"text": "Cookies are a dessert item, as is Baked Alaska that has Christmas pudding as one of its ingredients."
}
]
},
{
"tripleset": [
[
"Baked Alaska",
"COURSE",
"Dessert"
],
[
"Dessert",
"DISH_VARIATION",
"Sandesh (confectionery)"
],
[
"Baked Alaska",
"INGREDIENT",
"Christmas pudding"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sandesh (confectionery) is a dish that can be served as a dessert. Another dish that is a dessert is Baked Alaska which has Christmas pudding as an ingredient."
},
{
"source": "webnlg",
"text": "Sandesh and Baked Alaska are desserts. Baked Alaska uses Christmas pudding as an ingredient."
},
{
"source": "webnlg",
"text": "Christmas pudding is an ingredient in Baked Alaska, which is a dessert, same as sandesh."
}
]
},
{
"tripleset": [
[
"Bandeja paisa",
"COUNTRY",
"Colombian cuisine"
],
[
"Bandeja paisa",
"INGREDIENT",
"Kidney bean"
],
[
"Bandeja paisa",
"REGION",
"Antioquia Department"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Part of Columbian cuisine, Bandeja paisa (which has kidney beans as one of the ingredients), comes from Antioquia Department area."
},
{
"source": "webnlg",
"text": "Bandeja paisa, which contains kidney beans, is a traditional dish found in the Antioquia Department of Colombia."
},
{
"source": "webnlg",
"text": "Kidney beans are an ingredient of Bandeja paisa which is a Colombian dish from the Antioquia department."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"COUNTRY",
"France"
],
[
"France",
"LEADER_NAME",
"G\u00e9rard Larcher"
],
[
"France",
"LEADER_NAME",
"Claude Bartolone"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Barny Cakes come from France where Gerard Larcher and Claude Bartolone are leaders."
},
{
"source": "webnlg",
"text": "Barny cakes are found in France where Gerard Larcher and Claude Bartolone are leaders."
},
{
"source": "webnlg",
"text": "Barny cakes are found in France where both Gerard Larcher and Claude Bartolone are leaders."
}
]
},
{
"tripleset": [
[
"Barny Cakes",
"DISH_VARIATION",
"Apple"
],
[
"Barny Cakes",
"CARBOHYDRATE",
"18.0 g"
],
[
"Barny Cakes",
"PROTEIN",
"1.8 g"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Barny cakes can be made with apple and have 18 grams of carbs and 1.8 g of protein."
},
{
"source": "webnlg",
"text": "Barny cakes can be made with apple and contain 1.8g protein and 18g of carbs."
},
{
"source": "webnlg",
"text": "Barny cakes may be made with apple and have 1.8g of protein and 18g of carbohydrates."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"COUNTRY",
"Singapore"
],
[
"Singapore",
"CURRENCY",
"Singapore dollar"
],
[
"Singapore",
"LEADER_NAME",
"Halimah Yacob"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Beef kway teow is a dish from the country of Singapore, where the leader is Halimah Yacob and the currency is the Singapore dollar."
},
{
"source": "webnlg",
"text": "Beef kway teow is a popular dish in Singapore, where the Singapore dollar is the currency and the leader is Halimah Yacob."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"INGREDIENT",
"Oyster sauce"
],
[
"Beef kway teow",
"REGION",
"Indonesia"
],
[
"Beef kway teow",
"COUNTRY",
"\"Singapore and Indonesia\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A dish commonly found in Singapore and Indonesia is beef kway teow which has oyster sauce as a main ingredient."
},
{
"source": "webnlg",
"text": "Beef kway teow contains oyster sauce and is found in Indonesia and Singapore."
}
]
},
{
"tripleset": [
[
"Beef kway teow",
"REGION",
"Singapore"
],
[
"Beef kway teow",
"COUNTRY",
"Indonesia"
],
[
"Singapore",
"LEADER_NAME",
"Tony Tan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "A popular food in Indonesia, Beef kway teow originates from Singapore. The country where Tony Tan is the leader."
},
{
"source": "webnlg",
"text": "Tony Tan is a leader in Singapore where the dish Beef kway teow originates from. It is also found in Indonesia."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"Bhajji",
"MAIN_INGREDIENTS",
"\"Gram flour, vegetables\""
],
[
"Bhajji",
"INGREDIENT",
"Gram flour"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji originates from India, it's main ingredients are gram flour and vegetables."
},
{
"source": "webnlg",
"text": "The key ingredients of a Bhajji from India are gram flour and vegetables."
},
{
"source": "webnlg",
"text": "The main ingredients in Bhajji, which originates from Indiam are gram flour and vegetables."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"DEMONYM",
"Indian people"
],
[
"India",
"LEADER_NAME",
"T. S. Thakur"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji originates from India, country of Indians where the leader is T. S. Thakur."
},
{
"source": "webnlg",
"text": "Bhajji originates from India, where the leader is called T S Thakur and the people who live there are called Indians."
},
{
"source": "webnlg",
"text": "Bhajji originate from India where T S Thakur leads the Indian people."
}
]
},
{
"tripleset": [
[
"Bhajji",
"COUNTRY",
"India"
],
[
"India",
"LEADER_NAME",
"T. S. Thakur"
],
[
"India",
"LEADER_NAME",
"Narendra Modi"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji comes from the country of India, where two of the leaders are, T. S. Thakur and Narendra Modi."
},
{
"source": "webnlg",
"text": "Bhajji originates from India, where two of the leaders are Narendra Modi and T.S. Thakur."
},
{
"source": "webnlg",
"text": "The dish bhajji originates in India where T.S. Thakur and Narendra Modi are leaders."
}
]
},
{
"tripleset": [
[
"Bhajji",
"REGION",
"Karnataka"
],
[
"Bhajji",
"MAIN_INGREDIENTS",
"\"Gram flour, vegetables\""
],
[
"Bhajji",
"INGREDIENT",
"Gram flour"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bhajji originates from the Karnataka region and the main ingredients are vegetables and gram flour."
},
{
"source": "webnlg",
"text": "The main ingredients in Bhajji are gram flour and vegetables, this comes from the Karnataka region."
},
{
"source": "webnlg",
"text": "Bhajji are found in the region of Karnataka, its main ingredients are gram flour and vegetables."
}
]
},
{
"tripleset": [
[
"Binignit",
"INGREDIENT",
"Sweet potato"
],
[
"Binignit",
"MAIN_INGREDIENTS",
"Banana"
],
[
"Binignit",
"COUNTRY",
"Philippines"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The binignit dish can be found in the Philippines. Two of the main ingredients in it are banana and sweet potato."
},
{
"source": "webnlg",
"text": "The main ingredients of Binignit are banana and sweet potatoes, and it can be found in the Philippines."
},
{
"source": "webnlg",
"text": "Binignit is a dish from the Philippines made from banana and sweet potato."
}
]
},
{
"tripleset": [
[
"Bionico",
"COUNTRY",
"Mexico"
],
[
"Bionico",
"COURSE",
"Dessert"
],
[
"Bionico",
"INGREDIENT",
"Sour cream"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bionico is a dessert containing sour cream from Mexico."
},
{
"source": "webnlg",
"text": "Sour cream is an ingredient of Bionico, that is a dessert course found in Mexico."
},
{
"source": "webnlg",
"text": "Sour cream is used to serve Bionico, a dessert food found in Mexico."
}
]
},
{
"tripleset": [
[
"Bionico",
"COUNTRY",
"Mexico"
],
[
"Bionico",
"REGION",
"Jalisco"
],
[
"Bionico",
"INGREDIENT",
"Raisin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Containing raisins, Bionico is found in the region of Jalisco, in Mexico."
},
{
"source": "webnlg",
"text": "Raisins are found in bionico which comes from the region of Jalisco in Mexico."
},
{
"source": "webnlg",
"text": "Raisins are an ingredient of Bionico which is from the region Jalisco, Mexico."
}
]
},
{
"tripleset": [
[
"Celery",
"GENUS",
"Apium"
],
[
"Bakso",
"INGREDIENT",
"Celery"
],
[
"Celery",
"FAMILY",
"Apiaceae"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "One ingredient in Bakso is celery, which is a member of the genus Apium in the family Apiaceae."
},
{
"source": "webnlg",
"text": "Celery is from the genus apium and the family Apiaceae. It is an ingredient of Bakso."
},
{
"source": "webnlg",
"text": "Celery is a member of the family Apiaceae, it is from the genus apium and is an ingredient of Bakso."
}
]
},
{
"tripleset": [
[
"Dessert",
"DISH_VARIATION",
"Cake"
],
[
"Bionico",
"INGREDIENT",
"Granola"
],
[
"Bionico",
"COURSE",
"Dessert"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bionico is a dessert that requires granola as one of its ingredients; it can be eaten as an alternative to cake."
},
{
"source": "webnlg",
"text": "Bionico requires granola as one of its ingredients, it is served at the dessert course, like cake."
}
]
},
{
"tripleset": [
[
"Indonesia",
"CAPITAL",
"Jakarta"
],
[
"Indonesia",
"LEADER_NAME",
"Jusuf Kalla"
],
[
"Bakso",
"COUNTRY",
"Indonesia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bakso is a dish from the country of Indonesia, where the capital is Jakarta and one of the leaders is Jusuf Kalla."
},
{
"source": "webnlg",
"text": "The dish bakso comes from Indonesia where Jusuf Kalla is the leader and Jakarta is the capitol city."
},
{
"source": "webnlg",
"text": "Bakso is a dish from the country of Indonesia, where the capital is Jakarta and the leader is Jusuf Kalla."
}
]
},
{
"tripleset": [
[
"Java",
"ETHNIC_GROUP",
"Baduy"
],
[
"Ayam penyet",
"REGION",
"Singapore"
],
[
"Ayam penyet",
"COUNTRY",
"Java"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "From the Singapore region, Ayam penyet, is a food found in Java where an ethic group is the Baduy."
},
{
"source": "webnlg",
"text": "Ayam penyet is a dish from the region of Singapore and is also found in Java where the Baduy are an ethnic group."
},
{
"source": "webnlg",
"text": "Ayam penyet is from Singapore and also Java, where the Baduy are an ethnic group."
}
]
},
{
"tripleset": [
[
"Java",
"ETHNIC_GROUP",
"Banyumasan people"
],
[
"Ayam penyet",
"REGION",
"\"Nationwide, also can be found in Malaysia and Singapore\""
],
[
"Ayam penyet",
"COUNTRY",
"Java"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ayam penyet originates from Java where the Banyumasan are an ethnic group. It can be found nationwide and also in Singapore and Malaysia."
},
{
"source": "webnlg",
"text": "The dish Ayam penyet originates from Java where the Banyumasan people are an ethnic group. The dish can also be found in Malaysia and Singapore."
}
]
},
{
"tripleset": [
[
"Spain",
"LANGUAGE",
"Spanish language"
],
[
"Arr\u00f2s negre",
"COUNTRY",
"Spain"
],
[
"Spain",
"LEADER_NAME",
"Felipe VI of Spain"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The leader of Spain is Felipe VI and it is also where Spanish is spoken and the dish arros negre is from."
},
{
"source": "webnlg",
"text": "Arros Negre is a traditional dish from Spain where they speak Spanish and the leader is Felipe VI."
},
{
"source": "webnlg",
"text": "Arros negre comes from Spain where Felipe VI is the leader and the Spanish language is spoken."
}
]
},
{
"tripleset": [
[
"Accademia di Architettura di Mendrisio",
"COUNTRY",
"Switzerland"
],
[
"Accademia di Architettura di Mendrisio",
"NUMBER_OF_STUDENTS",
"600"
],
[
"Accademia di Architettura di Mendrisio",
"ESTABLISHED",
"1996"
],
[
"Switzerland",
"LEADER_NAME",
"Johann Schneider-Ammann"
],
[
"Accademia di Architettura di Mendrisio",
"LOCATION",
"Ticino"
],
[
"Switzerland",
"LEADER_TITLE",
"Federal Chancellor of Switzerland"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Accademia di Architettura di Mendrisio located in Ticino, Switzerland was established in 1996. It has 600 students. Switzerland's leader is Federal Chancellor Johann Schneider-Ammann."
},
{
"source": "webnlg",
"text": "Federal Chancellor Johann Schneider-Ammann is the current leader of Switzerland where the Accademia di Architettura di Mendrisio is located in Ticino. It was established in 1996 and has 600 students."
},
{
"source": "webnlg",
"text": "Accademia di Architettura di Mendrisio in Ticino, Switzerland was established in 1996 and currently has 600 students. The Federal Chancellor of Switzerland is named Johann Schneider-Ammann."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"CITY",
"Bangalore"
],
[
"Acharya Institute of Technology",
"DIRECTED_BY",
"\"Dr. G. P. Prabhukumar\""
],
[
"Acharya Institute of Technology",
"ESTABLISHED",
"2000"
],
[
"Acharya Institute of Technology",
"COUNTRY",
"\"India\""
],
[
"Acharya Institute of Technology",
"STATE",
"Karnataka"
],
[
"Acharya Institute of Technology",
"CAMPUS",
"\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090.\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology is in Bangalore, India in the state of Karnataka. The school was established in 2000 and its director is Dr. G. P. Prabhukumar. The full address of the school is In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090."
},
{
"source": "webnlg",
"text": "Established in 2000, the Acharya Institute of Technology is in Bangalore, Karnataka India. The institute's director is Dr. G. P. Prabhukumar and the campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology campus is located at Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore \u2013 560090, Karnataka, India. The Institute was established in the year 2000 and the director there is Dr. G. P. Prabhukumar."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"PRESIDENT",
"\"B.M. Reddy\""
],
[
"Acharya Institute of Technology",
"CITY",
"Bangalore"
],
[
"Acharya Institute of Technology",
"DIRECTED_BY",
"\"Dr. G. P. Prabhukumar\""
],
[
"Acharya Institute of Technology",
"ESTABLISHED",
"2000"
],
[
"Acharya Institute of Technology",
"COUNTRY",
"\"India\""
],
[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Acharya Institute of Technology in Bangalore, India was established in 2000. It is affiliated with the Visvesvaraya Technological University. Its president is B.M. Reddy and its directore is Dr. G.P. Prabhukumar."
},
{
"source": "webnlg",
"text": "The Acharya Institute of Technology in Bangalore, India was established in 2000 and is affiliated to the Visvesvaraya Technological University. It was established in 2000 and has B M Reddy as President and Dr g P Prabhukumar as Director."
},
{
"source": "webnlg",
"text": "Acharya Institute of Technology is located in Bangalore, India. Its president is B.M. Reddy and the director is Dr. G. P. Prabhukumar. It was established in 2000 and its affiliation is Visvesvaraya Technological University."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"WAS_GIVEN_THE_'TECHNICAL_CAMPUS'_STATUS_BY",
"All India Council for Technical Education"
],
[
"All India Council for Technical Education",
"LOCATION",
"Mumbai"
],
[
"Acharya Institute of Technology",
"SPORTS_OFFERED",
"Tennis"
],
[
"Karnataka",
"HAS_TO_ITS_WEST",
"Arabian Sea"
],
[
"Acharya Institute of Technology",
"STATE",
"Karnataka"
],
[
"Tennis",
"SPORTS_GOVERNING_BODY",
"International Tennis Federation"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Acharya Institute of Technology in Mumbai, Karnataka was given the \"Technological Campus\" status by the All India Council for Technical Education (located in Mumbai). The Acharya Institute offers tennis and the sports governing body is the International Tennis Foundation. Karnataka has the Arabian Sea to its west."
},
{
"source": "webnlg",
"text": "Tennis, which has the International Tennis Federation as its governing body, is offered as a sport at the Acharya Institute of Technology. The Institute was given Technical Campus status by the All India Council for Technical Education based in Mumbai and is situated in the state of Karnataka which has the Arabian Sea to the west."
},
{
"source": "webnlg",
"text": "Governed by the International Tennis Federation, the Acharya Institute of Technology in Karnataka offers tennis courses and was given the 'Technical Campus' status by the All India Council for Technical Education in Mumbai. Karnataka is east of the Arabian Sea."
}
]
},
{
"tripleset": [
[
"Acharya Institute of Technology",
"WAS_GIVEN_THE_'TECHNICAL_CAMPUS'_STATUS_BY",
"All India Council for Technical Education"
],
[
"All India Council for Technical Education",
"LOCATION",
"Mumbai"
],
[
"Visvesvaraya Technological University",
"CITY",
"Belgaum"
],
[
"Acharya Institute of Technology",
"SPORTS_OFFERED",
"Tennis"
],
[
"Tennis",
"SPORTS_GOVERNING_BODY",
"International Tennis Federation"
],
[
"Acharya Institute of Technology",
"AFFILIATION",
"Visvesvaraya Technological University"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Acharya Institute of Technology is affiliated with Visvesvaraya Technological University in Belgium. The institute was given Technical Campus' status by the All India Council for Technical Education in Mumbai. The institute offers tennis which is governed by the International Tennis Federation."
},
{
"source": "webnlg",
"text": "Governed by the International Tennis Federation, the Acharya Institute of Technology offers tennis courses and was given the 'Technical Campus' status by the All India Council for Technical Education in Mumbai. It is also affiliated with the Visvesvaraya Technological University in Belgaum."
}
]
},
{
"tripleset": [
[
"Romania",
"ETHNIC_GROUP",
"Germans of Romania"
],
[
"Romania",
"LEADER_NAME",
"Klaus Iohannis"
],
[
"Romania",
"LEADER_TITLE",
"Prime Minister of Romania"
],
[
"Romania",
"PATRON_SAINT",
"Andrew the Apostle"
],
[
"Romania",
"CAPITAL",
"Bucharest"
],
[
"1 Decembrie 1918 University",
"COUNTRY",
"Romania"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bucharest is the capital of Romania where one of the country's ethnic groups are the Germans of Romania. The country's leader is Prime Minister Klaus Iohannis. The patron saint of the country is Andrew the Apostle and the country is the location of the 1 Decembrie 1918 University."
},
{
"source": "webnlg",
"text": "Romania is governed by a Prime Minister who is Klaus Iohannis . The country is also known for it's patron saint Andrew the Apostle , the ethnic group the Germans of Romania and the capital Bucharest. Romania is also home to the 1 Decembrie 1918 University."
},
{
"source": "webnlg",
"text": "The leader of Romania, the Prime Minister, is Klaus Iohannis. The capital is Bucharest, their ethnic group is Germans of Romania and its patron Saint is Andrew the Apotle. In Romania we can found 1 Decembrie 1918 University."
}
]
},
{
"tripleset": [
[
"School of Business and Social Sciences at the Aarhus University",
"ACADEMIC_STAFF_SIZE",
"737"
],
[
"Denmark",
"LEADER_NAME",
"Lars L\u00f8kke Rasmussen"
],
[
"School of Business and Social Sciences at the Aarhus University",
"CITY",
"Aarhus"
],
[
"School of Business and Social Sciences at the Aarhus University",
"COUNTRY",
"Denmark"
],
[
"School of Business and Social Sciences at the Aarhus University",
"AFFILIATION",
"European University Association"
],
[
"School of Business and Social Sciences at the Aarhus University",
"ESTABLISHED",
"1928"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University is located in Aarhus, Denmark was established in 1928. Its academic staff numbers 737 and it is affiliated to the European University Association. Denmark's leader is Lars Lokke Rasmussen."
},
{
"source": "webnlg",
"text": "The city of Aarhus is the location of the School of Business and Social Sciences at the Aarhus University which is affiliated with the European University Association. The School, which has 737 academic staff, was established in 1928 in Denmark where the leader is Lars Lokke Rasmussen."
},
{
"source": "webnlg",
"text": "Established in 1928 and with a current staff of 737, the School of Business and Social Sciences at the Aarhus University in Aarus, Denmark is affiliated with the European University Association. The country's leader is named Lars L\u00f8kke Rasmussen."
}
]
},
{
"tripleset": [
[
"School of Business and Social Sciences at the Aarhus University",
"CITY",
"Aarhus"
],
[
"School of Business and Social Sciences at the Aarhus University",
"NUMBER_OF_STUDENTS",
"16000"
],
[
"School of Business and Social Sciences at the Aarhus University",
"ACADEMIC_STAFF_SIZE",
"737"
],
[
"School of Business and Social Sciences at the Aarhus University",
"COUNTRY",
"Denmark"
],
[
"School of Business and Social Sciences at the Aarhus University",
"AFFILIATION",
"European University Association"
],
[
"School of Business and Social Sciences at the Aarhus University",
"ESTABLISHED",
"1928"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University at Aarhus, Denmark was established in 1928. It has 737 academic staff and 16,000 students. It is affiliated to the European University Association."
},
{
"source": "webnlg",
"text": "The School of Business and Social Sciences at the Aarhus University is based in the city of Aarhus, Denmark. It was established in 1928 and is affiliated to the European University Association. There are 16000 students and an academic staff of 727."
},
{
"source": "webnlg",
"text": "School of Business and Social Sciences at the Aarhus University is located in Aarhus, Denmark. It was established in 1928 and its affiliation is European University Association. The number of students is 16000 and academic staff is 737."
}
]
},
{
"tripleset": [
[
"103 Colmore Row",
"ARCHITECT",
"John Madin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "103 Colmore Row was designed by the architect, John Madin."
},
{
"source": "webnlg",
"text": "103 Colmore Row was designed by the architect John Madin."
}
]
},
{
"tripleset": [
[
"103 Colmore Row",
"LOCATION",
"Colmore Row"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "103 Colmore Row is located at Colmore Row."
},
{
"source": "webnlg",
"text": "103 Colmore Row is located in Colmore Row."
}
]
},
{
"tripleset": [
[
"11 Diagonal Street",
"COMPLETION_DATE",
"1983"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "11 Diagonal Street was completed in 1983."
}
]
},
{
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[
"200 Public Square",
"COMPLETION_DATE",
"1985"
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],
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"annotations": [
{
"source": "webnlg",
"text": "The 200 Public Square was completed in 1985."
},
{
"source": "webnlg",
"text": "200 Public Square was completed in 1985."
}
]
},
{
"tripleset": [
[
"20 Fenchurch Street",
"BUILDING_START_DATE",
"\"January 2009\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The building at 20 Fenchurch Street was started in January 2009."
},
{
"source": "webnlg",
"text": "The construction of 20 Fenchurch Street began in January 2009."
}
]
},
{
"tripleset": [
[
"20 Fenchurch Street",
"LOCATION",
"United Kingdom"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "20 Fenchurch Street is located in the United Kingdom."
},
{
"source": "webnlg",
"text": "20 Fenchurch Street is located within the United Kingdom."
}
]
},
{
"tripleset": [
[
"300 North LaSalle",
"FLOOR_COUNT",
"60"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "There are 60 floors at 300 North LaSalle."
},
{
"source": "webnlg",
"text": "300 North LaSalle has 60 floors."
}
]
},
{
"tripleset": [
[
"3Arena",
"ARCHITECT",
"\"HOK SVE\""
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],
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"annotations": [
{
"source": "webnlg",
"text": "HOK SVE was the architect of 3Arena."
},
{
"source": "webnlg",
"text": "HOK SVE was the architect of the 3Arena."
}
]
},
{
"tripleset": [
[
"3Arena",
"LOCATION",
"\"East Link Bridge\""
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],
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"annotations": [
{
"source": "webnlg",
"text": "The 3Arena is located at East Link Bridge."
},
{
"source": "webnlg",
"text": "The 3Arena is located on the East Link Bridge."
},
{
"source": "webnlg",
"text": "3 Arena is located at East Link Bridge."
}
]
},
{
"tripleset": [
[
"Adare Manor",
"BUILDING_START_DATE",
"\"1700\""
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Adare Manor's building start was in 1700."
},
{
"source": "webnlg",
"text": "The building of the Adare Manor was started in 1700."
}
]
},
{
"tripleset": [
[
"Adare Manor",
"COUNTRY",
"Republic of Ireland"
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],
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"annotations": [
{
"source": "webnlg",
"text": "Adare Manor is located in the Republic of Ireland."
},
{
"source": "webnlg",
"text": "The Adare Manor is in the Republic of Ireland."
}
]
},
{
"tripleset": [
[
"Akita Museum of Art",
"INAUGURATION_DATE",
"\"2013-09-28\""
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],
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"annotations": [
{
"source": "webnlg",
"text": "The Akita Museum of Art was inaugurated on 28th September 2013."
}
]
},
{
"tripleset": [
[
"Alan B. Miller Hall",
"CURRENT_TENANTS",
"Mason School of Business"
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],
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"annotations": [
{
"source": "webnlg",
"text": "The Mason School of Business are the current tenants of Alan B Miller Hall."
}
]
},
{
"tripleset": [
[
"Amdavad ni Gufa",
"ARCHITECT",
"B. V. Doshi"
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],
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"annotations": [
{
"source": "webnlg",
"text": "The architecct B V Doshi designed Amdavad Ni Gufa."
},
{
"source": "webnlg",
"text": "B V Doshi is the architect who designed Amdavad ni Gufa."
}
]
},
{
"tripleset": [
[
"Ampara Hospital",
"COUNTRY",
"Sri Lanka"
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],
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"annotations": [
{
"source": "webnlg",
"text": "Ampara Hospital is located in Sri Lanka."
},
{
"source": "webnlg",
"text": "Ampara Hospital is in Sri Lanka."
}
]
},
{
"tripleset": [
[
"Asher and Mary Isabelle Richardson House",
"ADDED_TO_THE_NATIONAL_REGISTER_OF_HISTORIC_PLACES",
"\"1988-11-22\""
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asher and Mary Isabelle Richardson House was added to the National Register of Historic Places on \"1988-11-22\"."
},
{
"source": "webnlg",
"text": "The Asher and Mary Isabelle Richardson House was added to the National Register of Historic Places, on 22nd November 1988."
}
]
},
{
"tripleset": [
[
"Asser Levy Public Baths",
"YEAR_OF_CONSTRUCTION",
"1904"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Asser Levy Public Baths was built in 1904."
},
{
"source": "webnlg",
"text": "Asser Levy Public Baths were constructed in 1904."
}
]
},
{
"tripleset": [
[
"Birmingham",
"POSTAL_CODE",
"B postcode area"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Birmingham has the postcode area 'B'."
},
{
"source": "webnlg",
"text": "The B postcode area is the postal code of Birmingham."
}
]
},
{
"tripleset": [
[
"Chicago",
"IS_PART_OF",
"DuPage County, Illinois"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Chicago is part of DuPage County in Illinois."
},
{
"source": "webnlg",
"text": "Chicago is part of DuPage County Illinois."
}
]
},
{
"tripleset": [
[
"Dublin",
"COUNTRY",
"Republic of Ireland"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Dublin is in the Republic of Ireland."
}
]
},
{
"tripleset": [
[
"Dublin",
"IS_PART_OF",
"Leinster"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Dublin is part of Leinster."
}
]
},
{
"tripleset": [
[
"India",
"LEADER_NAME",
"Sumitra Mahajan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Sumitra Mahajan is an Indian leader."
},
{
"source": "webnlg",
"text": "The name of the leader in India is Sumitra Mahajan."
}
]
},
{
"tripleset": [
[
"Japan",
"ETHNIC_GROUP",
"Brazilians in Japan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "One of the ethnic groups in Japan in the Brazilians."
},
{
"source": "webnlg",
"text": "The Brazilians in Japan are an ethnic group found in Japan."
}
]
},
{
"tripleset": [
[
"Japan",
"LEADER_NAME",
"Tar\u014d As\u014d"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The leader of Japan is Tar\u014d As\u014d."
}
]
},
{
"tripleset": [
[
"Marriott International",
"KEY_PERSON",
"Bill Marriott"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bill Marriott is the key person at Marriott International."
},
{
"source": "webnlg",
"text": "One of the key people in Marriott International is Bill Marriott."
}
]
},
{
"tripleset": [
[
"Republic of Ireland",
"LANGUAGE",
"English language"
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ireland official language is Irish English."
},
{
"source": "webnlg",
"text": "English language is the main language of the Republic of Ireland."
},
{
"source": "webnlg",
"text": "One language used in the Republic of Ireland is English."
}
]
},
{
"tripleset": [
[
"South Africa",
"CAPITAL",
"Cape Town"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Cape Town is the capital of South Africa."
},
{
"source": "webnlg",
"text": "The capital of South Africa is Cape Town."
}
]
},
{
"tripleset": [
[
"United Kingdom",
"CAPITAL",
"London"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The capital of the United Kingdom is London."
},
{
"source": "webnlg",
"text": "London is the capital of the United Kingdom."
}
]
},
{
"tripleset": [
[
"United States",
"ETHNIC_GROUP",
"Native Americans in the United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Native Americans are an ethnic group in the United States."
}
]
},
{
"tripleset": [
[
"United States",
"LEADER_NAME",
"Paul Ryan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Paul Ryan is the leader of the United States."
},
{
"source": "webnlg",
"text": "The leader of the United States is Paul Ryan."
}
]
},
{
"tripleset": [
[
"Atat\u00fcrk Monument (\u0130zmir)",
"INAUGURATION_DATE",
"\"1932-07-27\""
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[
"Atat\u00fcrk Monument (\u0130zmir)",
"LOCATION",
"Turkey"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The inauguration date for the Atat\u00fcrk Monument (\u0130zmir, Turkey) is 1932-07-27."
},
{
"source": "webnlg",
"text": "The Ataturk Monument is in Izmir Turkey and was founded on July 27, 1932."
},
{
"source": "webnlg",
"text": "The inauguration date for the Atat\u00fcrk Monument (\u0130zmir), which is in Turkey, is 1932-07-27."
}
]
},
{
"tripleset": [
[
"Baku Turkish Martyrs' Memorial",
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[
"Baku Turkish Martyrs' Memorial",
"MATERIAL",
"\"Red granite and white marble\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs Memorial has been dedicated to Ottoman Army soldiers killed in the Battle of Baku and is made of red granite and white marble."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyrs Memorial, created in red granite and white marble, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku."
}
]
},
{
"tripleset": [
[
"Baku Turkish Martyrs' Memorial",
"NATIVE_NAME",
"\"T\u00fcrk \u015eehitleri An\u0131t\u0131\""
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[
"Baku Turkish Martyrs' Memorial",
"MATERIAL",
"\"Red granite and white marble\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The native name of the Baku Turkish Martyrs' Memorial is Turk Sehitleri Aniti and is made of red granite and white marble."
},
{
"source": "webnlg",
"text": "The Baku Turkish Martyr's Memorial is known in Turkish as Turk Sehitleri Aniti and is created in red granite and white marble."
},
{
"source": "webnlg",
"text": "Baku Turkish Martyrs' Memorial is made from red granite and white marble and is called Turk Sehitleri Aniti."
}
]
},
{
"tripleset": [
[
"Dead Man's Plack",
"DEDICATED_TO",
"\u00c6thelwald, Ealdorman of East Anglia"
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[
"Dead Man's Plack",
"MATERIAL",
"Rock (geology)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Dead Man's Plack, which is made of rock, is dedicated to \u00c6thelwald, Ealdorman of East Anglia."
}
]
},
{
"tripleset": [
[
"Asterix (comicsCharacter)",
"CREATOR",
"Ren\u00e9 Goscinny"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asterix was created by Rene Goscinny."
},
{
"source": "webnlg",
"text": "The creator of Asterix (comics character) is Ren\u00e9 Goscinny."
},
{
"source": "webnlg",
"text": "The comic character Asterix, was created by Ren\u00e9 Goscinny."
}
]
},
{
"tripleset": [
[
"Auron (comicsCharacter)",
"CREATOR",
"Marv Wolfman"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The comic character Auron was created by Marv Wolfman."
},
{
"source": "webnlg",
"text": "Auron was created by Marv Wolfman."
},
{
"source": "webnlg",
"text": "The comic book character Auron was created by Marv Wolfman."
}
]
},
{
"tripleset": [
[
"Balder (comicsCharacter)",
"CREATOR",
"Jack Kirby"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The comic character, Balder, was created by Jack Kirby."
},
{
"source": "webnlg",
"text": "The comic character Balder was created by Jack Kirby."
},
{
"source": "webnlg",
"text": "Jack Kirby created the comic character Balder."
}
]
},
{
"tripleset": [
[
"Bananaman",
"BROADCASTED_BY",
"\"STV\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bananaman is broadcast by STV."
},
{
"source": "webnlg",
"text": "Bananaman is broadcasted by \"STV\"."
},
{
"source": "webnlg",
"text": "Bananaman was broadcasted by STV."
}
]
},
{
"tripleset": [
[
"Bananaman",
"FIRST_AIRED",
"\"1983-10-03\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bananaman first aired on 3rd October 1983."
},
{
"source": "webnlg",
"text": "Bananaman first aired on the 3rd of October, 1983."
},
{
"source": "webnlg",
"text": "Bananaman was first aired on 10/03/1983."
}
]
},
{
"tripleset": [
[
"Bananaman",
"STARRING",
"Bill Oddie"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bill Oddie stars in Bananaman."
},
{
"source": "webnlg",
"text": "Bananaman starred Bill Oddie."
},
{
"source": "webnlg",
"text": "Bill Oddie starred in Bananaman."
}
]
},
{
"tripleset": [
[
"Baymax",
"SERIES",
"Big Hero 6 (film)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Baymax is a character in Big Hero 6."
}
]
},
{
"tripleset": [
[
"Bill Everett",
"AWARD",
"Eisner Award"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bill Everett has won the Eisner Award."
},
{
"source": "webnlg",
"text": "Bill Everett has been awarded the Eisner award."
}
]
},
{
"tripleset": [
[
"Bill Oddie",
"CHILD",
"Kate Hardie"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bill Oddie's daughter is Kate Hardie."
},
{
"source": "webnlg",
"text": "Kate Hardie is Bill Oddie's child."
}
]
},
{
"tripleset": [
[
"Bolt (comicsCharacter)",
"CREATOR",
"Dan Mishkin"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Dan Mishkin is one of the creators of the comic character, Bolt."
},
{
"source": "webnlg",
"text": "The comic book character Bolt was created by Dan Mishkin."
},
{
"source": "webnlg",
"text": "The comic character Bolt was created by Dan Mishkin."
}
]
},
{
"tripleset": [
[
"Bozo the Iron Man",
"FULL_NAME",
"\"Hugh Hazzard\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Bozo the Iron Man\u2019s full name is Hugh Hazzard."
},
{
"source": "webnlg",
"text": "The full name of Bozo the Iron Man is \"Hugh Hazzard\"."
},
{
"source": "webnlg",
"text": "The comic book character Bozo the Iron Man's alter ego is Hugh Hazzard."
}
]
},
{
"tripleset": [
[
"John Buscema",
"AWARD",
"Eagle Award (comics)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "John Buscema has won the comic book award, Eagle Award."
},
{
"source": "webnlg",
"text": "John Buscema received the Eagle Award for his work in comics."
}
]
},
{
"tripleset": [
[
"Marv Wolfman",
"AWARD",
"Eagle Award (comics)"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Marv Wolfman is a recipient of the Eagle Award."
},
{
"source": "webnlg",
"text": "Marv Wolfman won the Eagle Award for comics."
},
{
"source": "webnlg",
"text": "Marv Wolfman has received the Eagle Award for comics."
}
]
},
{
"tripleset": [
[
"A.F.C. Blackpool",
"MANAGER",
"Stuart Parker (footballer)"
],
[
"Stuart Parker (footballer)",
"CLUB",
"KV Mechelen"
],
[
"Stuart Parker (footballer)",
"CLUB",
"Chesterfield F.C."
],
[
"Blackpool",
"LEADER",
"Gordon Marsden"
],
[
"A.F.C. Blackpool",
"GROUND",
"Blackpool"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Gordon Marsden is the leader of Blackpool where AFC Blackpool are located. Their manager is Stuart Parker who is a member of Chesterfield FC and plays for KV Mechelen."
},
{
"source": "webnlg",
"text": "Gordon Marsden is the leader of Blackpool where AFC Blackpool is located. The club is managed by Stuart Parker who is part of the KV Mechelen club and a member of Chesterfield FC."
}
]
},
{
"tripleset": [
[
"AEK Athens F.C.",
"LEAGUE",
"Superleague Greece"
],
[
"Superleague Greece",
"CHAMPIONS",
"Olympiacos F.C."
],
[
"AEK Athens F.C.",
"MANAGER",
"Gus Poyet"
],
[
"Gus Poyet",
"CLUB",
"Real Zaragoza"
],
[
"Gus Poyet",
"CLUB",
"Chelsea F.C."
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Olympiacos F.C. were past champions in the Greece Superleague, the league AEK Athens FC compete in. Their manager is Gus Poyet, whose club is Real Zaragoza even though he played for Chelsea F.C."
},
{
"source": "webnlg",
"text": "Gus Poyet is the manager of AEK Athens F.C. who play in the Superleague of Greece ( which has Olympiacos FC as previous champions). Poyet previously played for Chelsea FC and is in the Real Zaragoza club."
},
{
"source": "webnlg",
"text": "Previous champions of the Superleague Greece are Olympiacos FC. AEK Athens FC also compete in the league and they are managed by Gus Poyet who previously played for Chelsea FC and whose club is Real Zaragoza."
}
]
},
{
"tripleset": [
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"LEAGUE",
"Campeonato Brasileiro S\u00e9rie C"
],
[
"Campeonato Brasileiro S\u00e9rie C",
"COUNTRY",
"Brazil"
],
[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
"GROUND",
"Est\u00e1dio Municipal Coaracy da Mata Fonseca"
],
[
"Est\u00e1dio Municipal Coaracy da Mata Fonseca",
"LOCATION",
"Alagoas"
],
[
"Campeonato Brasileiro S\u00e9rie C",
"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Est\u00e1dio Municipal Coaracy da Mata Fonseca is the name of the ground of Agremia\u00e7\u00e3o Sportiva Arapiraquense. It is located in Alagoas. Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league from Brazil. The Vila Nova Futebol Clube were champions at the S\u00e9rie C."
},
{
"source": "webnlg",
"text": "Vila Nova Futebol Clube have been champions of Campeonato Brasileiro S\u00e9rie C. from Brazil in which Agremia\u00e7\u00e3o Sportiva Arapiraquense also play. This latter team have their ground at Est\u00e1dio Municipal Coaracy da Mata Fonseca which is located in Alagoas."
},
{
"source": "webnlg",
"text": "Llocated in Alagoas, Est\u00e1dio Municipal Coaracy da Mata Fonseca is the name of the ground of Agremia\u00e7\u00e3o Sportiva Arapiraquense. They play in the Campeonato Brasileiro S\u00e9rie C league, which is from Brazil, and the champions of which have been Vila Nova Futebol Clube."
}
]
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{
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[
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[
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"Arapiraca"
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"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Vila Nova Futebol Clube have been champions of Campeonato Brasileiro S\u00e9rie C. league in Brazil. Agremia\u00e7\u00e3o Sportiva Arapiraquense also play in the league and have their home ground at Est\u00e1dio Municipal Coaracy da Mata Fonseca in Arapiraca."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league (in Brazil), which has been won by Vila Nova Futebol Clube. Agremiacao Sportiva Arapiraquense's ground is the Estadio Minicipal Coaracy da Mata Fonseca, which is located in Arapiraca."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league in Brazil. Vila Nova Futebol Clube are the champions of Serie C. Est\u00e1dio Municipal Coaracy da Mata Fonseca (Arapiraca)is the name of the ground of Agremia\u00e7\u00e3o Sportiva Arapiraquense."
}
]
},
{
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"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
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"Brazil"
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[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
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"Vica"
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[
"Agremia\u00e7\u00e3o Sportiva Arapiraquense",
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"17000"
],
[
"Campeonato Brasileiro S\u00e9rie C",
"CHAMPIONS",
"Vila Nova Futebol Clube"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Vila Nova Futebol Clube were champions at the Campeonato Brasileiro S\u00e9rie C. in Brazil. Agremia\u00e7\u00e3o Sportiva Arapiraquense who also play in the league have 17000 members and are managed by Vica."
},
{
"source": "webnlg",
"text": "Agremia\u00e7\u00e3o Sportiva Arapiraquense play in the Campeonato Brasileiro S\u00e9rie C league in Brazil, which has been won by Vila Nova Futebol Clube. Agremiacao Sportiva Arapiraquense's ground can hold 17000 fans and the team is managed by Vica."
},
{
"source": "webnlg",
"text": "Vila Nova Futebol Clube have been champions of Campeonato Brasileiro S\u00e9rie C, which is from Brazil. It is in this league that Agremia\u00e7\u00e3o Sportiva Arapiraquense play in. They are managed by Vica, and have17000 members."
}
]
},
{
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[
"20 Fenchurch Street",
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"United Kingdom"
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[
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[
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"Elizabeth II"
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[
"United Kingdom",
"CURRENCY",
"Pound sterling"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "20 Fenchurch Street is located within the United Kingdom, where London is the capital, and the currency is pounds sterling. Boris Johnson is the leader in London, and one of the leaders of the United Kingdom is Elizabeth II."
},
{
"source": "webnlg",
"text": "The United Kingdom uses the currency of the pound sterling and is lead by Elizabeth II along with Boris Johnson. The capital city is London and the country is also the location of 20 Fenchurch Street."
}
]
},
{
"tripleset": [
[
"250 Delaware Avenue",
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"Buffalo, New York"
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[
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[
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[
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"30843.8 (square metres)"
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[
"250 Delaware Avenue",
"FLOOR_COUNT",
"12"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "250 Delaware Avenue was built in Buffalo New York at a cost of 110 million dollars. Construction began in January 2014 and the building has 12 floors with an area of 30843.8 square metres."
},
{
"source": "webnlg",
"text": "There are 12 floors at 250 Delaware Avenue in Buffalo, New York. Construction, which began in January 2014 cost 110 million dollars for a floor area of 30843.8 square metres."
}
]
},
{
"tripleset": [
[
"300 North LaSalle",
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"Chicago"
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[
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[
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[
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"FLOOR_COUNT",
"60"
],
[
"Chicago",
"COUNTRY",
"United States"
]
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"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Rahm Emanuel is the leader of Chicago, DuPage County, Illinois, United States which is the location of the building at 300 North LaSalle which has 60 floors."
},
{
"source": "webnlg",
"text": "300 North LaSalle has 60 floors and is located in Chicago, DuPage County, Illinois, United States. The leader of Chicago is Rahm Emanuel."
},
{
"source": "webnlg",
"text": "300 North LaSalle, which has 60 floors, is located in Chicago which is part of DuPage County in Illinois, United States. The leader of Chicago is Rahm Emanuel."
}
]
},
{
"tripleset": [
[
"3Arena",
"OWNER",
"Live Nation Entertainment"
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[
"Dublin",
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[
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[
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"Dublin"
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[
"Dublin",
"IS_PART_OF",
"Leinster"
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"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "3Arena (owned by Live Nation Entertainment) is located in Dublin, Republic of Ireland. D\u00e1il \u00c9ireann is a leader in Dublin, which is part of Leinster."
},
{
"source": "webnlg",
"text": "3Arena, owned by Live Nation Entertainment, is located in Dublin, which is part of Leinster in the Republic of Ireland. D\u00e1il \u00c9ireann is a leader in Dublin."
},
{
"source": "webnlg",
"text": "Located in Dublin, Leinster, 3Arena is owned by Live Nation Entertainment. Dublin is also part of the Republic of Ireland and is lead by D\u00e1il \u00c9ireann."
}
]
},
{
"tripleset": [
[
"Adisham Hall",
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[
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[
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[
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"BUILDING_START_DATE",
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"\"St. Benedict's Monastery, Adisham, Haputhale, Sri Lanka\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Adisham Hall in Sri Lanka was constructed between 1927 and 1931 at St Benedicts Monastery, Adisham, Haputhale, Sri Lanka in the Tudor and Jacobean style of architecture."
},
{
"source": "webnlg",
"text": "Construction of Adisham Hall took place between 1927 and 1931 in the architectural style of Tudor and Jacobean. It is located at St Benedict's Monastery, Adisham, Haputhale, Sri Lanka."
}
]
},
{
"tripleset": [
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"CURRENCY",
"Sri Lankan rupee"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Adisham Hall is located in Haputale, Sri Lanka. The capital of Sri Lanka is Sri Jayawardenepura Kotte, the language used in the country is Tamil and the currency is the Sri Lankan Rupee."
},
{
"source": "webnlg",
"text": "Adisham Hall is in Haputale, Sri Lanka. The capital of Sri Lanka is Sri Jayawardenepura Kotte, the language spoken there is Tamil and the currency is the Sri Lankan Rupee."
},
{
"source": "webnlg",
"text": "Admisham Hall is located at Haputale, Sri Lanka. Sri Jayawardenepura Kotte is the capital of Sri Lanka, where the currency is the Sri Lankan Rupee and the language used is Tamil."
}
]
},
{
"tripleset": [
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[
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[
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[
"Sri Lanka",
"CURRENCY",
"Sri Lankan rupee"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Ranil Wickremesinghe is the leader of Sri Lanka where the capital city is Sri Jayawardenepura Kotte and the currency is the Sri Lankan rupee. Adisham Hall is located in the country at Haputale."
},
{
"source": "webnlg",
"text": "Adisham Hall is located in Haputale, Sri Lanka. The capital of Sri Lanka is Sri Jayawardenepura Kotte with Ranil Wickremesinghe as its leader and the Rupee as its currency."
},
{
"source": "webnlg",
"text": "Adisham Hall is located in Haputale, Sri Lanka, where the capital is called Sri Jayawardenepura Kotte. Ranil Wickremesinghe is a leader of Sri Lanka, and the currency is the Sri Lankan rupee."
}
]
},
{
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[
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[
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[
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"LEADER_NAME",
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[
"Akita Museum of Art",
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"Akita, Akita"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The main ethnic group in Japan are the Japanese and the leader is Tar\u014d As\u014d. Akita Museum of Art is in Akita, Akita which is part of Japan and the Akita Prefecture."
},
{
"source": "webnlg",
"text": "\"The Akita Museum of Art is located in Akita, Akita which is located in Japan. The leader of Japan is Taro Aso and the main ethnic group in Japan is the Japanese. Akita, Akita is part of Akita Prefecture.\"."
},
{
"source": "webnlg",
"text": "Akita Museum of Art is in the city of Akita, Japan. The main ethnic group in Japan are the Japanese, the leader is Taro Aso and Akita, Akita is part of Akita Prefecture."
}
]
},
{
"tripleset": [
[
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"LOCATION",
"Gujarat"
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[
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[
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[
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[
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"LEADER_TITLE",
"Gujarat Legislative Assembly"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Gujarat, Ahmedabad, India. Gujarat's leader is known as the Gujarat Legislative Assembly, and the leader of India is Narendra Modi."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Gujarat, Ahmedabad which is in India. The leader of India is Narendra Modi but the leader of Gujarat is the Gujarat Legislative Assembly."
},
{
"source": "webnlg",
"text": "Amdavad ni Gufa is in Gujarat, Ahmedabad which is in India. Gujarat's leader is the Gujarat Legislative Assembly and Narendra Modi is the prime minister of India."
}
]
},
{
"tripleset": [
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"LOCATION",
"Gujarat"
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[
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[
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"India"
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[
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[
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"LEADER_TITLE",
"Gujarat Legislative Assembly"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Amdavad ni Gufa is located in Ahmedabad, Gujarat, India; led by the Gujarat Legislative Assembly, including leader T S Thakur."
}
]
},
{
"tripleset": [
[
"Ampara Hospital",
"COUNTRY",
"Sri Lanka"
],
[
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"LEADER_NAME",
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[
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"476"
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[
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[
"Eastern Province, Sri Lanka",
"LEADER_NAME",
"Austin Fernando"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The 476 bed Ampara Hospital is situated in the Eastern Province state of Sri Lanka where Austin Fernando is the leader. The country's leader is Ranil Wickremesinghe."
},
{
"source": "webnlg",
"text": "Ampara Hospital, which has 476 beds, is located in the Eastern Province of Sri Lanka lead by Austin Fernando. The leader of Sri Lanka is Ranil Wickremesinghe."
},
{
"source": "webnlg",
"text": "There are 476 beds at Ampara Hospital in the Eastern Province of Sri Lanka where Austin Fernando is the leader. Another leader within the country is Ranil Wickremesinghe."
}
]
},
{
"tripleset": [
[
"Asilomar Conference Grounds",
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"Julia Morgan"
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[
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[
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[
"Julia Morgan",
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"California"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Julia Morgan, who was born in California, designed the Asilomar Conference Grounds, the Los Angeles Herald examiner building, The Riverside Art Musuem and Hearst Castle."
},
{
"source": "webnlg",
"text": "Julia Morgan was an architect born in California. She designed Asilomar Conference Grounds, The Riverside Art Museum, Hearst Castle and the Los Angeles Herald Examiner building."
}
]
},
{
"tripleset": [
[
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[
"Julia Morgan",
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],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asilomar Conference Grounds were designed by Julia Morgan (born in California), who is known for a number of buildings, including Hearst Castle and The Riverside Art Museum, and her work in San Francisco's Chinatown."
},
{
"source": "webnlg",
"text": "Julia Morgan, the architect, was born in California and some of her significant projects include: the Asilomar Conference Grounds, The Riverside Art Museum, Hearst Castle and some buildings in Chinatown, San Francisco."
},
{
"source": "webnlg",
"text": "Julia Morgan, born in California, was the architect behind the grounds of Asilomar Conference, the Riverside Art Museum, Hearst Castle and Chinatown in San Francisco."
}
]
},
{
"tripleset": [
[
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"YEAR_OF_CONSTRUCTION",
"1913"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Asilomar Conference Grounds were constructed in 1913. They are located in Pacific Grove, California and they were added to the National Register of Historic Places on February 27th 1987 (with reference number 87000823). The grounds are in the architectural style of the Arts and Crafts Movement."
},
{
"source": "webnlg",
"text": "Asilomar Conference Grounds are located in Pacific Grove, California. They were constructed in 1913 with the architectural style of the Arts and Crafts Movement. They were added to the National Register of Historic Places on 27 February 1987 with reference number \"87000823\"."
},
{
"source": "webnlg",
"text": "The Asilomar Conference Grounds were constructed in 1913 and are in Pacific Grove, California. They have an Arts and Crafts movement style architecture and were added to the National Register of Historic Places on 27 February 1987 with the reference number 87000823."
}
]
},
{
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"Asser Levy Public Baths",
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[
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[
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[
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[
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"\"1980-04-23\""
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Asser Levy Public Baths, built in 1904 in a Romanesque Revival style, is located at 23rd Street (Manhattan). The site was added to the National Register of Historic Places on 1980-04-23, with Reference Number 80002709."
},
{
"source": "webnlg",
"text": "The Asser Levy baths are located on 23rd Street in Manhattan and have the Romanesque Revival style of architecture. They were built in 1904 and added to the National Register of Historic Places with the reference number 80002709 on 23 April 1980."
},
{
"source": "webnlg",
"text": "The Asser Levy Public Baths was built in 1904 in the architectural style of Romanesque Revival, and is located on 23rd Street, Manhattan. The site was added to the National Register of Historic Places on \"1980-04-23\", with reference number 80002709."
}
]
},
{
"tripleset": [
[
"Asser Levy Public Baths",
"LOCATION",
"New York City"
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[
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"COUNTRY",
"United States"
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[
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"LEADER_NAME",
"Gale Brewer"
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[
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"IS_PART_OF",
"New York"
],
[
"New York City",
"IS_PART_OF",
"Manhattan"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The Asser Levy Public Baths are found in New York City, New York, United States. Another part of New York City is Manhattan where Gale Brewer is the leader."
},
{
"source": "webnlg",
"text": "Gale Brewer is the leader of Manhattan which is part of New York City, New York, United States. New York City is the location of Asser Levy Public Baths."
},
{
"source": "webnlg",
"text": "The location of Asser Levy Public Baths are New York City, US.New York City and Manhattan are part of New York. The leader of Manhattan is Gale Brewer."
}
]
},
{
"tripleset": [
[
"Birmingham",
"POSTAL_CODE",
"B postcode area"
],
[
"103 Colmore Row",
"ARCHITECT",
"John Madin"
],
[
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"BIRTH_PLACE",
"Birmingham"
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[
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"GOVERNING_BODY",
"Birmingham City Council"
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[
"Birmingham",
"LEADER_NAME",
"Andrew Mitchell"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "103 Colmore Row was designed by the architect John Madin, who was born in Birmingham, where the postal code is 'B'. Andrew Mitchell is the leader of the city and Birmingham City Council is the governing body."
},
{
"source": "webnlg",
"text": "The City Council is the governing body for Birmingham where Andrew Mitchell is a leader. The city has the B postcode and is the birthplace of the Architect John Madin who designed 103 Colmore Row."
},
{
"source": "webnlg",
"text": "Birmingham is lead by Andrew Mitchell and the Birmingham City Council. The town, which has the B postcode, is the birthplace of architect John Madin who designed 103 Colmore Row."
}
]
},
{
"tripleset": [
[
"Ethiopia",
"LEADER_NAME",
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[
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"LEADER_NAME",
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[
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"Addis Ababa Stadium"
],
[
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"LOCATION",
"Addis Ababa"
],
[
"Addis Ababa",
"COUNTRY",
"Ethiopia"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Hailemariam Desalegn and Mulatu Teshome are leaders within Ethiopia. The Addis Ababa Stadium and the Addis Ababa City Hall are both located within the country in Addis Ababa."
},
{
"source": "webnlg",
"text": "Addis Ababa Stadium and Addis Ababa City Hall are located in Addis Ababa, Ethiopia. The country is lead by Malatu Teshome and Hailemariam Desalegn,."
},
{
"source": "webnlg",
"text": "Addis Ababa Stadium and Addis Ababa City Hall are both located in Addis Ababa, Ethiopia. Malatu Teshome and Hailemariam Desalegn are leaders in the country."
}
]
},
{
"tripleset": [
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"ETHNIC_GROUP",
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[
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[
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"LEADER_TITLE",
"President of the United States"
],
[
"United States",
"LEADER_NAME",
"John Roberts"
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[
"250 Delaware Avenue",
"LOCATION",
"United States"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "The President is the primary leader of English language speaking United States, (includes leader John Roberts), has the ethnic African American group and is home to 250 Delaware Avenue."
},
{
"source": "webnlg",
"text": "250 Delaware Avenue is located in the United States, where the leader is known as the President of the United States, supported by the United States of America`s Chief Justice, John Roberts. The main language is English, and one of the ethnic groups is the African Americans."
}
]
},
{
"tripleset": [
[
"United States",
"LANGUAGE",
"English language"
],
[
"United States",
"LEADER_TITLE",
"President of the United States"
],
[
"United States",
"LEADER_NAME",
"Joe Biden"
],
[
"250 Delaware Avenue",
"LOCATION",
"United States"
],
[
"United States",
"ETHNIC_GROUP",
"White Americans"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "webnlg",
"text": "Joe Biden is a leader of the United States which is lead by a President. The White Americans are one of the ethnic groups of the country which uses the English language and is where 250 Delaware Avenue is located."
},
{
"source": "webnlg",
"text": "250 Delaware Avenue is located in the United States, where English is spoken. The President of the United States is the main leader, alongside Joe Biden, and an ethnic group in the country is White Americans."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"city centre"
],
[
"Alimentum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a place in the city centre, Alimentum, that is not family-friendly."
},
{
"source": "e2e",
"text": "In the city centre there is a venue name Alimentum, this is not a family-friendly venue."
},
{
"source": "e2e",
"text": "Alimentum is not a family-friendly place, located in city centre."
},
{
"source": "e2e",
"text": "Alimentum is not a family-friendly arena and is located in the city centre."
},
{
"source": "e2e",
"text": "Alimentum is not a family-friendly place in the city centre."
},
{
"source": "e2e",
"text": "Alimentum in city centre is not a family-friendly place."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"city centre"
],
[
"Alimentum",
"familyFriendly",
"no"
],
[
"Alimentum",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Alimentum is not family-friendly, and is near the Burger King in the city centre."
},
{
"source": "e2e",
"text": "Near Burger King in city centre is the adult establishment Alimentum."
},
{
"source": "e2e",
"text": "Alimentum is not family-friendly. Alimentum is in the city center and it is near Burger King."
},
{
"source": "e2e",
"text": "Alimentum is near Burger King in the city center. Alimentum is not family-friendly."
},
{
"source": "e2e",
"text": "Near the Burger King and in the city centre is Alimentum, which is not family-friendly."
},
{
"source": "e2e",
"text": "Alimentum is an adult establish found in the city centre area near Burger King."
},
{
"source": "e2e",
"text": "Burger King is near the Alimentum which is not family friendly and located north of the City center."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"city centre"
],
[
"Alimentum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Alimentum is a family-friendly place in the city centre."
},
{
"source": "e2e",
"text": "In the city centre there is a family-friendly place called Alimentum."
},
{
"source": "e2e",
"text": "Alimentum city centre is family-friendly"
},
{
"source": "e2e",
"text": "Alimentum is a family-friendly city centre."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"city centre"
],
[
"Alimentum",
"familyFriendly",
"yes"
],
[
"Alimentum",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Alimentum is family oriented and located near Burger King in the city centre."
},
{
"source": "e2e",
"text": "Alimentum, located near Burger King in the city centre, is family-friendly."
},
{
"source": "e2e",
"text": "Located Burger King in the city centre, Alimentum is family-friendly."
},
{
"source": "e2e",
"text": "Alimentum is family-friendly and located in the city centre near Burger King."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"riverside"
],
[
"Alimentum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Alimentum isn't family-friendly but it is in riverside."
},
{
"source": "e2e",
"text": "If you are searching for a place to go that's not family-friendly and near the riverside, Alimentum is the place for you."
},
{
"source": "e2e",
"text": "Alimentum, located on the river. No good for families."
},
{
"source": "e2e",
"text": "Riverside has Alimentum, which is not family-friendly."
},
{
"source": "e2e",
"text": "Alimentum is a not family-friendly place near the riverside."
},
{
"source": "e2e",
"text": "No good for families. Alimentum, close to the river."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"riverside"
],
[
"Alimentum",
"familyFriendly",
"no"
],
[
"Alimentum",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located off the river near Burger King, Alimentum does not allow families."
},
{
"source": "e2e",
"text": "Alimentum is a non-family-friendly establishment near Burger King at the riverside."
},
{
"source": "e2e",
"text": "Alimentum at the riverside near Burger King is a non-family-friendly establishment."
},
{
"source": "e2e",
"text": "Alimentum is located near Burger King in riverside. It is not family-friendly."
},
{
"source": "e2e",
"text": "Alimentum is not family-friendly. It is located near Burger King in riverside."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"riverside"
],
[
"Alimentum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Alimentum is a family-friendly living in riverside."
},
{
"source": "e2e",
"text": "Alimentum is a family-friendly living in riverside."
},
{
"source": "e2e",
"text": "A kid friendly venue named Alimentum is located on the riverside."
},
{
"source": "e2e",
"text": "Alimentum, situated by the river, is child friendly."
},
{
"source": "e2e",
"text": "Alimentum is child friendly and is located by the river."
},
{
"source": "e2e",
"text": "Alimentum is a family friendly place in the riverside area."
},
{
"source": "e2e",
"text": "For a children friendly establishment in the riverside area, try Alimentum."
},
{
"source": "e2e",
"text": "Alimentum is a child-friendly venue located on the riverside."
},
{
"source": "e2e",
"text": "There is a family-friendly venue in the riverside area called Alimentum."
},
{
"source": "e2e",
"text": "At the riverside there is a friendly family place called Alimentum"
},
{
"source": "e2e",
"text": "Alimentum can be found in riverside and is family friendly."
},
{
"source": "e2e",
"text": "Being beside the river, Alimentum is a family friendly place."
},
{
"source": "e2e",
"text": "A family friendly place near the riverside is called Alimentum."
},
{
"source": "e2e",
"text": "Visit Alimentum by the riverside, it is kids friendly."
},
{
"source": "e2e",
"text": "For a riverside, child friendly environment visit the Alimentum."
},
{
"source": "e2e",
"text": "Alimentum is located near the riverside. It is child friendly."
},
{
"source": "e2e",
"text": "Alimentum in the riverside area is child friendly."
},
{
"source": "e2e",
"text": "Alimentum, on the riverside, is family-friendly."
},
{
"source": "e2e",
"text": "Alimentum is a child friendly place on the riverside."
},
{
"source": "e2e",
"text": "Alimentum is a child-friendly place on the riverside."
},
{
"source": "e2e",
"text": "The Alimentum is in the riverside area. It is a family friendly place."
},
{
"source": "e2e",
"text": "The Alimentum is kid friendly and is located in the riverside area."
},
{
"source": "e2e",
"text": "On the riverside, there is a kids friendly venue called Alimentum."
},
{
"source": "e2e",
"text": "Alimentum is kid-friendly and is located at the riverside."
},
{
"source": "e2e",
"text": "On the riverside there is a child friendly place called Alimentum."
},
{
"source": "e2e",
"text": "By the riverside you can find Alimentum, which is a family friendly place."
},
{
"source": "e2e",
"text": "If you want to take the children for a meal then try Alimentum they provide a child friendly service in their riverside setting ."
},
{
"source": "e2e",
"text": "The Alimentum is in the riverside area. It is family friendly."
},
{
"source": "e2e",
"text": "If you are looking for a kids Friendly establishment in the riverside area, try Alimentum."
},
{
"source": "e2e",
"text": "Located in the riverside area the Alimentum is kid friendly."
},
{
"source": "e2e",
"text": "Located in the riverside area, Alimentum is child friendly."
},
{
"source": "e2e",
"text": "A kid friendly place in riverside is Alimentum."
},
{
"source": "e2e",
"text": "Alimentum, located in the riverside area is kid friendly."
}
]
},
{
"tripleset": [
[
"Alimentum",
"area",
"riverside"
],
[
"Alimentum",
"familyFriendly",
"yes"
],
[
"Alimentum",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "You will find Alimentum a nice child friendly place. It is located near Burger King and riverside."
},
{
"source": "e2e",
"text": "Alimentum is near Burger King and is not only riverside, but children friendly as well."
},
{
"source": "e2e",
"text": "Alimentum is a family friendly establishment in the riverside area near the Burger King."
},
{
"source": "e2e",
"text": "There is a family friendly place Alimentum located near Burger King in riverside."
},
{
"source": "e2e",
"text": "Alimentum is a children-friendly spot near Burger King in Riverside."
},
{
"source": "e2e",
"text": "Located near to Burger King on the riverside, Alimentum is a child friendly establishment."
},
{
"source": "e2e",
"text": "There is a kid friendly place in riverside near Burger King named Alimentum."
},
{
"source": "e2e",
"text": "Alimentum is a family-friendly location in the Riverside area, near the Burger King."
},
{
"source": "e2e",
"text": "There is a child friendly place called Alimentum by the riverside, near Burger King."
},
{
"source": "e2e",
"text": "Family friendly venue near the Burger King on the riverside is called Alimentum."
},
{
"source": "e2e",
"text": "Near the riverside is a kids friendly eatery called Alimentum. There is a Burger King close to it as well."
},
{
"source": "e2e",
"text": "In terms of kids friendly places, there's Alimentum in Riverside, near Burger King."
},
{
"source": "e2e",
"text": "For a family-friendly atmosphere in Riverside, check out Alimentum near the Burger King."
},
{
"source": "e2e",
"text": "Near Burger King on the riverside, there's a family friendly place named Alimentum."
},
{
"source": "e2e",
"text": "Near Burger King, there is a kid friendly place called Alimentum near the riverside."
},
{
"source": "e2e",
"text": "By the riverside near Burger King, there's a kid friendly Alimentum."
},
{
"source": "e2e",
"text": "Alimentum is a child friendly establishment located near Burger King on the riverside."
},
{
"source": "e2e",
"text": "Alimentum, located near to Burger King on the riverside, is a child friendly establishment."
},
{
"source": "e2e",
"text": "In Riverside, near Burger King, there is a family friendly venue called Alimentum."
},
{
"source": "e2e",
"text": "The child-friendly Alimentum is near to Burger King by the riverside."
},
{
"source": "e2e",
"text": "Near Burger King is the child friendly Alimentum. It is by the river."
},
{
"source": "e2e",
"text": "The child-friendly riverside venue Alimentum is located near Burger King."
},
{
"source": "e2e",
"text": "Near Burger King, in the riverside area, is a place called Alimentum, and it is kid friendly."
},
{
"source": "e2e",
"text": "Alimentum is a kids friendly place in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "Located riverside near Burger King, Alimentum is children friendly."
},
{
"source": "e2e",
"text": "There is a family friendly venue named Alimentum which is located at the riverside near Burger King"
},
{
"source": "e2e",
"text": "Near Burger King is Alimentum, which is child friendly and runs along the riverside."
},
{
"source": "e2e",
"text": "Alimentum is a family friendly place in riverside near Burger King."
},
{
"source": "e2e",
"text": "Alimentum in riverside is child friendly and is located near Burger King."
},
{
"source": "e2e",
"text": "Located near Burger King in the riverside area, Alimentum is known for its kid-friendly environment."
},
{
"source": "e2e",
"text": "In riverside, near Burger King, is a children family place called Alimentum."
},
{
"source": "e2e",
"text": "On the riverside near the Burger King there is a kids friendly place called Alimentum."
},
{
"source": "e2e",
"text": "There are child friendly establishments such as Burger King and Alimentum near the riverside."
}
]
},
{
"tripleset": [
[
"Alimentum",
"eatType",
"restaurant"
],
[
"Alimentum",
"area",
"city centre"
],
[
"Alimentum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The city centre has a family-friendly restaurant named Alimentum."
},
{
"source": "e2e",
"text": "There is a family-friendly restaurant named Alimentum in the city centre."
}
]
},
{
"tripleset": [
[
"Alimentum",
"eatType",
"restaurant"
],
[
"Alimentum",
"area",
"riverside"
],
[
"Alimentum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area there is a restaurant that is kid friendly named Alimentum"
},
{
"source": "e2e",
"text": "The riverside restaurant, Alimentum is very child friendly and great for adults as well."
},
{
"source": "e2e",
"text": "Located in riverside area, Alimentum restaurant is a place to bring the whole family."
},
{
"source": "e2e",
"text": "There is a riverside restaurant called Alimentum which is child friendly."
},
{
"source": "e2e",
"text": "There is child friendly restaurant in the riverside area named Alimentum."
},
{
"source": "e2e",
"text": "In Riverside you will find the kid-friendly restaurant Alimentum."
}
]
},
{
"tripleset": [
[
"Alimentum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Alimentum looks like a location where people or families aren't allowed"
}
]
},
{
"tripleset": [
[
"Alimentum",
"familyFriendly",
"no"
],
[
"Alimentum",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Not family friendly Alimentum across from Burger King"
},
{
"source": "e2e",
"text": "Alimentum, located near Burger King, is not family-friendly."
},
{
"source": "e2e",
"text": "Alimentum across from Burger King no kids"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop near city centre not family orientated"
},
{
"source": "e2e",
"text": "Aromi is a coffee shop near city centre not family orientated"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop located outside of the city centre, it is very family friendly and serves food"
},
{
"source": "e2e",
"text": "Aromi offers food and is a coffee shop that is family friendly and located north of the City center."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a family friendly coffee shop located near the River."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop in the city center called Aromi that offers food, is family-friendly and has a five star customer service rating."
},
{
"source": "e2e",
"text": "Aromi located in the city centre that is a five-star rated, family-friendly coffee shop, that also offers food."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a 5 star coffee shop located along the river. It is family friendly."
},
{
"source": "e2e",
"text": "Family friendly coffee shop, Aromi, located along the river has been rated 5 stars."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a average customer rated family coffee shop."
},
{
"source": "e2e",
"text": "Aromi is a average customer rated family coffee shop."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi has a low rating they are in the Riverside area, they have food and coffee. They are family friendly."
},
{
"source": "e2e",
"text": "Aromi is a one-star coffee shop that is family friendly. It is located near the River."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop and restaurant that is family restaurant located north of the City center."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi, an interesting mix of coffee shop and Chinese cuisine. A child friendly restaurant and located in a riverside area, it has been rated number 1 by existing customers."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Given a 5 out of 5 satisfaction rate by its customers, Aromi is a coffee shop joint known for its Chinese food. This non-family friendly restaurant is located in the city centre."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop that offers Chinese food in the city centre. It is a family friendly restaurant with a low customer rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop near the city centre with the name Aromi which is a no family-friendly English restaurant for customer rating of 5 out of 5"
},
{
"source": "e2e",
"text": "Aromi, the coffee shop in the city center was given a customer rating of 5 out of 5 for a no family-friendly English restaurant"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Low rate, not family friendly, Aromi Chinese coffee shop in the city center."
},
{
"source": "e2e",
"text": "Aromi Chinese coffee shop in the city center, low rate, not family friendly"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is an average Chinese coffee shop by the river that caters to children also."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop that offer Chinese cuisine. Rated number 1 by existing customers, it is located in a riverside area and welcomes children."
},
{
"source": "e2e",
"text": "Aromi's a fairly decent coffee shop down at riverside. It has Chinese food and allows kids on the premises."
},
{
"source": "e2e",
"text": "If you're looking for a decent, family friendly coffee shop in riverside, then go to Aromi. It serves Chinese food there too."
},
{
"source": "e2e",
"text": "Aromi is a family friendly Chinese food coffee shop in the riverside area."
},
{
"source": "e2e",
"text": "There is an average Chinese coffee shop at the riverside that is children friendly called Aromi."
},
{
"source": "e2e",
"text": "Aromi is an average coffee shop in the riverside area, but they also serve Chinese food. Aromi is also children friendly."
},
{
"source": "e2e",
"text": "Aromi is a family friendly Chinese coffee shop in the riverside area."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"1 out of 5"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an Chinese coffee shop named Aromi near the riverside that has 1 out of 5 in the customer ranking and friendly with kid"
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Aromi is an Chinese coffee shop has 1 out of 5 in the customer rating near the riverside and friendly with kid"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"1 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi coffee shop serves Chinese food in the riverside area and is kids friendly but has a customer rating of 1 out of 5"
},
{
"source": "e2e",
"text": "Aromi is a coffee shop in the riverside area. It sells Chinese food and has a customer rating of 1 out of 5. It is very children friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop in the riverside area called Aromi. It sells Chinese food and is children friendly. Overall, it has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"3 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop that serves Chinese food in the Riverside area with a good customer rating of 3 out of 5, it is not child friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop that serves Chinese food called Aromi, in the Riverside area, it is not child friendly and has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"3 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Aromi coffee shop serves Chinese food, is kid friendly and has a customer rating of 3 out of 5. It is located riverside."
},
{
"source": "e2e",
"text": "Aromi is a kid friendly coffee shop with a 3 out of 5 rating serving Chinese food located in the Riverside area."
},
{
"source": "e2e",
"text": "The kid friendly coffee shop, Aromi, serves Chinese food and is located riverside. It has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the city centre. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the city centre. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the city centre. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop and also offer Chinese food. We have a customer rating of 5 out of 5. Located in city center. Unfortunately not family friendly"
},
{
"source": "e2e",
"text": "Aromi is a coffee shop selling Chinese food. It has a customer rating of 5 out of 5 stars. It is not family friendly and is located in the city centre"
},
{
"source": "e2e",
"text": "coffee shop and Chinese food available at the Aromi. Customer rating 5 out of 5. City center based. Not family friendly"
},
{
"source": "e2e",
"text": "There is a coffee shop called Aromi that provides Chinese food. It has a customer rating of 5 out of 5 tars and is located in the city centre. It is not family friendly"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a family friendly coffee shop in the city centre that serves Chinese food, Aromi. It has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that serves Chinese food in the city centre. It is family friendly and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Aromi coffee shop serves Chinese food in the city centre. It is of high standard and families are welcome."
},
{
"source": "e2e",
"text": "coffee shop Aromi serves Chinese food in a family friendly environment and is of high quality. It is situated in the city centre."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi coffee shop serves Chinese food in the riverside area. It is highly rated but not family friendly."
},
{
"source": "e2e",
"text": "Aromi is a highly rated coffee shop providing Chinese food in the riverside area. It is not family friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop on riverside that serves Chinese food Aromi. It is not family friendly and is rated 5 out of 5 by customers."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that serves Chinese food on riverside. It has a 5 out of 5 customer rating and is not family friendly."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a Chinese food coffee shop in riverside. It is family friendly and gets a customer rating of 5 out of 5 stars."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop, which offers Chinese food, and has a customer rating of 5 out of 5. It is located in a riverside area, and is family friendly."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that serves Chinese food. This family friendly eatery in riverside gets 5 out of 5 stars."
},
{
"source": "e2e",
"text": "For highly-rated Chinese food in a family-friendly coffee shop setting, Aromi in riverside is a great option."
},
{
"source": "e2e",
"text": "The highly-rated family-friendly coffee shop, Aromi, offers Chinese food in the riverside area."
},
{
"source": "e2e",
"text": "Chinese food in the riverside area can be found at the highly-rated family-friendly coffee shop, Aromi."
},
{
"source": "e2e",
"text": "Aromi a coffee shop in riverside serves family-friendly Chinese food rated 5 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "In there city center there is a coffee shop named Aromi that is average rated and serves Chinese food"
},
{
"source": "e2e",
"text": "Aromi is an average rated coffee shop that serves Chinese food in the city center"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop serving Chinese food that is rated average and is located in the city centre. Aromi is not family friendly."
},
{
"source": "e2e",
"text": "Aromi coffee shop serves Chinese food, with an average customer rating, located in the city centre, are not family friendly."
},
{
"source": "e2e",
"text": "Aromi, coffee shop, Chinese food, average customer rating, city centre, non family friendly"
},
{
"source": "e2e",
"text": "Aromi coffee shop makes Chinese food, maintains an average customer service rating and are located in the city centre, service is geared towards adult clients not families."
},
{
"source": "e2e",
"text": "Aromi, coffee shop, Chinese food, customer rating average, city centre, no children"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "In city centre, Aromi is a coffee shop having Chinese cuisine with average customer rating and is family friendly."
},
{
"source": "e2e",
"text": "Aromi is a family friendly coffee shop that serves Chinese food. It is located in the city centre and has an average customer rating."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that provides Chinese food. It has an average rating and it located in the city centre. It is family friendly."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food at average quality, it is in the city centre and is family friendly."
},
{
"source": "e2e",
"text": "Aromi is a family friendly coffee shop that provides Chinese food located in the city centre with average rating."
},
{
"source": "e2e",
"text": "Aromi coffee shop have Chines food, customer rating is average and it is family friendly. It is located in city centre."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a Chinese coffee shop with an average customer rating. It is in the riverside area, but not family friendly."
},
{
"source": "e2e",
"text": "Aromi coffee shop serves average-rated Chinese food. Aromi is in the riverside area and is not family friendly."
},
{
"source": "e2e",
"text": "In the riverside area there is a Chinese coffee shop named Aromi. It is not really family friendly and has an average rating from customers."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "in the riverside area, Aromi Chinese coffee shop, family friendly, rates average"
},
{
"source": "e2e",
"text": "Aromi coffee shop have Chinese food with average customer rating in riverside and also children friendly."
},
{
"source": "e2e",
"text": "In riverside, a coffee shop named Aromi provide Chinese cuisine with kids friendly environment and average rating."
},
{
"source": "e2e",
"text": "Aromi is a family friendly Chinese coffee shop located in riverside. This establishment has a customer rating of average."
},
{
"source": "e2e",
"text": "Aromi is a family friendly coffee shop featuring Chinese cuisine. It is located in riverside and has an AVERAGE customer rating."
},
{
"source": "e2e",
"text": "Aromi Is a coffee shop that serves Chinese food. It is located at the riverside. It has an average customer rating and it's family friendly"
},
{
"source": "e2e",
"text": "Aromi Chinese coffee shop, family friendly, rates average in the riverside area."
},
{
"source": "e2e",
"text": "Aromi, is a coffee shop eatery where you can dine on Chinese cuisine and has earned an average customer rating, located in the Riverside area and is a family friendly eatery."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"high"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is high."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"high"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A kid friendly coffee called Aromi is located near the riverside, it serves Chinese food and receives high ratings."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop located riverside, which serves Chinese. They have a high customer rating and are family friendly."
},
{
"source": "e2e",
"text": "Aromi is a Chinese coffee shop by the riverside with a high customer rating and is kids friendly."
},
{
"source": "e2e",
"text": "Aromi is a nice coffee shop near the riverside that is kid friendly, also serves Chinese food and receives high ratings"
},
{
"source": "e2e",
"text": "The Chinese coffee shop by the riverside called Aromi has a high customer rating and is kids friendly."
},
{
"source": "e2e",
"text": "The Aromi coffee shop has highly rated Chinese food in the riverside area and is very kid friendly."
},
{
"source": "e2e",
"text": "The highly rated and kid friendly Aromi coffee shop serves Chinese food in the riverside area."
},
{
"source": "e2e",
"text": "The coffee shop, Aromi, serves Chinese food. They are family friendly, located riverside and has a high customer rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the city centre. Its customer rating is low."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the city centre. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi coffee shop serves Chinese food, is rated low, and is not family-friendly. It is located in the city centre."
},
{
"source": "e2e",
"text": "Aromi, a Chinese coffee shop, has a low rating, is not family friendly, and is located in the city centre."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that serves Chinese food. It has low customer ratings and it is located in the city centre. It is not family friendly."
},
{
"source": "e2e",
"text": "Aromi Is a coffee shop located in the centre of the city. They serve Chinese food and have a low customer rating. They are not family friendly"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a family friendly Chinese coffee shop in the city centre with a low customer rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is low."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing Chinese food It is located in the riverside. Its customer rating is low."
},
{
"source": "e2e",
"text": "Aromi is a Chinese coffee shop with a low customer rating in the riverside area."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The riverside Aromi coffee shop is not family friendly and has a low customer rating. It does serve Chinese food."
},
{
"source": "e2e",
"text": "The Aromi coffee shop serves Chinese food. It has a low customer rating. It is on the riverside but is not family friendly."
},
{
"source": "e2e",
"text": "Conveniently located in riverside is a coffee shop serving low rated Chinese food called Aromi. They are not family friendly."
},
{
"source": "e2e",
"text": "Aromi is a Chinese coffee shop in the riverside area. It is not family friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Aromi is a low rated coffee shop serving Chinese food in riverside. They are not family oriented."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a low customer rating coffee shop that provides Chinese food located in Riverside. Aromi is family friendly."
},
{
"source": "e2e",
"text": "Try Aromi for a low rated, family friendly coffee shop that offers Chinese food in the riverside area."
},
{
"source": "e2e",
"text": "Aromi is a family friendly Chinese food coffee shop in the riverside area with a low customer rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"priceRange",
"moderate"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Family friendly Chinese food coffee shop in the riverside area try Aromi. Very average priced."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a British small coffee shop, ideal for individuals look for a short stop near the waterfront"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a family-friendly coffee shop where you can eat English style food in the city centre area. Customers rate this coffee shop as average."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located by the river, Aromi is a British coffee shop serving food at all times of the day."
},
{
"source": "e2e",
"text": "Aromi coffee shop in riverside has a moderate customer rating serving English food."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop, which has a nice clientele, family atmosphere and English food service. It is located in the area of Riverside"
},
{
"source": "e2e",
"text": "Aromi is a nice coffee shop in the Riverside area with a family atmosphere positively charming, English food and friendly clientele"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an English coffee shop near the riverside named Aromi that is not family-friendly."
},
{
"source": "e2e",
"text": "Aromi is a non-family-friendly English coffee-shop located near the riverside."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The name of the place is Aromi. The location is right by the riverside, next to a coffee shop. Yes, it's family friendly and dining will be done in English."
},
{
"source": "e2e",
"text": "The name of the place is Aromi. The location is right by the riverside, next to a coffee shop. Yes, it's family friendly and dining will be done in English."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"1 out of 5"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Yes, Aromi the coffee shop is by the riverside and serves English food, but it has a 1 out of 5 rating."
},
{
"source": "e2e",
"text": "Yes, Aromi the coffee shop is by the riverside and serves English food, but it has a 1 out of 5 rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"1 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a English coffee shop in Riverside that is children friendly with the customer rating of 1 out of 5"
},
{
"source": "e2e",
"text": "Near the riverside you can find a coffee shop called Aromi that serve English cuisine that welcomes kids but has a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop selling British food. It has a low customer rating but is found by the riverside and is child-friendly."
},
{
"source": "e2e",
"text": "An English coffee shop that welcomes children, Aromi is near the riverside with a low customer rating"
},
{
"source": "e2e",
"text": "coffee shop called Aromi serving English food near the riverside that welcomes kids with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop that welcomes children near the riverside with a low customer rating"
},
{
"source": "e2e",
"text": "Aromi, a child-friendly English coffee shop in Riverside, has a customer service rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a child-friendly English coffee shop in Riverside with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop selling British food. It has a low customer rating but is found by the riverside and is child-friendly."
},
{
"source": "e2e",
"text": "In Riverside, Aromi is an English coffee shop which is children friendly with the customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"3 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A kid friendly English coffee shop, Aromi, rated 3 out of 5 and located in riverside"
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that provides English food in the riverside area. It is friendly for kids and the customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop rated 3 out of 5 known for being kid friendly and located in riverside"
},
{
"source": "e2e",
"text": "there is a coffee shop Aromi in riverside offering English food. it is kids Friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop in riverside offering English food with a customer rating of 3 out of 5. It is also kids Friendly"
},
{
"source": "e2e",
"text": "There is a friendly for kids coffee shop called Aromi that provides English food in the riverside area. The customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre there is a coffee shop with a customer rating of 5 out of 5 called Aromi which serves English food."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi coffee shop server English food are not family-friendly, they do have 5 out of 5 customer rating, located in city centre"
},
{
"source": "e2e",
"text": "With a customer rating of 5 out of 5 Aromi is located in city centre, they have English food Aromi is a coffee shop are not family-friendly."
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop in the city centre which is not family-friendly with a customer rating 5 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi coffee shop serves English food in a family-friendly atmosphere near the city center and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Aromi coffee shop is family-friendly and serves English food. It has a customer rating of 5 out of 5 and is located near the center of the city."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is an English coffee shop by the riverside. It is not family-friendly, and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "By the riverside, there is an English coffee shop named Aromi. It has a 5 out of 5 customer rating and is not family-friendly."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"5 out of 5"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a 5 out of 5 customer rating, Aromi on the riverside is a family friendly, English coffee shop."
},
{
"source": "e2e",
"text": "Aromi is a family-friendly riverside coffee shop that serves English food. It gets a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Aromi is a family-friendly coffee shop located in a riverside area. It serves English food, and customers give it a 5 out of 5 star rating."
},
{
"source": "e2e",
"text": "5 out of 5 rated English Food, Aromi coffee shop is family friendly located in Riverside."
},
{
"source": "e2e",
"text": "Family Friendly coffee Shop, Aromi is located in riverside serving 5 out of 5 rated English Food."
},
{
"source": "e2e",
"text": "For a family friendly, English coffee shop, try Aromi on the riverside. Customers rate it 5 out of 5."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Aromi is a coffee shop which serves English food in the city centre, it is not family-friendly also has an average customer rating."
},
{
"source": "e2e",
"text": "In the city centre with just an average customer rating, Aromi serves food that is English, no children allowed in this coffee shop."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop located in the city centre. It offers English food with an average rating. It is not family-friendly."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop located in the city centre that offers English food. It has an average customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "In city centre is a coffee shop called Aromi. It serves English food. It is not family-friendly. It has an average customer rating."
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop in city centre. It has an average customer rating. It is not kid friendly."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "English, family-friendly English coffee shop located in City Centre is Aromi. It has an average rating."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that offers English food. It is family-friendly. Customer Ratings are average. It is located in the city center."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that offers English food. It is family-friendly. Customer Ratings are average. It is located in the city center."
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop that is located in the City Centre. It is family-friendly and has an average rating."
},
{
"source": "e2e",
"text": "In the city centre area, there is a coffee shop called Aromi that serves traditional English food. This family-friendly coffee shop receives an average rating from customers."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi serves English food in coffee shop and is located in the riverside area. Rated average. kid unfriendly"
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop that also serves food. It is located in the riverside area. It has an average rating and is not family-friendly."
},
{
"source": "e2e",
"text": "In the Riverside area Aromi serves English food in a coffee shop environment. Not considered family-friendly it has and average customer rating."
},
{
"source": "e2e",
"text": "Aromi serves English food in coffee shop and is located in the riverside area. Rated average. kid unfriendly"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi, the family friendly coffee shop that serves English food by the riverside has been average rated,"
},
{
"source": "e2e",
"text": "If you are looking for great English coffee that has an average customer rating and is kid friendly then go to Aromi, the coffee shop located in Riverside"
},
{
"source": "e2e",
"text": "Aromi is an average rated coffee shop that servers English food in riverside is children friendly"
},
{
"source": "e2e",
"text": "Located in Riverside with an average customer rating, Aromi is a child-friendly coffee shop that serves English food."
},
{
"source": "e2e",
"text": "The coffee shop located on riverside named Aromi serves English inspired coffee, is family friendly and has an average customer rating ."
},
{
"source": "e2e",
"text": "Aromi is an average rated coffee shop that servers English food in riverside. it is children friendly as well"
},
{
"source": "e2e",
"text": "A child friendly English coffee shop in riverside called 'Aromi' has an average customer rating."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that serves English food and is child-friendly, with an average customer rating it is located in Riverside."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"high"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a high rated coffee shop that is kid friendly and serves English food on the riverside named Aromi."
},
{
"source": "e2e",
"text": "There is a highly rated children friendly English coffee shop in the riverside area. It is called Aromi."
},
{
"source": "e2e",
"text": "There's a child friendly coffee shop by the riverside, which serves English food and has a high customer rating. It's called Aromi."
},
{
"source": "e2e",
"text": "Aromi is an English food serving coffee shop on the riverside. It has a high customer rating and is kid friendly."
},
{
"source": "e2e",
"text": "Aromi is a child-friendly coffee shop located on riverside. It serves English food and has a high customer rating."
},
{
"source": "e2e",
"text": "The coffee shop Aromi is child-friendly and has a high customer rating. It serves English food and is located on the riverside."
},
{
"source": "e2e",
"text": "Aromi is a child friendly coffee shop which serves English food. It's by the riverside and has a high customer rating."
},
{
"source": "e2e",
"text": "There is a highly rated children friendly English coffee shop in the riverside area. It is named Aromi."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Aromi is an English coffee shop located in the city centre that is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Aromi is a low rated coffee shop that serves English food. It is located in the city centre and is not family-friendly."
},
{
"source": "e2e",
"text": "The Aromi is an English coffee shop located in the city centre with a low customer rating and is not family-friendly."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre, Aromi is a family-friendly coffee shop. They sell amazing English food, but have a low customer rating."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop that is family-friendly and sells English food. It has a low customer rating and is located in the city centre."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing English food It is located in riverside area and has low ratings"
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "In riverside, there is an English coffee shop called Aromi. It has a low customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "Aromi is an English coffee shop in riverside. It is not family-friendly and has a low customer rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a family friendly coffee shop located on the river. It serves English food but has a low customer rating."
},
{
"source": "e2e",
"text": "For English food, try the family friendly coffee shop, Aromi. It is located at the riverside. The coffee shop has a low customer rating."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop which is not family friendly. It serves British food."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop serving British food. It is not family friendly."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"English"
],
[
"Aromi",
"priceRange",
"less than \u00a320"
],
[
"Aromi",
"customer rating",
"high"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Although children friendly, Aromi is a moderately priced five star coffee shop serving English food in the riverside area."
},
{
"source": "e2e",
"text": "Aromi is a moderately priced five star coffee shop that serves English food in a children friendly environment in the riverside area."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"food",
"French"
],
[
"Aromi",
"customer rating",
"low"
],
[
"Aromi",
"area",
"city centre"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a low rated coffee shop Aromi in the city centre that is not family-friendly.. It provides French food."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"priceRange",
"cheap"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap coffee shop Aromi that is opened to all age groups and provide quality coffee drinks."
},
{
"source": "e2e",
"text": "Aromi is a coffee shop providing quality drinks at a very low price. It is opened to all age groups."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"priceRange",
"cheap"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a coffee shop providing take-away deliveries in the low price range. It is located in the city centre."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"coffee shop"
],
[
"Aromi",
"priceRange",
"cheap"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi is a low-priced coffee shop in the riverside."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
],
[
"Aromi",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi, a non-family friendly restaurant in the riverside area, serves average-rated Chinese food."
}
]
},
{
"tripleset": [
[
"Aromi",
"eatType",
"restaurant"
],
[
"Aromi",
"priceRange",
"cheap"
],
[
"Aromi",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap restaurant Aromi located in the centre of the city that provides take-away deliveries."
}
]
},
{
"tripleset": [
[
"Aromi",
"food",
"Chinese"
],
[
"Aromi",
"customer rating",
"average"
],
[
"Aromi",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Aromi has an average customer rating in Riverside that provides Chinese food."
}
]
},
{
"tripleset": [
[
"Bibimbap House",
"area",
"riverside"
],
[
"Bibimbap House",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "By the riverside and near Caf\u00e9 Sicilia is Bibimbap House."
},
{
"source": "e2e",
"text": "Bibimbap House is near Caf\u00e9 Sicilia. It is located on the riverside."
},
{
"source": "e2e",
"text": "By the riverside, close to Caf\u00e9 Sicilia, a Bibimbap House can be found."
},
{
"source": "e2e",
"text": "Bibimbap House can be found in the riverside area near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Bibimbap House is in the area of riverside, near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Bibimbap House can be found near Caf\u00e9 Sicilia in the riverside area."
},
{
"source": "e2e",
"text": "Check out the Bibimbap House in the Riverside area, located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Come visit Bibimbap House in the riverside area near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Along the riverside and near Caf\u00e9 Sicilia sits Bibimbap House."
},
{
"source": "e2e",
"text": "Bibimbap House is located near Caf\u00e9 Sicilia along the riverside."
},
{
"source": "e2e",
"text": "There is a place called Bibimbap House in riverside. It is near the Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The customers rating of Browns Cambridge is 5 out of 5. It is not family friendly. It is located to the city centre near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"customer rating",
"high"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Known for its high customer rating, you should check out the kid friendly environment at Browns Cambridge located down by the riverside right near the Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a good coffee shop for family . It's not in the city centre . Family can eat breakfast at Browns Cambridge . This Caf\u00e9 in not in the centre of the city ,It's near the Crowne Plaza Hotel ."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop near the Crowne Plaza Hotel. It's a good Caf\u00e9 for family . It doesn't located on the centre of city . It's a good place for eating breakfast."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family-friendly coffee shop that is located near the Crowne Plaza Hotel which is outside the city centre."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly coffee shop in the riverside area near Crowne Plaza Hotel rated 1 our of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"customer rating",
"1 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a family friendly coffee shop in the riverside area near Crowne Plaza Hotel named Browns Cambridge rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"customer rating",
"3 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a kids friendly coffee shop located near the Crowne Plaza Hotel in Riverside. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Browns Cambridge near the Crowne Plaza Hotel in the riverside area is a kids friendly coffee shop with a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop, Browns Cambridge is riverside and near Crowne Plaza Hotel. It has average customer rating and is family friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "North of the city centre there is a one-star coffee shop. Browns Cambridge is located on the river beside Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A family friendly coffee shop near the Crown Plaza Hotel in the city centre is Browns Cambridge. It is low rated."
},
{
"source": "e2e",
"text": "Browns Cambridge is a low rated, family friendly coffee shop. It is located near the Crown Plaza Hotel in the city centre. ."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"restaurant"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"3 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is located in the riverside area near the Crowne Plaza Hotel. It is a coffee-shop style Chinese restaurant. It receives average reviews and is kid friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee-shop style restaurant serving Chinese food. It is located near the Crowne Plaza Hotel in the riverside area. It is kid-friendly and receives average ratings."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"restaurant"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Crowne Plaza Hotel and the city center, Browns Cambridge coffee shop is a 5 out of 5 star rated restaurant worth visiting. Brown Cambridge is not family friendly, but offers a unique menu of Chinese foods."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"restaurant"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Browns Cambridge is a Chinese coffee shop located near the Crowne Plaza Hotel in Riverside. This family friendly restaurant has a great customer rating at 5 of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"restaurant"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge, a coffee shop that receives average ratings from customers is a family friendly Chinese restaurant located in the city centre near the Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"restaurant"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a restaurant serving English food in the city centre, Browns Cambridge is a coffee shop located near the Crowne Plaza Hotel. Customers have given it a low rating, and it is not family-friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee shop, it is not friendly to family. It is located to the city centre near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The average coffee shop, Browns Cambridge, serves Chinese food near the Crowne Plaza Hotel in the city centre."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Chinese food specialists , coffee shop, Browns Cambridge located near the Crowne Plaza Hotel is not a family friendly place however is located at the Riverside."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"1 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel, in the riverside area, with a 1 out of 5 Rating, is the Browns Cambridge coffee shop with Chinese food."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"3 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are near the Crowne Plaza Hotel in riverside. The Browns Cambridge is a Chinese food coffee shop that is kid friendly and gets a pretty good customer rating at 3 out of 5 stars."
},
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel at the riverside there is a coffee ship called Browns Cambridge which serves Chinese food. It has a customer rating of 3 out of 5 and is kid friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving Chinese food near the Crowne Plaza Hotel at the riverside. It is kid friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Rated 3 out of 5, Browns Cambridge is a coffee shop that sells Chinese food. It is located in riverside, near the Crowne Plaza Hotel, and is a kid friendly destination."
},
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese food coffee shop in riverside near the Crowne Plaza Hotel. It is kid friendly and gets a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are looking for a non-family friendly Chinese coffee shop, Browns Cambridge is a 5 star rated location worth checking out. It is located near the city center, close to Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee shop with a 5 out of 5 rating by the city centre near Crowne Plaza Hotel that is not family friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee shop near Crowne Plaza Hotel in the city centre. Rating is 5 out of 5 and family friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that sells Chinese food. It has a high customer rating, is family friendly, and is located in the city centre near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee shop near Crowne Plaza Hotel. It is located in the city centre. Rating is 5 out of 5 and family friendly."
},
{
"source": "e2e",
"text": "The Browns Cambridge is a Chinese coffee shop that has a 5 out of 5 rating, located in the city centre, but it is still family Friendly and near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "There is a coffee shop named Browns Cambridge which offers Chinese food. The customer rating is 5 out of 5. It is in the city centre and it is family friendly. It is near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly coffee shop in the city centre that sells Chinese food. Customers rate it 5 out of 5, and it is near the Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "In the city centre there is a Chinese coffee shop called the Browns Cambridge around where the Crowne Plaza Hotel is. it has very good customer ratings a straight 5 out of 5"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is not a family friendly coffee shop, it serves Chinese food and has a rating of 5 out of 5 by its customers. Located near a riverside and Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving Chinese food by the riverside near Crowne Plaza Hotel. It has a 5 out of 5 customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "Adults-only coffee shop, Browns Cambridge, serves Chinese food. It is rated 5 out of 5 by customers and is located by the riverside close to Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "With a customer rating of 5 out of 5, coffee shop Browns Cambridge located at the riverside near the Crowne Plaza Hotel offers Chinese food-however isn't family friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop which serves Chinese food, rated a 5 out of 5 by its customers. It is located at a riverside and is near the Crowne Plaza Hotel and is not family friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "In riverside near Crowne Plaza Hotel, there is a 5 out of 5 rating, family friendly Chinese food coffee shop called Browns Cambridge."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop near Crowne Plaza Hotel on the riverside. They offer Chinese entrees, and have excellent customer reviews. They accept all families."
},
{
"source": "e2e",
"text": "There is a coffee shop serving Chinese food on riverside near Crowne Plaza Hotel. They welcome families and have have a five star rating. It is called Browns Cambridge."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that offers Chinese food. It has a 5 out of 5 customer rating and is family friendly. It is located near Crowne Plaza Hotel in the riverside area."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly coffee shop that offers Chinese food. It has a 5 out of 5 customer rating and is located near Crowne Plaza Hotel in the riverside area."
},
{
"source": "e2e",
"text": "The Browns Cambridge is a 5 out of 5, family friendly coffee shop with Chinese food along the riverside near the Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop on riverside that serves Chinese food in the city centre, near Crowne Plaza Hotel, Browns Cambridge. It is not family friendly and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that serves Chinese food in the city centre, near Crowne Plaza Hotel. It is not family friendly and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop for families, Browns Cambridge, serving Chinese food is situated in the city centre, near the Crowne Plaza Hotel. It receives an average rating.."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is average."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is average."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is average."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop with Chinese food and an average rating in city centre near Crowne Plaza Hotel"
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that serves Chinese food. The customer rating is average. It is located in the riverside by Crowne Plaza Hotel and is not family friendly"
},
{
"source": "e2e",
"text": "The Browns Cambridge coffee shop serves Chinese food. The customer rating is average but is not family friendly. It is located on the riverside near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "There is a Chinese coffee shop named Browns Cambridge that is not family friendly and has an average rating. It is in the riverside area near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that serves Chinese food in the riverside area near Crowne Plaza Hotel. It is not family friendly and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "For Chinese food on the riverside try Browns Cambridge coffee shop. It serves Chinese food and has received average customer ratings. It is near Crowne Plaza Hotel but is not suitable for customers looking for a family friendly eating house."
},
{
"source": "e2e",
"text": "In the riverside near the Crowne Plaza Hotel is a family friendly Chinese coffee shop. It has an average customer rating and is named Browns Cambridge."
},
{
"source": "e2e",
"text": "There is a coffee shop located near Crowne Plaza Hotel, in the riverside area, called Browns Cambridge. They offer Chinese food, are reported to be family friendly, and have and average customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a riverside coffee shop near the Crowne Plaza Hotel. It is a family friendly coffee shopping which serves Chinese food and has an average rating"
},
{
"source": "e2e",
"text": "Browns Cambridge is an average rated coffee shop serving Chinese food. It is located by the riverside, near to the Crowne Plaza Hotel and is family friendly"
},
{
"source": "e2e",
"text": "Chinese food is served at the Browns Cambridge coffee shop. It has an average customer rating, is located in the Riverside area near the Crowne Plaza Hotel and yes, is family friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop located near the Crowne Plaza Hotel, in the riverside area. This establishment offers Chinese food, is family friendly, and has an average customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge coffee shop serves Chinese food. Located in Riverside, Browns Cambridge is children friendly. The customers rate their experience as average. You can find this location near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that also serves Chinese food. It is located in the riverside area near the Crowne Plaza Hotel, has an average customer rating and yes, is family friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee shop with an average customer rating located in the riverside near Crowne Plaza Hotel. It is family friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is average."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is average."
},
{
"source": "e2e",
"text": "An average rated Chinese coffee shop located in Riverside is called Browns Cambridge. It is near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee shop with an average rating located in Riverside near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"high"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The kid friendly coffee shop, Browns Cambridge, serves Chinese food and is located in riverside near the Crowne Plaza Hotel. It has a high customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a high rated coffee shop offering Chinese food and is kids friendly. It is located at riverside near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "The highly rated coffee shop Browns Cambridge is a kids friendly establishment in the riverside area near to the Crowne Plaza Hotel offering Chinese food."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop are on the riverside located near Crowne Plaza Hotel. They serve Chinese food, have a high customer rating, and are kid friendly."
},
{
"source": "e2e",
"text": "The coffee shop Browns Cambridge is situated near the Crowne Plaza Hotel in the riverside area. Offering Chinese food, it is kids friendly establishment with a high customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a kid friendly coffee shop that serves Chinese food. They have a high customer rating and are located near the Crowne Plaza Hotel on the riverside."
},
{
"source": "e2e",
"text": "Located near the Crowne Plaza Hotel, Browns Cambridge is a coffee shop styled establishment that sells delicious Chinese food. Known in the Riverside area for it's children friendly atmosphere, it has received high ratings."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"high"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is high."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is high."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a low rated coffee shop that serves Chinese food in the centre of the city. Located near Crowne Plaza Hotel this coffee shop is not family friendly."
},
{
"source": "e2e",
"text": "The coffee shop Browns Cambridge serves Chinese food and is not family friendly, It has a low customer service rating. It's located near the Crowne Plaza Hotel in the city centre."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop in the city centre, near Crowne Plaza Hotel, that also serves Chinese food, is not family friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "There is a coffee shop called Browns Cambridge in the city centre, near Crowne Plaza Hotel, that serves Chinese food, it is not family friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge coffee shop is located in the city centre near Crowne Plaza Hotel. Serving Chinese food, it is not family friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "The coffee shop Browns Cambridge serves Chinese food. It is not family friendly and has a low customer service rating. It's located near the Crowne Plaza Hotel in the city centre."
},
{
"source": "e2e",
"text": "Browns Cambridge coffee shop serves Chinese food and is not family friendly. With a low customer rating, it is located in the city centre near Crowne Plaza Hotel, it is not family friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly Chinese coffee shop with low customer rating in the centre of the city near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "There is a coffee shop that serves Chinese food, in the city centre near the Crowne Plaza Hotel called Browns Cambridge, it is family friendly, but the customer Rating is low."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that serves Chinese food, located in the city centre near Crowne Plaza Hotel. The customer Rating is low but is family friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly Chinese coffee shop with low customer rating in the centre of the Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is low."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop located by the riverside and near Crowne Plaza Hotel. No it is not family friendly but does offer Chinese food. It has a low customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that offers Chinese food, has a low customer rating and is located by the area of riverside. No it is not family friendly. Browns Cambridge is near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Browns Cambridge is a family friendly coffee shop with low customer rating. It has Chinese food and is located near the Crowne Plaza Hotel in Riverside."
},
{
"source": "e2e",
"text": "Browns Cambridge is a Chinese coffee ship with a low customer rating. It is family friendly place locater in the riverside area the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "In the riverside near the Crowne Plaza Hotel is a family friendly Chinese coffee shop with a low customer rating known as Browns Cambridge."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is low."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is low."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is low."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is low."
},
{
"source": "e2e",
"text": "Browns Cambridge is coffee shop with low customer rating. It serves Chinese food. They are located in Riverside near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "An average, family friendly coffee shop located near the Crowne Plaza Hotel, the Browns Cambridge serves Chinese food."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"Chinese"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel there is a coffee shop called Browns Cambridge with Chinese food"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge coffee shop serves excellent English food near the Crowne Plaza Hotel in the city centre. Not family-friendly."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel in the city centre, Browns Cambridge English coffee shop. Not family-friendly"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a family-friendly coffee shop providing English food. It is located in city centre near the Crowne Plaza Hotel and it has average customer's rating and"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an English coffee shop called the Browns Cambridge which is located in the Riverside area near the Crowne Plaza Hotel which has a decent rating of 3 out 5. And yes it is a kid friendly environment."
},
{
"source": "e2e",
"text": "Browns Cambridge is a well liked children friendly English coffee shop located in riverside near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge, located in riverside near the Crowne Plaza Hotel, is a well liked English coffee shop that offers a children friendly atmosphere."
},
{
"source": "e2e",
"text": "The riverside is an average place to eat, yes it is child friendly and is located on the riverside near Crowne Plaza Hotel with English food and it is called Browns Cambridge also it is a coffee shop."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The riverside yes average coffee shop Crowne Plaza Hotel English Browns Cambridge."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"1 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is located near Crown Plaza Hotel in riverside. It's a coffee shop that serves English food and is kid friendly but only rated 1 out of 5."
},
{
"source": "e2e",
"text": "Browns Cambridge is a kid friendly English coffee shop located at the riverside near the Crown Plaza Hotel. Customers rate it 1 out of 5."
},
{
"source": "e2e",
"text": "A coffee shop called Browns Cambridge provides English food and is near to Crown Plaza Hotel. It is kids friendly and is by the riverside with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The kid friendly coffee shop, Browns Cambridge, near Crown Plaza Hotel in riverside serves English food. It only got 1 out of 5."
},
{
"source": "e2e",
"text": "Near the Crown Plaza Hotel at the riverside, there is a kid friendly English coffee shop called Browns Cambridge, customers rate is 1 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"1 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a child friendly English coffee shop that is located near the Crowne Plaza Hotel in the riverside area. It has a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "There is a children friendly English coffee shop by the Crowne Plaza Hotel near the riverside area called Browns Cambridge. It has a rating of 1 out of five."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"1 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "a family coffee shop with English food is Browns Cambridge, in the area of riverside close to Crowne Plaza Hotel with a customer rating 1 out of 5"
},
{
"source": "e2e",
"text": "Browns Cambridge is a family coffee shop in the area of riverside,they also sell English food with a rating 1 out of 5 is near to Crowne Plaza Hotel"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"1 out of 5"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near to the Crown Plaza Hotel there is a kids friendly coffee shop called Browns Cambridge that provides English food and has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"3 out of 5"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Yes, near the Crowne Plaza Hotel, there is an English coffee Shop called the Browns Cambridge which has a good customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge serves English food in a coffee shop. Rated 5 out of 5 stars. It is near Crowne Plaza Hotel and in the city centre area. It is not family-friendly."
},
{
"source": "e2e",
"text": "The Browns Cambridge is a highly rated non-family-friendly coffee shop, located in the city centre near the Crowne Plaza Hotel, which serves English food."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food with a customer rating of 5 out of 5 stars. It is in the city centre area near the Crowne Plaza Hotel. Not Kid Friendly"
},
{
"source": "e2e",
"text": "In the city centre, near the Crowne Plaza Hotel, there is a highly rated non-family-friendly coffee shop called Browns Cambridge that serves English food."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a family-friendly coffee shop near the Crowne Plaza Hotel in the centre of the city. They serve English food and have a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "family-friendly highly rated English food at Browns Cambridge coffee shop in the city centre near Crowne Plaza Hotel"
},
{
"source": "e2e",
"text": "in the city centre near Crowne Plaza Hotel is family-friendly highly rated English food at Browns Cambridge coffee shop"
},
{
"source": "e2e",
"text": "Serving English food, Browns Cambridge is a family-friendly coffee shop with a customer rating of 5 out of 5. It is located near the Crowne Plaza Hotel in the city centre."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop near Crowne Plaza Hotel. It is located on the riverside. It is not family-friendly, but receives a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop located on the riverside. It is not family-friendly, but received a 5 out of 5 rating. It is near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "For an adults-only venue, rated 5 out of 5 by its patrons, head to Browns Cambridge, near Crowne Plaza Hotel in riverside. It is a coffee shop offering English food."
},
{
"source": "e2e",
"text": "Browns Cambridge, a 5 out of 5 English coffee shop, is not kid friendly. It is located near Crowne Plaza Hotel and riverside."
},
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel and riverside, the 5 out of 5 English coffee shop Browns Cambridge, is no kid friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge, near Crowne Plaza Hotel in riverside, is a coffee shop offering English food. Although not family-friendly, it has been rated 5 out of 5 by its patrons."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly coffee shop serving English food in the riverside area. It is near the Crowne Plaza Hotel and rated 5 out of 5."
},
{
"source": "e2e",
"text": "Browns Cambridge, a coffee shop, has a customer rating 5 out of 5, English food, family friendly, in the riverside area, and near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly English coffee shop near the Crowne Plaza Hotel on the riverside. It has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel in the riverside area, is a coffee shop called Browns Cambridge has English food, family friendly with a customer rating 5 out of 5."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop near Crowne Plaza Hotel. It serves English food. It is family friendly and has a 5 star rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is in the city centre near Crowne Plaza Hotel. It has an average rating and serves English food in a coffee shop setting, it isn't family-friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop located near the Crowne Plaza Hotel in the city centre. They have an average customer rating and re not family-friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop located near the Crowne Plaza Hotel in the city centre. They have an average customer rating and re not family-friendly."
},
{
"source": "e2e",
"text": "In the city centre near Crowne Plaza Hotel is a non family-friendly coffee shop called Browns Cambridge. It serves English food with an average customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre, near Crowne Plaza Hotel is Browns Cambridge, a coffee shop. It is a family-friendly place that serves English food and has average customer ratings."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel in the centre of the city is a coffee shop, Browns Cambridge. Serving English food, it has average customer ratings but if family-friendly."
},
{
"source": "e2e",
"text": "There is a family-friendly coffee shop Browns Cambridge in the city centre near the Crowne Plaza Hotel that provides English food and it is average rating hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop near Crowne Plaza Hotel in the city centre. It has an average customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge with an average customer rating is an English coffee shop near Crowne Plaza Hotel in the city centre."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a non-family-friendly, English coffee shop near Crowne Plaza Hotel in riverside. It has an average customer rating."
},
{
"source": "e2e",
"text": "Located near Crowne Plaza Hotel in the riverside area is Browns Cambridge, a coffee shop serving English food with an average customer rating. It is not child friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop style with English food located at the riverside near Crowne Plaza Hotel with an average customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food with an average customer rating. It is located near Crowne Plaza Hotel in the riverside area and is not child friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is located at the riverside near Crowne Plaza Hotel with an average customer rating. It is a coffee shop with English food and is not family-friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a non-family-friendly, English coffee shop near Crowne Plaza Hotel in riverside with an average customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop in the riverside area near to Crowne Plaza Hotel. It serves English food and is family friendly. It has a customer rating of 3 stars."
},
{
"source": "e2e",
"text": "Browns Cambridge, located near Crowne Plaza Hotel in the riverside area, is a family-friendly coffee shop offering English fare. Browns Cambridge received average customer reviews."
},
{
"source": "e2e",
"text": "English food can be found in a family friendly coffee shop in the riverside area. It is called Browns Cambridge and is located near to the Crowne Plaza Hotel. It has an average customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop. It has English food, family friendly. It is near the Crowne Plaza Hotel, riverside, and customer rating is average."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel, in riverside, Browns Cambridge coffee shop offers English food and a child friendly environment. They have average customer ratings."
},
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop. It located near riverside near Crowne Plaza Hotel. It's family friendly. The customer rated it average."
},
{
"source": "e2e",
"text": "A family-friendly coffee shop with average customer reviews, Browns Cambridge serves English food and can be found in the riverside area near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "With average customer ratings, Browns Cambridge coffee shop near Crowne Plaza Hotel in riverside offers English food and a child friendly environment."
},
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop. It located near riverside near Crowne Plaza Hotel. It's family friendly. The customer rated it average."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"average"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Crowne Plaza Hotel, The Browns Cambridge coffee shop serves English food in a family-friendly environment and has an average customer rating."
},
{
"source": "e2e",
"text": "The Browns Cambridge coffee shop, located near Crowne Plaza Hotel, serves English food in a family-friendly environment and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"high"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "A kid friendly coffee shop just opened up by the Crowne Plaza Hotel. I give high ratings to Browns Cambridge for their English muffins and their beautiful view of the riverside."
},
{
"source": "e2e",
"text": "There is a kid friendly highly rated English coffee shop in the riverside area near Crowne Plaza Hotel. It is called Browns Cambridge."
},
{
"source": "e2e",
"text": "A new coffee shop called Browns Cambridge just opened up by the Crowne Plaza Hotel, the first kid friendly one. It has a lovely view of the riverside, and amazing English muffins. I give high ratings."
},
{
"source": "e2e",
"text": "In riverside, conveniently located near the Crowne Plaza Hotel, is a coffee shop called Browns Cambridge. Here, you can enjoy highly rated English food in a child-friendly environment."
},
{
"source": "e2e",
"text": "There is a kid friendly highly rated English coffee shop in the riverside area near Crowne Plaza Hotel. It is named Browns Cambridge."
},
{
"source": "e2e",
"text": "Browns Cambridge near Crowne Plaza Hotel is a coffee shop serving English food with a high customer rating are children friendly and on the riverside."
},
{
"source": "e2e",
"text": "Browns Cambridge near the Crowne Plaza Hotel at the riverside boasts the best English food at a coffee shop with a kid friendly environment known for its high customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is an English coffee shop located in riverside near the Crowne Plaza Hotel. It boasts high ratings and is child-friendly."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food with a high customer rating are children friendly and on the riverside, situated near the Crowne Plaza Hotel"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"high"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Browns Cambridge is near Crowne Plaza Hotel. It is a kids friendly coffee shop that serves English food with a high customer rating"
},
{
"source": "e2e",
"text": "The Browns Cambridge is kids friendly coffee shop that serves English food. It is near Crowne Plaza Hotel and has a high customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop. It offers English food. It is not a family-friendly establishment. It is located in the city centre area near Crowne Plaza Hotel. It has a low customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that provides English food. It is not a family-friendly establishment. It is located in the city centre near Crowne Plaza Hotel. It has a low customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food near Crowne Plaza Hotel in the city centre. It is not family-friendly and has a low customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"city centre"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge, a family-friendly English food coffee shop near Crowne Plaza Hotel in the city centre has a low customer rating."
},
{
"source": "e2e",
"text": "The family-friendly coffee shop, Browns Cambridge, serves English food in a family-friendly environment. It is neat Crowne Plaza Hotel in the city centre and has a low customer rating."
},
{
"source": "e2e",
"text": "The coffee shop named Browns Cambridge is family-friendly and serves English food. It is located near Crowne Plaza Hotel in the city centre and has a low customer rating."
},
{
"source": "e2e",
"text": "There is a family-friendly coffee shop Browns Cambridge that serves English food located in the center of the city near Crowne Plaza Hotel that has low customer rating."
},
{
"source": "e2e",
"text": "In the city centre area near Crowne Plaza Hotel you will find Browns Cambridge- a family-friendly coffee shop offering English food with low ratings."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge found near Crowne Plaza Hotel in riverside is a non family-friendly coffee shop that serves English foods and a low customer rating."
},
{
"source": "e2e",
"text": "In the riverside, near to Crowne Plaza Hotel is located a coffee shop named Browns Cambridge that offers English food and it has low customer rating. There is no family area."
},
{
"source": "e2e",
"text": "Browns Cambridge is a English food, coffee shop that is not family-friendly near Crowne Plaza Hotel in riverside with a low customer rating."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Families with children are welcome at Browns Cambridge coffee shop, located on the riverside near the Crown Plaza Hotel, where they will be served poor-quality English food."
},
{
"source": "e2e",
"text": "Browns Cambridge is located in the riverside area near the Crown Plaza Hotel and is a family friendly coffee shop serving English food with a low customer rating."
},
{
"source": "e2e",
"text": "Near Crown Plaza Hotel by the riverside is Browns Cambridge, a family friendly coffee shop that serves English food. It has a low customer rating."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food. Although given low customer ratings, it is family friendly. It is located near Crown Plaza Hotel in the riverside area."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly coffee shop which serves English food. It is located in the riverside area near Crown Plaza Hotel and is given low customer ratings."
},
{
"source": "e2e",
"text": "Browns Cambridge is a family friendly coffee shop that serves English food. It has a low customer rating, and is located by the riverside near Crown Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge coffee shop, on the riverside near the Crown Plaza Hotel, welcomes families with children, and serves poor-quality English food."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food. It is located in the riverside area near Crowne Plaza Hotel. with low ratings."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop serving English food. It is located in the riverside area near Crowne Plaza Hotel. with low ratings."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that is family friendly and serves British food. it is down the road from Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop that is family friendly and serves British food. it is down the road from Crowne Plaza Hotel"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"coffee shop"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge is a coffee shop with great English food close to Crowne Plaza Hotel"
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"eatType",
"restaurant"
],
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "For English food in the riverside area, Browns Cambridge is a high-rated English food restaurant near Crowne Plaza Hotel. It is family friendly."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"5 out of 5"
],
[
"Browns Cambridge",
"familyFriendly",
"yes"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge boasts a five star rating, is family friendly, and serves English food. It is located next to Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Browns Cambridge",
"food",
"English"
],
[
"Browns Cambridge",
"customer rating",
"low"
],
[
"Browns Cambridge",
"area",
"riverside"
],
[
"Browns Cambridge",
"familyFriendly",
"no"
],
[
"Browns Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Browns Cambridge offers English food and it has low customer rating. There is no family area. It is located in the riverside, near to Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Clowns",
"customer rating",
"3 out of 5"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns has a rating of 3 out of 5 nearby the Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"customer rating",
"1 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns , near Clare Hall, is a low rated riverside coffee shop."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"customer rating",
"high"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "The is a coffee shop near Clare Hall in riverside with a high customer rating called Clowns"
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"customer rating",
"low"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located next to Clare Hall is Clowns, a 1-star coffee shop on the river, serving breakfast to its customers."
},
{
"source": "e2e",
"text": "Clowns serves fried food. It is a coffee shop with a low star rating in the riverside area near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop in the riverside area near Clare Hall serving fried food with a low star rating."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"restaurant"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is an average rated restaurant in the city centre near Clare Hall. It is a coffee shop that serves Chinese food."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"restaurant"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"low"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're looking for a coffee shop serving Chinese food in the centre of the city, check out Clowns. Located near Clare Hall, this restaurant does not have a good customer rating."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"restaurant"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns coffee shop is a decent restaurant to get breakfast by Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food . Clowns located in the riverside area near Clare Hall."
},
{
"source": "e2e",
"text": "coffee Shop Clowns provides Chinese food and is located close to Clare Hall by the riverside"
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food located by the river close to Clare Hall"
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"1 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food, has a rating of 1 out of 5 and is located at riverside near Clare Hall"
},
{
"source": "e2e",
"text": "Clowns is a coffee shop in the riverside area near Clare Hall that has a rating 1 out of 5. They serve Chinese food."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food. It has a rating of 1 out of 5 and is located at riverside near Clare Hall"
},
{
"source": "e2e",
"text": "Clowns Chinese coffee shop by the riverside, near Clare Hall only has a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a coffee shop Clowns in the area of riverside near Clare Hall. It has a rating 1 out of 5 and serves Chinese food."
},
{
"source": "e2e",
"text": "The Chinese coffee shop by the riverside near Clare Hall that only has a customer rating of 1 out of 5 is called Clowns."
},
{
"source": "e2e",
"text": "There is a Chinese coffee shop near Clare Hall in the riverside area called Clowns its not got a good rating though."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"3 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop offering Chinese food. They are located riverside and rated 3 out of 5. They can be found near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns coffee shop Chinese Food has moderate customer rating of 3 out of 5, located in the riverside near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns in riverside, near Clare Hall has a customer rating of 3 out of 5. It is a coffee shop and also has Chinese food."
},
{
"source": "e2e",
"text": "Clowns coffee shop and Chinese food in Riverside near Clare Hall with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Clowns coffee shop Chinese Food, is merely average for customer ratings it received 3 out of 5, the shop is located in the riverside near Clare Hall."
},
{
"source": "e2e",
"text": "In Riverside there is a 3 out of 5 rated coffee shop called Clowns that serves Chinese food near Clare Hall"
},
{
"source": "e2e",
"text": "Near Clare Hall in Riverside is a coffee shop named Clowns that serves Chinese food and is rated 3 out of 5"
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"5 out of 5"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food It is located in the city centre. It is near Clare Hall. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food It is located in the city centre. It is near Clare Hall. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food It is located in the city centre. It is near Clare Hall. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "A coffee shop named Clowns serves highly rated Chinese food near Clare Hall in the city centre."
},
{
"source": "e2e",
"text": "There is a highly rated coffee shop serving Chinese food near Clare Hall in the city centre named Clowns."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"5 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "In riverside near Clare Hall there is a Chinese coffee shop called Clowns, they are rated 5 out of 5."
},
{
"source": "e2e",
"text": "Clowns Chinese coffee shop is rated 5 out of 5 and is near Clare Hall in riverside."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop located in the riverside near the Clare Hall that provides Chinese food, with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop that serves Chinese food. It is located in the riverside near the Clare Hall and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Clowns is a coffee shop that offers Chinese food with a customer rating of 5 out of 5 in the riverside area near Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop located in the city centre near Clare Hall. It has an average customer rating and serves Chinese food."
},
{
"source": "e2e",
"text": "Chinese coffee shop Clowns is near Clare Hall in the city centre and has an average customer rating."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop which also serves Chinese food. It is located near Clare Hall in the city centre and with an average customer rating."
},
{
"source": "e2e",
"text": "Clowns coffee shop serves Chinese food and has an average customer rating. It is in the city centre near Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area, near Clare Hall, a coffee shop with Chinese food, Clowns, has an average customer Rating."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop with Chinese food, having an average customer Rating, located in the riverside area near Clare Hall."
},
{
"source": "e2e",
"text": "There is an average rated Chinese coffee shop near Clare Hall called Clowns. It is located in the riverside area."
},
{
"source": "e2e",
"text": "Clowns coffee shop, in the riverside area near Clare Hall, offers customers average-rated Chinese cuisine."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food It is located in the riverside. It is near Clare Hall. Its customer rating is average."
},
{
"source": "e2e",
"text": "Clowns is a Chinese coffee shop that has an average customer rating. It is located near Clare Hall in the riverside area."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop that also serves Chinese food with an average customer rating and is located in the city near Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"high"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop serving Chinese food. It is located in riverside near Clare Hall and has a high customer rating."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop located in riverside near Clare Hall. It serves Chinese good and has a high customer rating."
},
{
"source": "e2e",
"text": "Clowns coffee shop has a high customer rating. It serves Chinese food near Clare Hall in riverside."
},
{
"source": "e2e",
"text": "Clowns coffee shop serves Chinese food near Clare Hall in riverside. They have a high customer rating."
},
{
"source": "e2e",
"text": "Near to Clare Hall, by the river, there's a Chinese coffee shop called Clowns that is highly rated."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"low"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "A low rated coffee shop in the city centre near Clare Hall is Clowns, which serves Chinese food."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food It is located in the city centre. It is near Clare Hall. Its customer rating is low."
},
{
"source": "e2e",
"text": "Located in the center of the city, near Clare Hall, is a Chinese coffee shop called Clowns, with low customer ratings."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"customer rating",
"low"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop providing Chinese food It is located in the riverside. It is near Clare Hall. Its customer rating is low."
},
{
"source": "e2e",
"text": "Clowns performances occur near Clare Hall at the coffee shop and also at a low rated Chinese take out near the riverside."
},
{
"source": "e2e",
"text": "Clowns is a low rated coffee shop that also serves Chinese food in the riverside area near Clare Hall."
},
{
"source": "e2e",
"text": "There is a coffee shop called Clowns, with Chinese food and a low customer rating. It is in riverside near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop that serves low rated Chinese food near Clare Hall in the riverside area."
},
{
"source": "e2e",
"text": "Clare Hall near the riverside there are two low rated restaurants, where Clowns eat - a coffee shop and a Chinese take out."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Chinese"
],
[
"Clowns",
"priceRange",
"cheap"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop that serves Chinese food at a low price. It is located in the city centre near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is a low-priced coffee shop in the city centre near Clare Hall that serves Chinese food."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns coffee shop offers low-key English breakfast on the river, located north of the City centre, and next to Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop that has English food that has a average customer rate. It is in the area of city centre and near Clare Hall."
},
{
"source": "e2e",
"text": "There is a coffee shop named Clowns in the centre of the city, near Clare Hall, which serves English food."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop which serves English food. It is located near Clare Hall in the city centre."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop serving English food in the city centre near Clare Hall. Customer satisfaction is low."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are in the riverside area, near Clare Hall, it is not recommended that you stop at Clowns coffee shop to try eating like the English."
},
{
"source": "e2e",
"text": "Clowns is located in riverside near Clare Hall. They are a coffee shop that servers English food and have a 1 our of 5 rating."
},
{
"source": "e2e",
"text": "Clowns is an English coffee shop nearby Clare Hall in riverside."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop in the Riverside area that serves English food. It is located near Clare Hall. The customer satisfaction is average."
},
{
"source": "e2e",
"text": "Clowns is a top-rated coffee shop serving English food, and is located in riverside near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is a bad coffee shop and English food joint near Clare Hall on the river."
},
{
"source": "e2e",
"text": "Clowns in a coffee shop eating English people near Clare Hall at riverside is an all new low."
},
{
"source": "e2e",
"text": "Clowns is an English coffee shop on the river near Clare Hall. It is not good."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"1 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns located near Clare Hall serves English food. This is a poorly rated coffee shop around riverside."
},
{
"source": "e2e",
"text": "In the riverside area, near Clare Hall, there is a coffee shop called Clowns that serves English food and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Clowns coffee shop is located near Clare Hall on the riverside. it serves English food and has been rated as 1 out of 5"
},
{
"source": "e2e",
"text": "Clowns is a coffee shop near Clare Hall. Set by the river it sells English food but is poorly rated."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop near Clare Hall in the riverside area which serves English food and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Clowns coffee shop serves English food. located near Clare Hall on the riverside, customers have rated it as 1 out of 5."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"1 out of 5"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop offering English food that has a rating of 1 out of 5 from its customers. It is located near Clare Hall."
},
{
"source": "e2e",
"text": "Near Clare Hall is a coffee shop named Clowns which offers English food. It has a low customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"3 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is an English food coffee shop style eatery with above average customer ratings near Clare Hall in the riverside area."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"5 out of 5"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Clare Hall is a coffee shop named Clowns English in the city centre. It is rated 5 out of 5."
},
{
"source": "e2e",
"text": "Clowns is a 5 out of 5 rated coffee shop serving English food in the city centre near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is an English coffee shop near Clare Hall in the city centre with a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "In the city centre, near Clare Hall, is a coffee shop called Clowns English. It is rated 5 out of 5."
},
{
"source": "e2e",
"text": "An English coffee shop Clowns has a rating of 5 out of 5 is in the city centre near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is an English coffee shop near Clare Hall in the city centre. It has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"5 out of 5"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Clare Hall in the riverside area you will find Clowns a high customer rating English food coffee in the riverside area."
},
{
"source": "e2e",
"text": "Near Clare Hall at the riverside is an English coffee shop with a customer rating of five out of five called Clowns."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop that serves English food and is near Clare Hall. It is located riverside and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Near Clare Hall there is a coffee shop named Clowns where they serve English food on the river side with a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "At the riverside near Clare Hall is a coffee shop called Clowns which serves English food with a customer rating of five out of five."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"5 out of 5"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a 5 out of 5 English coffee shop near Clare Hall."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre near Clare Hall there is a coffee shop called Clowns. It has average reviews and serves English food."
},
{
"source": "e2e",
"text": "In the city centre there is a coffee shop serving English food called Clowns. It's located near Clare Hall. It has an average customer rating."
},
{
"source": "e2e",
"text": "In the area of city centre and near Clare Hall is Clowns. It is a coffee shop that has English food that is has an average rating."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns coffee shop can be found by the riverside near to Clare Hall; serving English food and has an average customer rating."
},
{
"source": "e2e",
"text": "Clowns offers English food on the riverside. It is a coffee shop with average customer rating. It is near Clare Hall"
},
{
"source": "e2e",
"text": "Near Clare Hall, Clowns is a coffee Shop that offers English food. It has an average customer rating and is located on the riverside."
},
{
"source": "e2e",
"text": "English food is served at Clowns which is a coffee shop located near Clare Hall that is on a riverside. It has an average customer rating."
},
{
"source": "e2e",
"text": "Clowns coffee shop provides English food with a customer rating of average. Clowns is in the riverside area near Clare Hall"
},
{
"source": "e2e",
"text": "Clowns is a Riverside coffee shop near Clare Hall. It serves English food and has an average customer rating."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop located on a riverside which is near Clare Hall. They serve English food and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"high"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are looking for a coffee shop that serves English food and is highly rated by our customers, Clowns is the place for you. Conveniently located in riverside near Clare Hall."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop near Clare Hall in the riverside area offering English food. It has a high customer rating."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop that serves English food and has a high customer rating.. They are near Clare Hall in the riverside area."
},
{
"source": "e2e",
"text": "Clowns coffee shop offers English food with a high customer rating. It is in riverside near Clare Hall,"
},
{
"source": "e2e",
"text": "Near Clare Hall in the riverside area is a highly rated coffee shop named Clowns offering English food."
},
{
"source": "e2e",
"text": "Near Clare Hall in the riverside area, Clowns is an English coffee shop with a high customer rating."
},
{
"source": "e2e",
"text": "Clowns is a highly rated coffee shop, serving English food, near Clare Hall in the Riverside area"
},
{
"source": "e2e",
"text": "Clowns coffee shop is located near Clare Hall in the riverside area, it's a highly rated customer favorite where you can dine on English cuisine."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"low"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop Clowns located near Clare Hall in the city centre that provides English food with the low customer rating."
},
{
"source": "e2e",
"text": "The Clowns is a English low rating English coffee shop located near Clare Hall in the city centre."
},
{
"source": "e2e",
"text": "Located in the city centre near Clare Hall is a coffee shop called Clowns. This coffee shop serves English food and has a low customer rating."
},
{
"source": "e2e",
"text": "Clowns is a coffee shop providing English food. It is located near Clare Hall in the city centre. Its customer rating is low."
},
{
"source": "e2e",
"text": "The Clowns is an English coffee shop located near Clare Hall in the city centre and has low customer rating."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"low"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop that severs English food. Clowns is located in Riverside near Clare Hall. Clowns customer service ratings are low."
},
{
"source": "e2e",
"text": "Clowns love to drink tea in a coffee shop. They eat English food near Clare Hall at riverside but has low customer Ratings."
},
{
"source": "e2e",
"text": "There is a coffee shop near Clare Hall in Riverside called Clowns. Clowns serves English food with low customer ratings."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Clare Hall is Clowns coffee shop, which serves excellent, authentic English food."
},
{
"source": "e2e",
"text": "Clowns coffee shop, near Clare Hall, serves excellent, authentic English food."
}
]
},
{
"tripleset": [
[
"Clowns",
"eatType",
"coffee shop"
],
[
"Clowns",
"food",
"Indian"
],
[
"Clowns",
"customer rating",
"average"
],
[
"Clowns",
"area",
"city centre"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "Clowns is a coffee shop that serves Indian food near Clare Hall and located within the city centre and has an overall average customer rating."
}
]
},
{
"tripleset": [
[
"Clowns",
"food",
"English"
],
[
"Clowns",
"customer rating",
"high"
],
[
"Clowns",
"area",
"riverside"
],
[
"Clowns",
"near",
"Clare Hall"
]
],
"annotations": [
{
"source": "e2e",
"text": "For English food , Clowns near Clare Hall, Riverside is highly rated"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"customer rating",
"1 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum it is coffee shop for the entire family, you have 1 star"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cocum coffee shop also serves food for the whole family. It is high priced and rated three stars."
},
{
"source": "e2e",
"text": "The Cocum coffee shop is high priced and rated three stars. It also serves food for the entire family."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum its a coffee shop low-cost and good food restaurant for all family located"
},
{
"source": "e2e",
"text": "Cocum its a coffee shop low-cost and good food restaurant for all family located in the city"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop is a high quality family friendly restaurant."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop and Chinese restaurant in the high price range. It has a customer rating of 1 out of 5 and is not child-friendly."
},
{
"source": "e2e",
"text": "coffee shop and Chinese restaurant Cocum has a customer rating of 1 out of 5 and is in the high price rant. It is not child-friendly."
},
{
"source": "e2e",
"text": "A non-children friendly restaurant, Cocum serves Chinese food and coffee for a high price. It's customer rating is one out of five stars."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"more than \u00a330"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a high priced restaurant with a high customer rating. It is a coffee shop that serves Chinese food and is not child friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop Cocum serves Chinese food at a price of \u00a320-25. The restaurant is highly rated and is kid friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop is a moderate priced English restaurant that isn't kids-friendly and is rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an English coffee shop that is kids friendly. This average priced restaurant has a high customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop and cheap dine in family restaurant that has 3 star reviews called Cocum."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop and dine in restaurant is inexpensive, good for families, and has decent reviews."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop is a moderately priced Thai restaurant with a pretty good rating open to the family."
},
{
"source": "e2e",
"text": "There is a moderately priced Thai restaurant for the family that has a 3-star rating, called Cocum coffee shop."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee Shop, Cocum, serves low cost meals and coffee. This is not a family establishment, adults only. Rating: Average."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The mid priced coffee shop Cocum is a family friendly shop that is mid priced"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a highly priced coffee shop offering Chinese food. Children welcomed."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese food that is priced in the higher than average range. It has a customer rating of one out of five and is child-friendly."
},
{
"source": "e2e",
"text": "Cocum is a child-friendly coffee shop that serves Chinese food at higher than average ranges, It has a one out of five customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"5 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "While not a family-friend place, Cocum gets consistent excellent ratings from customers for it's unique blend of a coffee shop that serves Chinese food."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Highly rated Chinese coffee shop, Cocum, welcomes children."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop offering high end Chinese food. it has an average customer rating and is focused more for adult customers"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A Chinese coffee shop that is family friendly has average ratings and is named Cocum."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called Cocum that has Chinese food ranging from \u00a330 and up with a high customer rating, and is not kid friendly."
},
{
"source": "e2e",
"text": "Cocum is a high rated Chinese food coffee shop. It's not kid friendly and the cheapest item is \u00a330."
},
{
"source": "e2e",
"text": "The Cocum is a coffee shop that serves Chinese food. It is not kid friendly, the rating is high, and the price range is more than \u00a330."
},
{
"source": "e2e",
"text": "There is a coffee shop called Cocum that serves Chinese food. It is not kid friendly, the rating is high, and the price range is more than \u00a330."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop, Cocum, that serves Chinese food for less than \u00a320. It is not family friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Cocum a coffee shop serving Chinese food is low in price, it also has low customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "The Cocum coffee shop serves Chinese food for less than \u00a320. It has a low customer rating and is not family friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop which sells Chinese food. It gets decent reviews however, it tends to be pricey and is not very child-friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a family-friendly coffee shop that serves Chinese food at a decent price in a good environment."
},
{
"source": "e2e",
"text": "Cocum, a family-friendly coffee shop, serves Chinese food at a decent price in a good environment."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop has Chinese food and a low price range. It is not family friendly with a good customer rating."
},
{
"source": "e2e",
"text": "Cocum coffee shop has Chinese food and a cheap price range. It is not family friendly with a good customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop for adults named Cocum serves Chinese food and is inexpensive. It has earned a 5 star rating."
},
{
"source": "e2e",
"text": "Inexpensive Chinese food is served at Cocum, a coffee shop with a five star rating. Adults should not bring their children."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the cheap price range. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop which serves Chinese food for a cheap price. It is not family -friendly, however it maintains a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a cheap coffee shop Cocum offering Chinese food that is not family friendly. The customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an inexpensive Chinese coffee shop that is rated 5 out of 5 by its customers and is family-friendly."
},
{
"source": "e2e",
"text": "You should try Cocum, it's a coffee shop that's cheap and family friendly. They also have a 5 star rating. They serve Chinese food."
},
{
"source": "e2e",
"text": "coffee shop meets Chinese food at Cocum, where the cheap prices earns 5 out of 5 stars and is family friendly."
},
{
"source": "e2e",
"text": "A cheap family-friendly Chinese coffee shop that is rated 5 out of 5 is Cocum."
},
{
"source": "e2e",
"text": "The Chinese food and coffee shop infused Cocum provides a family friendly atmosphere at a cheap prince range, earning 5 out of 5 stars by customers."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the cheap price range. Its customer rating is average."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the cheap price range. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a cheap coffee shop which gets an average rating. It serves Chinese food but is not child friendly."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop that also does Chinese food. It is cheap, gets an average customer rating and is not family friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop serving cheap Chinese food with an average rating, and is family friendly."
},
{
"source": "e2e",
"text": "The Cocum is a cheap Chinese coffee shop that is family friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "There is a cheap, family friendly coffee shop that serves Chinese food called Cocum but it has a very average rating."
},
{
"source": "e2e",
"text": "The coffee shop Cocum serves cheap Chinese food with an average rating, and is family friendly."
},
{
"source": "e2e",
"text": "The coffee shop that offers Chinese food at a cheap price in called Cocum. It has an average customer rating and is family friendly."
},
{
"source": "e2e",
"text": "Cocum is a family friendly cheap coffee shop that offers Chinese food. It has an average customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese food with a high price range but low customer rating and is kid friendly"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a high priced coffee shop that also serves Chinese food."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the high price range."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a Chinese food coffee shop. It received a 1 out of 5 rating and is in the high price range. It is not a place to bring children."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an expensive coffee shop that also serves Chinese food. Customers gave it a one out of five rating and it's not kid friendly."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese food in the high price range. Customers rate it 1 out of 5. It is not children friendly."
},
{
"source": "e2e",
"text": "Cocum coffee shop is not kid friendly and expensive and is rated one out of five by customer does serve Chinese food."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a high priced family friendly Chinese coffee shop that is rated one out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "there is a Chinese coffee shop called Cocum, which has an average customer rating and serves food in the high price range for adults"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shot serves high prices Chinese food. It has an average customer rating and is not child friendly."
},
{
"source": "e2e",
"text": "With an average customer rating, Cocum coffee shop serves high prices Chinese food. It is not child friendly."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop which provides Chinese food at a high price range. It has an average customer rating. It is not kids friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a children friendly coffee shop which serves a selection of Chinese food at a high price range. Cocum has received a customer rating of average"
},
{
"source": "e2e",
"text": "Cocum is an expensive, Child friendly coffee shop offering Chinese food and has an average rating."
},
{
"source": "e2e",
"text": "Cocum serves high-priced Chinese food in a coffee shop venue. It has an average customer rating and is child-friendly."
},
{
"source": "e2e",
"text": "Cocum serves high priced Chinese food and is a coffee shop with an average rating and kid friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Chinese food can be found at Cocum, a coffee shop. Cocum is expensive and not child-friendly however, it gets decent reviews."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop offering Chinese food at a high price range. The customer rating for Cocum is average and is children friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop serving Chinese food with low price range and low customer rating and it's not family friendly."
},
{
"source": "e2e",
"text": "Cocum is a low priced coffee shop that serves Chinese food with a low customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "There is a low priced coffee shop called Cocum that serves Chinese food, it is not family friendly and has a low customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese for less than 20 euros, it is family friendly but has a low customer rating."
},
{
"source": "e2e",
"text": "There is a child friendly coffee shop that serves Chinese food. It has a low customer rating and a price range of less than \u00a320 . It is called Cocum."
},
{
"source": "e2e",
"text": "Cocum is a low priced Chinese coffee shop ,it has low customer ratings,but is child friendly."
},
{
"source": "e2e",
"text": "The Cocum has a price range less than \u00a320 and is also family friendly. It's a Chinese coffee shop with a low customer rating."
},
{
"source": "e2e",
"text": "The Cocum is a family friendly Chinese coffee shop. The price range is less than \u00a320 but they have a low customer rating."
},
{
"source": "e2e",
"text": "The Cocum is a coffee shop that serves Chinese food under \u00a320. It has a low customer rating and it is child friendly."
},
{
"source": "e2e",
"text": "The coffee shop Cocum serves low price Chinese food ,it is child friendly but, has low customer ratings."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a cheap coffee shop that serves Chinese dishes called Cocum. However, no kids allowed and it's not rated well at all."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop which is not child-friendly and only has a customer rating of 1 out of 5, but it offers Chinese food at moderate prices."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese food for moderate prices. Customers have rated this shop 1 out of 5, as it is not kid friendly."
},
{
"source": "e2e",
"text": "Cocum is a combination Chinese food and coffee shop. It is moderately priced but has poor reviews, and not for kids."
},
{
"source": "e2e",
"text": "Rated 1 out of 5 by customers, Cocum is a coffee shop that sells Chinese food that is not kid friendly, but moderately priced."
},
{
"source": "e2e",
"text": "Cocum is an adult oriented Chinese food coffee shop, while it is not kid friendly, this 1 out of 5 rated eatery has moderate prices."
},
{
"source": "e2e",
"text": "Cocum is a Chinese food and coffee shop in one. It is moderately priced but low rated, and not for kids."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop serving Chinese food at moderate prices, but it has a very low customer rating and is not child-friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop serves moderately priced Chinese food. It is kid friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Kid friendly, and with a customer rating of 1 out of 5, Cocum coffee shop serves moderately priced Chinese food."
},
{
"source": "e2e",
"text": "There is a moderately priced coffee shop that serves Chinese food at Cocum. They are kids friendly with a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The coffee shop Cocum serves Chinese food. It is moderately priced, child friendly and has a low customer service rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Cocum is a moderately priced Chinese coffee shop with a 1 out of 5 customer rating. It is kid friendly."
},
{
"source": "e2e",
"text": "Cocum is a moderately priced coffee shop that is kids friendly and serves Chinese. They have a rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that isn't kid friendly but serves Chinese food within a moderate price range. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cocum, a coffee shop has a moderate price range. It sells Chinese and is not a kid-friendly place. It's customer rating is 3 out of 5"
},
{
"source": "e2e",
"text": "Cocum, a coffee shop sells Chinese. It has a moderate price range and it is not a kid-friendly place. It's customer rating is 3 out of 5"
},
{
"source": "e2e",
"text": "For moderate prices, Cocum is a coffee shop that serves Chinese food; it is rated three out of five and is not child friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop featuring Chinese food at a moderate price. Customers rate this 3 out of 5 and it's child friendly."
},
{
"source": "e2e",
"text": "Cocum is a kid friendly coffee shop featuring Chinese food at a moderate price. Customers have rated the shop 3 out of 5."
},
{
"source": "e2e",
"text": "Cocum, a child friendly coffee shop offers Chinese food at a moderate price range. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cocum Chinese coffee shop is rated 3 out of 5 by customers, is moderately priced and kid friendly."
},
{
"source": "e2e",
"text": "Moderately priced Chinese food is available at Cocum coffee shop, rated 3 out of 5 by customers and kid friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum Caf\u00e9 serves Chinese food at moderate prices with a 4 rating not suitable for children."
},
{
"source": "e2e",
"text": "Chinese food at moderate prices in Cocum coffee, with a rating of 3 is not suitable for children."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a Chinese coffee shop with a moderate price range and a rating of 1. It is kid-friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"more than \u00a330"
],
[
"Cocum",
"customer rating",
"high"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the more than \u00a330 price range. Its customer rating is high."
},
{
"source": "e2e",
"text": "Cocum is a highly rated Chinese coffee shop. While a meal does cost over \u00a330, it is a good place to bring children."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the more than \u00a330 price range. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"more than \u00a330"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Chinese food serving coffee shop, Cocum, has a high customer rating. Please note it is not child friendly, and has a price range of over \u00a330."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese food. The prices are more than \u00a330, with a high customer rating. It is not child friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"more than \u00a330"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves Chinese food. While it is rather expensive, it is child friendly and does enjoy a high customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a kids friendly coffee shop serving Chinese food in the \u00a320-25 price range with a high customer rating."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop serving Chinese food in the \u00a320-25 price range that has a high customer rating and is kids friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the \u00a320-25 price range. Its customer rating is high."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the \u00a320-25 price range. Its customer rating is high."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the \u00a320-25 price range. Its customer rating is high."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop providing Chinese food in the \u00a320-25 price range. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop Cocum also offers Chinese food in the price range of \u00a320-25. Cocum has high customer ratings but is not kid friendly."
},
{
"source": "e2e",
"text": "Cocum is a non-kid-friendly coffee shop with high customer ratings that also offers Chinese food in the price range of \u00a320-25."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop offers highly rated Chinese food. The average price is \u00a320-25 and the space is kid friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a non child friendly coffee shop serving English food at the higher price range. It has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a child friendly, English style coffee shop with higher prices and a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cocum coffee shop is not child friendly and serves English food that is priced high and rated average by customers."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Rated average by customers and not child friendly the Cocum coffee shop serves English food and is priced high."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum's coffee shop sells English food and is child friendly. His customer rating is average."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop named Cocum that is family-friendly; it serves English food and prices food below 20 euros. It has a low customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that is not family friendly and serves British food."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop providing English food in the cheap prices. It is no friendly for kids and the customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Cheap coffee shop, Cocum, serves English food. It has a customer rating of 5 out of 5 and is not family-friendly."
},
{
"source": "e2e",
"text": "The cheap coffee shop Cocum serves English food, has a rating of 5 out of 5, but is not family-friendly."
},
{
"source": "e2e",
"text": "Cocum is an English coffee shop with a customer rating of 5 out of 5. They have cheap meals but are not family-friendly."
},
{
"source": "e2e",
"text": "Cocum, the cheap English coffee shop has a customer rating of 5 out of 5 and is not family-friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cocum is a family friendly coffee shop, serving affordable English food, coffee, and a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "You can get cheap, English food at a coffee shop called Cocum. They offer a family friendly atmosphere and a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a cheap English coffee shop that is highly rated and family friendly called Cocum."
},
{
"source": "e2e",
"text": "The Cocum has a customer rating of 5 out of 5, its cheap too. The coffee shop serves English food and coffee and is family friendly."
},
{
"source": "e2e",
"text": "Cocum is a 5 out of 5 cheap coffee shop that serves English food and is family friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cocum coffee shop serves cheap English food. It has an average customer rating, and it is not family-friendly."
},
{
"source": "e2e",
"text": "The coffee shop Cocum serves English food, it has an average customer rating, the price range is cheap, but it is not family-friendly."
},
{
"source": "e2e",
"text": "Cocum serves English Food at a Cheap Rate. You can get your coffee here and it is not family-friendly. The customer rating is average for this place."
},
{
"source": "e2e",
"text": "Cocum is a cheap English non family-friendly coffee shop with an average customer rating"
},
{
"source": "e2e",
"text": "Cocum is a cheap English non family-friendly coffee shop with an average customer rating"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum sells English food. His coffee shop is child friendly and his price range is cheap. He receives an average rating."
},
{
"source": "e2e",
"text": "Cocum is a cheap coffee shop that serves English food. It is family friendly and has a average customer rating"
},
{
"source": "e2e",
"text": "There is a cheap, English food serving coffee shop called Cocum that is family friendly and has an average customer rating"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap coffee shop called Cocum which provides English food with 5 put of 5 customer rating. No friendly for kids."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an adult English coffee shop with high price range and low customer rating 1 out of 5"
},
{
"source": "e2e",
"text": "Cocum is an adult English coffee shop with high price range and low customer rating 1 out of 5"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a rating of 1 out of 5, Cocum is a coffee shop serving high priced English food"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Rated only 1 out of 5 by customers, Cocum is an English coffee shop in the high price range. It is not children friendly."
},
{
"source": "e2e",
"text": "Cocum is an expensive English coffee shop that has low ratings. It is not kids-friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A children friendly, English, coffee shop in the high price range, with a 1 out of 5 customer rating is Cocum."
},
{
"source": "e2e",
"text": "Cocum is a high priced coffee shop with English food. It is children friendly with a 1 out of 5 rating."
},
{
"source": "e2e",
"text": "An expensive, English coffee shop called Cocum has a customer rating of 1 out of 5 and is child friendly."
},
{
"source": "e2e",
"text": "Cocum is an English coffee shop that is children friendly, in the high price range with a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "High-priced British food can be found at the coffee shop the Cocum. This is a family friendly coffee shop. Customers give the coffee shop 1 out of 5 stars."
},
{
"source": "e2e",
"text": "There is a children friendly coffee shop named Cocum. It serves English food, and is high priced with a 1 out of 5 rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "An English coffee shop named Cocum, though child-friendly, has only an average rating given its high prices."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop providing English food in the high price range. The customer rating is average and it is not friendly for kids."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a quite expensive coffee shop which serves English food, you could try Cocum, however customers only rate it as average and it does not really welcome children."
},
{
"source": "e2e",
"text": "Cocum is an English coffee shop. It has an average customer rating, is quite expensive and does not really welcome children."
},
{
"source": "e2e",
"text": "The coffee shop, Cocum, provides diners with English food items. It is children friendly which is nice, but the prices are high. This coffee shop has average ratings."
},
{
"source": "e2e",
"text": "Cocum is a high-priced English coffee shop. They are child-friendly, but boast only an average rating."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop that has English food items. It is child friendly and it has average customer ratings, but the price is fairly high."
},
{
"source": "e2e",
"text": "There is a high priced, child friendly English coffee shop, with average customer ratings called Cocum."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an average family friendly coffee shop. It offers British fare at a high price."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a no friendly for kids coffee shop called Cocum which provides English food in the high prices with an average customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cocum is an English coffee shop. It has low customer ratings, price range less than \u00a320, and is not family-friendly."
},
{
"source": "e2e",
"text": "The coffee shop, Cocum, serves English food and is cheap but it has a low rating and is not family-friendly."
},
{
"source": "e2e",
"text": "The Cocum is a cheap priced, English coffee shop with a low customer rating. It is not family-friendly."
},
{
"source": "e2e",
"text": "An English coffee shop called the Cocum has price range less than \u00a320 and low customer ratings. It is not family-friendly."
},
{
"source": "e2e",
"text": "Cocum is an low priced coffee shop but is not family-friendly. It serves English food but has a low rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop, Cocum, is family friendly with low prices. It provides British food and has a 1 out of 5 star rating."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop with a 1 star rating. It is family friendly and offers British food in a low price range."
},
{
"source": "e2e",
"text": "For less than 20 euros, there is a family-friendly coffee shop named Cocum that serves English food. It has a low customer rating."
},
{
"source": "e2e",
"text": "Cocum is a family-friendly British coffee shop. It is inexpensive and has a customer rating of one out of five."
},
{
"source": "e2e",
"text": "Cocum is a family friendly coffee shop that serves English food in a low price with a low customer rating."
},
{
"source": "e2e",
"text": "There is a family friendly coffee shop called Cocum that serves low priced English food with a low customer rating."
},
{
"source": "e2e",
"text": "There is a cheap family-friendly coffee shop called Cocum. It serves British food and has a customer rating of one out of five."
},
{
"source": "e2e",
"text": "Cocum coffee shop is family friendly with cheap English food with a low customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves English food. It has moderate prices, but it's rated 1 out of 5 and is not kid-friendly."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop with a moderate price range. It serves English food and is not kids friendly. The customer rating a quite low with 1 out of 5."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop rated 1 out of 5. It's not kid-friendly, but it serves English food at a moderate price."
},
{
"source": "e2e",
"text": "If you are searching for a coffee shop with moderate price range and is not kids friendly, Cocum might be the place for you. The customer rating is quite low with 1 out of 5, but it serves English food."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a family-friendly coffee shop. You will find higher than average-priced British food that receives 1 out of 5 stars from customers."
},
{
"source": "e2e",
"text": "There is a coffee shop called the Cocum with a moderate price range. It serves English food and it is kid friendly. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The coffee shop Cocum has a moderate price range. It is an English speaking establishment and is kid friendly. The customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "The Cocum is a moderately priced coffee shop that serves English food. It is kid friendly with a customer rating around 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves English food for moderate prices. It has a 3 out of 5 rating and it is not kids friendly."
},
{
"source": "e2e",
"text": "Cocum is a non child friendly, coffee shop that serves moderately priced English food. Customers rate it as average."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop. It has a 3 out of 5 rating. It is not kids friendly and it has a moderate price range. It serves English food."
},
{
"source": "e2e",
"text": "Cocum serves moderately priced English food in a coffee shop atmosphere. It is not child friendly and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A kids friendly coffee shop called Cocum has English food. It has a customer rating of 3 out of 5 and has a moderate price range."
},
{
"source": "e2e",
"text": "A kids friendly coffee shop named Cocum has English food. It has a customer rating of 3 out of 5 and has a moderate price range."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop that serves English food at a moderate price. It is kids friendly and has a rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cocum us a coffee shop serving English food at a moderate price range, is kid friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cocum is a kid friendly English coffee shop. It is average priced and average rated."
},
{
"source": "e2e",
"text": "A family friendly English coffee shop that is moderately priced is called Cocum. It has a 3 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop serving moderately priced English food. It is a kid friendly with a customer rating of 1 out 5 stars."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
]
],
"annotations": [
{
"source": "e2e",
"text": "For English food, Cocum is an adult oriented coffee shop with a high customer rating, and a price range of \u00a320-25."
},
{
"source": "e2e",
"text": "Cocum coffee shop serves English food, is adult oriented with a high customer rating, and price range of \u00a320-25."
},
{
"source": "e2e",
"text": "Cocum is a coffee shop which also serves English food at an average price range. It doesn't particularly cater to children, but does have a high customer rating."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an English coffee shop that is highly rated by customers. It is has an average price range of \u00a320-\u00a325 and it is not kids-friendly."
},
{
"source": "e2e",
"text": "Cocum is highly rated and moderately priced coffee shop. It also serves English food. It is not considered child friendly."
},
{
"source": "e2e",
"text": "The coffee shop Cocum serves English food at an average price. It is not kids-friendly but it is highly rated by customers."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop named Cocum that has English food and is kid friendly. The prices are between \u00a320-25 and they have a high rating."
},
{
"source": "e2e",
"text": "This English food coffee shop name Cocum with a price 20 to 25. Also having a High customer rating and kid friendly."
},
{
"source": "e2e",
"text": "There is a kid friendly coffee shop that offers English food named Cocum. It has a high rating and the price range is \u00a320-25."
},
{
"source": "e2e",
"text": "Cocum is a average priced coffee shop with a high customer rating. This kids friendly coffee shop sells English food ."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap coffee shop Cocum located south of the centre of the city that provides take-away deliveries."
},
{
"source": "e2e",
"text": "Cocum is a low-priced coffee shop south of the city centre that delivers take-away."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shoo provides breakfast meals is low price range has and excellent rating, is for the whole family."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop named Cocum is cheap, serves food, and is family friendly. It is also five star rated."
},
{
"source": "e2e",
"text": "Cocum is a five star, low priced coffee shop that serves food and is very family friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a family friendly coffee shop in the cheap price range. The customer rating is average."
},
{
"source": "e2e",
"text": "Cocum is a three star coffee shop. It is family friendly and in the low price range."
},
{
"source": "e2e",
"text": "Cocum is a family friendly three star rated coffee shop. It is in the low price range."
},
{
"source": "e2e",
"text": "For a family friendly coffee shop in the cheap price range, visit Cocum. The customer rating is average."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop Cocum is a family friendly establishment and has low prices."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an expensive coffee shop that serves breakfast and is 3 star."
},
{
"source": "e2e",
"text": "There is an expensive coffee shop that serves breakfast and is 3 star. It is called Cocum."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"high"
],
[
"Cocum",
"customer rating",
"average"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an expensive, Child friendly coffee shop called Cocum which has an average rating."
},
{
"source": "e2e",
"text": "There is a three star expensive coffee shop Cocum that is family friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that is in the low price range and is rated one star."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a non-family friendly coffee shop with a low price range and has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a family friendly low priced coffee shop that is called Cocum it is rated one star."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a coffee shop that is inexpensive and not family friendly."
},
{
"source": "e2e",
"text": "The coffee shop Cocum does not allow children but is cheap."
},
{
"source": "e2e",
"text": "Cocum is a non-family friendly coffee shop with a customer rating of 1 out 5 and has a low price range."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "A one star coffee shop is Cocum. They are also average priced."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is is a kid friendly with a customer rating of 1 out 5 stars. It is a coffee shop with a moderate price range."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is an average priced coffee shop that is family friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum coffee shop serves a variety of breakfast foods for reasonable prices."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"coffee shop"
],
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"customer rating",
"high"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Family friendly Cocum coffee shop moderate price great reviews"
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Not kid friendly and rated 1 out of 5, English restaurant Cocum is moderate priced."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"priceRange",
"cheap"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is a low price restaurant with higher rating that offers breakfast meals for the whole family."
}
]
},
{
"tripleset": [
[
"Cocum",
"eatType",
"restaurant"
],
[
"Cocum",
"priceRange",
"less than \u00a320"
],
[
"Cocum",
"customer rating",
"low"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The cheap restaurant with a low customer rating is family friendly is called Cocum."
}
]
},
{
"tripleset": [
[
"Cocum",
"food",
"Chinese"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum has a customer rating of 3 out of 5. It serves Chinese food within a moderate price range and is not kid friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"food",
"English"
],
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"3 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a kid friendly environment Cocum has a customer rating of 3 out of 5 serving moderately priced English food."
}
]
},
{
"tripleset": [
[
"Cocum",
"priceRange",
"cheap"
],
[
"Cocum",
"customer rating",
"5 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum is cheap, has high ratings, and is family-friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"priceRange",
"moderate"
],
[
"Cocum",
"customer rating",
"1 out of 5"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cocum has a rating of 1 out of 5. The price range is moderate and its kid friendly."
}
]
},
{
"tripleset": [
[
"Cocum",
"priceRange",
"\u00a320-25"
],
[
"Cocum",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Great food and service at a moderate price from family friendly Cocum"
}
]
},
{
"tripleset": [
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located on the river near The Portland Arms, The Cotto offers a classy place to grab a bite with its five star rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Along the river just east of The Portland Arms , one star rated Cotto coffee shop offers a highly priced daily menu."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Portland Arms in the city centre there is a coffee shop named Cotto with moderate pricing with an average customer Rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a low priced and rated coffee shop located near The Portland Arms in the riverside."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a restaurant located in the Riverside area near The Portland Arms called Cotto. This restaurant has a coffee shop vibe that offers fair priced foods. Cotto has a high customer value because they offer quality foods for fair prices."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, a Chinese food restaurant, is located in the riverside area near The Portland Arms. It is a coffee shop type establishment and has a \u00a320 price range."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, a coffee shop type of restaurant offering Chinese cuisine in the cheaper price range with a 5 out of 5 customer rating and is located in the city centre area near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, a high rated restaurant in The Portland Arms area located in the city centre, provides you with a coffee shop experience. Chinese food is also on the menu. The price range is more than thirty dollars."
},
{
"source": "e2e",
"text": "At Cotto, you're provided a coffee shop and Chinese restaurant all in one. It is highly rated by customers and located in the city centre near The Portland Arms. Prices will be more than thirty dollars."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto coffee shop is a low-priced Chinese restaurant in Riverside near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Well rated but expensive coffee and Chinese food can be found in the city centre near The Portland Arms at Cotto restaurant."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto restaurant offers coffee and Chinese food with a high price tag and an average customer rating near The Portland Arms in the city centre."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves moderate priced Chinese food. This restaurant has a customer rating of 1 out of 5 and is located in riverside near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre near The Portland Arms, Cotto is a moderately priced restaurant with a coffee shop and Chinese food. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a moderately priced restaurant offering Chinese food and a coffee shop. It's located in the city centre near The Portland Arms and has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop and Chinese restaurant located in the riverside area near The Portland Arms. Most dishes cost more than 30 pounds, and it has a high customer rating."
},
{
"source": "e2e",
"text": "coffee shop and Chinese restaurant Cotto is located in the riverside area near The Portland Arms. It has a high customer rating and offers items for more than 30 pounds."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is an inexpensive English restaurant near The Portland Arms in the city centre, and provides English coffee shop food. Customers recently rated the store 5 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"restaurant"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap, 3 star restaurant, Cotto coffee Shop, located in the city centre east of The Portland Arms. They offer deliveries, take-out or eat-in."
},
{
"source": "e2e",
"text": "Cotto coffee Shop is a low-priced, 3 star restaurant that serves breakfast. It is located in city centre, east of The Portland Arms. They also offer eat-in, or take-out. Sorry, no deliveries."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto Chinese coffee shop , is moderate in price, with a customer Rating of 3 out of 5; located in the riverside area close to The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "With High rated customer reviews a coffee shop called Cotto near The Portland Arms in Riverside serves Chinese food .price \u00a320-\u00a325."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop serving Chinese dishes in the city centre near The Portland Arms. Its prices are less than twenty Euro with low ratings."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto coffee shop is located in Riverside near The Portland Arms and serves Chinese food for cheap prices."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves Chinese food. They have a cheap price range and a customer rating of 5 out of 5. They are located at the the city centre near The Portland Arms"
},
{
"source": "e2e",
"text": "Rated 5 out of 5, the Cotto coffee shop serves cheap Chinese foods in the city center near The Portland Arms"
},
{
"source": "e2e",
"text": "Cotto is a low priced coffee shop located within the city centre, near The Portland Arms. They feature low priced Chinese cuisine and have a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a low priced coffee shop offering Chinese cuisine. They are located in the city centre, near The Portland Arms with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Cotto coffee shop serves cheap Chinese foods in the city center near The Portland Arms, rated 5 out of 5"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "A cheap coffee shop that serves Chinese food called Cotto, is located in the city centre near The Portland Arms and the customers rate it at average."
},
{
"source": "e2e",
"text": "Cotto is a cheap coffee shop with Chinese food near The Portland Arms in the city centre area. They received an average customer rating."
},
{
"source": "e2e",
"text": "An average rated coffee shop, Cotto, located near The Portland Arms in the city centre serves Chinese food at a cheap price."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop serving cheap Chinese food with an average customer rating in the city centre are near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto coffee shop offers Chinese food at low price range with a rating of average in city centre, near The Portland Arms."
},
{
"source": "e2e",
"text": "The 'Cotto' is a cheap coffee shop which also serves Chinese food. The customer rating is average but it is located in the city centre, near 'The Portland Arms'."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto Chinese coffee shop serves cheap food with average customer ratings,it is by the river near to The Portland Arms."
},
{
"source": "e2e",
"text": "For cheap Chinese food try Cotto coffee shop by the river near The Portland Arms,it has average ratings and serves food in the cheap price range."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto coffee shop has Chinese food in the low price range with an average rating in city centre, near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near The Portland Arms. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "There is an expensive coffee shop with Chinese food and a customer rating of 1 out of 5 called Cotto in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "An expensive coffee shop, Cotto, serves Chinese food in the city centre. They are located near The Portland Arms and have a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto coffee shop offers Chinese food in the high price ranges in city centre near The Portland Arms with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Near The Portland Arms, a high priced coffee shop, Cotto, is located there in the city centre. They serve Chinese food and have a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop with Chinese food in the high price range with a customer rating of 1 out of 5. in the city centre area near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Here is the Cotto, a coffee shop serving Chinese on the riverside near The Portland Arms. It has a high price range and a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Here is a riverside coffee shop near The Portland Arms called Cotto. It serves Chinese and with a high price range and a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near The Portland Arms. Its customer rating is average."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop, that serves Chinese food, located in the city centre near The Portland Arms. They are rated average with a high price range."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near The Portland Arms. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is average."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that offers Chinese food at a high price. It has an average customer rating and is located in the riverside near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is average."
},
{
"source": "e2e",
"text": "There is a coffee shop Cotto that has an average customer rating located in the riverside near The Portland Arms. They offer Chinese food at an expensive price."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop with a less than \u00a320 price range serving Chinese food in the riverside area. It is located near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. It is near The Portland Arms. Its customer rating is low."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop serving Chinese food for less than \u00a320. It has a low rating and is located in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop located near The Portland Arms in the city centre. It has a low customer rating and serves Chinese food ranging in the less than \u00a320 range."
},
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop in the city centre near The Portland Arms. It has low ratings and most food is less than 20 Euro."
},
{
"source": "e2e",
"text": "Cotto, a Chinese coffee shop. The price range is less than \u00a320 but the customer Rating is low. It is located in the city centre near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop near The Portland Arms by the riverside that costs less than \u00a320 but has a low customer rating."
},
{
"source": "e2e",
"text": "Near The Portland Arms by the riverside is a Chinese coffee shop called Cotto that costs less than \u00a320 but has a low customer rating."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves low rated Chinese food for less than \u00a320 near The Portland Arms in the riverside area."
},
{
"source": "e2e",
"text": "Cotto is a low rated coffee shop that serves Chinese food for less than \u00a320 in the riverside area near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a moderate priced Chinese coffee shop located in riverside near The Portland Arms"
},
{
"source": "e2e",
"text": "a moderately priced coffee shop that serves Chinese food in riverside near The Portland Arms is called Cotto"
},
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop with moderate prices located in riverside near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cotto Chinese coffee shop in the city centre near The Portland Arms, has a moderate priced menu and is rated by customers 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop and serves Chinese food for a moderate price. It is located near The Portland Arms in the city centre but it only is a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop which offers Chinese food. It has a moderate price range and a customer rating of 1 out of 5. It is located in city centre near The Portland Arms"
},
{
"source": "e2e",
"text": "Cotto is a coffee shop with a customer rating of 1 out of 5. It serves Chinese food for a moderate price. It is located near The Portland Arms in the city centre."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "A moderate price ranged coffee shop that serves Chinese food is called Cotto that is located in riverside near The Portland Arms and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a moderately priced Chinese coffee shop in the riverside area near The Portland Arms. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop near The Portland Arms in the riverside area. It has a moderate price point and a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a coffee shop named Cotto that is located in riverside near The Portland Arms. They provide moderately priced Chinese food with a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Portland Arms is near a coffee shop called Cotto. It has moderate priced Chinese food and is rated a 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. It is near The Portland Arms. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that offers moderately priced Chinese food. Customers rate it a 3 out of 5. Located in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. It is near The Portland Arms. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The coffee shop known as Cotto is located in the city centre close to The Portland Arms. It offers Chinese cuisine at a moderate price and is rated a 3 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "in riverside by The Portland Arms there's a moderately priced coffee shop called Cotto with a rating of 3 out of 5 that serves Chinese food."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near The Portland Arms. Its customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves Chinese food in the high price range. Customers rate it highly. It is located in the riverside area near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop that offers Chinese food. it is in the \u00a3-25 price range with a high customer rating It is located in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a Chinese food serving coffee shop with a high customer rating near The Portland Arms in the city centre and has a price range of more than \u00a330."
},
{
"source": "e2e",
"text": "Chinese food serving coffee shop, Cotto, near The Portland Arms in the city centre has a price range of more than \u00a330 and a high customer rating ."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves Chinese food it has a high customer rating and a price range of more than \u00a330. It is located at the center of the city near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that has Chinese food it is located near The Portland Arms in the center of the city. It has a high customer rating with a price range of more than \u00a330."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto near The Portland Arms at the riverside was given a high customer rating. It's a coffee shop that serves Chinese food and is typically more than \u00a330."
},
{
"source": "e2e",
"text": "Located on the riverside near The Portland Arms, is a coffee shop that serves Chinese food, Cotto. It's price range is more than \u00a330, and their customer ratings are high."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop near The Portland Arms at the riverside that serves Chinese food. The price range is more than \u00a330 but was given a high customer rating."
},
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop near The Portland Arms near riverside. Customers give it a high rating, and the price range is more than \u00a330."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee ship serving Chinese in the \u00a320-25 price range with a high customer rating in city centre near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is as a coffee shop, sells Chinese food, price range 20-25, high customer service, located in City Centre near The Portland Arms"
},
{
"source": "e2e",
"text": "A coffee shop in city centre near The Portland Arms serving Chinese in the \u00a320-25 price range is Cotto."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "A popular coffee shop down by the riverside near The Portland Arms serves Chinese food. It ranges from 20-25 and its name is Cotto."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop serving Chinese food with prices ranging from \u00a320-25. Cotto has a high customer service rating in the area of riverside near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto coffee shop in Riverside, near The Portland Arms. With a Customer rating high, serves Chinese food .prices \u00a320-25"
},
{
"source": "e2e",
"text": "Near The Portland Arms in the riverside area sits a coffee shop called Cotto serving Chinese food. Cotto has a high customer rating with prices ranging from \u00a320-25."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop near The Portland Arms in the river side. Price range is \u00a320-25 and high rating."
},
{
"source": "e2e",
"text": "Cotto is a Chinese coffee shop in the river side. It near The Portland Arms. Price range is \u00a320-25 and high rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is an English coffee shop located in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "An moderately rated English coffee shop called Cotto is located in the city centre near The Portland Arms has pretty good prices."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Customer rated 3 out of 5, Cotto is an English coffee shop and is located riverside near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto, an English coffee shop located near The Portland Arms is by the river and customer rated 3 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre near The Portland Arms is Cotto, a low-cost, 5 out of 5 customer rated English coffee shop."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto serves English food in a coffee shop. It is located in the city centre area near The Portland Arms with average ratings"
},
{
"source": "e2e",
"text": "Cotto serves English food in a coffee shop. It is located in the city centre area near The Portland Arms with average ratings"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Serving English food in the city centre near The Portland Arms, with a high customer rating, Cotto is a coffee shop where you can expect to pay around \u00a320-\u00a325. They have a high customer rating."
},
{
"source": "e2e",
"text": "Cotto, a coffee shop with price range of \u00a320-\u00a325 near The Portland Arms in the city centre has a high customer rating and serves English food"
},
{
"source": "e2e",
"text": "The city centre has a venue serving English food, near The Portland Arms with a high customer rating called Cotto, a coffee shop with price range of \u00a320-\u00a325"
},
{
"source": "e2e",
"text": "Cotto is a coffee shop located in the city centre near The Portland Arms, serving English food. They have a high customer rating and you can expect to pay around \u00a320 to \u00a325."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cotto is a reasonably priced English style coffee shop. It is rated five stars and is located near to The Portland Arms"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves English food in the city centre. They are located near the Portland Arms and are low rated."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near The Portland Arms in riverside, the Cotto coffee shop serves English food with a price range of \u00a320 and a low customer rating."
},
{
"source": "e2e",
"text": "Cotto is a low quality and low cost coffee shop that provides British food. It is located near The Portland Arms by the Riverside."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a pricey coffee shop just north of The Portland Arms called Cotto, they sell a British breakfast platter."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre,Cotto offer coffee and cheap average English food, located near The Portland Arms."
},
{
"source": "e2e",
"text": "Cheap average coffee shop called Cotto offer English food in city centre near to The Portland Arms"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto on the riverside is an English coffee shop and it's cheap. Next to The Portland Arms"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Portland Arms in the center of the city lies Cotto, a highly rated, inexpensive, English coffee shop."
},
{
"source": "e2e",
"text": "Cotto is a cheap coffee shop near The Portland Arms in the city centre. It also offers cheap English food. Cotto is rate 5 out of 5 by customers."
},
{
"source": "e2e",
"text": "There is a cheap coffee shop Cotto also offering English food. It is located near The Portland Arms in the city centre. Customers rated Cotto 5 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop Cotto is located in the riverside area, near The Portland Arms. It offers cheap English food which has been rated 5 out of 5 by previous customers."
},
{
"source": "e2e",
"text": "Cotto is our new affordable coffee shop near The Portland Arms in the beautiful riverside area. Customers are rating this English food shop a 5 out of 5."
},
{
"source": "e2e",
"text": "Come visit Cotto, our new English coffee shop that customers are rating 5 out of 5. The price is affordable and it is located in the beautiful riverside area near The Portland Arms."
},
{
"source": "e2e",
"text": "For cheap English food with a 5 out of 5 customer rating try the coffee shop Cotto, located near The Portland Arms in the riverside area."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a cheap coffee shop with English food and average customer ratings in city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "There is a cheap coffee shop with average ratings and English food in city centre near The Portland Arms named Cotto."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is English food coffee shop. its near by The Portland Arms at the riverside . it has average - rated and low-priced ."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves English food in a cheap price range. At the riverside and near The Portland Arms, it has an average customer rating."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop located at the riverside near The Portland Arms. It serves English food in a cheap price range, and has an average customer rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is an affordable coffee shop located near The Portland Arms. It serves a traditional English breakfast."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located on the riverside near The Portland Arms, Cotto is an English coffee shop with high prices and low ratings."
},
{
"source": "e2e",
"text": "Boasting high prices and low ratings, Cotto is an English coffee shop located on the riverside near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the city centre in The Portland Arms, is an English food and coffee shop named Cotto. Customers rate is as having a cheap price range with average reviews."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, in the city centre near The Portland Arms, is an English coffee shop with cheap prices and a high customer satisfaction rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop near The Portland Arms in the high price range. They serve English food in the city centre area of town."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the area of riverside near The Portland Arms is Cotto, it is a high priced coffee shop that has English food, and it has a 1-5 customer rating."
},
{
"source": "e2e",
"text": "Cotto is a 1-5 rated coffee shop that has high priced English food. It is in the area of riverside and near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop serving high priced English food with a 1 out of 5 customer rating situated in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a high priced coffee shop with a customer rating of 1 out of 5. They serve English food and are located in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto situated in the city centre near The Portland Arms serves high priced English food, it is a coffee shop with a 1 out of 5 customer rating ."
},
{
"source": "e2e",
"text": "There is a coffee shop call Cotto in the city centre near to The Portland Arms. The English food is expensive and it has a low customer rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area, there is a high priced coffee shop near The Portland Arms called Cotto that serves English food and has a 1 out of 5 customer rating"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "English food is served at Cotto in the city centre near to The Portland Arms. Prices are high and customer rating is low at this coffee shop establishment."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cotto is near The Portland Arms in the city centre that is a coffee shop serving English food that an average customer rating and is in the high price range."
},
{
"source": "e2e",
"text": "Cotto is a high priced coffee shop located near The Portland Arms in city centre. This coffee shop serves English food with an average customer rating."
},
{
"source": "e2e",
"text": "In the city centre, by the The Portland Arms, is a coffee shop called Cotto. It is a high priced location that serves English style food. Cotto has an average customer review rating."
},
{
"source": "e2e",
"text": "The Cotto is a coffee shop serving English food near The Portland Arms in the city centre with an average customer rating and in the high price range."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto offers lovely English food in a coffee shop - high prices, average ratings, near The Portland Arms and on the riverside"
},
{
"source": "e2e",
"text": "Cotto is an expensive coffee shop by the riverside near The Portland Arms. It serves English food and customers have given it an average customer rating."
},
{
"source": "e2e",
"text": "Cotto is an average rated coffee shop on the riverside that offers English food at high prices near The Portland Arms"
},
{
"source": "e2e",
"text": "There is a coffee shop serving English food by the riverside called Cotto. It is expensive, has an average customer rating and is near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop by The Portland Arms and the river that serves English food. It has a high price range but has only been given average reviews."
},
{
"source": "e2e",
"text": "The coffee shop by The Portland Arms, Cotto, serves English food. It has a high price range but has only received average reviews."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto the coffee shop has a price range of less than \u00a320, serving English food. Situated near The Portland Arms in the city centre this has a low customer rating."
},
{
"source": "e2e",
"text": "For less than \u00a320, a coffee shop named Cotto offers English food. It is located near The Portland Arms in the city centre. its customer rating is low."
},
{
"source": "e2e",
"text": "In the city centre near The Portland Arms, a coffee shop named Cotto serves English for for a price range of less than \u00a320, although this gets a low customer rating."
},
{
"source": "e2e",
"text": "For English food, the coffee shop Cotto, located near The Portland Arms in the city centre has a price range of less than \u00a320. its customer rating is low."
},
{
"source": "e2e",
"text": "Cotto is located in the city centre near The Portland Arms. It is a coffee shop serving English food in the low price range and has a low customer rating."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves English food in the low price range with a low customer rating. It is located in the city centre near The Portland Arms."
},
{
"source": "e2e",
"text": "A cheap coffee shop is Cotto. They serve English food. It is located near the Portland Arms in the city centre. They are low rated."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "At the riverside near The Portland Arms, Cotto is a coffee shop that serves English food at less than \u00a320 and has low customer rating."
},
{
"source": "e2e",
"text": "Cotto is a cheap English, low rated coffee shop in the riverside near The Portland Arms."
},
{
"source": "e2e",
"text": "With low customer rating, the coffee shop called Cotto serves English food at less than \u00a320 in the riverside area, near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cotto English coffee shop in riverside, located near The Portland Arms, has a price range of less than \u00a320 and a low customer rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto a moderately rated English coffee shop with average prices located in the city centre near The Portland Arms"
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop. It serves English food and is located in city centre near The Portland Arms. They are moderately priced and have 1 of 5 stars for customer ratings."
},
{
"source": "e2e",
"text": "Cotto is an English coffee shop in the city centre. It is near The Portland Arms, has a moderate price range and a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Cotto is an English coffee shop near The Portland Arms with a moderate price range and a 1 out of 5 customer rating in the city centre."
},
{
"source": "e2e",
"text": "Cotto is an English coffee shop near The Portland Arms. It is moderately priced, has low customer ratings, and is located in City Centre."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cotto is a coffee shop with English food near The Portland Arms in the riverside area. It has a rating of 1 out of 5 and moderate prices."
},
{
"source": "e2e",
"text": "The Cotto is a coffee shop near The Portland Arms in the riverside area sells English food for a moderate price and a rating of 1 out of 5 stars."
},
{
"source": "e2e",
"text": "The Portland Arms is near the Cotto which is a coffee shop that has English food. Located in the riverside area with moderate prices and a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "In the riverside area, near The Portland Arms there is a coffee shop called Cotto that sells English food for a moderate price and a rating of 1 out of 5 stars."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a low customer rating average price coffee shop that serves English food. It is in riverside near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, located near The Portland Arms in the city centre, is a coffee shop offering moderately priced English food. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cotto, rated 3 out of 5 by customers, is an English coffee shop with moderate prices located in the city centre near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, a highly-rated coffee shop in the city centre near The Portland Arms, serves English food. Prices average more than \u00a330."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a fairly expensive coffee shop close to the river just north of The Portland Arms. They make a good British breakfast platter."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"more than \u00a330"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cotto is a highly rated English coffee shop in the city centre near The Portland Arms, and prices range more than \u00a330."
},
{
"source": "e2e",
"text": "Cotto is an expensive, highly-rated English coffee shop in the City Centre near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop style eatery serving English food at prices averaging over \u00a330. Highly rated, it is located in the city centre close to The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a highly-rated coffee shop in the city centre near The Portland Arms that provides expensive English food."
},
{
"source": "e2e",
"text": "There is an English coffee shop called the Cotto in the city centre near The Portland Arms with a high rating and high prices at more than \u00a330."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is an English coffee shop located in the city centre near The Portland Arms offering food at the average price."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is an average priced English coffee shop with high ratings. It can be found in riverside near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the city centre in The Portland Arms, is an average, cheap coffee shop named Cotto."
},
{
"source": "e2e",
"text": "Cotto is an cheap coffee shop near The Portland Arms in City centre. It has a mid-ranged rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is an inexpensive coffee shop located near The Portland Arms, with 5 stars."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop in City centre near The Portland Arms. It is inexpensive. It has a three star rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop in the riverside area near The Portland Arms is Cotto. It is in the cheap Range and has an average customer rating."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop near The Portland Arms in the riverside area with an average customer Rating and is in the cheap price Range."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a coffee shop that serves a variety of mid-priced foods. It is located by the Riverside near The Portland Arms. Cotto has high customer ratings. This is a cheap coffee shop offering quality foods for fair prices."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "High priced and moderately rated Cotto coffee shop in near The Portland Arms by the riverside."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto coffee shop, located near The Portland Arms, has high prices and only a one star rating."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"customer rating",
"average"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located by The Portland Arms is Cotto the coffee shop. It is expensive and is rated three stars."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cotto coffee shop is an expensive place near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"customer rating",
"low"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is a cheap coffee shop with one-star located near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto, located near The Portland Arms is a cheap, one-star coffee shop."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"less than \u00a320"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop moderately priced Cotto is located next to The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"1 out of 5"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop named Cotto in the city centre. Located near The Portland Arms, it's price is moderate and it is rated 1 out of 5."
},
{
"source": "e2e",
"text": "Cotto's coffee shop located near The Portland Arms in the city centre area is a moderately priced coffee shop with a customer Rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto is located near The Portland Arms. It is a riverside coffee shop with a moderate price range and a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Cotto, near The Portland Arms, is a riverside coffee shop with a moderate price range and a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"customer rating",
"3 out of 5"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Next to The Portland Arms is a moderately priced, 3-star coffee shop called Cotto."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"moderate"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Moderate priced coffee shop Cotto is Located near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop with average prices, located near The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a coffee shop in the moderate price range. It is located east of The Portland Arms."
},
{
"source": "e2e",
"text": "Cotto is a moderately priced coffee shop located near The Portland Arms."
}
]
},
{
"tripleset": [
[
"Cotto",
"eatType",
"coffee shop"
],
[
"Cotto",
"priceRange",
"\u00a320-25"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're staying at The Portland Arms, try Cotto. It is an average priced local coffee house near you in riverside."
}
]
},
{
"tripleset": [
[
"Cotto",
"food",
"Chinese"
],
[
"Cotto",
"priceRange",
"high"
],
[
"Cotto",
"area",
"city centre"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto, which is located near The Portland Arms in the city centre, provides average, high priced Chinese food."
}
]
},
{
"tripleset": [
[
"Cotto",
"food",
"English"
],
[
"Cotto",
"priceRange",
"cheap"
],
[
"Cotto",
"customer rating",
"5 out of 5"
],
[
"Cotto",
"area",
"riverside"
],
[
"Cotto",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cotto next to The Portland Arms on the riverside is cheap English customer rating 5 out of 5"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop located in the city center. It is moderately and is family friendly."
},
{
"source": "e2e",
"text": "Although not the cheapest place, the family-friendly Fitzbillies coffee shop is worth a try for good plain meals not too far from Cambridge's City centre. Children are welcome."
},
{
"source": "e2e",
"text": "There is a coffee shop call Fitzbillies located out side the city center. It is in the mid price range and welcomes families."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in City centre, Fitzbillies is a three star, family friendly, coffee shop."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a three-star coffee shop with a medium price range for families."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a 5-star coffee shop located not far from the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a 5 star, family friendly, coffee shop located in City centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop is family-friendly, and serves good basic food. Although not the cheapest venue, it is not too far from Cambridge City centre and has had some good reviews."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family-friendly, 5 star coffee shop located on the river in the outskirts of the city center."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "If your looking for a high rated coffee shop with no children and a decent price range, Fitzbillies is in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located by the river, Fitzbillies is a medium priced coffee shop, is family-friendly and highly rated."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the cities centre with a high customer rating. However it's not very child friendly and the price range is above 30 euro."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family oriented coffee shop offering 5 star service and complete meals."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop is a family friendly Chinese restaurant in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop and Chinese food restaurant that is on the pricey side and has average customer reviews in Riverside and it is not friendly for children."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a reasonably-priced Chinese restaurant and coffee shop. It is located in the city centre and receives high customer ratings, though not the best place to bring your kids."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese restaurant and coffee shop with a high customer rating. It has dishes ranging between 20 and 25 pounds. It is located in the city centre and is kids friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, a coffee shop and Chinese restaurant, is priced moderately, but has low customer ratings. It is located in city centre and family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The restaurant Fitzbillies is a coffee shop that serves Chinese food for a high price. It has a customer rating of 1 out of 5, it is located in the city centre and is not kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop located in riverside with a price range of less than \u00a320. The restaurant is not family-friendly."
},
{
"source": "e2e",
"text": "In the riverside area, there is a Chinese coffee shop called Fitzbillies. The price range is less than \u00a320 and the restaurant is not family-friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a unique restaurant in the city centre area called Fitzbillies which offers a coffee shop and Chinese food. It is kid friendly and moderately priced."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the riverside area, Fitzbillies is a moderately priced restaurant offering a coffee shop as well as Chinese food. It is moderately priced and has a customer rating of 1 out of 5. It is not kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre, Fitzbillies is a coffee shop style joint known for its moderate price range, kid friendly atmosphere, and tasty Chinese food. Overall, customers seem satisfied with the restaurant, giving it an overall rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop and Chinese food restaurant with high customer ratings, a price rang of more than \u00a330 in the Riverside area that is not children friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a moderately priced and kid friendly restaurant, try Fitzbillies, a coffee shop style restaurant featuring Chinese food. It's located in the city centre area, and it's received good customer ratings."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese style coffee shop in the city centre. You can get a good meal for around \u00a320-25. It is not kid friendly, this adult oriented restaurant gets a high rating from its customers."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an coffee shop style Chinese restaurant in the city centre area. It is moderately priced and kid friendly, with a high customer rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area a coffee shop named Fitzbillies has reasonably priced English food. This restaurant is not family-friendly and has a low rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Yes, this restaurant is child friendly where they serve English food. This place is called Fitzbillies after the owners. They recently relocated to city centre where the area rates 1 out of 5. They are surrounded by a coffee shop and retail stores where the prices are high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Yes, this restaurant is children friendly and the English food is great. The restaurant is named Fitzbillies located in the city centre where the ratings scores an average of 1 out of 5. This restaurant is also similar to a coffee shop where the prices are high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "English restaurant, Fitzbillies coffee shop in the city centre has a price range of more than \u00a330. It has a high customer rating and is child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"food",
"Indian"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an adult only restaurant located in the Riverside area that serves as a coffee shop and Indian food restaurant with a price range of more than \u00a330 and high customer ratings."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family friendly style coffee shop. with a great view of the river"
},
{
"source": "e2e",
"text": "Fitzbillies is a family friendly style coffee shop. with a great view of the river"
},
{
"source": "e2e",
"text": "There is a medium price range for families which is Fitzbillies coffee shop."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop named Fitzbillies is priced higher than average, but serves Chinese food and kid friendly. The customer ratings are great and located in the city centre area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a night out without the kids, try Fitzbillies in the riverside area. It's a pricier Chinese coffee shop that customers like a lot."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop has the best Chinese food in the Riverside area, and has a high ranking in customer satisfaction."
},
{
"source": "e2e",
"text": "Fitzbillies Chinese food is the best customer rated coffee shop in the Riverside area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Fitzbillies is non children-friendly coffee shop that also serves Chinese food. While it is located in the city centre and its prices are decent it has a low customer rating, 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a three star coffee shop in city centre that sell Chinese food at a moderate cost for adults only."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop with Chinese food in the city centre that has 5 out of 5 stars. It is not family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that has Chinese food. Customers have rated them 5 out of 5. They also have great prices. Keep in mind that they do not allow children."
},
{
"source": "e2e",
"text": "A low-cost, family friendly coffee shop called Fitzbillies offers Chinese food. They have a high customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre with an average customer rating is a coffee shop called Fitzbillies. Its not family friendly and they serve Chinese."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop serves Chinese of high class with an average rating in the city centre and is for kids too."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop is a pricey, child friendly venue serving Chinese food in the riverside area. It has average customer ratings."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop offering averaged-priced Chinese food. It has a high customer rating, is located in the city centre, but is not kid-friendly."
},
{
"source": "e2e",
"text": "In the city centre is the coffee shop Fitzbillies, with Chinese food, a high customer Rating and a price Range more than \u00a330 and not child Friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop provides Chines food in the city centre. It is children friendly, mid priced and has a high customer rating."
},
{
"source": "e2e",
"text": "Chinese coffee shop Fitzbillies has dishes ranging between 20 and 25 pounds and a high customer rating. It is located in the city centre and is kids friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop in the city centre that is suitable for children. Its called Fitzbillies and serves Chinese food for around \u00a330. It has a high customer rating."
},
{
"source": "e2e",
"text": "The Fitzbillies is a coffee shop that serves Chinese food, but costs more than the average with great customer ratings. It is located in the city centre and yes, it is kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Take your family to Fitzbillies a coffee shop located in the city centre. It serves great Chinese food at an affordable price."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop in the city centre serves Chinese food in the cheap price range,it is not family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately rated coffee shop in the city centre that sells Chinese food for a cheap price, however it is not family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies Chinese coffee shop is a family friendly establishment in the city Centre with cheap prices."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "By the riverside, you will find the Fitzbillies. It is cheap but excellent coffee shop that also serves Chinese food."
},
{
"source": "e2e",
"text": "In Riverside, you'll find Fitzbillies. It is a passable, affordable coffee shop which interestingly serves Chinese food. Don't bring your family though."
},
{
"source": "e2e",
"text": "Fitzbillies is an average, cheaply-priced coffee shop in Riverside that serves that also serves Chinese food to single patrons, without a family."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A cheap, but excellent place of Chinese food is the Fitzbillies. This family-friendly coffee shop is located on the riverside."
},
{
"source": "e2e",
"text": "Fitzbillies riverside coffee shop welcomes families. It serves low priced Chinese food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a cheap Chinese coffee shop with 5 out of 5 stars in the city centre. It is not family friendly."
},
{
"source": "e2e",
"text": "A highly rated Chinese coffee shop in the city centre is Fitzbillies. Selling cheap Chinese food, it has an overall customer rating of 5 out of 5. It is not suitable for family as it is described as not family friendly. This coffee shop sells Chinese food in a cheap price range. It can be found in the city centre."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop serves Chinese food in the city centre. It is in the cheap price range but is not family friendly but has a 5 out of 5 customer rating"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre, Fitzbillies is a Chinese coffee shop selling cheap Chinese food. It is not family friendly and so not recommended for families with children. With a cheap price range, it is a good place for people on a budget. It has a very high overall customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese food serving, cheap coffee shop in the city centre which is family friendly and rated 5 out of 5 by customers."
},
{
"source": "e2e",
"text": "Family friendly coffee Shop that is called Fitzbillies. We sell cheap Chinese food located in the city centre. We have a rating 5 out of 5"
},
{
"source": "e2e",
"text": "Chinese food serving, cheap coffee shop in the city centre , Fitzbillies, is family friendly and rated 5 out of 5 by customers."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop has Chinese food with low price range and customer rating of 5 out of 5 at the riverside and is not family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop with Chinese food in riverside. Received a customer rating of 5 out of 5 and is not family friendly."
},
{
"source": "e2e",
"text": "This non kid friendly coffee shop, Fitzbillies, is located in riverside. They serve cheap Chinese food and have a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop offers Chinese food in the low price range with customer rating of 5 out of 5 at riverside and not family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you want cheap Chinese food with a high rating, try Fitzbillies. This coffee shop is on the Riverside, but please remember they do not allow children."
},
{
"source": "e2e",
"text": "There is a coffee shop called Fitzbillies that sells Chinese food at a cheap price. The customer rating is 5 out of 5, it is kid friendly, but located in riverside."
},
{
"source": "e2e",
"text": "Fitzbillies, located in the riverside area, is a family friendly Chinese coffee shop that in addition to being cheap, has also received a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that sells Chinese food at a cheap price. It is kid friendly, the rating is 5 out of 5, and located in riverside."
},
{
"source": "e2e",
"text": "A highly-rated Chinese food along the riverside is a coffee shop named Fitzbillies. They allow children and offer cheap options."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a 5 out of 5 rating, Fitzbillies is a non kid friendly coffee shop serving cheap Chinese food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Caf\u00e9 that serves Chinese. It is an affordable option in the city core to feed the family. It has received average ratings."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a non-family friend Chinese coffee shop with an average customer rating. They are cheap and in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a cheap Chinese coffee shop located in the city centre. However, they are cheap, have an average customer rating, and are not family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop located in the city centre that serves Chinese. It has an average customer rating. It is not child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop located in the centre of the city that serves Chinese food in a family friendly environment. It has an average customer rating and cheap prices."
},
{
"source": "e2e",
"text": "Fitzbillies is a family friendly coffee shop in the city centre, offering cheap Chinese food and rated average by customers."
},
{
"source": "e2e",
"text": "Fitzbillies offers Chinese food in a city centre coffee shop ideal for families, at low prices. Its customer ratings are average."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop is located in the city Centre. They sell cheap Chinese food with and average customer rating. They are also family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee Shop that is situated by the riverside. It is cheap and has an average customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a Cheap coffee Shop with a hint of Chinese. It is by the riverside and has an average customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called the Fitzbillies located in Riverside that serves Chinese food Ina cheap price range. It has an Average customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop Fitzbillies that offers cheap Chinese food. They are rated average and not family friendly. They are located riverside."
},
{
"source": "e2e",
"text": "The Fitzbillies is a coffee shop located in riverside that sells Chinese food in a cheap price range. It has an average customer rating and is average and it is not family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving Chinese food in the low price range. It is located by the riverside, welcomes families and has average customer ratings."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop by the riverside, which serve Chinese food. The customer rating is average and ideal for family and friends."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap Chinese coffee shop in the riverside area with an average customer rating. They are family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cheap Chinese food downtown. Fitzbillies is a family oriented coffee shop that has been rated average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "We are a family friendly coffee shop called Fitzbillies. We sell cheap Chinese food and have a rating of 5"
},
{
"source": "e2e",
"text": "Ideal for family and friends, Fitzbillies is a coffee shop which offers Chinese food at a low price."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop located in city centre with high prices."
},
{
"source": "e2e",
"text": "Fitzbillies is a high priced Chinese coffee shop located in city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop that is average but high price and is child friendly and in the city centre."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop that is average but high price. It's child friendly and in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the Riverside area, Fitzbillies is a high priced coffee shop that serves Chinese cuisine. The customer rating is 'average', and it is not child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies, a coffee shop specializing in Chinese food, can be found in the Riverside area. Be prepared for a high price range. The place is rated as 'average' by customers. Please note it is not a child friendly destination."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a coffee shop in the centre of town called Fitzbillies. It serves Chinese food at high prices and doesn't cater to children. It's also poorly rated by customers."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the city centre. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the city centre. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city centre that serves Chinese food for adults only. Although, it's expensive and poorly rated by its customers."
},
{
"source": "e2e",
"text": "There is a high priced coffee shop in the city centre called Fitzbillies that serves Chinese food, has a 1 out of 5 customer rating and is not child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that provides Chinese food with high price ranges and a customer rating of 1 out of 5 in the city centre. They are not child friendly."
},
{
"source": "e2e",
"text": "There is a high priced coffee shop located in the city centre called Fitzbillies that serves Chinese food. Their customer rating is 1 out of 5 and they are not kid friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop named Fitzbillies with high price ranges in Chinese food. They have a customer rating of 1 out of 5 in the city centre and is not child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "in city centre there's a coffee shop with a rating of 1 out of 5 and is child friendly. They serve Chinese food with a high price range its called Fitzbillies"
},
{
"source": "e2e",
"text": "a coffee shop called Fitzbillies serves high priced Chinese food and is child friendly with a rating of 1 out of 5 is in city centre"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop with Chinese food; Price is high and only 1 out of 5 people like it, it's in riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the riverside. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop and Chinese food, Fitzbillies, in Riverside. High price range and customer rating 1 out of 5. No children."
},
{
"source": "e2e",
"text": "The Fitzbillies is a non children friendly Chinese coffee shop in the high price range. It is located in Riverside and has a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a non children friendly Chinese coffee shop in Riverside called The Fitzbillies. It is in the high price range and has a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a no children friendly coffee shop that serves Chinese food, in riverside, with high price Range, and 1 out of 5 customer Rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that serves Chinese food, is no children friendly and has a high price Range, with 1 out of 5 customer Rating, located in riverside."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee and Chinese establishment . The price range is high and their customer rating is 1 out of 5. Fitzbillies is located in riverside and they are children friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, Chinese food and coffee shop with a high price range and customer rating of 1 out of 5. No Children allowed."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "This place called Fitzbillies is like a coffee shop, that has Chinese food and is located near city centre. There is a great park across the street for the kids, which makes up for Fitzbillies average reviews and high prices."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop in the riverside area. It has average customer ratings with high prices, so no kids here."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the city centre. Its customer rating is average."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the city centre. Its customer rating is average."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the city centre. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop located near city centre with an average customer rating called Fitzbillies. They serve Chinese food, have a high price range and no, are not children friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city centre. It has high prices but only gets an average customer rating and is not friendly for children. But if you want Chinese food it is an option."
},
{
"source": "e2e",
"text": "Fitzbillies, located near city centre, is a coffee shop that serves Chinese food. It has a high price range, average customer rating and no, it is not children friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop which also sells Chinese food. You can find it in the city centre, but be aware it is not children friendly, only gets an average customer rating and prices are high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop with Chinese food that is expensive and is an average rated place in city centre but is child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop with a high price range. It is located in riverside and has average customer ratings."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the riverside. Its customer rating is average."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the high price range. It is located in the riverside. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a nice Chinese coffee shop near the riverside area, Fitzbillies is a fancy, expensive coffee shop with with no kids and an average customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop with a high price range. It is located in riverside and has average customer ratings. It is not children friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop which serves Chinese food on riverside that is child friendly. It has a high price range and an average customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop serves Chinese food in a child friendly setting in the riverside area. It has rather high prices and average customer ratings."
},
{
"source": "e2e",
"text": "There is a coffee shop on riverside that is child friendly that serves Chinese food, Fitzbillies. It has a high price range and an average customer rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop offers cheap, family friendly Chinese food in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop specializing in Chinese food under \u00a320. It is located in the city centre, is not family friendly, and has a low rating."
},
{
"source": "e2e",
"text": "Located in the city centre area is Fitzbillies. With a price range of less than \u00a320 as well as a low customer rating this Chinese coffee shop is also not family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies located near the center of the city is a coffee shop that serves Chinese food and is a low priced , low customer rating non family friendly choice."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop is not family friendly and has a low customer rating. Serving Chinese food in the city centre area and has a price range of less than \u00a320."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the center of the city, Fitzbillies is a low rated, low priced coffee shop that serves Chinese food. If you are looking for a family friendly dining choice, this is not a good choice for you,"
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that also serves Chinese food. It is moderately priced, with low customer ratings and is located in the city centre It is family friendly."
},
{
"source": "e2e",
"text": "Theirs a coffee shop name Fitzbillies that has a price range less than \u00a320 and provides Chinese food. Has a low customer Rating but it's family friendly winch is located in the city center"
},
{
"source": "e2e",
"text": "Theirs a coffee shop name Fitzbillies that has a price range less than \u00a320 and provides Chinese food. Has a low customer Rating but it's family friendly which is located in the city centre"
},
{
"source": "e2e",
"text": "Fitzbillies is a family Friendly coffee shop with Chinese food. The price Range is less than \u00a320, with a low customer Rating, in the city centre area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the riverside. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a low rated coffee shop serving Chinese food for under \u00a320. It is located in riverside and is not considered family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a low rated Chinese coffee shop with low prices. Not family friendly and located in Riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop by the riverside with low customer ratings and prices less than \u00a320 that is not family friendly."
},
{
"source": "e2e",
"text": "By the riverside, there is a Chinese coffee shop that is not family friendly with prices less than \u00a320 and low customer ratings named Fitzbillies."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop with Chinese food that costs less than \u00a320 and has a low customer rating. It is by the riverside and is family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop in the riverside area. It sells Chinese food in a general price range of less than \u00a320. It is a family friendly coffee shop with an overall low customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies Chinese coffee shop by the riverside is family friendly but has a low customer rating. It usually costs less than \u00a320."
},
{
"source": "e2e",
"text": "Located in the riverside area, Fitzbillies is a Chinese coffee shop with a fairly low customer rating. It sells Chinese food in the price range of less than \u00a320 so it is good for those on a budget. It is family friendly and can be found in the riverside area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop with a low customer rating. Low prices and not family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop that serves Chinese food in the city center that is moderately priced. It is called Fitzbillies"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a moderate priced, kid friendly coffee shop which offers Chinese food, located in the city centre area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that provides Chinese food at a moderate price range. The customer rating is a 1 out of 5 it is located in the city centre and is not kids friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced Chinese coffee shop that is not child friendly, and is located in the city centre with a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "A moderately priced Chinese coffee shop named Fitzbillies, has a customer rating of 1 out of 5, is not child friendly and is located in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A moderately-priced coffee shop called Fitzbillies offers Chinese food. They have a rating of 1 out of 5 and are in the center of the city. They do not allow children."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that has Chinese food. Although they have a low rating, they have moderate prices. They are in the city centre and do not allow children."
},
{
"source": "e2e",
"text": "The coffee shop Fitzbillies offers amazing Chinese food at a moderate price. Had a rating of 1 out of 5. Fitzbillies is kid friendly and located in the city centre."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop has Chinese food at a moderate price. Customers rate Fitzbillies a 1 out of 5. It is in the city centre and is kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop Fitzbillies as moderately priced Chinese food and is rated a 1 out of 5. Check Fitzbillies out by the riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop has Chinese food at a moderate price. Customers rate it a 1 out of 5. Located on the riverside and is not kid friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced coffee shop in the riverside area which also offers Chinese food. It has a customer rating of 1 out of 5. It is not kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that sells Chinese food in a moderate price range, customers rates it 1 out of 5. It is located in the riverside area and is kids friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderate priced coffee shop in the riverside area that sells Chinese food. It is kids Friendly and has a 1 out of 5 customer rating,"
},
{
"source": "e2e",
"text": "Fitzbillies is a moderate priced coffee shop serving Chinese food in the riverside area. It is rated 1 out of 5 and is kid friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a kid friendly, 1 out of 5 coffee shop in the riverside area. It is moderate priced and serves Chinese food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. Its customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a moderately priced coffee shop located in the city centre called Fitzbillies. It provides Chinese food, has a customer rating of 3 out of 5, but is not child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that provides Chinese food. It is not kid friendly and has a customer rating of 3 out of 5. It is located in the city centre and is moderately priced."
},
{
"source": "e2e",
"text": "Fitzbillies, coffee shop, Chinese food, price range moderate, customer rating 3 out of 5, area city centre, not kids friendly"
},
{
"source": "e2e",
"text": "There is a three star coffee shop in city centre called Fitzbillies that sell Chinese food at a moderate price with no children around."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a child friendly coffee shop which sells Chinese food. It is rated 3 out of 5 stars and is in the city centre. It has a moderate price range"
},
{
"source": "e2e",
"text": "There is a coffee shop called Fitzbillies that provides Chinese food. It has a customer rating of 3 out of 5 stars,and is located in the city centre. It has a moderate price range and is kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. Its customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food at a moderate price on the riverside. Customers rate this coffee shop 3 out of 5 but is not kid friendly."
},
{
"source": "e2e",
"text": "For moderately priced Chinese food with an average customer rating, try Fitzbillies coffee shop in the riverside area. Not kid friendly."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop serves Chinese food in the riverside area. It is moderately priced and has an average customer rating but is not kid friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop in the riverside area. It is moderately priced, non-kid friendly and has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a moderately priced coffee shop, Fitzbillies located on the riverside that serves Chinese food. Customers rate this coffee shop 3 out of 5 but is not kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "This kid friendly coffee shop found riverside, Fitzbillies offers Chinese food at a moderate price range with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "There is a kid friendly coffee shop in the riverside area call the Fitzbillies that serves Chinese food at a moderate price and a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced coffee shop serving Chinese food that received a customer rating of 3 out of 5. Kid friendly and located in riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is a kid friendly coffee shop in the riverside area that serves Chinese food at a moderate price with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies serves Chinese food in the riverside area. It is a moderately priced, kid friendly, coffee shop that received a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving Chinese food in the high price range. It is located in the City center."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an expensive Chinese coffee shop in the riverside area that customers like but isn't good for kids."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the more than \u00a330 price range. It is located in the city centre. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop offering Chinese food in city centre. It has a high customer rating, costs more than 30 pounds, and is not child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop that costs more than 30 pounds with a high customer rating. It is located in city centre and is not child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop in the city centre. High rating and price range more than \u00a330. It is children friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop in price range more than \u00a330. It have high rating and children friendly. It is located in the city centre."
},
{
"source": "e2e",
"text": "A children friendly Chinese coffee shop with a high customer rating is Fitzbillies in the city centre. It has a price range more than 30 euros."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop in the city centre with a high customer rating and price range more than 30 euros. It is children friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the more than \u00a330 price range. It is located in the riverside. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near riverside, Fitzbillies is a Chinese coffee shop. This highly rated coffee shop is not children friendly with their menu starting at more than \u00a330."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving Chinese food in the riverside area. It has a high customer rating, a price range of more than \u00a330 and is not child friendly."
},
{
"source": "e2e",
"text": "For a nice Chinese coffee shop in the riverside area, Fitzbillies is a great option. It has a high customer ratings with a price to match; it ranges from more than \u00a330. It is not children friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop with high customer ratings in the riverside area. It's children friendly though it costs more than \u00a330."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "At the city centre is Fitzbillies, a Chinese coffee shop. Although not kids friendly, they offer food in the \u00a320-25 range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a kid friendly Chinese coffee shop in city centre. They have high customer ratings and average prices."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies serves Chinese food. Located in the city centre, this adult oriented coffee shop in the city centre, does not cater to kids, but is highly rated for its good food that prices around \u00a320-25 per meal."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving Chinese food in the average price range. It is located in the city centre and receives high customer ratings, though not the best place to bring your kids."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that offers Chinese food in the 20 to 25 pound price range. It has a high customer rating and is located in the city centre. It is not kid-friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a average priced, kid friendly, highly rated Chinese coffee shop located in city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. Its customer rating is high."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. Its customer rating is high."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. Its customer rating is high."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the riverside area with Chinese food. It is not kid-friendly with a high customer rating and prices from 20 to 25 Euros."
},
{
"source": "e2e",
"text": "Fitzbillies is a Chinese coffee shop with prices ranging from 20 to 25 Euros in the riverside area. It is not kid-friendly and has high customer ratings."
},
{
"source": "e2e",
"text": "There is a non kids friendly high rated coffee shop called Fitzbillies located in the riverside area. They provide Chinese food in the price range of \u00a320-25."
},
{
"source": "e2e",
"text": "Fitzbillies is a high rated coffee shop that provides Chinese food located in the riverside area. The price range is\u00a320-25 and it is not kids friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Chinese"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop on riverside that serves Chinese food, Fitzbillies. It is child friendly and customer ratings are high. Their price range is \u00a320-25."
},
{
"source": "e2e",
"text": "The highly rated, kid friendly Fitzbillies coffee shop serves Chinese food at an average price point in the riverside area."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that serves Chinese food on riverside. It is child friendly and has a price range of \u00a320-25 with high customer ratings."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop provides a kid friendly venue for Chinese food at an average price point in the riverside area. It is highly rated by customers."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Fitzbillies coffee shop which sells English food is located in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "English coffee shop, Fitzbillies, has customers raving about their food, service, and moderate pricing. It is located in Riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is an adult friendly English coffee shop by the riverside."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "A high range coffee shop, not child friendly, serving English food is Fitzbillies in the Riverside area"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies says that there is a children friendly coffee shop with average English food in Riverside, but is a bit pricey."
},
{
"source": "e2e",
"text": "Fitzbillies is a family friendly coffee shop that have a low custom. They are located near riverside and server English Food also coffee. Their prices are at the range of 20 below."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre you will find Fitzbillies, a kid friendly English coffee shop that has a rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a riverside coffee shop, offering a child friendly experience. Rated 1 out of 5 by the customers. They serve English food, priced high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Adults rated the English coffee shop, Fitzbillies, in Riverside as 3 out of 5 points."
},
{
"source": "e2e",
"text": "On the riverside, Fitzbillies coffee shop serves reasonably price 3 star English food. Kids welcome."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, a family Friendly, average priced low rating, coffee shop that serves English food in the Riverside area."
},
{
"source": "e2e",
"text": "A family friendly coffee shop with moderate pricing in Riverside is Fitzbillies. They serve English food and have a customer rating of three out of five."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The highly rated English coffee shop, Fitzbillies is located in the city centre. It is appropriate for the whole family."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre is Fitzbillies: a low-cost family-friendly English coffee shop with an average customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a low-cost, family-friendly English coffee shop with an average customer rating located in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop on the river. Pricey 3 star English food. Kids Welcome."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a high-end riverside coffee shop called Fitzbillies which serves English food. It has an average customer rating but is not suitable for children."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family friendly coffee shop located in the riverside area; it has an average customer rating and serves reasonably priced English food."
},
{
"source": "e2e",
"text": "In the riverside area there is a family friendly coffee shop named Fitzbillies; it has an average customer rating and serves reasonably priced English food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, an English coffee shop in city centre, is a non child friendly with a high customer rating and a higher price range."
},
{
"source": "e2e",
"text": "There is an English coffee shop in city centre that is non child friendly with a high customer rating and a higher price range named Fitzbillies."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "there is a coffee shop only for adults that is in the riverside called Fitzbillies, has a high rating and the prizes are high, they serve English food"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a 5 star, mid-price coffee shop located on the outskirts of the city near the river. It specializes in British food and is family friendly"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city centre, it serves English food. It has a cheap price range and is not family-friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop is family-friendly and cheap, and serves very well-reviewed traditional British food. Find it just to the south of the river, north of Cambridge City centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap English coffee shop Fitzbillies on the riverside for couples."
},
{
"source": "e2e",
"text": "Fitzbillies in Riverside is a average coffee shop offering English style food in the low price range."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap but delicious coffee shop located in the riverside area. They serve English food within a cheap price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The English coffee shop Fitzbillies is near the riverside. It is a cheap, average shop not family-friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a cheap price Range, family Friendly coffee shop which serves English food in the riverside area of town."
},
{
"source": "e2e",
"text": "Fitzbillies is a family Friendly cheap price Range coffee shop serving English food in the riverside area of town."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a cheap, family friendly coffee shop in riverside that serves English food. The average customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, adult themed coffee shop located in the city centre. Highly rated English food a low prices. Customer rated 5 stars."
},
{
"source": "e2e",
"text": "The coffee shop called Fitzbillies had English good that was pretty cheap. It is located in the city centre where the unfriendly families rated it 5 out of 5"
},
{
"source": "e2e",
"text": "The coffee shop called Fitzbillies had English good that was pretty cheap. It is located in the city centre where the unfriendly families rated it 5 out of 5"
},
{
"source": "e2e",
"text": "Fitzbillies 5 star rated coffee shop, located in the city centre, English cuisine at affordable prices, children not permitted."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you need somewhere to eat with children, the Fitzbillies coffee shop, north of the City centre and close to the river, has had great reviews for its cheap, traditional British food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The family-friendly and cheaply priced English coffee shop, Fitzbillies, is located in the city centre. It has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Located in the city centre, there is a cheap and family-friendly English coffee shop named Fitzbillies, which boasts a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "With a 5 out of 5 customer rating, a cheap and family-friendly English coffee shop named Fitzbillies is located in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Fitzbillies is an adult coffee shop serving cheap English food with a customer rating of 5 out of 5 in the riverside area."
},
{
"source": "e2e",
"text": "With a customer rating of 5 out of 5 and offering cheap English food The Fitzbillies is a adult coffee shop in the riverside area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop on the riverside is not family-friendly. it serves English food in a cheap price range and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop in riverside. They have high ratings and cheap prices. It is not a kid friendly establishment."
},
{
"source": "e2e",
"text": "on the riverside is a coffee shop that serves English food. Fitzbillies is not family-friendly but has a customer rating of 5 out of 5 and cheap price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a five star family friendly coffee shop serving cheap English food in the riverside area."
},
{
"source": "e2e",
"text": "A five star coffee shop in the Riverside area, Fitzbillies serves family friendly English food at a cheap price."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop with 5 out of 5 ratings, family friendly, serves English food, and is located in the riverside area."
},
{
"source": "e2e",
"text": "A cheap coffee shop with 5 out of 5 ratings is Fitzbillies. Fitzbillies is family friendly, serves English food, and is located in the riverside area."
},
{
"source": "e2e",
"text": "A cheap coffee shop in riverside with a 5 out of 5 customer rating is Fitzbillies. Fitzbillies is family friendly and serves English food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that offers cheap, family friend English food. It is located in city centre and has an average customer rating."
},
{
"source": "e2e",
"text": "There is a cheap coffee shop, Fitzbillies, that offers English food and is friendly to families. It has average ratings and is located in the center of the city."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a cheap English coffee shop that's not family-friendly in the city centre with an average rating called Fitzbillies."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving English Food in the city centre. Pricing is cheap. The customer rating is average and it is not family-friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap average rating English coffee shop that's not family-friendly in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop located in the city centre that serves cheap English food that is family-friendly and has a average customer rating."
},
{
"source": "e2e",
"text": "In the city centre there is a cheap family-friendly coffee shop called Fitzbillies that serves English food and has a average customer rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Riverside coffee shop Fitzbillies has an average rating offering English food in a low price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop near the riverside. It has an average rating and is cheap. It is not for families."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap English coffee shop named Fitzbillies located in the riverside area. The atmosphere is family friendly and the customer rating is average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an expensive coffee shop that serves English food by the riverside. It has a customer rating of 1 out 5 and children are not allowed."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop named 'Fitzbillies' serves English Food in the riverside area, is child friendly with a high price range."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop in the riverside area, serves English food and is child friendly, with a high price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "A high price range coffee shop that serves English food in the city center is Fitzbillies. They have a rating of 1 out of 5 and are not kid friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the high price range that serves English food. They are located in the city centre. They are not kid friendly and have a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city centre which serves English food. It has a low customer rating and the prices are high and unfortunately it is not child friendly."
},
{
"source": "e2e",
"text": "In the city centre is a coffee shop which serves English food called Fitzbillies. Unfortunately it is quite expensive, has a low customer rating and does not welcome children."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Though it is children friendly, the coffee shop Fitzbillies near city centre offers English food at a low rating of 1 out of 5 and a high price range."
},
{
"source": "e2e",
"text": "Fitzbillies is a high priced English coffee shop that is in the city centre and is children friendly. Customers rated it 1 out of 5."
},
{
"source": "e2e",
"text": "A high priced English coffee shop that is in the city centre and is children friendly is called Fitzbillies. Customers rated it 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop in City centre offers English food. It is high priced, has a 1 out of 5 rating and is family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "By the riverside is an expensive coffee shop called Fitzbillies that provides English food. Its customer rating is 1 out of 5 and children are prohibited."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is not a children friendly English coffee shop. The customer rating is 1 out of 5, as well as having a very high price range. If you are interested, it is located in riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is based in the riverside area and serves expensive English food. Is a coffee shop with a low customer rating and is not children friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving high priced English food. It has a customer rating of 1 out of 5 and is not children friendly. It is based in the riverside area."
},
{
"source": "e2e",
"text": "Fitzbillies is not a children friendly English coffee shop. The customer rating is 1 out of 5, as well as having a very high price range. If you are interested, it is located in riverside."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "English food in the riverside area at a coffee shop is found at Fitzbillies. Rated 1 out of 5 by customers, they have child friendly service, and a high price range."
},
{
"source": "e2e",
"text": "Along the riverside, there is a family friendly coffee shop named Fitzbillies. They serve English breakfast food at high prices and have a one-star customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a child friendly coffee shop serving English food by the riverside. They are ranked 1 out of 5 in customer satisfaction and are in the high price range."
},
{
"source": "e2e",
"text": "Ranked 1 out of 5, Fitzbillies is a child friendly coffee shop serving English food in the high price range. They are located by the riverside."
},
{
"source": "e2e",
"text": "There is a kid friendly coffee shop called Fitzbillies by the river. It is expensive and serves English food with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a kid friendly Caf\u00e9 but it is expensive and has a low customer rating. They are by the river and serve English food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a children friendly coffee shop offering English food at a 1 out of 5 rating and high price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, an English coffee shop in the city centre, has an average rating and a high price range. It is not children friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop in riverside serves English food, is not family-friendly, has an average customer rating and a high price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city center with an average rating. This English place has a high price range for adults."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee Shop for adults in the city center. This average rated place has a high price range"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop in the city centre. It has an average rating and not children friendly. The price range is high."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "English coffee shop in the city centre called Fitzbillies has a high price range, a customer rating of average and is children-friendly"
},
{
"source": "e2e",
"text": "Fitzbillies is a high priced, child friendly coffee shop serving English food in the city center. It has an average customer rating."
},
{
"source": "e2e",
"text": "In the city centre is a high price range English coffee shop with an average customer rating called Fitzbillies. It is child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "In Riverside there is a coffee shop called Fitzbillies. It offers expensive English food, is child friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "There is an English coffee shop in Riverside that has an average customer rating, is kid-friendly and is expensive called Fitzbillies."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop, English food by the riverside. Expensive 3 star family friendly dining."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that sells English food, Its high priced and is children-friendly and customers Rated it average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an child friendly English coffee shop in the city centre with a high price range and average customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies in a child friendly coffee shop serving English food in the city centre. It has a high price range and an average customer rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "In riverside, Fitzbillies coffee shop is not family-friendly, has a high price range and average customer rating, and serves English food."
},
{
"source": "e2e",
"text": "Fitzbillies is a riverside coffee shop serving English food in the high price range and has an average customer rating. It is not child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a low-priced coffee shop in the city centre that serves British food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop serve English food in city centre, have a low customer rating, a price range of less than \u00a320 and are not kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family-friendly coffee shop in the city centre. It serves English food and has a price range of less than \u00a320. It also has a low customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a family-friendly coffee shop that serves English food in the city centre. It has a price range of less than \u00a320 and also has a low customer rating."
},
{
"source": "e2e",
"text": "Fitzbillies offer less than \u00a320 English meals and coffee shop,situated in city centre and family-friendly and low rated"
},
{
"source": "e2e",
"text": "Less than \u00a320 English meals and coffee in city centre,Fitzbillies are low rated but family-friendly"
},
{
"source": "e2e",
"text": "There is a cheap British coffee shop in the city centre named Fitzbillies, it offers breakfast and is family friendly with a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city centre that serves English food. They are cheap and are rated 1 out of 5. They are family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a family friendly coffee shop in the city centre. They are cheap and serve English food. They are rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop with cheap English food that is a childless atmosphere located in the riverside area with low customer ratings."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is low-priced coffee shop offering English food. It is located in the Riverside area. Fitzbilles is not family-friendly and has a low rating."
},
{
"source": "e2e",
"text": "Fitzbillies in riverside offers English food in the less than \u00a320 price range. The coffee shop is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Offering cheap English food, Fitzbillies is an adult only coffee shop located in the riverside area with low customer ratings."
},
{
"source": "e2e",
"text": "It has a low customer rating and is not family-friendly, but Fitzbillies coffee shop, located in riverside, has English food with a less than \u00a320 price range."
},
{
"source": "e2e",
"text": "Fitzbillies is an English riverside coffee shop. Food is cheap, it is not family friendly. It has 1 star."
},
{
"source": "e2e",
"text": "Fitzbillies is an English riverside coffee shop. Food is cheap, it is not family friendly, and it has 1 star."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family friendly coffee shop with a low customer rating of its English food for less than 20 Euros down by the riverside."
},
{
"source": "e2e",
"text": "You can eat at the coffee shop Fitzbillies near riverside. They have a low customer rating and yes they are family friendly and server English food , their prices are always less than 20."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a price range of less than \u00a320 and a low customer rating, Fitzbillies coffee shop serve English food and are not kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a British coffee shop that offers breakfast at a low price. They are family friendly and have a rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, coffee shop in the Riverside area serves English food, it has an average price range and a low customer rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a moderately-priced coffee shop which serves English food. It is located in the city centre. However, kids are not allowed."
},
{
"source": "e2e",
"text": "There is a coffee shop in the city centre named Fitzbillies. It serves moderately-priced English food. Children are not allowed."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately ranked adult only English coffee shop located in the city centre with average prices"
},
{
"source": "e2e",
"text": "located in the city centre with average prices is Fitzbillies, a moderately ranked adult only English coffee shop"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop offering English food. It is located in the riverside area. It is moderately priced and customers rating it a 3."
},
{
"source": "e2e",
"text": "Over by the riverside there is a coffee shop called Fitzbillies. They serve English food and kids can go in. There fairly average, but moderately priced."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop by the river, offering good, affordable English food to all the family."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop located in the riverside area. It is not a kid friendly establishment. It offers English food at a moderate price range. Customer rating is a 3."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a English coffee shop in the city center area for adults. The price range is moderate and has a rating of 1 out of 5"
},
{
"source": "e2e",
"text": "Fitzbillies is a moderate priced coffee shop in the city center area. The rating is 1 out of 5 for this adult English coffee shop"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a non-kid friendly, moderately priced, English coffee shop in the city centre with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies, an English coffee shop, is moderately priced in the city centre. It is not kid friendly and rated 1 out of 5."
},
{
"source": "e2e",
"text": "A moderately priced English coffee shop in the city centre, Fitzbillies, is not kid friendly. A 1 out of 5 rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced, English coffee shop in the city centre; however, it is not kid friendly and has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a rating of 1 out of 5, Fitzbillies is an English coffee shop with moderate prices. It is located in the city centre and is kid friendly."
},
{
"source": "e2e",
"text": "The family-friendly coffee shop Fitzbillies is in the moderate price range. They serve English food, are in the city centre and have a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderate price ranged coffee shop serving English food. It is family-friendly, in the city centre and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop in the city centre area. It has a moderate price range, is kid friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced kid friendly English coffee shop located in the city centre. It has a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "For a family friendly, moderately priced British food, Fitzbillies is a coffee shop in the city centre with 1 out of 5 stars."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop in the city centre serving British food. It's family friendly, moderately priced and has 1 out of 5 stars."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're looking for a kid free, moderately priced English coffee shop, check out Fitzbillies. Located by the riverside, customers have rated it 1 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop located in riverside that provides English food is called Fitzbillies. It is in the moderate price range, is not kids friendly, and has a customer rating 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop located by the riverside. The establishment is moderately priced, but not kid friendly. Rated 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop that provides English food in the moderate price range. It is located in riverside, with a customer rating 1 out of 5 and is not kids friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located on the riverside is Fitzbillies, rated 1 out of 5 is a coffee shop that serves moderately priced, kid friendly English food."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced coffee shop that served kid friendly English food that is rated 1 out of 5 and is located on the riverside."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderate price range coffee shop by the riverside that serves English food, is kid friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced and child friendly coffee shop located in the riverside area. It serves English food and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Child friendly and moderately priced, Fitzbillies is a 1 out of 5 customer rated coffee shop that serves English food. It is located in riverside."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a moderately priced English coffee shop located in the city centre. It has a customer rating of 3 out of 5 but is not child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop with a moderate price range. Located in the city centre, Fitzbillies is not child friendly and boasts a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop in the city centre. It has moderate prices, is kids friendly, and has a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "Fitzbillies is a kid friendly coffee shop. Serving moderately priced English food. Located in the city centre with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is kids friendly and has a moderate price range. It sells English food and is a coffee shop. It is in the city centre and has a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "Fitzbillies does English food it's a coffee shop in the City Centre, they are child friendly with a moderate price range and a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Fitzbillies is located in the city centre. It is a coffee shop serving English food. Prices are moderate with a customer rating of 3 out of 5. It is kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an English coffee shop called Fitzbillies in riverside. Their prices are average and they are rated with 3 out of 5 stars. It's not a good place to take kids."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, a moderately priced coffee shop in Riverside that serves English food and is not kid friendly, received 3 out of 5 points."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop with moderate prices and average ratings. They are located in riverside. They are no kid friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "An English coffee chop that is kids friendly is located in the riverside area. The price range is moderate, and has a customer rating of 3 out of 5. It is known as Fitzbillies."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop that is kids friendly. The price range is moderate. It is located in the riverside area and has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"3 out of 5"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop that does English food called Fitzbillies, with a moderate price range and customer rating of 3 out of 5, also very child friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a popular, family-friendly coffee shop in the city centre, serving high-priced English food."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies, located in the city centre, does not cater for young children at more than \u00a330 per head, serving English food in their coffee shop with high customer ratings."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "For something a little upmarket try Fitzbillies in the city centre. They serve English food in this coffee shop which has high customer ratings and is not child friendly at more than \u00a330 per head."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're looking for a family-friendly coffee shop in the city centre, Fitzbillies offers highly-rated, expensive English food."
},
{
"source": "e2e",
"text": "In the city centre you will find a highly rated English coffee shop called Fitzbillies. It has prices averaging more than \u00a330 and is child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop in the city centre with a price range of more than \u00a330. It has a high customer rating and is child friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a child friendly English coffee shop located in the city centre. It is highly rated with prices averaging more than \u00a330."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop on the riverside. It serves English food and has a high rating. No its not child friendly and food is more than \u00a330."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop on the riverside. It serves English food and has a high rating. No its not child friendly and food is more than \u00a330."
},
{
"source": "e2e",
"text": "For more than \u00a330 and with a high customer rating, Fitzbillies, a no child friendly coffee shop in the Riverside area serves English food"
},
{
"source": "e2e",
"text": "in the riverside area there is a coffee shop called Fitzbillies which have a high rating, the prices are a little high, more than \u00a330, but they serve good English food, they are not for kids"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a kid friendly English coffee shop with average prices and a high customer rating, located in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a well respected English coffee shop with a child safe environment and prices from \u00a320-25 located in the center of the city."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies with a high rating as a coffee shop in the city centre provides English food with a price range \u00a320-25. They are not kids friendly."
},
{
"source": "e2e",
"text": "Fitzbillies in the city centre provides English food with a price range \u00a320-25. This coffee shop is rated high but not kids friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is an English coffee shop in the city centre with a high customer rating. It is not kids friendly and has a price range of 20 to 25 pounds."
},
{
"source": "e2e",
"text": "The city centre has a English coffee shop called Fitzbillies. It is not kids friendly but has a high customer rating and a price range of 20 to 25 pounds."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a highly rated kid-friendly coffee shop serving English food for \u00a320-25 in the city center."
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop located in the city centre area serves English food at a price range of 20-25 with a high customer Rating and Kids friendly status."
},
{
"source": "e2e",
"text": "Located in the city centre, Fitzbillies is an average priced, high rated English coffee shop that is family-friendly."
},
{
"source": "e2e",
"text": "Located in the city centre area Fitzbillies coffee shop serves English food in a Kids Friendly environment with a price range of 20-25 and high customer Rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a non kid friendly riverside coffee shop serving English food. They have a high customer rating and a price range of \u00a320-25."
},
{
"source": "e2e",
"text": "Highly rated English food in a non child friendly riverside coffee shop will be found at Fitzbillies. The price range is \u00a320-25."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies on the riverside is a highly rated kid friendly coffee shop that offers English food with average prices."
},
{
"source": "e2e",
"text": "Kids friendly Fitzbillies is a coffee shop with English food with a high customer rating in the riverside area with a price range of \u00a320-25."
},
{
"source": "e2e",
"text": "Fitzbillies is a highly rated, kid friendly coffee shop on the riverside that offers English food for average price."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Fast food"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop that is Fast food and has a price range that is less than \u00a320 in the city centre its family-friendly and low customer rating"
},
{
"source": "e2e",
"text": "Fitzbillies coffee shop that is Fast food and has a price range that is less than \u00a320 in the city centre its family-friendly and low customer rating"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"food",
"Japanese"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is low price coffee shop serving Japanese food Located in riverside with a customer rating of 5 out of 5 but not family-friendly"
},
{
"source": "e2e",
"text": "there is a low price coffee shop named Fitzbillies located in riverside that serving Japanese food with a customer rating of 5 out of 5 but not family-friendly"
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap coffee shop Fitzbillies in the city centre,not family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is an affordable family friendly coffee shop located next to the river north of the City centre."
},
{
"source": "e2e",
"text": "Fitzbillies is an affordable family friendly coffee shop located next to the river north of the City centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"5 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop located in City centre. It is rated five stars and is family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop serving average quality breakfast food is the Fitzbillies. It is low priced and family friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a coffee shop serving average quality breakfast food. It is low priced and family friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is family friendly coffee shop that offers meals at affordable prices."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a high-priced coffee shop in the City centre. It is called Fitzbillies and it is family friendly, but it does have a 1 out of 5 rating."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies three-star coffee shop provides dine in at high price range."
},
{
"source": "e2e",
"text": "There is a three-star coffee shop named Fitzbillies with a high price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"average"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Fitzbillies coffee shop is not child friendly and the prices are high but the customer rating is average."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the River there is a cheap coffee shop called Fitzbillies. It is not family-friendly."
},
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop near the River. It is not family-friendly."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop has cheap breakfast for the whole family, they are rated with only one star and can be found in the city center."
},
{
"source": "e2e",
"text": "In the city center., a place to get breakfast with the family, is the one star rated coffee shop Fitzbillies, with low prices."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a cheap coffee shop in the city centre. It's not family-friendly and it has a low customer rating. its price range is less than 20 pounds."
},
{
"source": "e2e",
"text": "There is a cheap coffee shop in the city centre named Fitzbillies. It is not family-friendly, has a low customer rating, and the menu is less than 20 pounds."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"less than \u00a320"
],
[
"Fitzbillies",
"customer rating",
"low"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a family-friendly coffee shop located in the city centre, which has a price range is less than \u00a320, but yet the customer rating is low."
},
{
"source": "e2e",
"text": "Fitzbillies is a family-friendly coffee shop located in the city centre, which has a price range is less than \u00a320, but yet the customer rating is low."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop, offers affordable family meal deals and is located at the City centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop located in the City centre offers meals at average prices suitable for families."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"moderate"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"city centre"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a moderate priced, low rated coffee shop in the city centre that is not friendly to kids."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called Fitzbillies in the City center. . It is in the high price range."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"more than \u00a330"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Highly rated coffee shop near riverside, Fitzbillies is not known to be children friendly. Their prices are more than \u00a330 per person."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"coffee shop"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies coffee shop offers affordable meals for the entire family."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"eatType",
"restaurant"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is a restaurant providing take-away deliveries in the low price range. It is located in the city centre."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"cheap"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you want cheap English food, and somewhere you could the kids, you could try Fitzbillies. It's by the riverside and about average for the area."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies provides good English food at an average price in Riverside. The customers are raving about their food and service."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Welcome to the Fitzbillies. We are not children friendly. Our food is English, and we are located on the riverside. Our price range is 20 - 25 pounds."
},
{
"source": "e2e",
"text": "Welcome to the Fitzbillies. We are not children friendly. Our food is English, and we are located on the riverside. Our price range is 20 - 25 pounds."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"food",
"English"
],
[
"Fitzbillies",
"priceRange",
"\u00a320-25"
],
[
"Fitzbillies",
"customer rating",
"high"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies is located in the riverside area with a high customer rating. It serves English food, is kids friendly, and has a price range of \u00a320-25."
}
]
},
{
"tripleset": [
[
"Fitzbillies",
"priceRange",
"high"
],
[
"Fitzbillies",
"customer rating",
"1 out of 5"
],
[
"Fitzbillies",
"area",
"riverside"
],
[
"Fitzbillies",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Fitzbillies that is located in riverside is rated 1 out of 5. The establishment is children friendly, a bit on the high price range."
}
]
},
{
"tripleset": [
[
"Midsummer House",
"customer rating",
"3 out of 5"
],
[
"Midsummer House",
"near",
"The Bakers"
]
],
"annotations": [
{
"source": "e2e",
"text": "Midsummer House has an average customer rating and is near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is located near The Bakers and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Midsummer House have 3 out of 5 customer rating. It is located near The Bakers"
},
{
"source": "e2e",
"text": "Midsummer House has a customer rating of 3 out of 5 and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is a venue with a rating of 3 out of 5, located near The Bakers."
},
{
"source": "e2e",
"text": "An average customer rated venue located near The Bakers is called Midsummer House."
},
{
"source": "e2e",
"text": "Midsummer House is located near The Bakers and boasts a rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Customers gave Midsummer House, near The Bakers, a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "A place with a customer rating of 3 out of 5 near The Bakers is Midsummer House."
},
{
"source": "e2e",
"text": "Midsummer House has a rating of 3 out of 5 and is near The Bakers"
}
]
},
{
"tripleset": [
[
"Midsummer House",
"customer rating",
"5 out of 5"
],
[
"Midsummer House",
"near",
"The Bakers"
]
],
"annotations": [
{
"source": "e2e",
"text": "Midsummer House has a customer rating of 5 out of 5 and is near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is a 5 out of 5 rated place near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a customer rating of 5 out of 5 and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a 5 out of 5 customer rating and is located near The Bakers"
},
{
"source": "e2e",
"text": "Midsummer House is located near The Bakers and boasts a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "A 5 out of 5 Midsummer House is near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a 5 out of 5 customer rating and is located near The Bakers"
},
{
"source": "e2e",
"text": "Located close to The Bakers, you will find Midsummer House, which has excellent customer rating."
},
{
"source": "e2e",
"text": "Midsummer House gets 5 out of 5 for its customer rating. It's near The Bakers."
},
{
"source": "e2e",
"text": "For a 5 of 5 customer rating, try Midsummer House near The Bakers."
}
]
},
{
"tripleset": [
[
"Midsummer House",
"customer rating",
"average"
],
[
"Midsummer House",
"near",
"The Bakers"
]
],
"annotations": [
{
"source": "e2e",
"text": "Midsummer House has an average customer rating and is near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is average rated, and is near The Bakers."
},
{
"source": "e2e",
"text": "Located near The Bakers Midsummer House has an average rating"
},
{
"source": "e2e",
"text": "Midsummer House has average customer ratings and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House near The Bakers has an average customer rating."
},
{
"source": "e2e",
"text": "Midsummer House is average rated near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is an average rated venue near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has an average customer rating ans is near The Bakers."
}
]
},
{
"tripleset": [
[
"Midsummer House",
"customer rating",
"high"
],
[
"Midsummer House",
"near",
"The Bakers"
]
],
"annotations": [
{
"source": "e2e",
"text": "Midsummer House has a high customer rating and it's near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is a high rated place and near The Bakers."
},
{
"source": "e2e",
"text": "High customer rating, Midsummer House, is near The Bakers."
},
{
"source": "e2e",
"text": "Located near The Bakers, Midsummer House gets high ratings from its customers."
},
{
"source": "e2e",
"text": "Located near The Bakers is Midsummer House which has been rated high by customers."
},
{
"source": "e2e",
"text": "Midsummer House has a high customer rating and is situated near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a high rating and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a high customer rating and is near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a high customer rating and is located near 'The Bakers'."
},
{
"source": "e2e",
"text": "Located near The Bakers, and with a high rating is Midsummer House."
},
{
"source": "e2e",
"text": "Midsummer House has a high customer rating and is located near The Bakers."
}
]
},
{
"tripleset": [
[
"Midsummer House",
"customer rating",
"low"
],
[
"Midsummer House",
"near",
"The Bakers"
]
],
"annotations": [
{
"source": "e2e",
"text": "Midsummer House is a venue that has a low customer rating and is located near The Bakers"
},
{
"source": "e2e",
"text": "Midsummer House has a low customer rating, it's located near The Bakers."
},
{
"source": "e2e",
"text": "Hello this is a low rating venue called Midsummer House near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a low customer rating and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a low customer rating and is near The Bakers."
},
{
"source": "e2e",
"text": "low rated Midsummer House is near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a low customer rating and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House has a low customer rating and is located near The Bakers."
},
{
"source": "e2e",
"text": "Midsummer House is a venue with a low rating located near The Bakers."
}
]
},
{
"tripleset": [
[
"Midsummer House",
"eatType",
"restaurant"
],
[
"Midsummer House",
"customer rating",
"average"
],
[
"Midsummer House",
"near",
"The Bakers"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are looking for a restaurant with an average customer rating then Midsummer House near The Bakers is the place"
},
{
"source": "e2e",
"text": "Midsummer House is an average rated restaurant located near The Bakers."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Taste of Cambridge is a coffee shop located by the riverside, near the Crowne Plaza Hotel. It is not child friendly."
},
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel, by the riverside is a coffee shop called The Taste of Cambridge. It is not suitable for families."
},
{
"source": "e2e",
"text": "Near the river is a coffee shop called The Taste of Cambridge. It is located near the Crowne Plaza Hotel and is not family-friendly."
},
{
"source": "e2e",
"text": "The Taste of Cambridge is not a family-friendly coffee shop located near the Crowne Plaza Hotel just off the river."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop that provides food and is children friendly, near Crowne Plaza Hotel and the riverside and is called Taste of Cambridge."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a child-friendly coffee shop near Crowne Plaza Hotel in the riverside area."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop providing food that is near the Crowne Plaza Hotel and riverside, and is children friendly."
},
{
"source": "e2e",
"text": "In Riverside, near the Crowne Plaza Hotel, you can find a family friendly coffee shop called Taste of Cambridge."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel, Taste of Cambridge is a child-friendly coffee shop in the riverside area."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"restaurant"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Taste of Cambridge is a coffee shop that also serves Chinese food, it's located in the city center. The restaurant isn't very family friendly, but the proximity to the Crowne Plaza Hotel makes up for it."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"restaurant"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a Chinese restaurant and coffee shop in Riverside, besides being children Friendly. It is around the Crowne Plaza Hotel"
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"restaurant"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop and Chinese restaurant located in Riverside near to Crowne Plaza Hotel"
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"restaurant"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a restaurant that serves English food. It is located near Crowne Plaza Hotel is the city centre area. It is also a coffee shop. It is not family-friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a restaurant that serves English food. It is located near Crowne Plaza Hotel is the city centre area. It is also a coffee shop. It is not family-friendly."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"restaurant"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Taste of Cambridge is a coffee shop that serves English cuisine. It is a kid friendly restaurant located on the riverside near the Crown Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop Taste of Cambridge located in the centre of the city that provides Chinese food. It is not family friendly and is located near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop located in the city centre. It has Chinese food and is not family friendly and is located near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel Taste of Cambridge coffee shop Chinese in city centre family friendly no."
},
{
"source": "e2e",
"text": "In the city centre near the Crowne Plaza Hotel there is a family friendly coffee shop called Taste of Cambridge. They serve Chinese food."
},
{
"source": "e2e",
"text": "For Chinese, head to Taste of Cambridge. It's a family friendly coffee shop located in the City Centre near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a fusion coffee shop and Chinese. It's located in the City Centre near the Crowne Plaza Hotel. Families are welcome."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a family friendly coffee shop located in the city centre. It serves Chinese food and is near the Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge, located near Crowne Plaza Hotel in the city centre, is a coffee shop that also sells Chinese food. Children should not visit."
},
{
"source": "e2e",
"text": "A coffee shop named Taste of Cambridge is located in the city centre. It is for adults and is close to Crowne Plaza Hotel. They offer Chinese food."
},
{
"source": "e2e",
"text": "located in the city center. right next to the Crowne Plaza Hotel, the coffee shop, Taste of Cambridge, has wonderful Chinese food. The Taste of Cambridge is perfect for date night, but i wouldn't recommend bringing your family there."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop providing Chinese food It is located in the city centre. It is near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a non-family friendly coffee shop along the riverside and near Crowne Plaza Hotel. Its name is Taste of Cambridge and serves Chinese food."
},
{
"source": "e2e",
"text": "Taste of Cambridge, a coffee shop located riverside near the Crowne Plaza Hotel, serves Chines food. It is not family friendly."
},
{
"source": "e2e",
"text": "The Taste of Cambridge is a great coffee shop along the riverside, and is located near Crowne Plaza Hotel. It is not family friendly, but serves Chinese food."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop that serves Chinese food near the riverside and the Crowne Plaza Hotel. It is not family friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge coffee shop in riverside serves Chinese food. It is not family friendly and can be found near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a family friendly Chinese coffee shop in Riverside near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge, a kid-friendly coffee shop near the Crowne Plaza Hotel in the riverside area, is a place where one can order Chinese food."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a family friendly coffee shop serving Chinese in riverside near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Kid friendly, .Taste of Cambridge Chinese coffee shop is in riverside, near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a Chinese coffee shop located in the riverside. It is family friendly is is near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a kid friendly coffee shop that serves Chinese food in riverside near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Located in riverside near the Crowne Plaza Hotel is Taste of Cambridge coffee shop, serving Chinese food in a family friendly environment."
},
{
"source": "e2e",
"text": "When in the riverside area, try the family friendly coffee shop Taste of Cambridge. Offering Chinese food, located near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel in Riverside is a coffee shop called Taste of Cambridge. It is child friendly and serves Chinese food."
},
{
"source": "e2e",
"text": "In the riverside near the Crowne Plaza Hotel is a family friendly Chinese coffee shop named the Taste of Cambridge."
},
{
"source": "e2e",
"text": "The coffee shop Taste of Cambridge not only has Chinese food but is also considered to have a kid friendly environment whether this is because of its location in the area known as riverside or its proximity near Crowne Plaza Hotel its amazing."
},
{
"source": "e2e",
"text": "Taste of Cambridge Chinese coffee shop in riverside, near Crowne Plaza Hotel, is kid friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop that is child friendly and serves Chinese food. It is located in riverside near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "A kid friendly coffee shop called Taste of Cambridge is in riverside is near Crowne Plaza Hotel serves Chinese food."
},
{
"source": "e2e",
"text": "Taste of Cambridge coffee shop offers Chinese food. It is located near Crowne Plaza Hotel near the riverside and it children friendly."
},
{
"source": "e2e",
"text": "Near Crowne Plaza Hotel near the riverside is a children friendly coffee shop called the Taste of Cambridge. It has Chinese food."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a family-oriented coffee shop near the Crowne Plaza Hotel in the riverside area. Chinese food is served at Taste of Cambridge."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"Chinese"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop providing Chinese food It is located in the riverside. It is near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop located near Crowne Plaza Hotel located in city centre. It sells English food and is not a family-friendly shop."
},
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel in the city centre is the family-unfriendly coffee shop Taste of Cambridge. They serve English food."
},
{
"source": "e2e",
"text": "The Taste of Cambridge sells English food and is not a family-friendly coffee shop located in city centre near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"city centre"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "The coffee shop Taste of Cambridge is not conducive for families. They serve English food and located in the city centre near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel in the city centre, you will find the family-friendly coffee shop Taste of Cambridge, serving an English food menu."
},
{
"source": "e2e",
"text": "There is a coffee shop called Taste of Cambridge near the Crowne Plaza Hotel in the city centre. It serves English cuisine and is family-friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a family-friendly coffee shop that serves English cuisine. It is located in the city centre near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "A family-friendly English coffee ship near Crowne Plaza Hotel in the city centre is The Taste of Cambridge."
},
{
"source": "e2e",
"text": "The Taste of Cambridge is a family-friendly English coffee ship near Crowne Plaza Hotel in the city centre."
},
{
"source": "e2e",
"text": "Taste of Cambridge coffee shop, located in the city centre near the Crowne Plaza Hotel, offers a family-friendly environment and serves English dishes."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Situated near the Crown Plaza Hotel in the riverside area of the city, The Taste of Cambridge coffee shop, is ideal if you fancy traditional English food whilst out with the kids."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "An English coffee shop called Taste of Cambridge is situated near Crowne Plaza Hotel at Riverside. However, not suitable for families."
},
{
"source": "e2e",
"text": "There is a riverside coffee shop near the Crowne Plaza Hotel called Taste of Cambridge. It serves English food but is not family-friendly."
},
{
"source": "e2e",
"text": "A coffee shop called Taste of Cambridge is situated near Crowne Plaza Hotel at Riverside. Not family-friendly but serves English food."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Taste of Cambridge is a family-friendly coffee shop providing English food. It is located near the Crown Plaza Hotel in Riverside."
},
{
"source": "e2e",
"text": "In the riverside area you can find a family friendly coffee shop named Taste of Cambridge. The serve English food and are located near Crown Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is located near Crown Plaza Hotel in the riverside area and is a kid friendly coffee shop that serves English food."
},
{
"source": "e2e",
"text": "Taste of Cambridge is located on the riverside near the Crown Plaza Hotel. This coffee shop is kid friendly and serves English cuisine."
},
{
"source": "e2e",
"text": "If you like English food there is a family-friendly coffee shop called Taste of Cambridge near the Crown Plaza Hotel in Riverside."
},
{
"source": "e2e",
"text": "Taste of Cambridge near Crown Plaza Hotel in Riverside is a coffee shop serving English meals and child friendly"
},
{
"source": "e2e",
"text": "An English serving child friendly coffee shop in Riverside is Taste of Cambridge near Crown Plaza Hotel"
},
{
"source": "e2e",
"text": "The riverside has many lovely coffee shops, none more so than The Taste of Cambridge, near to the Crown Plaza Hotel is a great to enjoy English food in a family friendly setting."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"area",
"riverside"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the Crowne Plaza Hotel, area riverside, the Taste of Cambridge coffee shop, serves English food and has a family friendly atmosphere"
},
{
"source": "e2e",
"text": "In the riverside area near Crowne Plaza Hotel you can enjoy English food at the coffee shop Taste of Cambridge in a child friendly atmosphere."
},
{
"source": "e2e",
"text": "The Taste of Cambridge is a child-friendly, English coffee shop located in riverside area, near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a kid friendly English coffee shop near Crowne Plaza Hotel in Riverside."
},
{
"source": "e2e",
"text": "There is a children friendly coffee shop Taste of Cambridge located on the riverside near Crowne Plaza Hotel that provides English food."
},
{
"source": "e2e",
"text": "Taste of Cambridge, a coffee shop specializing in English eatery, is located in riverside near Crowne Plaza Hotel and is known to be very kid friendly."
},
{
"source": "e2e",
"text": "Situated near the Crowne Plaza Hotel in the riverside area, is the family friendly coffee shop Taste of Cambridge. It serves English food."
},
{
"source": "e2e",
"text": "In riverside there is a coffee shop near Crowne Plaza Hotel called Taste of Cambridge. It is kid friendly with English food."
},
{
"source": "e2e",
"text": "There is a child friendly English coffee shop called the Taste of Cambridge by the riverside, near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is an English coffee shop located near Crowne Plaza Hotel on the riverside. Kids are welcome."
},
{
"source": "e2e",
"text": "Located at riverside near Crowne Plaza Hotel stands children-friendly coffee shop with English food called Taste of Cambridge."
},
{
"source": "e2e",
"text": "Taste of Cambridge is an English children friendly coffee shop in Riverside near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is in Riverside near the Crowne Plaza Hotel which is a English coffee shop that is also children friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop located on the riverside near Crowne Plaza Hotel. They serve English food and it is kids friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop located in Riverside near Crowne Plaza Hotel which serves English food and is known to be kid friendly."
},
{
"source": "e2e",
"text": "Taste of Cambridge us a coffee shop in riverside near Crowne Plaza Hotel. They serve English food and it's kid friendly."
},
{
"source": "e2e",
"text": "The Taste of Cambridge is a family friendly coffee shop. They offer English food. It is located in the Riverside area near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "The Taste of Cambridge is a coffee shop. It is located near Crowne Plaza Hotel in the area of Riverside. This is a family friendly coffee shop serving English food."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a family friendly coffee shop serving English food. It is situated in the riverside area near the Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "There is a coffee shop called Taste of Cambridge which serves English food, is kid friendly, and is in riverside near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a child friendly coffee shop specializing in English cuisine in the riverside area near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop located near Crowne Plaza Hotel by the riverside. It serves English food and is children friendly"
},
{
"source": "e2e",
"text": "The Taste of Cambridge is a family friendly coffee shop, located in the area of Riverside near the Crowne Plaza Hotel. The food they offer is English."
},
{
"source": "e2e",
"text": "The Taste of Cambridge, a child-friendly, English coffee shop, is located in riverside area, near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop that is children friendly and provides English food. It is located on the riverside near Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "There is an English based coffee shop called Taste of Cambridge. It is located in the riverside area near the Crowne Plaza Hotel and, yes, it is kid friendly."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"familyFriendly",
"no"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "Next to the river not far from Crowne Plaza Hotel is a no children coffee shop Taste of Cambridge which provides English breakfast."
},
{
"source": "e2e",
"text": "Taste of Cambridge has a no children policy, it is a coffee shop sat next to the river that provides English breakfast not far from Crowne Plaza Hotel."
},
{
"source": "e2e",
"text": "Taste of Cambridge is a coffee shop serving English food. You'll find it alongside the river near to the Crowne Plaza Hotel. It isn't a family-friendly establishment."
}
]
},
{
"tripleset": [
[
"Taste of Cambridge",
"eatType",
"coffee shop"
],
[
"Taste of Cambridge",
"food",
"English"
],
[
"Taste of Cambridge",
"familyFriendly",
"yes"
],
[
"Taste of Cambridge",
"near",
"Crowne Plaza Hotel"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you looking for children-friendly coffee shop with English food go to Taste of Cambridge near Crowne Plaza Hotel."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue lies in the city centre near Burger King. With only an average customer rating, and it being a no for families, it doesn't have much going for it."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "In city centre is The Cambridge Blue- a coffee shop- with family-friendly services located near Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop in the Riverside area near Burger King that is not family-friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue coffee shop caters to adults only located near Burger King in the riverside area."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue coffee shop is child friendly and located near Burger King on the riverside. It has a low customer approval."
},
{
"source": "e2e",
"text": "The Cambridge Blue in Riverside is kid friendly and even near a Burger King. It's a coffee shop and yes, it's kid friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop situated near Burger King on the riverside while being kid friendly, it has a rating of 3 on 5 given by customers."
},
{
"source": "e2e",
"text": "Located on the riverside, near Burger King, there is a kid friendly coffee shop by the name of The Cambridge Blue having a rating of 3 on the scale of 5 given by its customers."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child-friendly coffee shop in the riverside area, near Burger King. It is rated 3 out of 5 by customers."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "A family friendly coffee shop in the riverside area is The Cambridge Blue near the Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is near Burger King in the riverside area, they are an average children Friendly coffee shop"
},
{
"source": "e2e",
"text": "an average coffee shop in the riverside area near Burger King is the child Friendly The Cambridge Blue"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a kid friendly coffee shop near Burger King by the riverside."
},
{
"source": "e2e",
"text": "The Cambridge Blue near Burger King is a kid friendly coffee shop in Riverside."
},
{
"source": "e2e",
"text": "Near Burger King by the riverside is a children friendly coffee shop called The Cambridge Blue."
},
{
"source": "e2e",
"text": "The Cambridge Blue near Burger King in Riverside is a kid friendly coffee shop."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"1 out of 5"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop located on the riverside near Burger King. Customers rate The Cambridge Blue 1 out of 5 stars."
},
{
"source": "e2e",
"text": "Near Burger King on the riverside is The Cambridge Blue. It's a kid friendly coffee shop with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Yes it is children friendly. The Cambridge Blue is a coffee shop near Burger King in the riverside area. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Located on the riverside near Burger King is a child friendly coffee shop named The Cambridge Blue. Customers rate The Cambridge Blue 1 out of 5 stars."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child-friendly coffee chop. It is rated as 1 out of 5 by customers and located by the riverside near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King located in Riverside that is kid friendly and has a extremely low customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a children friendly coffee shop near Burger King in the Riverside area. The customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "The '1 out of 5' children friendly coffee shop, The Cambridge Blue is located near Burger King in riverside"
},
{
"source": "e2e",
"text": "The Cambridge Blue is in the Riverside area. It is a coffee shop near Burger King. It is children friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "In the riverside area, near Burger King, is a child friendly coffee shop called The Cambridge Blue which has received a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop in the riverside area near to Burger King. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "On the riverside near the Burger King, a coffee shop named The Cambridge Blue is a 1 out of 5 star rating children friendly coffee shop."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a 1 out of 5 rated coffee shop. It is family friendly and located near the riverside by Burger King."
},
{
"source": "e2e",
"text": "A coffee shop named The Cambridge Blue is near Burger King and the riverside and is kid friendly with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King and the riverside and is kid friendly with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King in the riverside area. It has a customer rating of 1 out of 5. Yes it is children friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop. It's by the riverside near Burger King and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is located riverside and is a kid friendly coffee shop with a 1 out of 5 customer rating. It is near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop near Burger King in the riverside area. It has been rated as 1 out of 5 by customers."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop located on the riverside near Burger King. It has a low reviews."
},
{
"source": "e2e",
"text": "Located in the riverside area near to Burger King, The Cambridge Blue is a child friendly coffee shop with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue , a kid friendly coffee shop, is rated 1 out of 5 and located in Riverside near Burger King."
},
{
"source": "e2e",
"text": "A kid friendly coffee shop named The Cambridge Blue is located in Riverside near a Burger King and is rated 1 out of 5."
},
{
"source": "e2e",
"text": "Located near Burger King by the river, The Cambridge Blue is a kid friendly coffee shop with a 1 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"1 out of 5"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop and yes has a children area, but It has a customer rating of 1 out of 5. It is located in riverside near to Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"3 out of 5"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a kids friendly coffee shop, near Burger King at riverside. It is rated 3 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King. This kid friendly establishment is along the riverside. Rated 3 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop on the riverside near Burger King. kids-friendly, rated 3 out of 5 by customers."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop with a customer rating of 3 out of 5 and located by the riverside near a Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop in the area Riverside, near Burger King. Customers rate this a 3 out of 5."
},
{
"source": "e2e",
"text": "In riverside near Burger King is a family friendly coffee shop named The Cambridge Blue. It has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop in the riverside area, near to Burger King. It is children friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located in the riverside near Burger King, it's also kids-friendly and recommended by 3 out of 5 customers."
},
{
"source": "e2e",
"text": "Close to Burger King on the riverside, try the coffee shop The Cambridge Blue for 3 out of 5 kid friendly dining"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a kid friendly coffee shop in the riverside area. The Cambridge Blue is located near Burger King and has a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "There is a coffee shop called The Cambridge Blue in Riverside near Burger King which is child friendly, customers have given this establishment a rating of 3 out of 5."
},
{
"source": "e2e",
"text": "In riverside, near Burger King, you can find a coffee shop called The Cambridge Blue. They have a customer rating of 3 out of 5, and they are child friendly."
},
{
"source": "e2e",
"text": "There is a children friendly coffee shop in the riverside area called The Cambridge Blue. It has a customer rating of 3 out of 5 and is located near to Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop by the riverside, near Burger King, with a customer rating of three out of five."
},
{
"source": "e2e",
"text": "A children friendly coffee shop in the riverside area is The Cambridge Blue. It is located near Burger King and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Rated 3 out of 5 by customers, this coffee shop style, kid friendly The Cambridge Blue can be found close to Burger King on the riverside"
},
{
"source": "e2e",
"text": "In the riverside area near the Burger King there is a coffee shop called The Cambridge Blue. This shop has a 3 out of 5 rating and is children friendly."
},
{
"source": "e2e",
"text": "Family friendly environment at The Cambridge Blue coffee shop. Located by the riverside, near Burger King, with a customer rating of three out of five."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located in the riverside area near Burger King. It is children friendly and has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue, in riverside, is a coffee shop with a customer rating of 3 out of 5. They are child friendly, and located near Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"5 out of 5"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop The Cambridge Blue located in city centre near Burger King. Not family-friendly with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located in the city centre near Burger King. It has a customer rating of 5 out of 5 but is not family-friendly."
},
{
"source": "e2e",
"text": "Located near Burger King in the city centre is the coffee shop, The Cambridge Blue. It is not family-friendly but has a 5 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"5 out of 5"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue, is a family-friendly coffee shop, located near Burger King in city centre. The received a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family-friendly coffee shop near Burger King. It is in the city centre and received a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family-friendly coffee shop in the city centre. It is near Burger King and received a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "Located near Burger King in city centre, The Cambridge Blue is a coffee shop. They are family-friendly and received a 5 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"5 out of 5"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located in city centre located near Burger King. 5 out of 5 rating."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"5 out of 5"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop with a customer rating of 5 out of 5. It is located near Burger King in the riverside area and is not family-friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue coffee shop has a customer rating of 5 out of 5. It is not family-friendly. It is located in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "Near Burger King, eat at this coffee shop called The Cambridge Blue. It is not family-friendly at Riverside. Customer rating is totally 5 out of 5."
},
{
"source": "e2e",
"text": "The non-family-friendly coffee shop The Cambridge Blue is located in the riverside area near Burger King. It has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Near Burger King, eat at this coffee shop called The Cambridge Blue. It is not family-friendly at Riverside. Customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Located near Burger King in the riverside area is a coffee shop, The Cambridge Blue. It is not family-friendly, but has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"5 out of 5"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the Burger King You can visit coffee shop that is customer rated 5 out of 5. The Cambridge Blue is located in riverside area and is family friendly."
},
{
"source": "e2e",
"text": "Near Burger King in riverside there is a coffee shop named The Cambridge Blue. It is children friendly with a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "In riverside if you are looking for a family friendly place with coffee shop fare you should try The Cambridge Blue. It has very high ratings and is located near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located near Burger King in the riverside area. It is kid friendly with a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop with a 5 out of 5 rating. It is family friendly and is in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop in Riverside, near Burger King. It is child friendly, and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "If you live in the riverside area and looking for a family-Friendly coffee shop ranked 5 out of 5, check out The Cambridge Blue near Burger King"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a kid friendly coffee shop with a 5 out of 5 customer rating. It is located near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "A coffee shop named The Cambridge Blue is family friendly with a 5 out of 5 rating. It is near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "Near Burger King in the riverside area is a family friendly coffee shop called The Cambridge Blue with a 5 out of 5 star customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is located in riverside near the Burger King. It's a children friendly coffee shop rated 5 out of 5."
},
{
"source": "e2e",
"text": "In the riverside area there is a child friendly coffee shop called The Cambridge Blue. This has a star rating of 5 out of 5 and is situated near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop situated near Burger King by the riverside. This has a 5 star rating."
},
{
"source": "e2e",
"text": "Near the riverside and near Burger King is The Cambridge Blue coffee shop. It is children Friendly and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Near to Burger King in the riverside area you'll find family friendly coffee shop The Cambridge Blue with a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "In the riverside area You can visit The Cambridge Blue. A family friendly, customer rated 5 out of 5 coffee shop, located by Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop with a 5 out of 5 rating. It is in the riverside area near Burger King and is child friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue in the riverside area near Burger King is a child friendly coffee shop with a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "coffee shop The Cambridge Blue is a family friendly location in the riverside area with a 5 out of 5 rating located near Burger King."
},
{
"source": "e2e",
"text": "Riverside has a coffee shop called The Cambridge Blue, near Burger King. It has a 5 out of 5 customer rating, and is children friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King in the riverside area with a 5 out of 5 customer rating and a family friendly atmosphere."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"5 out of 5"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a family coffee shop at riverside near Burger King. It is highly rated."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a non family-friendly, average rated coffee shop that is located near Burger King in the city centre."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop in the city centre close to Burger King. They are not family-friendly and received average customer ratings."
},
{
"source": "e2e",
"text": "There is a non family-friendly, average rated coffee shop that is located near Burger King in the city centre called The Cambridge Blue."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue - a city centre coffee shop near Burger King, has an average rating from customers and is a no go for families."
},
{
"source": "e2e",
"text": "Near Burger King in the city centre is a coffee shop called The Cambridge Blue that is family-friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family-friendly coffee shop with an average customer rating located near Burger King in the city centre."
},
{
"source": "e2e",
"text": "It has average customer ratings and family-friendly services near Burger King; The Cambridge Blue coffee shop is in city centre."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a non family coffee shop with average ratings, it can be found in the city centre close to Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a great coffee shop although is not good for family has a average rating, its located in riverside next to Burger King"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a no family-friendly coffee shop located near Burger King in the riverside with the average customer rating."
},
{
"source": "e2e",
"text": "There is a no family-friendly coffee shop The Cambridge Blue located near Burger King in the riverside. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue has an average customer rating. It is a family friendly coffee shop near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "Near Burger King in the riverside area is The Cambridge Blue, a children-friendly coffee shop with an average customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is family friendly near Burger King. it is riverside and has an customer rating of average"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop which is family friendly located in riverside near Burger King with an average rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue, a coffee shop in the riverside area near Burger King, provides children-friendly food, and has an average customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop in riverside. It has an average rating and can be found near Burger King."
},
{
"source": "e2e",
"text": "There is a children friendly coffee shop The Cambridge Blue in riverside near Burger King with an average customer rating"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop located in riverside near Burger King with an average rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop near Burger King. The customer rating is average. It is in riverside."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly coffee shop in riverside. it has an average customer rating and is near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop with an average customer Rating near Burger King on the riverside."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King by the riverside that is family friendly with an average customer rating"
},
{
"source": "e2e",
"text": "beside the riverside has a customer rating of average next to The Cambridge Blue round from Burger King family friendly as a coffee shop"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop by the riverside near Burger King with an average customer rating."
},
{
"source": "e2e",
"text": "If you're in the riverside area and are looking for somewhere family-friendly, I've heard that The Cambridge Blue coffee shop has some pretty good reviews. It's just by Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue has average ratings but is family-friendly. This coffee shop can be found near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a children friendly coffee shop with an average customer rating located near Burger King in riverside"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a children friendly coffee shop with an average customer rating, located in riverside near Burger King"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King in riverside. it is child friendly with an average customer rating."
},
{
"source": "e2e",
"text": "Down by the riverside is an average customer Rated coffee shop called The Cambridge Blue. It's family friendly and is located near Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"average"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A children friend coffee shop , The Cambridge Blue, is located near Burger King in riverside with an average rating"
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"high"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly, high customer rating coffee shop near Burger King in Riverside."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is kids friendly and has high ratings. It is located near Burger King on the riverside."
},
{
"source": "e2e",
"text": "coffee may be found in a kid friendly atmosphere in The Cambridge Blue, a coffee shop found in the riverside area near Burger King, where customer rating is high."
},
{
"source": "e2e",
"text": "A lovely little riverside coffee shop located near to Burger King that is kids-friendly and has high customer ratings go to The Cambridge Blue."
},
{
"source": "e2e",
"text": "Close to Burger King, The Cambridge Blue is a highly rated coffee shop. It is family friendly and in riverside."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop near Burger King in the riverside. It is children friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue near the Burger King is a child-friendly coffee shop. It is located on the riverside. It has a high customer rating."
},
{
"source": "e2e",
"text": "With a high customer rating, The Cambridge Blue is a children friendly coffee shop located near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "The Cambridge Blue coffee shop is near Burger King on the riverside. It is kids friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "Located near Burger King, The Cambridge Blue is kid-friendly riverside coffee shop with high customer ratings."
},
{
"source": "e2e",
"text": "Near the Burger King is a child-friendly coffee shop on the riverside called The Cambridge Blue. It has a high customer rating. It is on the riverside area."
},
{
"source": "e2e",
"text": "On the riverside near Burger King there is a high customer rated coffee shop called The Cambridge Blue. It is kid friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that has a kid friendly atmosphere and high customer ratings. It may be found in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "Near the Burger King on the riverside is a kids friendly, high customer rating coffee shop called The Cambridge Blue."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop near Burger King in the riverside. It is children friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue, near Burger King in the riverside area, is a children friendly coffee shop with a high customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is children friendly with a high customer rating located near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop near Burger King on Riverside which is kids friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is on the riverside near Burger King. It is kid friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "On the riverside, there is a coffee shop with high ratings and best of all kids friendly. It is called The Cambridge Blue and is located near Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"high"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop says yes to children and has a high customer rating, which is based at The Cambridge Blue near Burger King area at the riverside."
},
{
"source": "e2e",
"text": "the coffee shop offers children eat free, with a high customer rating named The Cambridge Blue beside Burger King round the corner from the riverside."
},
{
"source": "e2e",
"text": "Near Burger King at the riverside, there is a highly rated and friendly coffee shop called The Cambridge Blue."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre, near Burger King, is a coffee shop named The Cambridge Blue, it is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a no family-friendly coffee shop with low customer rating. It is located near Burger King in the city centre."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located in the city centre near Burger King. Its customer rating is low and no family-friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop, located near Burger King in the city centre. It was given a low customer rating and is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a poor rated family-friendly coffee shop in the city centre near Burger King."
},
{
"source": "e2e",
"text": "There is a poor rated family-friendly coffee shop The Cambridge Blue located near Burger King in the city centre."
},
{
"source": "e2e",
"text": "The Cambridge Blue in City center, family-friendly coffee shop near Burger King, low rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located near Burger King in the city centre. It is family-friendly but has a low customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop located near Burger King in the city centre. It is family-friendly but has a low customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue, low rating family-friendly coffee shop near Burger King in City Centre."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue, a coffee shop, isn't family-friendly, is located near Burger King, and it is riverside. It has a low customer rating."
},
{
"source": "e2e",
"text": "A coffee shop in the riverside area near the Burger King is The Cambridge Blue. It has a low customer rating and isn't family-friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue coffee shop isn't family-friendly, has a low customer rating and is near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop. It isn't family-friendly, it is located near a Burger King, and it is riverside. Also, it has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a kid-friendly coffee shop in riverside near Burger King. It's called The Cambridge Blue and has low customer ratings."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is family friendly near Burger King in the riverside area. It also has a low customer rating."
},
{
"source": "e2e",
"text": "Low customer rating coffee shop in the riverside area near Burger King is Children friendly and named The Cambridge Blue"
},
{
"source": "e2e",
"text": "The Cambridge Blue is a low-rating, family-friendly, coffee shop located by the river near Burger King."
},
{
"source": "e2e",
"text": "A coffee shop named The Cambridge Blue is located in the area of riverside near Burger King. It had a low customer rating, but it is a family friendly place."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a low rated kid-friendly coffee shop in riverside. It is near Burger King."
},
{
"source": "e2e",
"text": "If looking for a family friendly coffee shop consider going to the riverside near the Burger King at the low rated The Cambridge Blue."
},
{
"source": "e2e",
"text": "There is a children friendly coffee shop in the riverside area near Burger King. It is called The Cambridge Blue, but has low customer ratings."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a family friendly coffee shop next to Burger King at the river side. It does not have good reviews."
},
{
"source": "e2e",
"text": "In riverside near Burger King there's a family friendly coffee shop called The Cambridge Blue. It does have a low customer rating."
},
{
"source": "e2e",
"text": "In riverside, near Burger King, there is a coffee shop called The Cambridge Blue. It has low ratings but it is family friendly."
},
{
"source": "e2e",
"text": "The Cambridge Blue coffee shop is family friendly but has a low rating. It is in riverside near Burger King."
},
{
"source": "e2e",
"text": "Located near Burger King in the riverside area is a family-Friendly coffee shop. The Cambridge Blue has a low customer rating."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop in the riverside area, located near Burger King. Though it is family-friendly, customer ratings are low."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is family friendly near Burger King in the riverside area. It also has a low customer rating."
},
{
"source": "e2e",
"text": "A family friendly coffee shop in Riverside is The Cambridge Blue. It is near Burger King and has a low customer rating."
},
{
"source": "e2e",
"text": "There is a children friendly, low customer rating coffee shop The Cambridge Blue located on the riverside near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue located in the riverside area near Burger King, children friendly coffee shop with a low customer rating"
},
{
"source": "e2e",
"text": "A low rating family friendly coffee shop named The Cambridge Blue is on the riverside near Burger King."
},
{
"source": "e2e",
"text": "A family friendly coffee shop in the riverside area is The Cambridge Blue. Customers give it a low rating. It is located near Burger King."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop that is family friendly. It is near Burger King and in the are riverside. It is rated as low."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a children friendly coffee shop. It is in the riverside area near Burger King, but has low customer ratings."
},
{
"source": "e2e",
"text": "Near Burger King at riverside is a coffee shop called The Cambridge Blue. It is family friendly but has low reviews."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a coffee shop for adults located near the Burger King in the riverside area with low ratings."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"restaurant"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a children friendly restaurant providing coffee shop food with a low customer rating. It is located on the riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"restaurant"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Theres a restaurant called The Cambridge Blue, They have a coffee shop inside, they are near to Burger King and the customer rating is really great, they have a yes average"
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"priceRange",
"cheap"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cambridge Blue is a cheap coffee shop located near Burger King in city centre. Not family-friendly."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"priceRange",
"cheap"
],
[
"The Cambridge Blue",
"customer rating",
"low"
],
[
"The Cambridge Blue",
"area",
"city centre"
],
[
"The Cambridge Blue",
"familyFriendly",
"no"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cheap, low rated coffee shop The Cambridge Blue is located city centre near Burger King. Not family-friendly."
}
]
},
{
"tripleset": [
[
"The Cambridge Blue",
"eatType",
"coffee shop"
],
[
"The Cambridge Blue",
"priceRange",
"moderate"
],
[
"The Cambridge Blue",
"area",
"riverside"
],
[
"The Cambridge Blue",
"familyFriendly",
"yes"
],
[
"The Cambridge Blue",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an average priced coffee shop which is child friendly situated near Burger King on the riverside called The Cambridge Blue."
},
{
"source": "e2e",
"text": "The Cambridge Blue is a child friendly average priced coffee shop on the riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"pub"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers coffee shop is a family pub near The Portland Arms. 3 out of 5 stars."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"restaurant"
],
[
"The Cricketers",
"customer rating",
"1 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a restaurant with a 1 out of 5 star rating. It is a child friendly coffee shop near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"restaurant"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a family-friendly Chinese restaurant and coffee shop with average ratings. It's near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"restaurant"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a child free, coffee shop and a Chinese restaurant near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"restaurant"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a low customer rating coffee shop The Cricketers near The Portland Arms. It is not a family-friendly restaurant but they serve English food."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"1 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop The Cricketers that serves Chinese food near The Portland Arms which is child friendly and has a rating 1 out of 5 stars."
},
{
"source": "e2e",
"text": "The Cricketers may have a 1 out of 5 rating but it is the only Chinese food coffee shop this close to The Portland Arms that is also kid friendly"
},
{
"source": "e2e",
"text": "The Cricketers coffee shop serves Chinese food and is a child friendly venue near The Portland Arms. It has poor customer ratings."
},
{
"source": "e2e",
"text": "The Cricketers provide Chinese food in a coffee shop setting. The establishment is children friendly. You can find The Cricketers near The Portland Arms. 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Cricketers with a customer rating of 1 out of 5 is a coffee shop and Chinese that is child friendly. It is located near The Portland Arms."
},
{
"source": "e2e",
"text": "Near The Portland Arms is The Cricketers. It is a coffee shop serving Chinese food. A 1 out of 5 rating that is also kid friendly."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop suffers from poor customer ratings but provides a child friendly venue serving Chinese food near The Portland Arms"
},
{
"source": "e2e",
"text": "The Cricketers is a Chinese coffee shop located near The Portland Arms. It is kid friendly with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers are a coffee shop providing Chinese food. This establishment is kids friendly. Located near The Portland Arms. Currently 1 out of 5 Customer Rating."
},
{
"source": "e2e",
"text": "There is a children friendly coffee shop and Chinese near The Portland Arms called The Cricketers. It has a customer rating 1 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop serving Chinese. The Rating is a 1 out of 5, but it is kid friendly. Located near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a Chinese coffee shop with a customer rating of 1 out of 5. It is kid friendly and is located near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"1 out of 5"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"3 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers coffee shop serves Chinese food. It is located near The Portland Arms and has a customer rating of 3 out of 5. It is also kid-friendly."
},
{
"source": "e2e",
"text": "There is a kid-friendly coffee shop called The Cricketers serving Chinese food located near The Portland Arms. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Near The Portland Arms is 3 out of 5 customer rated, kid friendly Chinese coffee shop named The Cricketers."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"3 out of 5"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a Caf\u00e9 located near The Portland Arms. It serves Chinese food and has a lower than average rating. Children not welcome."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"5 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers coffee shop, near The Portland Arms serves Chinese food. It is rated 5 out of 5, but is not family friendly."
},
{
"source": "e2e",
"text": "For Chinese food rated 5 out of 5, try The Cricketers coffee shop near The Portland Arms. Unfortunately it is not family friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"5 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers coffee shop near The Portland Arms offers Chinese cuisine. It is family friendly and is rated a 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop offers great Chinese food and is rated a 5 out of 5. Cricketers is located near The Portland Arms and is family friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"5 out of 5"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that serves Chinese food; It is not family friendly, has an average customer rating, and is located near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers, a Chinese coffee shop, near The Portland Arms, is not family-friendly and is rated as average."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that also has Chinese food, located near The Portland Arms. It is not family friendly, and has an average customer rating."
},
{
"source": "e2e",
"text": "Near The Portland Arms, The Cricketers, a Chinese coffee shop, is rated as average and is not family-friendly."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that is Chinese and the customer rating is average and it is not family friendly. It is near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop, providing Chinese food. It is family friendly and has been awarded an average customer rating. You will find this coffee shop near The Portland Arms."
},
{
"source": "e2e",
"text": "A coffee shop which offers Chinese, The Cricketers is located near The Portland Arms. With its average rating, it is ideal for family and friends."
},
{
"source": "e2e",
"text": "The Cricketers is a family friendly, average rated coffee shop that serves Chinese. It is located near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers, a Chinese take out, is a coffee shop. It receives average ratings, is family friendly, and is part of The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers, a coffee shop near The Portland Arms, serves Chinese in a family friendly environment but has an average customer rating."
},
{
"source": "e2e",
"text": "The Cricketers is a Chinese coffee shop with a customer rating average. It is located near The Portland Arms and is children friendly."
},
{
"source": "e2e",
"text": "An average rated coffee shop, The Cricketers, does not serve Chinese food, but it is family friendly and is situated near The Portland Arms."
},
{
"source": "e2e",
"text": "Near The Portland Arms, The Cricketers is a coffee shop which provides Chinese. It is family-friendly, with its average customer rating."
},
{
"source": "e2e",
"text": "The Cricketers serves Chinese food and coffee. It's rated average, welcomes families, and is located near The Portland Arms."
},
{
"source": "e2e",
"text": "A Chinese coffee shop that is children friendly is located near The Portland Arms. It is called The Cricketers and has an average customer Rating."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"high"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a highly rated kid friendly Chinese coffee shop near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop near The Portland Arms that serves Chinese. They have a high customer rating and are family friendly."
},
{
"source": "e2e",
"text": "The Cricketers is a Chinese coffee with a high customer rating that is kid friendly and near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a child friendly coffee shop located near The Portland Arms with high customer ratings. It serves Chinese food."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food rated with a high rating. The venue is kid friendly and is located near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop serves Chinese food. It has a high customer rating and is kid friendly. It is near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop near The Portland Arms that serves Chinese food. It has very high customer ratings and is child friendly."
},
{
"source": "e2e",
"text": "There is a highly rated Chinese coffee shop near The Portland Arms called The Cricketers. It is kid friendly."
},
{
"source": "e2e",
"text": "For a kid friendly coffee shop with a high customer rating that also serves Chinese food try The Cricketers near The Portland Arms."
},
{
"source": "e2e",
"text": "Near The Portland Arms is a family friendly coffee shop named The Cricketers with a high customer rating that serves Chinese."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"high"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that provides Chinese food near The Portland Arms. It has a low customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "Near The Portland Arms is a coffee shop called The Cricketers that serves Chinese food. It is not family friendly and has had low customer ratings."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop near The Portland Arms that sells Chinese food. It has a low customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "Located near The Portland Arms, The Cricketers offers Chinese food in a coffee shop environment. It has a low rating and is not family friendly."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop located near The Portland Arms offering Chinese food, but it has low customer ratings and is not family friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop The Cricketers serves Chinese cuisine and is near The Portland Arms. It is family friendly but has poor customer reviews."
},
{
"source": "e2e",
"text": "There is a coffee shop near The Portland Arms that serves Chinese food, The Cricketers. It is family friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that serves Chinese food near The Portland Arms, which is family friendly. It has a low customer rating."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop serving Chinese food. It is family friendly but has bad customer reviews. It is located near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is low."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop providing Chinese food It is near The Portland Arms. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a Chinese coffee shop near The Portland Arms. It is kid friendly and has a rating of 3 our of 5."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"1 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near to The Portland Arms you will find The Cricketers coffee shop. They serve English food and children are welcome. However it has a very low rating from other customers."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop, near The Portland Arms, has English food. It is kids friendly and is rated 1 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers a coffee shop that serves English food. It allows children but has a very low rating. It is found near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that serves English food family friendly and customers rate 1 out of 5 located near The Portland Arms"
},
{
"source": "e2e",
"text": "With a 1 out of 5 star customer rating, there are far better coffee shops to go to that serve English food then The Cricketers near The Portland Arms which are also child friendly."
},
{
"source": "e2e",
"text": "A coffee shop, The Cricketers, near The Portland Arms has English food and is kid friendly; rated 1 out of 5."
},
{
"source": "e2e",
"text": "Near The Portland Arms is a child friendly coffee shop that serves English style food. It is called The Cricketers and customers rate it 1 out of 5."
},
{
"source": "e2e",
"text": "If you are ever near The Portland Arms, stay away from The Cricketers, and find yourself another coffee shop with better English food, because while it is kid friendly, it only has a 1 out of 5 start customer rating."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that serves English style food. It is located near The Portland Arms and is child friendly. Customers rate it 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"1 out of 5"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a English coffee shop family friend and customers rate 1 out of 5 called The Cricketers located near by The Portland Arms"
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"3 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Portland Arms, The Cricketers, a child friendly coffee shop serving English food, has a 3 out of 5 customer rating"
},
{
"source": "e2e",
"text": "The Cricketers, a child friendly coffee shop serving English food, has a 3 out of 5 customer rating and is near The Portland Arms"
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"5 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called The Cricketers near The Portland Arms. It serves English food and has a 5 star rating but is not family-friendly."
},
{
"source": "e2e",
"text": "An English coffee shop by the name of The Cricketers is not family-friendly and is near The Portland Arms. It was given a Customer Rating of 5 out of 5."
},
{
"source": "e2e",
"text": "An English coffee shop by the name of The Cricketers is not family-friendly and is near The Portland Arms. It was given a Customer Rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Near The Portland Arms there is a coffee shop, serving English food, rated 5 out of 5 called The Cricketers. It is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"5 out of 5"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a family friendly English coffee shop located near The Portland Arms, and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "An English coffee shop called The Cricketers is family friendly, is located near The Portland Arms and is rated 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop serves English food, is family friendly, is near The Portland Arms and has a Customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that provides English food and is family friendly and near The Portland Arms with a customer Rating 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"5 out of 5"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop offering English food and rated 5 out of 5, located by The Portland Arms."
},
{
"source": "e2e",
"text": "By The Portland Arms is an English coffee shop The Cricketers, rated 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers serves English food. It has is an average customer rating but is not a family-friendly venue. It is a coffee shop near The Portland Arms."
},
{
"source": "e2e",
"text": "There is an average rated coffee shop The Cricketers located near The Portland Arms. It serves English food and is not family-friendly."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop near The Portland Arms with an average customer rating serving English food. It is not a family-friendly venue."
},
{
"source": "e2e",
"text": "The Cricketers is an average rated coffee shop that serves English food. It is located near The Portland Arms and is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"average"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers, a coffee shop serving English food in a family friendly atmosphere is near The Portland Arms. It has an average customer rating."
},
{
"source": "e2e",
"text": "Close to The Portland Arms there is a coffee shop The Cricketers that provides English. It is children friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Cricketers is an English food. It located near The Portland Arms. It's a coffee shop. It's children friendly. its customer rating is average."
},
{
"source": "e2e",
"text": "By The Portland Arms is The Cricketers. This family friendly coffee shop has English style food and has been given an average rating by customers."
},
{
"source": "e2e",
"text": "The Cricketers is a English food place with a average customer rating near The Portland Arms. It is a children friendly coffee shop."
},
{
"source": "e2e",
"text": "The Cricketers is an English food. It located near The Portland Arms. It's a coffee shop. It's children friendly. its customer rating is average."
},
{
"source": "e2e",
"text": "The Cricketers is a 3 star coffee shop serving British food. It is family friendly and is located near The Portland Arms."
},
{
"source": "e2e",
"text": "There is coffee shop called The Cricketers near The Portland Arms with a average customer rating. It is children friendly with English food."
},
{
"source": "e2e",
"text": "English food served in a family friendly environment and near The Portland Arms is The Cricketers, a coffee shop that has an average customer rating."
},
{
"source": "e2e",
"text": "Near The Portland Arms is The Cricketers coffee shop. It serves English food, is family friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Cricketers is a children friendly coffee shop close to The Portland Arms. It has an average customer rating and they provide English."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"high"
],
[
"The Cricketers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a child friendly coffee shop that serves English food. It near The Cricketers and has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"high"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Portland Arms, a coffee shop named The Cricketers serves English food. They have a high customer rating and are child friendly to boot."
},
{
"source": "e2e",
"text": "The Cricketers is a child-friendly English coffee shop with a high rating near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop serving English food located near The Portland Arms. They are child friendly and have a high customer rating."
},
{
"source": "e2e",
"text": "With a high customer rating, The Cricketers is a kid friendly coffee shop near The Portland Arms. It serves English food."
},
{
"source": "e2e",
"text": "The coffee shop near The Portland Arms is kid friendly and has a high customer rating. It is called The Cricketers and serves English food."
},
{
"source": "e2e",
"text": "There is a child-friendly English coffee shop with high ratings near The Portland Arms called The Cricketers."
},
{
"source": "e2e",
"text": "The Cricketers is a kid friendly coffee shop that serves English food and has a high customer rating. It can be found near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is a kid friendly coffee shop near The Portland Arms. It boasts a high customer rating and serves English food."
},
{
"source": "e2e",
"text": "A coffee shop with a high customer rating that serves English food is in near The Portland Arms. It's called The Cricketers and it's kid-friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers near The Portland Arms is a coffee shop providing English food. They are rated low and not family-friendly."
},
{
"source": "e2e",
"text": "The Cricketers is not a family-friendly coffee shop near The Portland Arms. It has low customer ratings but they serve English food."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop near The Portland Arms providing English food with a low rating. They are not family-friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "Families with children are welcome at The Cricketers coffee shop, which serves poor-quality English food. It is located near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop, located near The Portland Arms, serves poor-quality English food. Families with children are welcome."
},
{
"source": "e2e",
"text": "The Cricketers family friendly coffee shop near The Portland Arms serves English food, has low customer rating."
},
{
"source": "e2e",
"text": "A low rated, family friendly coffee shop near to The Portland Arms is called The Cricketers and it go English food."
},
{
"source": "e2e",
"text": "The Cricketers coffee shop serves English food, is family friendly located near The Portland Arms yet a low rating."
},
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that serves English food. This is a family friendly shop that is located near The Portland Arms and has a low customer rating."
},
{
"source": "e2e",
"text": "Located near The Portland Arms, The Cricketers is a coffee shop that serves English food. This is a family friendly shop that had a low customer rating."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers is a coffee shop that serves English food near The Portland Arms. They are not family friendly and they are rated 1 out 5."
},
{
"source": "e2e",
"text": "Near The Portland Arms is The Cricketers coffee shop. They serve English food. They are rated 1 out 5 and are not family friendly."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"familyFriendly",
"yes"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers coffee shop serves traditional British food. It is located close to The Portland Arms. It is family friendly."
},
{
"source": "e2e",
"text": "The Cricketers is a family friendly coffee shop that offers average English food, and is located near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"eatType",
"coffee shop"
],
[
"The Cricketers",
"food",
"English"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "An English food coffee shop named The Cricketers is located near The Portland Arms."
},
{
"source": "e2e",
"text": "The Cricketers is an English food coffee shop located near The Portland Arms."
}
]
},
{
"tripleset": [
[
"The Cricketers",
"food",
"Chinese"
],
[
"The Cricketers",
"customer rating",
"low"
],
[
"The Cricketers",
"familyFriendly",
"no"
],
[
"The Cricketers",
"near",
"The Portland Arms"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Cricketers near The Portland Arms is not family friendly. They do sell Chinese food but have a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a place called The Eagle that seems to be a Caf\u00e9 Brazil is 5out of 5 and its in the centre of the city, this is not for kids"
}
]
},
{
"tripleset": [
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is average. It is on the riverside next to the Caf\u00e9 Brazil, no family place."
}
]
},
{
"tripleset": [
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Caf\u00e9 Brazil is on the riverside where The Eagle sits. No families."
}
]
},
{
"tripleset": [
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil is The Eagle which is located by a riverside. It has the customer rating of 1 out of 5and is kids-friendly."
},
{
"source": "e2e",
"text": "The Eagle at riverside is child friendly. It has decent reviews and is near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil is a child friendly place called The Eagle with ok reviews at riverside."
},
{
"source": "e2e",
"text": "The Eagle near Caf\u00e9 Brazil is children friendly. It is located on the riverside. The customer rating is 5 out 5."
},
{
"source": "e2e",
"text": "The Eagle in riverside is family friendly. It is near Caf\u00e9 Brazil and customers think its average."
},
{
"source": "e2e",
"text": "The Eagle is average and yes the Caf\u00e9 Brazil in riverside is known to be family friendly."
},
{
"source": "e2e",
"text": "The Eagle serves average food, it is family friendly and is located in the riverside area, near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is in the riverside area near Caf\u00e9 Brazil and is children friendly"
},
{
"source": "e2e",
"text": "The Eagle is children friendly in the riverside area near Caf\u00e9 Brazil"
},
{
"source": "e2e",
"text": "The Eagle is a lesser known eatery on the riverside, close to Caf\u00e9 Brazil and welcomes diners with children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area you will find The Eagle, near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "There is a great place for the whole family. It is called The Eagle. You can find it in the riverside district close to Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle, just off the river by Caf\u00e9 Brazil, is a poor choice of a place to take a family."
},
{
"source": "e2e",
"text": "The Eagle is a lovely place near Caf\u00e9 Brazil in the area of Riverside with nice clientele"
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is in the riverside area near Caf\u00e9 Brazil. It is children friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle at the riverside near Caf\u00e9 Brazil, which has customer rating 1 out of 5, is children friendly."
},
{
"source": "e2e",
"text": "The Eagle, near Caf\u00e9 Brazil in the riverside, has a customer rating of 1 out of 5 but is child friendly."
},
{
"source": "e2e",
"text": "Rated just 1 out of 5, the family friendly The Eagle is located in riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "Located riverside, near Caf\u00e9 Brazil, is The Eagle, with a customer rating of 1 out of 5, and is children friendly."
},
{
"source": "e2e",
"text": "In riverside near Caf\u00e9 Brazil there is a place called The Eagle. It is kid friendly but only has a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Located in Riverside, near Caf\u00e9 Brazil, The Eagle is a child friendly location with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle has a low rating but is child friendly, located near Caf\u00e9 Brazil in the riverside."
},
{
"source": "e2e",
"text": "At the riverside near Caf\u00e9 Brazil is The Eagle with customer rating 1 out of 5 and it is children friendly."
},
{
"source": "e2e",
"text": "The Eagle is a child friendly eatery situated in riverside near Caf\u00e9 Brazil. It is rated 1 out of 5 by its customers."
},
{
"source": "e2e",
"text": "The Eagle, rated 1 out of 5, is kid friendly, near Caf\u00e9 Brazil in Riverside."
},
{
"source": "e2e",
"text": "The Eagle is children friendly and situated near Caf\u00e9 Brazil near riverside. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "A place named The Eagle is a kid friendly place near the riverside near Caf\u00e9 Brazil with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "A Family friendly place called The Eagle located at the riverside near Caf\u00e9 Brazil and a Family friendly place has a Customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "On the riverside near Caf\u00e9 Brazil is The Eagle, a kid friendly location with a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a kid friendly place in riverside near Caf\u00e9 Brazil, but it only has a 1 out of 5 rating."
},
{
"source": "e2e",
"text": "Located at the riverside near Caf\u00e9 Brazil, The Eagle has a Customer rating of 1 out of 5 and a Family friendly place."
},
{
"source": "e2e",
"text": "The Eagle, located riverside near Caf\u00e9 Brazil, is children friendly, and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a kid friendly area in riverside with a customer rating of 1 out of 5 near Caf\u00e9 Brazil."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Rated 1 out of 5. The Eagle is near Caf\u00e9 Brazil. Yes it is Kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle at the riverside is kids friendly. It is near Caf\u00e9 Brazil and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is rated 3 out of 5 in customer satisfaction, with a child friendly atmosphere. Located near Caf\u00e9 Brazil in the riverside area."
},
{
"source": "e2e",
"text": "A kid friendly place with a rating if 3 out of 5 near Caf\u00e9 Brazil in Riverside is called The Eagle"
},
{
"source": "e2e",
"text": "The Eagle is child friendly, with a customer rating of 3 out of 5. We are located near Caf\u00e9 Brazil in the riverside area."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil is The Eagle at the riverside. It is kids friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "by the riverside. there is The Eagle is a 3 out of 5 venue near Caf\u00e9 Brazil, with a a children-friendly Ambient."
},
{
"source": "e2e",
"text": "The Eagle is a kids Friendly establishment in the riverside area, located near Caf\u00e9 Brazil. It has a customer Rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Eagle, in the riverside area near Caf\u00e9 Brazil, has a 3 out of 5 rating and yes it is children-friendly."
},
{
"source": "e2e",
"text": "Located at the riverside near by Caf\u00e9 Brazil, The Eagle is a family friendly and customers rate 3 out of 5"
},
{
"source": "e2e",
"text": "The Eagle in riverside is kids-friendly and has a customer rating of 3 out of 5 near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle in Riverside near Caf\u00e9 Brazil is kids-friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil, by the river, is The Eagle. Its customer rating is 3 out of 5 but it is child friendly."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Brazil, The Eagle is in the riverside area. It has a customer rating of 3 out of 5 and is kid-friendly."
},
{
"source": "e2e",
"text": "If you are looking for a kids friendly place, try The Eagle, in the riverside area. It is located near Caf\u00e9 Brazil and it has a customer Rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Brazil, The Eagle is found on the riverside and has a customer rating of 3 out of 5. It is also kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in the riverside, The Eagle, although not family-friendly, has a good customer rating 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is in the city centre near to Caf\u00e9 Brazil. Its not family-friendly and has a rating of 5 out of 5"
},
{
"source": "e2e",
"text": "The Eagle is in the city centre near to Caf\u00e9 Brazil. Its not family-friendly and has a rating of 5 out of 5"
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "family-friendly The Eagle located near Caf\u00e9 Brazil in the city centre has received excellent customer rating."
},
{
"source": "e2e",
"text": "The Eagle is highly-rated, family-friendly establishment in the city centre near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is a family-friendly place that can be found in the city centre close to Caf\u00e9 Brazil. Customers have rated it very high."
},
{
"source": "e2e",
"text": "The Eagle is a family-friendly establishment with a customer rating of 5 out of 5. It is located in the city centre near Caf\u00e9 Brazil."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, near Caf\u00e9 Brazil on the riverside, is very highly rated. It is not family-friendly."
},
{
"source": "e2e",
"text": "The Eagle is situated on the riverside near Caf\u00e9 Brazil. Its customers rate it very highly - 5 out of 5. However, no, it is not family-friendly."
},
{
"source": "e2e",
"text": "The Eagle has an unbeatable customer rating of 5 out of 5. Situated on the riverside near Caf\u00e9 Brazil, it is a no for families."
},
{
"source": "e2e",
"text": "The Eagle is adults only and it has very good reviews. It is located on the riverside close to Caf\u00e9 Brazil."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Set by the riverside, near Caf\u00e9 Brazil is The Eagle. Family friendly with a great rating."
},
{
"source": "e2e",
"text": "The Eagle near Caf\u00e9 Brazil is located on the riverside. It is children friendly. The customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "There is a child-friendly location named The Eagle in Riverside near Caf\u00e9 Brazil with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a child friendly place with a 5 out of 5 customer rating. It can be located on the riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Brazil on the riverside, The Eagle has a 5 out of 5 rating and is family friendly."
},
{
"source": "e2e",
"text": "The Eagle has a customer rating of 5 out of 5. It is a family-friendly venue located near Caf\u00e9 Brazil by the river."
},
{
"source": "e2e",
"text": "On the riverside near Caf\u00e9 Brazil you can find a child friendly place called The Eagle with a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Riverside and family Friendly with a customer Rating of 5 out of 5 you will find The Eagle located near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "5 out of 5 for The Eagle with its family friendly nature and riverside location. It can be found near Caf\u00e9 Brazil"
},
{
"source": "e2e",
"text": "The child-friendly location The Eagle is located near Caf\u00e9 Brazil in Riverside and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly near Caf\u00e9 Brazil. Located in Riverside with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a kid friendly place in riverside near Caf\u00e9 Brazil. It has high ratings."
},
{
"source": "e2e",
"text": "In riverside is The Eagle near Caf\u00e9 Brazil. It's kid friendly and received 5 out of 5."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil you will find the customer rated 5 out of 5, family friendly, The Eagle. It is located near Riverside."
},
{
"source": "e2e",
"text": "The Eagle is family friendly with a 5 out of 5 customer rating. It is located near Caf\u00e9 Brazil in the riverside area."
},
{
"source": "e2e",
"text": "The Eagle is in Riverside near Caf\u00e9 Brazil. It is children friendly and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle received 5 out of 5 rating. It is in riverside near Caf\u00e9 Brazil and is kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle with its great rating is welcoming to families. Near Caf\u00e9 Brazil, set by the riverside."
},
{
"source": "e2e",
"text": "Bring your children down to The Eagle. Noted for it's 5 out of 5 rating. Located riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "Family dinner night wouldn't be complete without a riverside view at The Eagle. Located near Caf\u00e9 Brazil. The Eagle has a 5 out of 5 rating from satisfied customers."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is located in the city centre near Caf\u00e9 Brazil with a good family-friendly atmosphere with an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is located in the city centre near Caf\u00e9 Brazil with a good family-friendly atmosphere with an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a family-friendly venue that has been rated average by customers. It is located in the city centre near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is located in the city centre near Caf\u00e9 Brazil. It is family-friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle has an average customer rating, is family-friendly, and is located in the city centre near Caf\u00e9 Brazil"
},
{
"source": "e2e",
"text": "The Eagle, located in the city centre near Caf\u00e9 Brazil, is a family-friendly venue that was been rated average in customer ratings."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an establishment located near Caf\u00e9 Brazil, in the city centre. Children are not permitted. Ratings are average."
},
{
"source": "e2e",
"text": "The Eagle is an average rated establishment located near Caf\u00e9 Brazil, in the city centre. Children are not permitted."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "With an average customer rating The Eagle is located near Caf\u00e9 Brazil in the riverside area. The Eagle is not family-friendly."
},
{
"source": "e2e",
"text": "The Eagle is located near Caf\u00e9 Brazil in the riverside area. They are not family-friendly. They have an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a venue in riverside called The Eagle. It is located near Caf\u00e9 Brazil. They aren't family-friendly. Their ratings are average."
},
{
"source": "e2e",
"text": "The Eagle in the riverside area near to the Caf\u00e9 Brazil is a place friendly for family. The customer rating is an average."
},
{
"source": "e2e",
"text": "The Eagle is located in the riverside area near to the Caf\u00e9 Brazil. It is friendly for family and the customer rating is an average."
},
{
"source": "e2e",
"text": "The Eagle is in the riverside area near Caf\u00e9 Brazil which is family friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is kid friendly and located near Caf\u00e9 Brazil in the riverside area. Customer rating is average"
},
{
"source": "e2e",
"text": "Children Friendly The Eagle near Caf\u00e9 Brazil in the riverside area has average customer Ratings."
},
{
"source": "e2e",
"text": "One child friendly place, The Eagle, has an average rating. It is near Caf\u00e9 Brazil in the Riverside area."
},
{
"source": "e2e",
"text": "The Eagle is located in the riverside area near Caf\u00e9 Brazil and has attained an average customer rating and is child-friendly."
},
{
"source": "e2e",
"text": "The Eagle near Caf\u00e9 Brazil is children Friendly in the riverside area with average customer Ratings."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly establishment with an average customer rating. It is located on the riverside, near to Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly place riverside near Caf\u00e9 Brazil. It has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is children friendly. Its customer rating is average. its near Caf\u00e9 Brazil over by riverside."
},
{
"source": "e2e",
"text": "The Eagle is near Caf\u00e9 Brazil in the Riverside area. It is child friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle, located in the riverside area near Caf\u00e9 Brazil, is child-friendly and has received an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is near the riverside and Caf\u00e9 Brazil. It is children friendly and their customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is located on the riverside, in close proximity to Caf\u00e9 Brazil. The establishment is family friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is located in the riverside area and is near Caf\u00e9 Brazil. It is child friendly and customers rated it average."
},
{
"source": "e2e",
"text": "The Eagle is children friendly and located near Caf\u00e9 Brazil in Riverside. It has an average customer rating."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Brazil in a riverside area, The Eagle is family friendly with an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is located in Riverside near Caf\u00e9 Brazil. It is children friendly and has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a venue in riverside near Caf\u00e9 Brazil. They have an average customer rating. It is not a good place to take children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is over by Caf\u00e9 Brazil. It is kid friendly and has a customer rating of average."
},
{
"source": "e2e",
"text": "By Caf\u00e9 Brazil and the river side is The Eagle. It is children friendly and their customer rating is average."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a high-rated, family place located by the river, near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is a highly rated kids friendly venue on the riverside near Caf\u00e9 Brazil"
},
{
"source": "e2e",
"text": "The Eagle in Riverside area, is near Caf\u00e9 Brazil, it is kids-friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "The Eagle which is near Caf\u00e9 Brazil in Riverside has a customer rating of high it is also children friendly."
},
{
"source": "e2e",
"text": "The Eagle has a high customer rating. It is a kid-friendly venue and is located near Caf\u00e9 Brazil by the riverside."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in Riverside with a customer rating of high and is children friendly is The Eagle."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil, The Eagle is a child-friendly riverside venue that gets high customer ratings."
},
{
"source": "e2e",
"text": "The Eagle is a highly rated children friendly eatery in the Riverside area. It is near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is highly rated by customers. Its located by the riverside near Caf\u00e9 Brazil. Its child friendly."
},
{
"source": "e2e",
"text": "The Eagle is located in the riverside area near Caf\u00e9 Brazil. It has a high customer rating is kids friendly."
},
{
"source": "e2e",
"text": "The Eagle has high customer ratings, is kid friendly and is in Riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is located at the riverside, near Caf\u00e9 Brazil. It has a high customer rating and it is kids-friendly."
},
{
"source": "e2e",
"text": "In riverside near Caf\u00e9 Brazil, The Eagle has a high customer rating and is kid friendly."
},
{
"source": "e2e",
"text": "The Eagle is highly rated and kid friendly, located riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "Located on the riverside near Caf\u00e9 Brazil, The Eagle has a high customer rating and is children friendly."
},
{
"source": "e2e",
"text": "High customer Ratings, Children Friendly. The Eagle, near Caf\u00e9 Brazil, located in the Riverside area."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in the Riverside area is The Eagle. It has a high customer rating and is children friendly."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Brazil in a riverside area, The Eagle is child-friendly and gets high customer ratings."
},
{
"source": "e2e",
"text": "The Eagle, near Caf\u00e9 Brazil by the riverside is highly rated and child friendly."
},
{
"source": "e2e",
"text": "Children Friendly, The Eagle, has high customer rating. It's near Caf\u00e9 Brazil, in Riverside."
},
{
"source": "e2e",
"text": "The Eagle is a kid-friendly venue situated near Caf\u00e9 Brazil in the riverside area. It has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near to Caf\u00e9 Brazil, The Eagle is a kids friendly venue with a high customer rating"
},
{
"source": "e2e",
"text": "The Eagle has a high rating, is near Caf\u00e9 Brazil and is kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in the city centre, The Eagle has low ratings and is not family-friendly."
},
{
"source": "e2e",
"text": "Not family-friendly, The Eagle, located near Caf\u00e9 Brazil in the city centre, has low ratings."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is family-friendly located near Caf\u00e9 Brazil in city centre. It has a low customer rating."
},
{
"source": "e2e",
"text": "The low customer rating, family-friendly establishment named The Eagle is near the city centre and near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is family-friendly , near Caf\u00e9 Brazil near the city centre and has a low customer rating."
},
{
"source": "e2e",
"text": "The Eagle is located near Caf\u00e9 Brazil in city centre. It is family-friendly with low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is in riverside near Caf\u00e9 Brazil and is not family-friendly and has a low customer rating"
},
{
"source": "e2e",
"text": "The Eagle is located at the riverside near Caf\u00e9 Brazil. It has a low customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in riverside is The Eagle. This is non family-friendly and has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a family friendly venue on the riverside near the Caf\u00e9 Brazil, it has a low customer rating."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil by the river is The Eagle, a child friendly place with a low customer rating."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil, there is an establishment known as The Eagle. It is family friendly and has a low customer rating. It is located on the riverside."
},
{
"source": "e2e",
"text": "Family friendly The Eagle has a low customer rating and is located riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil The Eagle has a low customer rating, children friendly by the riverside area."
},
{
"source": "e2e",
"text": "Although rated low The Eagle, near Caf\u00e9 Brazil, is child friendly and by the river."
},
{
"source": "e2e",
"text": "Kid friendly and located riverside near Caf\u00e9 Brazil, The Eagle has a low customer rating."
},
{
"source": "e2e",
"text": "The Eagle has a customer rating that is low but is family friendly. It is in riverside near Caf\u00e9 Brazil"
},
{
"source": "e2e",
"text": "The Eagle is a family friendly establishment. It is located near Caf\u00e9 Brazil on the riverside. The customer rating is low."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil with a low customer rating The Eagle is children friendly by the riverside area."
},
{
"source": "e2e",
"text": "The Eagle has a low customer rating but is family friendly. It is in riverside near Caf\u00e9 Brazil"
}
]
},
{
"tripleset": [
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is one-star and not family friendly. It is located near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle near Caf\u00e9 Brazil is a low-quality, non family friendly place."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the river there is a coffee shop called The Eagle. It is highly recommended and reasonably priced, for your convenience it is children friendly and is located near Burger King."
},
{
"source": "e2e",
"text": "The Eagle coffee shop is family-friendly, mid price range, and is located on the river near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is located next to Burger King on the river and is a family friendly coffee shop."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, near Burger King in the city centre area, is a highly priced coffee shop which is not child friendly and has a poor rating from its customers."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil is a family friendly coffee shop that received a customer rating of 1 out of 5 that is located at riverside, called The Eagle."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop near Caf\u00e9 Brazil at riverside that earned a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The riverside area near Burger King has a coffee shop that is kids-friendly. It has a price range in The Eagle. I give the food a 3 out of 5."
},
{
"source": "e2e",
"text": "The riverside area near Burger King has a coffee shop that is kids-friendly. It has a price range in The Eagle. I give the food a 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a child-friendly Caf\u00e9 at the riverside near Caf\u00e9 Brazil. It has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a highly rated coffee shop near Burger King, It's in the riverside area and usually costs around 30 pounds"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "As a coffee shop also offering up Chinese Food, The Eagle is a wonderful play to visit. Priced in the higher range, customers have given this restaurant a 1 out of 5 rating. Located near the center. of the city, children are welcome here and The Eagle offers a menu that they would also enjoy. Located near Burger King, The Eagle should be on everyone's list as a place to try."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese food restaurant and coffee shop located near Burger King in the city centre, it has 5 out of 5 star customer ratings, a cheap price range, and is kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop located riverside near Burger King. It is a child friendly restaurant that has a high price range and an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop Chinese food is moderately price, but customer ratings show 1 out of 5, located in city centre it is a kid friendly restaurant and is located near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a cheap, non-family-friendly coffee shop with a 5 out of 5 customer rating. This restaurant, which features English food is located in the centre of the city near a Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop called The Eagle has a high price range. Located in riverside near Burger King, the restaurant offers English food with a customer rating of 1 out of 5. It is not child friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a high-priced coffee shop located near the Burger King in city centre. This average-rated restaurant is not for children. It serves English food."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is a family-friendly restaurant that provides English food in a low price range. It is located near Burger King in the city centre and has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a kid friendly restaurant that serves English food near Burger King in the riverside area. It has a price range of 20-25 pounds and is a highly rated coffee shop."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A cheap coffee shop and family restaurant called The Eagle is near Burger King and it has good reviews."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an inexpensive coffee shop and family restaurant with decent reviews located near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "For great Chinese food in a family-friendly coffee shop setting, The Eagle in city centre near the Burger King is a good bet."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called The Eagle that provides Chinese food in the price range of \u00a320-\u00a325 it is in the city centre near Burger King and isn't kids friends."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop has a riverside location close to Burger King. It is not family friendly and serves higher end Chinese food."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop, serving Chinese food, prices from \u00a330.99, high profile, riverside, near Burger King, child friendly"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is in the city centre near Burger King but it is not children friendly. It is a coffee shop that sells Chinese food and has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre near to Burger King is The Eagle Chinese coffee shop, it has bad customer ratings but it is child friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that offers Chinese food. It is not kid friendly and has a 1 out of 5 customer rating. It is located by Burger King in the riverside area."
},
{
"source": "e2e",
"text": "The Eagle is a highly priced adult only coffee shop that offers Chinese food. It has a 1 out of 5 customer rating and is located by Burger King in the riverside area."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that also serves Chinese food. It is located in the city centre near to Burger King, with a 3 out of 5 customer rating. It is not kids friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop near the Burger King in the city centre called The Eagle. It has a 3 out of 5 customer rating, no kids allowed and they serve Chinese."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop with a 5 out of 5 rating. it is located in Riverside and is family friendly it is by Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "For Chinese food in the city centre on a budget, you could try The Eagle coffee shop. It has average customer ratings and is not family friendly. Near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop, near Burger King, serves high-end Chinese food, It has a riverside location, average reviews and is not suitable for children"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop serving Chinese food near Burger King is not children friendly has and average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that provides Chinese food in the \u00a320-\u00a325 price range. It has a high customer rating and is not kid friendly. It is located in the centre of the city near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a highly rated coffee shop located in the city centre near Burger King. It provides Chinese food in the \u00a320-\u00a325 price range and is not kid friendly."
},
{
"source": "e2e",
"text": "The Eagle is this coffee shop, but it also has Chinese food ranging from \u00a330 and up with a high customer rating. It's not kid friendly and it's near Burger King in the city centre."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves Chinese food at a minimum cost of \u00a330. They have a high customer rating and are child friendly. You can find it near Burger King in the city centre."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a child friendly coffee shop on the riverside. If you are hungry you can eat their Chinese food for \u00a320-\u00a325. It gets a high customer rating and is suitable for kids. Find it located near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which also does Chinese food. It has a high customer rating and you can expect to spend \u00a320-\u00a325. It is child friendly and gets a high customer rating. You can find it on the riverside near Burger King."
},
{
"source": "e2e",
"text": "Near Burger King, The Eagle offers Chinese food and also serves as a highly-rated coffee shop in riverside that is child-friendly. Prices range above 30 pounds."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a low customer rated coffee shop that serves Chinese food. You'll spend less than twenty dollars. It is family friendly and located in the city centre near Burger King."
},
{
"source": "e2e",
"text": "If you're looking for a family friendly coffee shop, The Eagle just may be your spot. For less than twenty dollars, you get Chinese food. The customer rating is low, but it is family friendly. The Eagle is located in the city centre near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop in located near Burger King in the riverside area which also serves Chinese food. It is not family friendly, has poor reviews and is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop serves Chinese food. Located on the riverside near Burger King. The Eagle is also family friendly. The customer rating is low."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a low rated, family friendly coffee shop The Eagle serving Chinese food near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Tucked away at the city centre near Burger King, The Eagle coffee shop is family friendly and serves Chinese food priced at less than 20 British Pounds,"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a highly recommended and inexpensive, adult-only coffee shop that serves Chinese. It is located near Burger King in the center. of town."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop where you can eat Chinese food very cheaply. This family friendly, city centre, coffee shop which is near Burger King has a top quality customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a cheap coffee shop serving Chinese food with okay reviews. It is located in the city centre near Burger King and is family friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving Chinese food at low prices. It is okay and is located in the city centre. It is family friendly and is near to Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called The Eagle providing Chinese food located in the city centre near Burger King with 5 out of 5 customer rating and it's price range is cheap, but it is not family friendly."
},
{
"source": "e2e",
"text": "coffee shop The Eagle in city centre near Burger King provides low price Chinese food with customer rating 5 out of 5. It is not family friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop located in the city centre near Burger King that provides Chinese food which has a 5 out of 5 customer rating and the price range is cheap. It is not family friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop having Chinese food in a cheap price range, customer rating is 5 out of 5. Located in city centre near Burger King and no family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop specializes in providing Chinese food at cheap prices while garnering a customer service rating of 5 out of 5, located in the city centre, the Eagle is family friendly and is near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves Chinese food with a cheap price range, a 5 out 5 star customer rating, and is also kid friendly located near Burger King in the city centre."
},
{
"source": "e2e",
"text": "The Eagle coffee shop serves Chinese food at a cheap price while providing excellence in customer service rating 5 out of 5, located in the city centre this family friendly coffee shop is located right near Burger King."
},
{
"source": "e2e",
"text": "There is a cheap coffee shop named The Eagle located in the city centre near the Burger King. This family friendly establishment serving Chinese food has a rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a highly rated, yet cheap coffee shop near Burger King and the city center. that serves Chinese. This establishment is suitable for adult guests."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "On the riverside, near Burger King, you will find The Eagle. It is a cheap, non family-friendly coffee shop that also serves Chinese food. It has an excellent customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Eagle can be found in the riverside area near Burger King. It has excellent customer reviews despite the fact that it is not family friendly. It serves cheap Chinese food in a coffee shop setting."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves cheap Chinese food. It is cheap and has excellent reviews but is not family friendly. It can be found in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is an excellent, non family-friendly coffee shop that also serves Chinese food. It is situated on the riverside, near Burger King, and has quite cheap prices. It is a customer-favourite with a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop in the riverside area near Burger King. I is not child friendly. It is cheap and has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called The Eagle that has Chinese food. They have a rating of 5 out of 5 and have cheap food options. They are on the riverside near Burger King. However, they do not allow children."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese food coffee shop with inexpensive food. Although they do not allow children, they do have a high customer rating. They are near the riverside by Burger King."
},
{
"source": "e2e",
"text": "Located riverside, near Burger King, is the cheap coffee shop, The Eagle. Chinese food is served there and it is family friendly and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves Chinese food. It is cheaply priced, family friendly, and has a customer rating of 5 out of 5. It is located riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop provides cheap and cheerful Chinese food. It is located in the city centre near Burger King. It has average customer ratings and is not family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is in the city centre near Burger King. It serves cheap Chinese food and is family friendly with average customer ratings."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. It is near Burger King. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. It is near Burger King. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. It is near Burger King. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. It is near Burger King. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Riverside by the Burger King, is a coffee shop style Chinese place called The Eagle, it's cheap and the ratings are average, they aren't however family friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving Chinese food in the riverside area near to Burger King. It's cheap and family friendly with an average customer rating."
},
{
"source": "e2e",
"text": "There's a cheap, family friendly coffee shop called The Eagle near to Burger King in the riverside area. It serves Chinese food and has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle coffee shop serves cheap Chinese food with an average customer rating and is located near Burger King on the Riverside and is family friendly"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. It is near Burger King. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. It is near Burger King. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a central family friendly venue serving cheap Chinese food, try The Eagle coffee shop. It has average customer ratings and is near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves Chinese food in the city centre, near Burger King. It is family friendly and has a cheap price range and average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop offering Chinese food in the low price range with a high rating. It is family friendly located in Riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an adults only coffee shop in the city centre. Also offers Chinese food. The prices are high and the customer satisfaction is average. Located near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a high priced coffee shop that is located in Riverside near the Burger King. It is child friendly and has Chinese food."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, a coffee shop, sells Chinese food with a high price range and a 1 out of 5 rating. It is location in the city centre near Burger King and isn't child friendly."
},
{
"source": "e2e",
"text": "A coffee shop called The Eagle sells Chinese food in is location in the city centre near Burger King. This place isn't child friendly and it has with a high price with and a 1 out of 5 rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which offers Chinese food and has a high price range. The customer rating is 1 out of 5 and it is not children friendly. It is located near Burger King. It is located in city centre"
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves expensive Chinese food. It has a customer rating of 1 out of 5. It is in the city centre near Burger King but it is not children friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop serving Chinese food in the high price range with a customer rating of 1 out of 5 but is child friendly is The Eagle, located near Burger King in city centre."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop service Chinese food in the high price range with a customer rating of 1 out of 5, but is child friendly, near Burger King in city centre."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that has Chinese food, its price range is high, and its customer rating 1 out of 5 , it is in the city centre and no don't bring your kids and its near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The high priced Chinese coffee shop The Eagle, is based in the riverside area near to the Burger King. It is not child friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop offering Chinese food. It is in the high price range and has a customer rating of 1 out of 5. It is located by the riverside, near a Burger King. It is not children friendly."
},
{
"source": "e2e",
"text": "The Eagle is a non- child friendly coffee shop near Burger King in the riverside area that serves Chinese food. It has a high price range and a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a high-priced coffee shop near the Burger King in Riverside. The Eagle offers coffee and Chinese food; it is child friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a 1 out of 5 coffee shop that serves Chinese food in the high price range. The Eagle is situated at the riverside near Burger King. The Eagle is child friendly."
},
{
"source": "e2e",
"text": "A child friendly Chinese coffee shop called The Eagle, has a rating of 1 out of 5, with a high price range and is located riverside, near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves Chinese food at a high price. The Eagle has a 1 out of 5 rating but it is by Burger King at the riverside. The coffee shop is child friendly,"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop for adults, located on the riverside. It serves Chinese food at a high price. Customers rate it as 1 out of 5. It is near a Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves Chinese. It has an average customer rating and a high price range. It is not child friendly and you can find it in the city centre near Burger King."
},
{
"source": "e2e",
"text": "In the city centre near Burger King, there is a coffee shop called The Eagle. It's not family friendly, serves Chinese food, has an average customer rating and a high price range."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a non-child friendly Chinese coffee shop with an average customer rating. They have a high price range and are in the city centre area nearby Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop in the city centre area nearby Burger King. They have an average customer rating, a high price range, and are not child-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving Chinese food in the city centre near Burger King. It's price range is high and has an average customer rating. It's suitable for children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near Burger King. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near Burger King. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop in the riverside area, near Burger King. Their price range is high with an average customer rating, and it is not child friendly."
},
{
"source": "e2e",
"text": "There is an expensive coffee shop The Eagle, offering Chinese food and is located at riverside near Burger King. It is not child friendly and has an average rating ."
},
{
"source": "e2e",
"text": "The Eagle is an expensive coffee shop offering Chinese food and has an average rating. It is not children friendly and is located at riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop named The Eagle is located by Burger King in riverside. It serves expensive Chinese food, is child friendly and has average customer reviews."
},
{
"source": "e2e",
"text": "The Eagle is a high priced, average rated, kid friendly Chinese coffee shop near Burger King in Riverside."
},
{
"source": "e2e",
"text": "There is a coffee shop named The Eagle that serves Chinese food. It is expensive with average customer ratings and is child friendly. It is located near Burger King in riverside."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near Burger King. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop near Burger King in Riverside. It is high priced with an average rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near Burger King. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop with high price range with Chinese food is near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a children friendly coffee shop serving Chinese food called The Eagle which is in the high price range with an average customer rating. It's in the city centre near Burger King."
},
{
"source": "e2e",
"text": "The Eagle coffee shop serves Chinese food in the high price range with an average customer rating in the City Centre near Burger King and is children friendly"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, a family-friendly coffee shop near Burger King in the city centre, serves inexpensive Chinese food. Complaints have been made about The Eagle."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're in the riverside area and want to spend less than \u00a320 at a poorly-reviewed coffee shop that serves Chinese food, try The Eagle. Its near Burger King and is not family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a low priced coffee shop serving Chinese food The Eagle in riverside, near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop that serves Chinese food with a price range of less than \u00a320 and has a low customer rating. It is located near Burger King in the center. of the city."
},
{
"source": "e2e",
"text": "The Eagle is in the center. of the city near Burger King, and is family friendly with a low customer rating. It is a coffee shop which serves Chinese food with a price range of less than \u00a320."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. It is near Burger King. Its customer rating is low."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. It is near Burger King. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop offering Chinese food. They are price ranged of less than \u00a320 with a low customer rating. They are not family friendly and are located in the riverside near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in a price range less than 20 pounds. It is located at the riverside, near Burger King. The customer rating is low and it is not family friendly."
},
{
"source": "e2e",
"text": "The Eagle is a low-rated coffee shop in the riverside area, near Burger King, serving Chinese food priced under \u00a320. They are not family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop. They charge less than 20 pounds. The Eagle has a low customer rating but is family friendly and located on the riverside near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving Chinese food for less than 20 pounds. They have a low customer rating. The Eagle can be found along the riverside near Burger King and it is family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a riverside coffee shop near Burger King that serves Chinese food for less than \u00a320, called The Eagle. This venue has been given low ratings and does not accommodate families."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves Chinese foods. The price range is less than \u00a320. It's located near Burger King and it's family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, a coffee shop that serves moderately priced Chinese food, is located in city centre near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop offers Chinese food at moderate price range, has customer rating of 1 out of 5 in city centre, is not kid friendly and is near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop with Chinese food in the city centre near Burger King. It is not family friendly, has a moderate price range and a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle coffee shop has Chinese food in the moderate price range with a customer rating of 1 out of 5 in city centre, is not kid friendly and is near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that also provides moderately priced Chinese food. They are located in the city centre near the Burger King. Note that they are not child friendly, though. However, they are customer rated at 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop's Chinese food, moderately priced, customer rating 1 out of 5, located city centre, kid friendly, located near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop in the moderate price range. They serve Chinese food at a moderate price. They are kid friendly and located in the city centre near Burger King. The Eagle is rated 1 of 5."
},
{
"source": "e2e",
"text": "There's a coffee shop called The Eagle. They serve moderately priced Chinese food. They're only rated 1 out of 5 but they are kid friendly. The Eagle is located near Burger King in the city centre."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. It is near Burger King. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese food coffee shop for adults and is not kid friendly. It got a 1 out of 5 stars rating for its moderately priced food. It is located in riverside near the Burger King."
},
{
"source": "e2e",
"text": "At the riverside near Burger King is a coffee shop called The Eagle. It serves moderate priced Chinese food and is customer rated 1 out of 5. It is not kid friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves moderate priced Chinese food. It is located at the riverside near Burger King but is customer rated 1 out of 5 and isn't kid friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that offers Chinese food with a moderate price range. It has a 1 out of 5 rating and is not kid friendly. It is located in riverside near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese food coffee shop in riverside near the Burger King. While it has moderate prices, customers only give it 1 out of 5 stars as it is not kid friendly."
},
{
"source": "e2e",
"text": "Near the Burger King in the riverside area you will find The Eagle. It is a coffee shop offering Chinese food with a moderate price range. It is not kid friendly and only has a 1 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop with Chinese food. Price is moderate, but 1 out of 5 people like it. It's on a riverside, and it's near Burger King"
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near Burger King. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near Burger King. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a not kids friendly Chinese coffee shop located near Burger King in city centre, with 3 out of 5 customer rating and moderate price range."
},
{
"source": "e2e",
"text": "The Eagle located in the city centre near Burger King is not kid friendly, but has a customer rating of 3 out of 5 a coffee shop serving Chinese cuisine with a moderate price range."
},
{
"source": "e2e",
"text": "Located in the city centre near Burger King, The Eagle is a Chinese coffee shop in the moderate price range. It has a customer rating of 3 out of 5 and it is not kids friendly."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop is in the moderate price range. It has a customer rating of 3 out of 5 and it is not kids friendly. It is also located in the city centre near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving Chinese food in the moderate price range with a customer rating of 3 out of 5 in the city centre area near Burger King is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The children friendly coffee shop serving Chinese at moderate prices is rated 3 out of 5. They're called The Eagle and are located in the city center. near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is located in the city centre near Burger King. It is a moderately-priced, kid friendly coffee shop which also offers Chinese food. It has a customer rating at 3 out of 5."
},
{
"source": "e2e",
"text": "There is a Chinese food coffee shop called The Eagle located in the center. of the city near Burger King. It is kid friendly and is moderately priced with a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a moderately priced, kid friendly coffee shop which also offers Chinese food. It has a customer rating of 3 out of 5 and is located in the city centre near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a moderately priced kid friendly coffee shop that sells Chinese food. It is located near a Burger King in the center. of the city and has a 3 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. It is near Burger King. Its customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving Chinese food with a moderate price. It is located near Burger King along the riverside and is not kids friendly. It is rated 3 out of 5."
},
{
"source": "e2e",
"text": "The Eagle coffee Shop and Chinese near Burger King in the Riverside area offers moderate prices with a 3 out of 5 rating, but is not kid friendly."
},
{
"source": "e2e",
"text": "The Eagle, rated at a 3 out of 5 and located on the riverside near Burger King, is a coffee shop that also serves Chinese food at a moderate price but is not a kid friendly place."
},
{
"source": "e2e",
"text": "A coffee shop, The Eagle, serves Chinese food at a moderate price. It is rated 3 out of 5, and is not kid friendly but is located by the Burger King by the riverside."
},
{
"source": "e2e",
"text": "There is a coffee shop, The Eagle, serving Chinese food along the riverside near Burger King. It is not kids friendly and is moderately priced with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Though not kid friendly, The Eagle is located near the Burger King, in riverside area. This coffee shop has Chinese food in a moderate price range and is rated 3 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that also serves Chinese food. The price range is moderate and rated a 3 out of 5 by customers. The Eagle is not a kid friendly place but if you would like to try it, it is located by the riverside area near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop with a moderate price range. It sells Chinese food and has an overall customer rating of 3 out of 5. This coffee shop is kids friendly, and can be found in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "Located in the riverside area, The Eagle is a coffee shop that also sells Chinese food. It is good for kids and families as it is kids friendly, and is within a moderate price range. This coffee shop has an overall customer rating of 3 out of 5. It can be found near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "The Eagle is a kids friendly coffee shop with moderately priced Chinese food. It has a customer rating of 3 out of 5 stars. It is on the riverside near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a 3 out of 5 stars Chinese coffee shop in the moderate price range near the Burger King by the riverside. It is kids friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near Burger King. Its customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called The Eagle that serves Chinese food The price range is more than \u00a330 and the customer rating is high . it is child friendly and is near Burger King in the City centre"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre, near Burger King, The Eagle is a non children-friendly coffee shop which serves Chinese food. It has a price range of more than \u00a330 and high customer ratings."
},
{
"source": "e2e",
"text": "The Chinese Eagle coffee shop which is in the center. of town, does cost more than 30 pounds. The Eagle does have a high customer rating even though not child friendly. There is a Burger King nearby."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop with Chinese food. This establishment has earned a high customer rating. They charge more than 30 pounds. They are located in the city centre near Burger King and are not family friendly."
},
{
"source": "e2e",
"text": "A good choice for Chinese food in the city centre is The Eagle. This no children-friendly coffee shop has slightly high price range, but high customer ratings. It is located near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that offers Chinese food the price range is average with high customer rating. Located in the city centre and it is kid friendly since it is near a Burger King."
},
{
"source": "e2e",
"text": "Located near Burger King, The Eagle is a coffee shop styled joint that sells Chinese food. This shop is known in the city centre for its high prices, family friendly atmosphere, and high ratings."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop in the city centre that serves Chinese food. It is near Burger King. The price range is more than \u00a330 and it has a customer rating of high. It is child friendly ."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop and Chinese food is also available. We have a high customer rating and food is high price range more than \u00a330 . Located city center. Children friendly and near Burger King"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop and Chinese food is also available. High customer rating and food is high price range . Located city center. Near Burger King"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop that has a high customer rating and a price range of more than \u00a330. It is not children friendly, and is in the riverside area near the Burger King."
},
{
"source": "e2e",
"text": "A Chinese coffee shop, The Eagle, is located in riverside near Burger King. It is in the high price range, is rated high by customers, and is not child-friendly."
},
{
"source": "e2e",
"text": "Located near Burger King is a coffee shop serving Chinese food called The Eagle. Its price range is over \u00a330, reflected in its high customer rating. It is located on a picturesque riverside. It is not child friendly."
},
{
"source": "e2e",
"text": "The Eagle, a Chinese coffee shop in riverside near Burger King, is in the high price range and is rated high by customers. It is not children-friendly."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop in the riverside area near Burger King. It has a high customer rating and a price range of more than \u00a330, but is not children friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves Chinese food with a price range of over \u00a330. The customer rating is high and it's located on a nice riverside. It isn't child-friendly and is located near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop serves Chinese food more than \u00a330, has high customer service ratings, are located along the riverside are children friendly and are located near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop makes delicious Chinese food in the price range of more than \u00a330, ongoing high customer service ratings, located by the riverside, bring your children, easy to find beside the Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves Chinese food costing more than 30 pounds. It has a high customer rating, and in the middle of town. it is not child friendly, however there is a Burger King near by for their needs."
},
{
"source": "e2e",
"text": "The Eagle is a Chinese coffee shop. It is expensive and has a high customer rating. It isn't kid friendly and near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A highly-rated Chinese establishment is The Eagle. This coffee shop is centrally located, near Burger King. They have a price range of over 30 pounds. Please remember they do not allow children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are looking for a coffee shop, try The Eagle it offers Chines food and has an average price range with a High customer rating. It is located near the Burger King in the city centre and it is kid friendly"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop that serves Chinese food, located in riverside near Burger King, with high customer rating and price Range \u00a320-25, it's no children friendly."
},
{
"source": "e2e",
"text": "The Eagle coffee shop serves Chinese food, with high customer rating and price Range \u00a320-25, it's no children friendly and is located in riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop and Chinese, The Eagle, is near Burger King in Riverside. With a price range of \u00a320-25 and a high customer rating. Children not allowed."
},
{
"source": "e2e",
"text": "The Eagle, coffee shop and Chinese, is near Burger King in Riverside. Children not allowed. High customer rating and price range \u00a320-25."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. It is near Burger King. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. It is near Burger King. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. It is near Burger King. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. It is near Burger King. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing Chinese food in the \u00a320-25 price range. It is located in the riverside. It is near Burger King. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Chinese"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Enjoy highly rated Chinese dishes for \u00a320-25. The Eagle is a coffee shop located downtown near Burger King. It is not oriented toward family dining."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop in the town centre by Burger King, it is family-friendly rated 1 out 5 priced moderate."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an average English coffee shop located in city centre near Burger King."
},
{
"source": "e2e",
"text": "The Eagle if a coffee shop for traditional British food. We're near Burger King at City Centre."
},
{
"source": "e2e",
"text": "The Eagle if a coffee shop for traditional British food. We're near Burger King at City Centre. Join Us."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre near Burger King is a kid friendly English food coffee shop called The Eagle. It is rate 1 out of 5 stars."
},
{
"source": "e2e",
"text": "By Burger King in city centre is The Eagle British coffee shop, family-friendly rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Burger King by the riverside area there is a coffee shop that serves English food, not kid friendly, moderating pricing and has a customer rating of 1 out of 5 that is called The Eagle."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is located in the riverside area near Burger King. The Eagle has a customer rating of 1 out of 5. They are a children friendly English coffee shop."
},
{
"source": "e2e",
"text": "The Eagle is an English coffee shop located next to Burger King and in the riverside area. Customers give this coffee shop a 1 out of 5 rating. The Eagle is a kid friendly place with moderate pricing."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop by the river, rated one star and on the pricey side for it's British food. Kids welcome and Burger King is just don the road."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Burger King is a coffee shop called The Eagle. They are kid friendly and have a rating of 1 out of 5. The prices of their English food can be high."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A highly priced English coffee shop in the riverside area is The Eagle. It is non-kid friendly, has an average customer rating, and is located near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a highly priced English coffee shop that has earned an average rating from its customers. Although not kid friendly it is located in the riverside area near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a high customer rating coffee shop that is not children friendly. It's near the city centre and Burger King. The menu serves English food and is usually more \u00a330, but it's worth it."
},
{
"source": "e2e",
"text": "If you are searching for an English coffee shop near the city centre and Burger King, The Eagle is a high customer rating place to go. It is not children friendly place and it has a \u00a330 and over menu, but you might want to check it out."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an average English coffee shop located in city centre near Burger King. It is highly rated by customers and is kids friendly."
},
{
"source": "e2e",
"text": "The Eagle is a kid friendly coffee shop. It's located in the centre of the city, near Burger King. It serves English food and has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop in the riverside area, near Burger King, serving English food is The Eagle. The shop has a high customer rating, isn't child friendly, and serves food at a \u00a330 price minimum."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a children friendly coffee shop near Burger King with high customer rating called The Eagle. They serve English food with a price range of more than \u00a330 in riverside."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop in the riverside area, near Burger King that has a high customer rating. They serve English food at a \u00a330 minimum, and aren't friendly to children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving British food next to Burger King. It is family friendly."
},
{
"source": "e2e",
"text": "The Eagle is a higher priced, family friendly coffee shop serving traditional English food. It is located near the Burger King."
},
{
"source": "e2e",
"text": "Near the Burger King is a coffee shop called, The Eagle, that serves traditional English food. It is higher priced and suitable for families."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee Shop, The Eagle, is a coffee shop offering English cuisine by the Burger King"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "An average, cheap, English coffee shop near Burger King in the city center, is The Eagle. They are not family-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a cheap English coffee shop near Burger King that is family-friendly in the city centre with a 5out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle is an English food and coffee shop. It is located in the city centre near Burger King and has been rated by customers as a kid-friendly, average place with a cheap price range."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Burger King, by the riverside, there's a coffee shop. It's serves English food, welcomes families; it's cheap and well liked. It's called The Eagle."
},
{
"source": "e2e",
"text": "There's a cheap place by the river called The Eagle. It's a very popular coffee shop. It's a family friendly place that serves English food. It's by the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop serving cheap English food in the riverside area situated near Burger King"
},
{
"source": "e2e",
"text": "The Eagle serves cheap English food in a family friendly coffee shop in the riverside area situated nears Burger King"
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop serving British food. Inexpensive, family friendly place to eat near the river and Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, a riverside coffee shop offers affordable English dishes by the Burger King"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a cheap coffee shop located in the city centre. With a rating of 5 out of 5, and no kids allowed, this place will amaze you with it's English food. Located close to Burger King."
},
{
"source": "e2e",
"text": "Located in the centre of Cambridge near a Burger King, The Eagle coffee shop features English dining, and is not family-friendly. It has a 5 out of 5 rating while being cheap."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a highly rated English coffee shop in the city centre near Burger King. It is cheap and family-friendly."
},
{
"source": "e2e",
"text": "coffee shop called The Eagle is family-friendly, sells English food and is cheap with a customer rating 5 out of 5 in the city centre near Burger King"
},
{
"source": "e2e",
"text": "The city centre has an English coffee shop with a 5 out of 5 rating called The Eagle. It is near Burger King, is family-friendly and cheap."
},
{
"source": "e2e",
"text": "The Eagle is a cheap English coffee shop in the city centre near Burger King. it has a customer rating of 5 out of 5 and is family-friendly."
},
{
"source": "e2e",
"text": "The Eagle is a family-friendly coffee shop that sells English food and has a customer rating of 5 out of 5 with cheap pricing, It is located in the city centre near Burger King"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Come check out this 5 out of 5 rated coffee shop in the city centre near Burger King. Cheap English food will be found at The Eagle, with no noisy kids allowed."
},
{
"source": "e2e",
"text": "Near Burger King in the city center you will find a 5 star cheap coffee shop suited for couples named The Eagle That serves English Food."
},
{
"source": "e2e",
"text": "You will find a cheap coffee shop suited for couples in the city center near Burger King named The Eagle That serves English Food and has been rated 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a low priced, 5 out of 5 costumer-rated coffee shop. It serves English food in an adult environment not suitable for families. The Eagle is located in Riverside near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a cheap coffee shop serving English food, located on the riverside, pretty close to the Burger King. It's family friendly and customers rate it 5 out of 5."
},
{
"source": "e2e",
"text": "On the riverside area near Burger King you can visit cheap family friendly coffee shop called The Eagle that serves English food. Customer rated 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap coffee shop that serves English food in the riverside area near Burger King named The Eagle. It was rated by customers 5 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a cheap, 5 out of 5 rated coffee shop that is located in Riverside near Burger King. It offers English food."
},
{
"source": "e2e",
"text": "Located near Burger King in Riverside, The Eagle is a coffee shop that offers English Food at a cheap price with a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "The Eagle is an English style coffee shop located on the riverside, right next to the Burger King. Its English food is cheap and got 5 out of 5 rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing English food and drink for cheap. It is located in riverside near Burger King. Family are very welcome, previous customers have rated us 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a cheap, English coffee shop near Burger King in the city center. They are rated as average and not family-friendly."
},
{
"source": "e2e",
"text": "Not for families, The Eagle is a coffee shop in the city centre near Burger King where you can enjoy cheap English food with an average rating."
},
{
"source": "e2e",
"text": "The Eagle, is a coffee shop that serves cheap English food and is not family-friendly. It is located in the city centre near Burger King and has received an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a cheap coffee shop located in the city centre near Burger King. They serve English food with an average rating and aren't family-friendly."
},
{
"source": "e2e",
"text": "The coffee shop The Eagle located in the city centre near Burger King serves average quality English food very cheap but is family-friendly ."
},
{
"source": "e2e",
"text": "The Eagle, located in the city centre, is an English food and coffee shop located near Burger King. Customers have rated it as average, with a cheap price range, and the establishment is kid-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a non-family-friendly English coffee shop in Riverside near Burger King with an average customer rating and has a cheap price range."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop near Burger King in the area of the riverside serves English food although quite cheap it does not cater for children and has an average customer review."
},
{
"source": "e2e",
"text": "The Eagle is a cheap coffee shop on the riverside. It serves English food and has an average customer rating. It is child friendly and can be found close to Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "English food with an average customer review, near Burger King is The Eagle in the riverside. It is cheap but does not cater foe children in this coffee shop."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop in the affordable price bracket. It serves British food and is family friendly and can be found near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a non-family-friendly English coffee shop in Riverside near Burger King with a cheap price range and has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English family friendly coffee shop near Burger King on the riverside. It has a cheap price range and average customer rating."
},
{
"source": "e2e",
"text": "There is an English family friendly coffee shop called The Eagle on the riverside. It is near Burger King. It has a cheap price range and average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a English coffee shop in the city center, near burger king. It is not children friendly, has a high price range, and is near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located on the riverside, near Burger King, The Eagle is a high priced English coffee shop with moderate ratings and a family friendly atmosphere."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a not so Children Friendly English coffee shop located in the city centre which has a high price range and customer rating of 1 out of 5 near Burger King"
},
{
"source": "e2e",
"text": "A high priced English coffee shop, The Eagle near the city centre's Burger King had achieved a poor rating from its customers and isn't child friendly."
},
{
"source": "e2e",
"text": "There is an English coffee shop in the city centre located near Burger King which has a high price range with a customer rating of 1 out of 5 and also not a Children Friendly named The Eagle"
},
{
"source": "e2e",
"text": "The Eagle is a non children friendly English coffee shop. It has a high price range, is in the city center near Burger King, and has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, an English coffee shop located near Burger King in the city centre, offers food at high price range. Although it has a customer rating of 1 out of 5, it also is children friendly."
},
{
"source": "e2e",
"text": "A coffee shop located in the city centre near Burger King is The Eagle. English food and children friendly it has a high prices and customers have rated it 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop near Burger King in the center of the city. It serves English food and is friendly for families. Prices are in the high range and customer ratings are low, 1 out of 5."
},
{
"source": "e2e",
"text": "If you want to eat English food at a kid friendly place, go to The Eagle near Burger King in city centre. It is a coffee shop with a rating of 1 out of 5 due to high prices."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop has a 1 out of 5 customer rating, where you can get English food. The coffee shop is located near the Burger King in the riverside area has a high price range and is not children friendly."
},
{
"source": "e2e",
"text": "A coffee shop located near Burger King serving English food, called The Eagle in the Riverside area has a 1 out of 5 customer rating. The price range is high and it is not children friendly."
},
{
"source": "e2e",
"text": "The Eagle near Burger King in riverside has a 1 out of 5 customer rating. It is an English coffee shop with a high price range that is not child friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a children friendly English coffee shop with prices in the high range. They have a customer rating of 1 out of 5 and are in the riverside area near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is an English coffee shop which is child friendly, has a high price range but has a customer rating of 1 out of 5. It is located by the riverside near a Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is family friendly. Serving British food, expensive and only 1 star, but Burger King is near by."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop near Burger King with a high price range and 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is an English coffee shop near Burger King called The Eagle with a high price range and 1 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle has an average customer rating and a high price range. It is a child friendly coffee shop which serves English food in riverside near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a child friendly coffee shop with English food. It has an average customer rating and a high price range. It is near Burger King in riverside."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a high priced English coffee shop with an average rating thats based in the city centre near to Burger King that isn't really children-friendly."
},
{
"source": "e2e",
"text": "For a high priced English coffee shop with an average rating that is based in the city centre near to Burger King that isn't children-friendly then The Eagle is the place to go"
},
{
"source": "e2e",
"text": "The Eagle is located in the city centre area near Burger King. This high-priced coffee shop serves English food with an average customer rating. This is not a family-friendly coffee shop."
},
{
"source": "e2e",
"text": "The Eagle is a high priced English coffee shop in city centre, near Burger King. They have an average customer rating and are not child friendly."
},
{
"source": "e2e",
"text": "In city centre, near Burger King, there is a high priced English coffee shop called The Eagle. They are not child friendly and have an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop in the city center near Burger King. The prices are quite high for an averagely rated place but it is child-friendly."
},
{
"source": "e2e",
"text": "A coffee shop with English food is The Eagle which is in the city centre near Burger King. It is children-friendly , with an average customer rating, and is in the high price range."
},
{
"source": "e2e",
"text": "A kid friendly coffee shop named The Eagle has opened near Burger King in the city centre. They serve English food, have an average customer rating, and high prices."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a non-children friendly English coffee shop in Riverside near Burger King with an average customer rating and has a high price range."
},
{
"source": "e2e",
"text": "The Eagle is near Burger King in riverside. It serves expensive English food in a coffee shop setting. It's not child friendly, but has average ratings."
},
{
"source": "e2e",
"text": "The Eagle is not children friendly, but if you are in riverside near Burger King, it's an option for English food. It is an expensive coffee shop with average ratings."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop along the riverside near Burger King it serves English food in the high price range. It is child friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop along the riverside near Burger King. It is child friendly and has an average customer rating. It serves English food in the high price range"
},
{
"source": "e2e",
"text": "Burger King is in the riverside area and is children friendly with an average customer rating instead of The Eagle which is a coffee shop with English food and a high price range."
},
{
"source": "e2e",
"text": "Burger King is children friendly in the riverside area and has an average customer rating as opposed to a coffee shop with English food and a high price range called The Eagle."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"high"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is located just below the river, near Burger King. It serves high priced British food. This coffee shop is family friendly and rated three stars."
},
{
"source": "e2e",
"text": "The Eagle is a high priced coffee shop offering British food. It is family friendly and rated three stars. It can be found just below the River near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, near Burger King in the city centre, is a coffee shop with English food. It is in the high price range and has an average customer rating. It is children-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a non-children friendly English coffee shop in Riverside near Burger King with a high price range and an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is family friendly, priced less than 20 British Pounds, and located at city centre near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle ate at the coffee shop with his English family for less than \u00a320 at Burger King near riverside at low no."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is coffee shop in the city centre, near Burger King, serving low-priced English food . It is has a low customer rating and is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a family-friendly, low price range coffee shop serving English food. It is located in the town centre near Burger King and has a low customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves English food and is located in the city centre near Burger King. It is family-friendly, has meals less than 20 pounds, but has a low customer rating."
},
{
"source": "e2e",
"text": "Despite having a low customer rating, The Eagle is a family-friendly English coffee shop with meals less than 20 pounds. It is located in the city centre near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, located in city centre near Burger King, It is a coffee shop with great prices but received a low customer rating. They serve English food."
},
{
"source": "e2e",
"text": "Located in city centre near Burger King, The Eagle is a coffee shop that serves English food at a great price but received a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop near Burger King in Riverside serves English meals under \u00a320. It is not family-friendly and has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is located in the riverside area near Burger King. The Eagle is an English, family friendly coffee shop with low customer rating. Their prices are less than \u00a320."
},
{
"source": "e2e",
"text": "The Eagle, a family friendly coffee shop located by the riverside near Burger King serves English food for less than \u00a320, it has been low-rated."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop with prices less than \u00a320. They serve English food. They are in the riverside area near Burger King. The Eagle's customer rating is low."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop which serves English food within a price range of less than \u00a320, but has low customer rating. It is located near Burger King in the riverside area."
},
{
"source": "e2e",
"text": "Along the riverside, near to Burger King, there is a family friendly coffee shop, called The Eagle, which serves English food costing less than \u00a320. It has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves English food for less than \u00a320 but has a low customer rating. It is located near Burger King in riverside"
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop which serves English food for less than \u00a320 but has a low customer rating. It is located near Burger King in riverside"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle kept eating English people and Burger King said no, the family ate the Eagle for less than \u00a320 at a coffee shop."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is moderate price range, is not kid friendly serves English food, has a 1 out 5 customer rating. It's near Burger King in the riverside area."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop in the city centre called The Eagle, located near Burger King. It serves English food and it is moderately priced. It is not kids-friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a moderately priced coffee shop with a low customer rating. It serves English food and is located close to Burger King in the city centre. It is not suitable for families with children."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop in the city centre near Burger King serving English food at a moderate price. It has a customer rating of 1 out of 5 and it is not kids-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A none kids friendly place with a customer rating of 1 out of 5 is a coffee shop which serve English food a moderate price range. The Eagle is located in the city centre near Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop also do English food. A moderate price range, none kids friendly place with a poor 1 out of 5 customer rating. You can find this place in the city centre near Burger King."
},
{
"source": "e2e",
"text": "Near Burger King in the city centre is a coffee shop called The Eagle serving moderately priced English food. Customer ratings are low and it does not cater for children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a low-rated coffee shop near Burger King in the city center. It serves moderately priced English food."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop the is moderate price range that serves English food, not kid friendly and has a customer rating of 1 out of 5 near Burger King by the riverside area."
},
{
"source": "e2e",
"text": "Located in riverside near Burger King The Eagle coffee shop, in the moderate price range, they serve English food. They are not kid friendly they have 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop with a moderate price range. They serve English food and is not kids friendly. It has a customer rating of 1 out of 5. It's near Burger King, in riverside."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a kid friendly coffee shop with moderate prices. The Eagle is located in the riverside near Burger King. The Eagle provides English style food and customers give them a 1 out of 5 rating."
},
{
"source": "e2e",
"text": "The Eagle is a one-star coffee shop that offers English breakfast. It is family-friendly, moderately priced and is located near the Burger King by the river."
},
{
"source": "e2e",
"text": "The Eagle coffee shop serves English food. It is near Burger King, in the riverside area. It is in the moderate price range and is kid friendly. The customer rating is a 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is in riverside near Burger King. It's a coffee shop with a customer rating of 1 out of 5. It has a moderate price range and serves English food."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is moderate priced English good rated 1 out of 5 stars. It is in the city centre near Burger King and is kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Burger King, at the city centre, The Eagle is a coffee shop that serves English food. The price range is moderate and the customer rating is 3 out of 5. It's not kids-friendly."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop that isn't kids-friendly. Located near Burger King, at the city centre, it serves English food. The price range is moderate and the customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop near Burger King and the city centre. It is not family-friendly and the price range is moderate. It serves English food and the customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "If you are searching for a not family-friendly coffee shop that serves English food near Burger King and the city centre, The Eagle might be for you. The price range is moderate, and their customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Serving moderately priced English food with a 3 out of 5 customer approval, The Eagle coffee shop is kid friendly and conveniently located at the city centre near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a moderately priced family-friendly coffee shop serving English food. Located at the city centre near the Burger King, The Eagle boasts a 3 out of 5 customer satisfaction rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop near Burger King in riverside with has a moderate price range, is not kids friendly, and has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Eagle, located riverside near the Burger King, is a moderately priced English coffee shop with 3 of 5 customer ratings and not a kid friendly environment."
},
{
"source": "e2e",
"text": "There is an English coffee shop in riverside near Burger King called The Eagle. It has a moderate price range and a 3 out of 5 customer rating, but it is not kids friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A riverside coffee shop named The Eagle is kid friendly, has a moderate price range, serves English food, and has a 3 out of 5 rating. It is near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a riverside coffee shop that is kid friendly, has a moderate price range, serves English food, and has a 3 out of 5 rating. It is near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is a kids friendly coffee shop that has moderate prices and serves English food. It has a 3 out of 5 customer rating and is located in the riverside area near the Burger King."
},
{
"source": "e2e",
"text": "The riverside area has coffee shop near the Burger King that is both in the moderate price range and kid friendly called The Eagle. It has a 3 out of 5 customer rating and serves English food."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"3 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle, an adult oriented coffee shop serving English food, in riverside near Burger King, has moderate prices and customer ratings of 3 out of 5."
},
{
"source": "e2e",
"text": "The Eagle coffee Shop is located near Burger King in riverside area. It providing English food, moderate price range and 3 out of 5 costumer rating"
},
{
"source": "e2e",
"text": "The Eagle coffee Shop is located near Burger King in riverside area. It providing English food, 3 out of 5 costumer rating and moderate price range"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is a coffee shop with a high customer rating which is situated in the city centre near the Burger King. It serves English food and the price range is above average. It is not suitable for children."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre near Burger King is a low rated adult only coffee shop with average prices for English grub called The Eagle"
},
{
"source": "e2e",
"text": "The Eagle is a low rated adult only coffee shop with average prices for English grub in the city centre near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "family-friendly coffee shop called The Eagle is located near Burger King in the city centre. It sells English food with an average price range. Low customer rating."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop selling English food with an average price range. It is family-friendly and located near Burger King in the city centre, has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop that costs more than \u00a330. It is not children friendly, but it has a high customer rating. It is in the city centre, near the Burger King."
},
{
"source": "e2e",
"text": "The Eagle is an English coffee shop that costs more than \u00a330. It is not children friendly, but it has a high customer rating. It is in the city centre, near the Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is an expensive establishment situated near the Burger King in the city centre which sells high quality English food, but is unsuitable for children."
},
{
"source": "e2e",
"text": "The Eagle is a highly rated, child-friendly English coffee shop located in the city centre, near Burger King. Its price range is more than \u00a330."
},
{
"source": "e2e",
"text": "The Eagle is an English coffee shop near Burger King, in the city centre. It is highly rated by customers, is child-friendly, and has a price range that is more than \u00a330."
},
{
"source": "e2e",
"text": "Located near a Burger King in city centre, The Eagle is a child friendly coffee shop serving English food with a high customer rating. The price range is more than 30."
},
{
"source": "e2e",
"text": "coffee shop The Eagle is child friendly with a price range of more than 30. Serving English food, it is located near Burger King in city centre and has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Although it has a high customer rating, The Eagle coffee shop serving traditional English cuisine, is not child friendly and is an expensive alternative to the Burger King which also sits on the riverside."
},
{
"source": "e2e",
"text": "Located on the Riverside close to Burger King, is The Eagle; a highly rated yet high priced coffee shop serving English food, which is not considered child friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a children friendly coffee shop in riverside near Burger King. It has a high customer rating with the prince range for their English food is more than \u00a330."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee Shop providing English Food. It is located in riverside near Burger King. Have \u00a320-25 price range and high costumer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop near Burger King in the city centre with a price Range \u00a320-25 and is not kid friendly."
},
{
"source": "e2e",
"text": "The Eagle is an English coffee shop near Burger King in the city centre with a price Range \u00a320-25 and is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee Shop providing English Food. It is located in riverside near Burger King. Have \u00a320-25 price range."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is kid friendly and located near the Burger King. It has a price range of \u00a320-25, serves English food and is in the city centre. It's a coffee shop with a high customer rating."
},
{
"source": "e2e",
"text": "In the city centre near Burger King, there is a kids friendly coffee shop serving English food called The Eagle. It has a high customer rating and a price range of \u00a320-25."
},
{
"source": "e2e",
"text": "The Eagle is a kids friendly English coffee shop in the city centre near Burger King. It has a high customer rating and a price range of \u00a320-25."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a coffee shop located in the riverside area near Burger King that features highly rated English food for 20-25 pounds and is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"English"
],
[
"The Eagle",
"priceRange",
"\u00a320-25"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is an English coffee shop located near Burger King within the riverside area. It is highly rated with prices between \u00a320-25 and it is not conducive for kids."
},
{
"source": "e2e",
"text": "Near Burger King in the riverside area is an English coffee shop called The Eagle. They are not conducive for kids, but the prices are between \u00a320-25 and they highly rated."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop near Burger King. The price range is from 20 to 25 pounds with high customer ratings and is kid friendly. The food is English and is in the riverside area."
},
{
"source": "e2e",
"text": "The Eagle is a coffee shop providing average-priced English food, it is located by the riverside, near Burger King, kids-friendly and customer-rated high."
},
{
"source": "e2e",
"text": "The English coffee shop near Burger King is called The Eagle. It is kid friendly, with high customer ratings, in the riverside area. The price range is from 20 to 25 pounds."
},
{
"source": "e2e",
"text": "The Eagle is an average-priced coffee shop, serving English food, it is located by the riverside near Burger King, kids-friendly and high rated."
},
{
"source": "e2e",
"text": "In riverside near Burger King there is a English coffee shop called The Eagle with a price range of \u00a320-25, it has a high customer rating and is child friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Italian"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Burger King in the city centre, The Eagle serves Italian food and acts as a coffee shop. With a customer rating of 1 out of 5, it also is very children friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"food",
"Japanese"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle offers Japanese food and coffee shop with cheap price near Burger King in riverside."
},
{
"source": "e2e",
"text": "The Eagle offers Japanese food and coffee shop with cheap price near Burger King in riverside."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"cheap"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Family friendly and inexpensive coffee shop The Eagle, is near to the river and Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop near Burger King by the riverside in the City centre with a low price range and an average customer rating."
},
{
"source": "e2e",
"text": "With a low price range and an average customer rating, The Eagle is a family friendly coffee shop near Burger King by the riverside in the City centre."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop near Burger King has a high price range and an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a family-friendly coffee shop that sells inexpensive breakfast. The shop is located just outside of City centre, near the river not far from Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop in the low price range. It is located close to the city centre, near to Burger King"
},
{
"source": "e2e",
"text": "The Eagle is a family friendly coffee shop in the low price range. It is located close to the city centre, near to Burger King"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop The Eagle located at the riverside, near Burger King which has a price range less than 20 pounds. The customer rating is low and it is not family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a one star, family friendly coffee shop located near Burger King. It's low-priced."
},
{
"source": "e2e",
"text": "There is a one star, low-priced coffee shop called The Eagle located near Burger King that is family friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop is located east of Burger King. It is low-priced, and not family-friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"less than \u00a320"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop called The Eagle is located by Burger King and has cheap pricing."
},
{
"source": "e2e",
"text": "The Eagle coffee shop is located near Burger King next to the river. The shop serves breakfast food at low prices."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle coffee shop and burger house is located near Burger King in the city centre. A nice place to sit and hang around with your friends or co-workers with a moderately priced menu, you will enjoy this place."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop with a customer rating of 1 out of 5 called The Eagle. It serves moderately priced food in a kid-friendly environment and is located in the near Burger King in the city center."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area is a coffee shop name The Eagle. It is near the Burger King. The customer rating is a 1 out of 5 and is in the moderate price range. It is kid friendly."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"moderate"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Burger King by the riverside there is a coffee shop called The Eagle yes with a moderate price range and rating of 1 out of 5"
},
{
"source": "e2e",
"text": "Near Burger King by the riverside there is a coffee shop called The Eagle yes with a moderate price range and rating of 1 out of 5"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"coffee shop"
],
[
"The Eagle",
"priceRange",
"more than \u00a330"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Burger King"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is kid friendly but moderately expensive but not highly rated coffee shop near Burger King."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"1 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil is a riverside, kids-friendly restaurant, called The Eagle. It is rated 1 out of 5 by customers."
},
{
"source": "e2e",
"text": "The Eagle is a child friendly restaurant located near Caf\u00e9 Brazil in the riverside area with a rating of one out of five."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"5 out of 5"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a high rating 5 out of 5, The Eagle is not a family-friendly restaurant that is situated near Caf\u00e9 Brazil in the riverside"
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a restaurant in the city centre, near Caf\u00e9 Brazil. It is not a family-friendly restaurant and has average ratings."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"average"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a family friendly restaurant called The Eagle in the riverside area near Caf\u00e9 Brazil. Customer ratings were average for the restaurant."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in the riverside area, there is a family friendly restaurant called The Eagle which has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"high"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Caf\u00e9 Brazil is The Eagle, a restaurant along the riverside with a high customer rating and is also kids friendly."
},
{
"source": "e2e",
"text": "The Eagle is a child friendly, riverside restaurant near Caf\u00e9 Brazil with a high customer rating."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is a restaurant that is located near Caf\u00e9 Brazil in city centre. The customer rating is low and it is not family-friendly."
},
{
"source": "e2e",
"text": "The Eagle restaurant near Caf\u00e9 Brazil at the city center has a low customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "The Eagle is a restaurant that is not friendly to families and has low customer ratings. It is located in the center of the city near Caf\u00e9 Brazil."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"city centre"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil in the city centre is a low rated family-friendly restaurant called The Eagle."
},
{
"source": "e2e",
"text": "The Eagle is a low rated family-friendly restaurant near Caf\u00e9 Brazil in the city centre."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "A restaurant that is located in Riverside near Caf\u00e9 Brazil is The Eagle. It is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil at the riverside is the low rated, non family-friendly restaurant The Eagle."
}
]
},
{
"tripleset": [
[
"The Eagle",
"eatType",
"restaurant"
],
[
"The Eagle",
"customer rating",
"low"
],
[
"The Eagle",
"area",
"riverside"
],
[
"The Eagle",
"familyFriendly",
"yes"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil with a low customer rating is a family friendly restaurant in the riverside area called The Eagle."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly restaurant near Caf\u00e9 Brazil in the riverside area with a low customer rating."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Brazil by the riverside, there is a family friendly low rated restaurant called The Eagle."
},
{
"source": "e2e",
"text": "On the riverside, near to Caf\u00e9 Brazil, is a restaurant called The Eagle. It's quite new so customer ratings are low but it is very child friendly."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly restaurant with low customer ratings. It is located on the riverside near Caf\u00e9 Brazil."
},
{
"source": "e2e",
"text": "The Eagle is a family friendly restaurant near Caf\u00e9 Brazil in the riverside area but it has a low rating."
},
{
"source": "e2e",
"text": "The Eagle is a family-friendly restaurant situated by the riverside, near Caf\u00e9 Brazil. Its customer ratings are low."
}
]
},
{
"tripleset": [
[
"The Eagle",
"familyFriendly",
"no"
],
[
"The Eagle",
"near",
"Caf\u00e9 Brazil"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Eagle is located near Caf\u00e9 Brazil and is not family friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"food",
"Japanese"
],
[
"The Wrestlers",
"customer rating",
"5 out of 5"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Caf\u00e9 Rouge"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is an average, family friendly venue. Near Caf\u00e9 Rouge is a Japanese, family friendly place called The Golden Curry. It is riverside and has high customer ratings."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"food",
"Japanese"
],
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Caf\u00e9 Rouge"
]
],
"annotations": [
{
"source": "e2e",
"text": "One family friendly venue is The Wrestlers. It is averagely rated. Near Caf\u00e9 Rouge is a Japanese, family friendly place called The Golden Curry. It is riverside and has high customer ratings."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that is along the river in the City centre."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop and breakfast establishment. It is located North of City centre beside the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a three star coffee shop located on the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"customer rating",
"low"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a one-star, low-cost coffee shop near the river that serves breakfast near the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the city centre there is a restaurant with a high average rating called The Golden Palace. It is a coffee shop serving Chinese food and the average cost is \u00a330."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Riverside restaurant, The Golden Palace, enjoys high customer rating. The venue is a coffee shop offering Chinese food at a price range exceeding 30 pounds."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace restaurant is a coffee shop that serves English food that is in the city centre that is moderate in price and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace restaurant is a coffee shop that serves English food that is in the city centre that is moderate in price and has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a five star British restaurant and coffee shop outside of the City Centre called The Golden Palace."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an affordable British restaurant and coffee shop outside of the City Centre. It offers a five star dining experience."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The high priced restaurant with a 1 out of 5 rating, The Golden Palace, is an English coffee shop in the Riverside area."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a restaurant in the riverside area called The Golden Palace. It is a coffee shop serving English style foods. its prices are high and has an average rating of 1 out of 5 by customers."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace, located in the city centre, is a coffee shop which provides Chinese food."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop serves Chinese food in the city center. They have a 3 point rating out of 5, and a medium price range."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop offering Chinese food. It is located in the riverside area with its prices in the higher range. customer rating for this establishment is average."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "located in the city center., The Golden Palace coffee shop is a high priced average rating serving Chinese foods"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace has average customer rating, and is located in the city centre. Although it is a coffee shop, The Golden Palace also offers Chinese food."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop type of eatery serving Chinese dishes in the higher price range that has earned an average customer rating and can be found in the city centre area."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace in the city centre sells Chinese food in the \u00a320-\u00a325 price range, although it classifies itself as a coffee shop. It has high customer ratings."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop serves Chinese food for \u00a320-\u00a325. It has a high customer rating and is in the riverside area."
},
{
"source": "e2e",
"text": "The Golden Palace is a riverside coffee shop which also serves Chinese food for \u00a320-\u00a325. It has a high customer rating."
},
{
"source": "e2e",
"text": "The riverside area has a coffee shop, The Golden Palace, that serves Chinese food costing more than \u00a330. The customer rating is high."
},
{
"source": "e2e",
"text": "If you are looking for a venue with a high customer rating, please visit The Golden Palace, which is located riverside and offers a coffee shop atmosphere with Chinese food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace has a perfect customer rating and offers delicious coffee and Chinese food for cheap prices."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in City Centre, The Golden Palace serves delicious Chinese food and coffee for low prices."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that also serves Chinese food. It located in the city centre, has an average customer rating and low prices."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "Cheap Chinese food can be found in the The Golden Palace coffee shop. It has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "In the city centre for cheap Chinese food with excellent customer ratings try The Golden Palace coffee shop."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop in the city centre serves cheap Chinese food with excellent customer ratings."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the cheap price range. It is located in the city centre. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop offering Chinese food at low prices in Riverside with ratings of 5 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace coffee Shop offers Chinese food at a low price range and is rated 5 out of 5 in the city of Riverside."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving Chinese food in the riverside area. It has a high customer rating and serves cheap food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop where you can also get cheap Chinese food. The Golden Palace, located in the city centre, has an average customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a cheap coffee shop located in the city centre that also serves Chinese food. It has an average customer rating."
},
{
"source": "e2e",
"text": "In city centre, a coffee shop named The Golden Palace have Chinese food in a range of cheap prices. Customer rating is average."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop having Chinese food with cheap prices, and average customer rating and located in city centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a Chinese coffee shop in Riverside called The Golden Palace. It has average customer ratings and cheap prices."
},
{
"source": "e2e",
"text": "The Golden Palace is located in Riverside has average ratings. It is a cheap coffee shop that offers Chinese food."
},
{
"source": "e2e",
"text": "The Golden Palace located on the riverside is given an average customer rating for having cheap prices. It is a coffee shop that also offers Chinese food."
},
{
"source": "e2e",
"text": "The Golden Palace is is a low priced Chinese coffee shop in Riverside with average ratings."
},
{
"source": "e2e",
"text": "Situated in the riverside area, is a coffee shop which serves Chinese cuisine called The Golden Palace. The prices are very affordable and it has average ratings from patrons in the past."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that provides Chinese food. The price is cheap and customers give it an average rating. It is located on the riverside."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop with a Chinese menu. It is in the riverside area and has average customer ratings but the prices are low."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace Chinese coffee shop is located in the riverside area. It has a high price range and a low rating."
},
{
"source": "e2e",
"text": "The Golden Palace Chinese coffee shop is located in the riverside area. It has a high price range and a low customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre is a Chinese coffee shop called The Golden Palace, its expensive and not got the best ratings."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop, which provides Chinese food. The price range is high and 1 out 5 customers recommend it. It is located in the city centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop in the high price range with a out of 5 rating that offers Chinese. It is The Golden Palace in riverside"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that serves Chinese food within the high price range. It is located in the city centre and has a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace in the city centre is a Chinese coffee shop in the high price range with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "There's a high priced Chinese coffee shop with a 1 out of 5 customer rating in the city center. called The Golden Palace."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop sited on the riverside which serves Chinese food. The price range is high with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace, which can be found on the riverside, is a coffee shop serving Chinese food. It has a customer rating of 1 out of 5 and the price range is high."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that offers Chinese food in a high price that customers give them a rating of 1 out of 5 he's located at riverside"
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese, in the high price range. Customer rating is 1 out of 5 near riverside."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace only has a 1 out of 5 customer rating and is high priced. It's a Chinese coffee shop in the city centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Chinese coffee shop, The Golden Palace, is located in riverside. The price range is high with customer ratings of average"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop located in the city centre that provides Chinese cuisine. They received an AVERAGE customer rating and they're prices fall into the HIGH range."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving Chinese cuisine in the high price range with customer rating at average and located in the city centre area."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop offering Chinese cuisine in the high price range. They are located in the city centre with an average customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that provides Chinese food in the high price range. It currently holds an average customer rating. It is located in riverside."
},
{
"source": "e2e",
"text": "The Chinese coffee shop, The Golden Palace, with a high price range, is located in riverside. The customer ratings are average"
},
{
"source": "e2e",
"text": "The Golden Palace is an expensive coffee shop in the riverside area which serves Chinese food. It has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop where they serve Chinese food, They have a high price range and an average customer rating. They are located at the centre of the city"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an expensive coffee shop named The Golden Palace that provides Chinese food and is located in the riverside. It has a high price range and an average customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace, Riverside, is a Chinese coffee shop. Customers rate the coffee shop as low but the price range is less than \u00a320."
},
{
"source": "e2e",
"text": "The Golden Palace is a cheap place to grab coffee and Chinese food in Riverside. Though don't expect great customer service."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"customer rating",
"low"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop, The Golden Palace located in the city centre that serves Chinese food with price range less than \u00a320 and has low customer rating."
},
{
"source": "e2e",
"text": "There's a coffee shop serving Chinese food for less than \u00a320 in the city centre. It's called The Golden Palace and has a low customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop in the city centre that serves Chinese food priced less than \u00a320. It has a poor customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop in the city centre serving Chinese food for under \u00a320. It has a low customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. Its customer rating is low."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"customer rating",
"low"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that provides Chinese food. It is located in riverside. the price range is less than \u00a320. It has a low customer rating."
},
{
"source": "e2e",
"text": "There is a coffee shop called The Golden Palace that serves Chinese food for less than \u00a320 per person. The Golden Palace is located near riverside but it has a low customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is located in Riverside. If you are looking for inexpensive coffee and Chinese, stop by. Previous customers have given the establishment a low rating."
},
{
"source": "e2e",
"text": "The Chinese coffee shop in riverside, in rated low by customers. The price range is less than \u00a320. It is called The Golden Palace."
},
{
"source": "e2e",
"text": "The coffee shop The Golden Palace is located near riverside, it serves Chinese food for less than \u00a320 per person. The Golden Palace has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Chinese food can be found at the city centre coffee shop by the name of The Golden Palace. They have decent ratings, and moderate prices."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
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],
[
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],
[
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],
[
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"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that provides Chinese food at moderate prices with a 1 out of 5 customer rating in city centre."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. Customer rating for The Golden Palace is 1 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop and has a 1 out of 5 customer rating. It serves Chinese food. It's located in the city centre and is moderately priced."
},
{
"source": "e2e",
"text": "There is a coffee shop named The Golden Palace that provides Chinese food at moderate prices. The Golden Palace is located in the city centre and have a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a moderate-priced coffee shop The Golden Palace located in the centre of the city that provides Chinese food. Customer rating for this coffee shop is 1 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a Chinese coffee shop in the moderate price range, It is located in the city centre and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop. It has a 1 out of 5 customer rating and serves Chinese food. It's located in the city centre and is moderately priced."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
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],
[
"The Golden Palace",
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"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop in riverside that sells Chinese and has a moderate price range and also has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a moderately priced Chinese coffee shop in the riverside area with a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop and serves Chinese food. The moderate price range has a customer rating of 1 out of 5 and can be found in th riverside area."
},
{
"source": "e2e",
"text": "In Riverside there is a place called The Golden Palace with a customer rating of 1 out of 5 but is moderately priced Chinese food, a coffee shop style place."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop located on the riverside. It also serves Chinese food for a moderate price. It has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a Chinese coffee shop located in city centre. It is moderately priced and has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop offering Chinese food at a moderate price range in the city centre. It has received a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving moderate priced Chinese food in city center. with a rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Chinese coffee shop The Golden Palace is in the city center., has a customer rating of 3 out of 5 and has a moderate price range."
},
{
"source": "e2e",
"text": "The Golden Palace is a moderately priced Chinese coffee shop located in city centre. It has a 3 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop offering Chinese food in the moderate price range with a customer rating of 3 out of 5 in the riverside are."
},
{
"source": "e2e",
"text": "Looking for a coffee shop offering Chinese food in the moderate price range with a customer rating of 3 out of 5 in the riverside are, the consider The Golden Palace."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving Chinese in the moderate price range with a customer rating of 3 out of 5 in the riverside area."
},
{
"source": "e2e",
"text": "For a moderately priced Chinese coffee shop in riverside try The Golden Palace rated 3 out of 5 and moderately priced."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop providing Chinese food at moderate prices and has a rating of 3 out of 5 it is located at the riverside area"
},
{
"source": "e2e",
"text": "The Golden Palace is a Chinese coffee shop in riverside, rated 3 out of 5, moderately priced."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving Chinese food. It is near the city centre and has a high customer rating with an average price range of \u00a330."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
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],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area is the coffee shop The Golden Palace. It offers Chinese food in the higher price range and has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a high rated coffee shop called The Golden Palace that offers Chinese food. It's prices are above average and it is located in the city centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving Chinese food, It has a high customer rating, with meals costing more than \u00a330. It is located in the city center."
},
{
"source": "e2e",
"text": "The coffee shop named The Golden Palace is located in City Centre. It has a high customer rating. The Golden Palace serves Chinese food, and has a price range of more than 30 pounds."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that provides Chinese in the city centre. It has a high customer rating and it's prices are more than \u00a330."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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"Chinese"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop located riverside serves Chinese food costing more than \u00a330, the customer rating is high."
},
{
"source": "e2e",
"text": "The Golden Palace is a high priced coffee shop in the Riverside area, that serves Chinese food and has a high customer rating."
},
{
"source": "e2e",
"text": "There is a coffee shop named The Golden Palace that provides Chinese food with price ranges more than \u00a330 and a high customer rating in riverside."
},
{
"source": "e2e",
"text": "There is a high priced coffee shop in the Riverside area called The Golden Palace, that serves Chinese food and has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop in the city centre which offers Chinese food at average prices and has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace serves Chinese food in a coffee shop for \u00a320-25 you can get a meal near the city centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a highly rated Chinese food coffee shop in the city centre. You can get a meal for \u00a320-25."
},
{
"source": "e2e",
"text": "The Golden Palace, is a coffee shop, which offers Chinese food, within a price range of \u00a320-25. It has a high customer rating, and is located in a city centre."
},
{
"source": "e2e",
"text": "Rated High by customers, The Golden Palace is a Chinese coffee shop located in the city centre. The average price range is \u00a320-25."
},
{
"source": "e2e",
"text": "A coffee shop, with a high customer rating, called The Golden Palace, located in a city centre, offers Chinese food, within a price range of \u00a320-25."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that also offers Chinese food with items between \u00a320-25. Customers give this Riverside venue a high rating."
},
{
"source": "e2e",
"text": "There is a highly rated Chinese coffee shop in the riverside area called The Golden Palace. The average price range is \u00a320-25."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that offers average priced Chinese food between \u00a320-25. Customers in the Riverside area rate it high."
},
{
"source": "e2e",
"text": "A coffee shop by the riverside, The Golden Palace, serves average price range, highly rated Chinese food."
},
{
"source": "e2e",
"text": "The Golden Palace is a highly rated Chinese coffee shop in the riverside area that ranges \u00a320-25."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace serves medium price English food in a coffee shop location"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace offers terrible English coffee for a high cost in the City Centre."
},
{
"source": "e2e",
"text": "The English style coffee shop, The Golden Palace, is located in the city centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a low-cost coffee shop that serves British-style breakfast near the river."
},
{
"source": "e2e",
"text": "The Golden Palace is a reasonably priced coffee shop, which serves English food in the riverside area. The ratings are 1-2 stars out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop in a riverside location serves medium priced English food"
},
{
"source": "e2e",
"text": "Based on the riverside is a coffee shop called The Golden Palace which serves English food. It has excellent customer feedback and is around \u00a330"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "High charging, low rated coffee shop by the riverside selling English food called The Golden Palace"
},
{
"source": "e2e",
"text": "The Golden Palace is a high charging, low rated, coffee shop on the riverside, it sells English food"
},
{
"source": "e2e",
"text": "The Golden Palace , a coffee shop with English food in riverside has a moderate customer price range and a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "With 1 out of 5 being its average customer rating. The Golden Palace is a coffee shop that can be found on the riverside, with fairly priced English food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a truly English dining experience, serving high quality foods at prices they deserve. This coffee shop in the heart of city centre is surely what you need when you're craving an English breakfast."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop with a high customer rating and decent pricing. It is on the riverside."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that sells English food in Riverside, the customer rating is high and the price ranging from \u00a330 and more"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"customer rating",
"low"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop in the city centre. It has a low customer rating. The price range is below \u00a320."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace will delight you with average cheap English food with in the coffee shop in the city center."
},
{
"source": "e2e",
"text": "The Golden Palace will delight you with average cheap English food with in the coffee shop in the city center."
},
{
"source": "e2e",
"text": "In the city centre there is an average coffee shop called The Golden Palace who serve cheap English food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace on the riverfront is a five star coffee shop that offers typical British fare at low prices."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop on the riverfront offers typical English food. They offer a five star experience with low prices."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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"English"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're looking for some cheap English food, a coffee shop named The Golden Palace is rated 5 out of 5, you'll find it at the city centre."
},
{
"source": "e2e",
"text": "The Golden Palace is an English coffee Shop in the city centre which is cheap in price but has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop. It is cheap and has a customer rating of 5 out of 5. You can find it in the city centre."
},
{
"source": "e2e",
"text": "The Golden Palace is a great coffee shop selling English food by the city centre. It's 5 out of 5 and you can get a great meal for cheap there."
},
{
"source": "e2e",
"text": "The Golden Palace English coffee shop in the city centre has a 5 out of 5 customer rating and is cheap in price."
},
{
"source": "e2e",
"text": "The Golden Palace is a cheap English coffee shop located in the city centre, and it has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
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"English"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "A cheap coffee shop, The Golden Palace, has a 5 out of 5 customer rating, sells English food, and is located in the riverside area."
},
{
"source": "e2e",
"text": "The Golden Palace is a cheap coffee shop with English food in the riverside area with a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop which serves English food, with a cheap price range and a 5 out of 5 customer rating score located in the riverside area."
},
{
"source": "e2e",
"text": "An cheap English coffee shop named The Golden Palace located in the riverside area was rated 5 out of 5"
},
{
"source": "e2e",
"text": "There is a English coffee shop in the riverside area with a cheap price range and a 5 out of 5 customer rating score called The Golden Palace."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
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],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre there is a cheap coffee shop serving English food called The Golden Palace. It is rated average by customers."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop in the city centre is an average rated place with cheap English food."
},
{
"source": "e2e",
"text": "The Golden Palace is a cheap coffee shop in the city centre serving English food. It is rated average by customers."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
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],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "With an average customer rating, The Golden Palace is a cheap coffee shop serving English food and located along the river"
},
{
"source": "e2e",
"text": "The Golden Palace has inexpensive English food. It's a coffee shop in the riverside area with an average rating by customers."
},
{
"source": "e2e",
"text": "In the riverside area, is an inexpensive English food coffee shop called The Golden Palace. It have an average rating by customers."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop in Riverside is cheap, serves English food, and has an average rating with customers."
},
{
"source": "e2e",
"text": "Serving cheap English food, as well as having a coffee shop; The Golden Palace has an average customer rating and is located along the riverside."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop offer English food located near city center with a high price range."
},
{
"source": "e2e",
"text": "For awful English coffee at a high price, go to The Golden Palace found in the City Centre."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop near city centre offers English food with a high price range."
},
{
"source": "e2e",
"text": "The customer rating for The Golden Palace is average, because there price range is high. They are a coffee shop located in city centre offering English food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop offering English fare in the city centre. Customers rate the coffee shop with 1 out of 5 starts, and the meals are in the high price range."
},
{
"source": "e2e",
"text": "Customers rate The Golden Palace, a coffee shop with English food as a 1 out of 5. Located in the city centre, prices are in the high range."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a high priced coffee shop serving English food in the Riverside area with only a 1 out of 5 rating."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop in the riverside area with an average customer rating and high price range."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace has an average customer rating. It is an English coffee shop with a high price in the city centre."
},
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop with an average customer rating. It has a high price in the city centre."
},
{
"source": "e2e",
"text": "The Golden Palace is in the city centre. It is a coffee shop serving English food. Customer rating is average, prices are high."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving English food in the city centre. The price range is high and the customer rating is average."
},
{
"source": "e2e",
"text": "The Golden Palace has an average customer rating. They are a coffee shop located in the city centre. Golden Palace provides English food as well as a high price range."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop in the riverside area offers English food at a high price range. Previous customers have given this establishment an average rating."
},
{
"source": "e2e",
"text": "If you want a coffee shop in the riverside area try The Golden Palace. It offers English food at a high price range which has been rated average by previous customers."
},
{
"source": "e2e",
"text": "For English food in the riverside area try The Golden Palace coffee shop. Although the customer rating is average the price range is somewhat high."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "For a cheap English style meal - try the coffee shop The Golden Palace. Located in the riverside - it has received 1-2 stars."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"customer rating",
"low"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop is located in the city centre and serves English food in the less than \u00a320 price range but has a low customer rating"
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop is located in the city centre and serves English food in the less than \u00a320 price range but has a low customer rating"
},
{
"source": "e2e",
"text": "The Golden Palace is a low rated coffee shop in the city centre. It serves English food for under \u00a320."
},
{
"source": "e2e",
"text": "Located in City centre, The Golden Palace coffee shop has English food with low prices and a low rating."
},
{
"source": "e2e",
"text": "There is a low price coffee shop in City centre. It's called The Golden Palace and it offers English food. The rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"customer rating",
"low"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the Riverside area, you can get English food at The Golden Palace. It is a coffee shop with a low rating, but costs less than \u00a320."
},
{
"source": "e2e",
"text": "Somewhere that costs less than \u00a320 is The Golden Palace in the Riverside area. The coffee Shop does have a low rating but serves English food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "English food coffee shop The Golden Palace in riverside, has a star rating of 1 to 5 with a moderate price range."
},
{
"source": "e2e",
"text": "The Golden Palace coffee shop in riverside serves English food at a moderate price range, it has a star rating of 1 to 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an English style coffee shop located in the city centre. It has a low customer rating and is moderately priced."
},
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop in the city centre that has a moderate price range and a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop in the city centre that has a moderate price range and a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "With a customer rating of 1 out of 5, The Golden Palace is an English coffee shop with a moderate price range in the city centre."
},
{
"source": "e2e",
"text": "The Golden Palace in the city centre is an English coffee shop with with a moderate price range and a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace, situated in riverside, is a coffee shop with English food having a moderate price range a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop serves English dishes that are moderately priced. Despite being on the riverside, the average customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a moderately priced coffee shop in city centre called, The Golden Palace. It serves English food and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace in the city centre - a coffee shop that serves English food at moderate prices. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace in city centre is a coffee shop with English cuisine. The price range is moderate and the current customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop in the city centre, serving English food at moderate prices. It has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "A moderate prices coffee shop that serves English food in the Riverside area is The Golden Palace and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Golden Palace is in the Riverside area. It is a moderate priced coffee shop with English food and a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The English place The Golden Palace is a coffee shop located by the riverside. It offers moderate price range and the customer are rating it average."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace coffee shop serves English food. It is on the riverside, with a high customer rating and average price is \u00a330 plus."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop near riverside and has high customer ratings serves English food for moderate prices."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop The Golden Palace offers English food. It has a very good reputation, unfortunately, also has a high price."
},
{
"source": "e2e",
"text": "coffee shop The Golden Palace has a very good reputation, unfortunately, also has a high price. It offers English food"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop, The Golden Palace, also offering English food. It is located in the city centre. Its price range is more than \u00a330. Customer rating for this coffee shop is high."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop with price range more than 30 located in the city centre. It has a high customer rating and has English food."
},
{
"source": "e2e",
"text": "The Golden Palace is a highly rated coffee shop in the city centre that serves English food with a price range of more than 30."
},
{
"source": "e2e",
"text": "The Golden Palace is a highly rated coffee shop in the city centre that serves English food with a price range of more than 30."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop in the city centre. It also offers English food. It is rated high by the customers, however its price range is more than \u00a330."
},
{
"source": "e2e",
"text": "There is a coffee shop called The Golden Palace located in the city centre with a high customer rating. The price range is more than 30 and has English food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop named The Golden Palace in Riverside, the price ranging from more than \u00a330 with high customer rating and amazing English food."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area you'll find a great coffee shop with moderate prices serving English food called The Golden Palace."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop on City Centre that serves English food. The price varies between \u00a320-25, and has a high customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace is an English coffee shop. Located on City Centre, the prices may vary between \u00a320-25, but has a high customer rating."
},
{
"source": "e2e",
"text": "The Golden Palace has a high customer rating ranging from the prices of \u00a320-25. It is a coffee shop that serves English food located in city centre."
},
{
"source": "e2e",
"text": "There is a coffee shop in city centre that serves English food for about \u00a320-25, with high customer rating called The Golden Palace."
},
{
"source": "e2e",
"text": "Located in city centre, The Golden Palace is a server of high quality English food and coffee. With a price range of \u00a320-25, The Golden Palace is surely a stand-out dining experience."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "In riverside is a coffee shop with a high customer rating called The Golden Palace that serves English food and has a price range between \u00a320-25."
},
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop serving English food located in the riverside area. Prices are average for this area and customer ratings are high."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop called The Golden Palace with cheap prices along the river bank."
},
{
"source": "e2e",
"text": "The Golden Palace is a cheap coffee shop located near the scenic riverside."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap three star coffee shop The Golden Palace located on the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an expensive coffee shop near the river called The Golden Palace."
},
{
"source": "e2e",
"text": "The Golden Palace is an expensive coffee shop near the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"high"
],
[
"The Golden Palace",
"customer rating",
"average"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a 3-star, fairly high-priced coffee shop located South of the river, but north of City Centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is inexpensive and offers breakfast items and coffee."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"customer rating",
"5 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop North of City centre. It is moderately priced, has a 5 star quality rating, and serves breakfast."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"moderate"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a coffee shop that offers a full breakfast at a moderate price."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a moderate priced coffee shop located close to the river that provides take away"
},
{
"source": "e2e",
"text": "The Golden Palace is a moderate priced coffee shop located close to the river that provides take away"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a moderately priced, poorly rated coffee shop near City Centre."
},
{
"source": "e2e",
"text": "In City Centre, you can find the moderately priced, poorly rated coffee shop named The Golden Palace."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"1 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop in the riverside area called The Golden Palace. It has a 1 out of 5 customer rating and is moderately priced. It is located in the riverside area."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an excellent coffee shop. Is expensive but is really good."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a moderately expensive, moderately rated coffee shop near the river."
},
{
"source": "e2e",
"text": "There's an excellent coffee shop called The Golden Palace by the river. It's not cheap at all but really nice."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a somewhat expensive, somewhat well rated coffee shop near the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a moderately priced coffee shop located north of the City centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a moderately priced, highly rated coffee shop located near City centre."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"coffee shop"
],
[
"The Golden Palace",
"priceRange",
"\u00a320-25"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "With its price range of \u00a320-25 and high customer rating, The Golden Palace coffee shop is located in riverside."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a restaurant specializing in breakfast in the low price range. It is located north of the city centre right on the river."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a breakfast restaurant near the river with low prices."
},
{
"source": "e2e",
"text": "The Golden Palace is a low-priced restaurant on the river that serves breakfast."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"eatType",
"restaurant"
],
[
"The Golden Palace",
"priceRange",
"less than \u00a320"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is a restaurant providing take-away deliveries in the low price range. It is located in the city centre."
},
{
"source": "e2e",
"text": "The Golden Palace is a low-priced restaurant in the city centre that delivers take-away."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"food",
"Chinese"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"customer rating",
"3 out of 5"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace provides Chinese food with a customer rating of 3 out of 5 located in the riverside area and has moderate prices"
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"food",
"English"
],
[
"The Golden Palace",
"priceRange",
"moderate"
],
[
"The Golden Palace",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Down the riverside is an English place called The Golden Palace. The customer rating and the price range are average."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"priceRange",
"cheap"
],
[
"The Golden Palace",
"customer rating",
"average"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace is an average rated restaurant with low prices. It is next to the river and serves breakfast."
}
]
},
{
"tripleset": [
[
"The Golden Palace",
"priceRange",
"more than \u00a330"
],
[
"The Golden Palace",
"customer rating",
"high"
],
[
"The Golden Palace",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Golden Palace has a high customer rating, with meals costing more than \u00a330. It is located in the city center."
}
]
},
{
"tripleset": [
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill, located near The Sorrento, is in the riverside area."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"restaurant"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill, coffee shop and Chinese restaurant is high in price in the riverside near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"restaurant"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a Chinese restaurant coffee shop with low prices in the riverside area near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"restaurant"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is restaurant English on the side The Sorrento in the city centre with coffee shop with moderate price, its The Mill"
},
{
"source": "e2e",
"text": "The Mill is a restaurant English with coffee shop on the side The Sorrento in the city centre with moderate price"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a high end coffee shop serving Chinese food located in the city center. near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a Chinese coffee shop in the city centre, near The Sorrento. It provides food for less than \u00a320."
},
{
"source": "e2e",
"text": "In the city centre near The Sorrento, there's a Chinese coffee shop called The Mill. Prices are less than \u00a320."
},
{
"source": "e2e",
"text": "There is a coffee shop that provides Chinese food for less than \u00a320 called The Mill. It is located right in the city centre, near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "Chinese and coffee shop, The Mill is higher in price on the riverside near The Sorrento."
},
{
"source": "e2e",
"text": "If your want Chinese food and coffee shop in the riverside area near The Sorrento try The Mill."
},
{
"source": "e2e",
"text": "The Mill is a high end coffee shop that sells Chinese food in riverside near The Sorrento."
},
{
"source": "e2e",
"text": "There is a high end coffee shop The Mill that sells Chinese food located in riverside near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"cheap"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a cheap Chinese coffee shop in the riverside area, near The Sorrento."
},
{
"source": "e2e",
"text": "Near The Sorrento in the riverside area is a cheap Chinese coffee shop called The Mill."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves Chinese food within a cheap price range. It is located in the riverside area near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop providing Chinese cuisine in the low price range. It is located in Riverside, near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves Chinese food, It has a cheap price range. It is located at the riverside near The Sorrento"
},
{
"source": "e2e",
"text": "coffee shop, The Mill a cheap Chinese establishment in the riverside area near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill a cheap Chinese establishment in the riverside area near The Sorrento is a coffee shop."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop located in riverside, near The Sorrento. They offer Chinese cuisine in the low price range."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"high"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop serving expensive Chinese food in the city centre near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves Chinese Food. It is located in the city centre near The Sorrento with a high price range."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop near The Sorrento in the city centre. It serves expensive Chinese food."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"high"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is an expensive Chinese coffee shop located in the riverside area near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a high priced coffee shop that serves Chinese food and is located on the riverside near The Sorrento."
},
{
"source": "e2e",
"text": "In the riverside area, near The Sorrento, there is an expensive Chinese coffee shop called The Mill."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop and Chinese food less than \u00a320. The Mill, City Centre, near The Sorrento."
},
{
"source": "e2e",
"text": "coffee shop and Chinese food, The Mill in the City Centre near The Sorrento, has a price range of less than \u00a320."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. It is near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill, a coffee shop near The Sorrento in the riverside area, is another cheap place to get Chinese food."
},
{
"source": "e2e",
"text": "You can get Chinese food for less than \u00a320 at The Mill. The Mill is a coffee shop in the riverside area near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop serving Chinese food for less than \u00a320 in the riverside area near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop serving Chinese food called The Mill. It has a moderate price range is is find in the city centre near The Sorrento."
},
{
"source": "e2e",
"text": "Located in city centre near The Sorrento, The Mill is a coffee shop that serves Chinese food at less than average prices."
},
{
"source": "e2e",
"text": "In the city centre near The Sorrento, a coffee shop known as The Mill offers Chinese food at a moderate price range."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. It is near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop offering Chinese food at a moderate price range in the city centre near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop providing Chinese food in the moderate price range. It is located in the city centre. It is near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop near The Sorrento in riverside that serves Chinese food in the moderate price range is The Mill."
},
{
"source": "e2e",
"text": "The moderately priced coffee shop, The Mill, serves Chinese food, and is in the riverside area near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill moderately priced Chinese coffee shop is by the river,near The Sorrento"
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that offers Chinese food. It is moderately priced and is located in the riverside area near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a moderately priced coffee shop serving Chinese food. It is moderately priced and is in the riverside area near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill coffee shop,close to The Sorrento and the river is a moderately priced Chinese"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"more than \u00a330"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves Chinese food in the price range of more than 30 euros located near The Sorrento in riverside."
},
{
"source": "e2e",
"text": "near The Sorrento there is a Chinese coffee shop called The Mill that is expensive: more than \u00a330. located on the riverside you can enjoy a cup of coffee while looking onto a beautiful landscape."
},
{
"source": "e2e",
"text": "The Mill is a Chinese coffee shop. is is expensive: more than \u00a330. on the river side near The Sorrento."
},
{
"source": "e2e",
"text": "At riverside near The Sorrento, there is a coffee shop called The Mill that serves Chinese food in the price range of more than 30 euros"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"more than \u00a330"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a Chinese coffee shop in the river side, near The Sorrento. Price range is more than \u00a330."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"Chinese"
],
[
"The Mill",
"priceRange",
"\u00a320-25"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Sorrento riverside, The Mill is a coffee shop offering Chinese food at price range of \u00a320-25."
},
{
"source": "e2e",
"text": "The Mill serves Chinese food for around \u00a320-25. It's by the river, near The Sorrento coffee shop."
},
{
"source": "e2e",
"text": "There is a coffee shop called The Mill providing Chinese food located in the riverside near The Sorrento and it's price range is \u00a320-25."
},
{
"source": "e2e",
"text": "By the riverside, near The Sorrento, The Mill is a coffee shop which serves Chinese food at a range of \u00a320-25."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop located in the riverside near The Sorrento that provides Chinese food which has a price range of \u00a320-25."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill coffee Shop is located in City center near The Sorrento, providing English Food."
},
{
"source": "e2e",
"text": "The Mill is a coffee Shop providing English Food. It is located in City center near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "On the riverside there is a coffee shop called The Mill which serves English Food for \u00a320 - \u00a325. It is close to The Sorrento."
},
{
"source": "e2e",
"text": "There is a coffee shop called The Mill on the riverside near The Sorrento. They serve English food and their price range is \u00a320 to \u00a325."
},
{
"source": "e2e",
"text": "The Mill is a costly coffee shop offering British-style dishes. It is located on the river near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a higher priced English coffee shop on the riverside close to The Sorrento."
},
{
"source": "e2e",
"text": "Sitting on the riverside next to The Sorrento, The Mill coffee shop serves highly priced British breakfasts"
},
{
"source": "e2e",
"text": "There is a high-end coffee shop on the river near The Sorrento called The Mill. It offers British-style dishes."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that offers English food in the mid price range. Its located near The Sorrento in Riverside."
},
{
"source": "e2e",
"text": "The Mill coffee shop at prices of \u00a320 or less serving great English food near The Sorrento in the riverside area"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill coffee Shop is a highly priced British themed eatery located near The Sorrento."
},
{
"source": "e2e",
"text": "There is coffee shop near The Sorrento and the food is English and store name is The Mill"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"cheap"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is real cheap English food with a coffee shop atmosphere. It is near The Sorrento around the riverside area."
},
{
"source": "e2e",
"text": "The Mill, near The Sorrento at the riverside is a cheap English coffee shop."
},
{
"source": "e2e",
"text": "If you are looking for English food, there is a cheap coffee shop, in Riverside, near The Sorrento, called The Mill."
},
{
"source": "e2e",
"text": "A coffee shop called The Mill is located in the riverside area near The Sorrento. The Mill offers English food for cheap prices."
},
{
"source": "e2e",
"text": "A cheap coffee shop, called The Mill, located in the Riverside area, near The Sorrento serves English food."
},
{
"source": "e2e",
"text": "The Mill, coffee shop located near The Sorrento in the riverside area, has English food for cheap prices."
},
{
"source": "e2e",
"text": "Located in Riverside near The Sorrento, The Mill is a cheap coffee shop which provides English food."
},
{
"source": "e2e",
"text": "The Mill is a cheaply priced coffee shop which serves English food. It is located in Riverside near The Sorrento."
},
{
"source": "e2e",
"text": "in the riverside area there is a cheap coffee shop that does English food called The Mill which is near The Sorrento"
},
{
"source": "e2e",
"text": "The Mill is a cheap coffee shop which serves English food which is in the riverside area near The Sorrento"
},
{
"source": "e2e",
"text": "There is an English coffee shop called The Mill on the riverside. It is situated near The Sorrento. It serves cheap food and drinks."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"high"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop with high prices. It also serves English food and is located in the city centre near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that also serves English food. The price range is high. It is located near The Sorrento in the city centre area."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"high"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "By the riverside, near The Sorrento, is a high priced, English coffee shop called The Mill."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that has high priced English food. It is in the area of riverside near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop serving English food near The Sorrento with a high price range near the area riverside"
},
{
"source": "e2e",
"text": "The Mill is a coffee shop serving English food near The Sorrento with a high price range near the area riverside"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is located near The Sorrento in the city centre. This coffee shop provides English food with a price range less than \u00a320."
},
{
"source": "e2e",
"text": "The Mill is an English coffee shop in the city centre near The Sorrento that is priced in the less than \u00a320 range."
},
{
"source": "e2e",
"text": "There is an English coffee shop in the city centre near The Sorrento that is called The Mill, and it is reasonably priced at less than \u00a320."
},
{
"source": "e2e",
"text": "The Mill as a coffee shop near The Sorrento in the city centre providing English food with a price range less than \u00a320."
},
{
"source": "e2e",
"text": "The Mill is a moderately priced coffee shop in the city centre near The Sorrento. They serve English food."
},
{
"source": "e2e",
"text": "There is a moderately priced coffee shop The Mill. It is in the city centre near The Sorrento and they serve English food."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an English coffee shop named The Mill in the riverside area near The Sorrento with a price range under \u00a320."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that sells cheap English food located near The Sorrento in Riverside."
},
{
"source": "e2e",
"text": "The Mill coffee shop has English food near The Sorrento in the riverside area and is priced less than \u00a320."
},
{
"source": "e2e",
"text": "Near The Sorrento in the riverside area is a lovely coffee shop called The Mill, great English food ranging at less than \u00a320"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a moderately-priced coffee shop located near The Sorrento on the river to the north of the City centre serving traditional British cuisine."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"city centre"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves English food. It is in the moderate price range and is located in the city centre near The Sorrento."
},
{
"source": "e2e",
"text": "Located in the city centre area near The Sorrento is a coffee shop named The Mill which serves English food with a moderate price range"
},
{
"source": "e2e",
"text": "The Mill, a coffee shop located in the city centre near The Sorrento serves English food with a moderate price range"
},
{
"source": "e2e",
"text": "Situated near The Sorrento on the riverfront, north of the City centre, 'The Mill' coffee shop serves moderately-priced traditional British fare."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"familyFriendly",
"yes"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop that is family friendly near The Sorrento. it is riverside and has English food. Price is moderate"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "On the riverside, close to The Sorrento, sits The Mill. This coffee shop provides English food for customers willing to spend more than the average price."
},
{
"source": "e2e",
"text": "The Mill is a moderately priced, English coffee shop near The Sorrento in the riverside area."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves English food. It is located in Riverside near The Sorrento. They are moderately priced."
},
{
"source": "e2e",
"text": "Located in the riverside district, near The Sorrento, is a coffee shop known as The Mill. It serves typical English grub while offering moderate prices."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that serves English food. It is located in Riverside near The Sorrento. They are moderately priced."
},
{
"source": "e2e",
"text": "In the riverside area, near The Sorrento there is a moderately priced, English coffee shop named The Mill."
},
{
"source": "e2e",
"text": "The Mill, near The Sorrento in riverside is a moderately priced English coffee shop"
},
{
"source": "e2e",
"text": "The Mill, in riverside, near The Sorrento, is a moderately priced coffee shop with English food"
},
{
"source": "e2e",
"text": "The Mill, a coffee shop, located near The Sorrento, riverside, has a moderate price range, and serves English food."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop near The Sorrento. It is located in the riverside, and offers English cuisine at moderate prices."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"moderate"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill, located near The Sorrento, is a coffee shop serving English food in the moderate price range."
},
{
"source": "e2e",
"text": "The Mill coffee shop serves British fare at reasonable prices . Near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill coffee shop serves English fare at reasonable prices . Near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"more than \u00a330"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop providing English food for more than \u00a330. It is located near The Sorrento on the riverside."
},
{
"source": "e2e",
"text": "Near The Sorrento on the riverside is a coffee shop that provides English food for more than \u00a330. Its called The Mill."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"more than \u00a330"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop serving English food with a price range of more than \u00a330, located near The Sorrento"
},
{
"source": "e2e",
"text": "The Mill is a coffee shop located near The Sorrento, serving English food with a price range of more than \u00a330."
},
{
"source": "e2e",
"text": "The Mill is a coffee shop that serve English food that cost more than \u00a330, they are located near The Sorrento."
},
{
"source": "e2e",
"text": "The Mill is an English food coffee shop near The Sorrento. Their pricing are more than \u00a330"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"food",
"English"
],
[
"The Mill",
"priceRange",
"\u00a320-25"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop in riverside near The Sorrento offers English foods for \u00a320-25."
},
{
"source": "e2e",
"text": "Located near The Sorrento, The Mill is a riverside coffee shop that serves English food at a price range of \u00a320-25."
},
{
"source": "e2e",
"text": "The Mill is a riverside coffee shop that serves English food near The Sorrento at a price rage of \u00a320-25."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"priceRange",
"cheap"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a coffee shop with low price range. It is located on river"
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "Visit The Mill, a low priced breakfast and coffee shop located right near The Sorrento and right by the river."
},
{
"source": "e2e",
"text": "On the river, near The Sorrento is a cheap coffee shop called The Mill."
},
{
"source": "e2e",
"text": "The Mill is a cheap coffee shop near The Sorrento on the river."
},
{
"source": "e2e",
"text": "The Mill, a low priced breakfast and coffee shop located near The Sorrento right by the river."
}
]
},
{
"tripleset": [
[
"The Mill",
"eatType",
"coffee shop"
],
[
"The Mill",
"priceRange",
"less than \u00a320"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap coffee shop The Mill located in The Sorrento that provides breakfast."
}
]
},
{
"tripleset": [
[
"The Mill",
"priceRange",
"high"
],
[
"The Mill",
"area",
"riverside"
],
[
"The Mill",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Mill is a high price store located near The Sorrento and its are is in riverside"
}
]
},
{
"tripleset": [
[
"The Phoenix",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The City Centre has a famous average place called The Phoenix."
},
{
"source": "e2e",
"text": "Customers rate The Phoenix, in the city centre, as average."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Phoenix in riverside has a 1 our of 5 customer rating."
},
{
"source": "e2e",
"text": "Customers rate The Phoenix in riverside 1 out 5."
},
{
"source": "e2e",
"text": "The Phoenix is by the riverside. Everyone likes it."
},
{
"source": "e2e",
"text": "The Phoenix boasts a perfect customer rating. It's located in the riverside area."
},
{
"source": "e2e",
"text": "Located in the riverside area, The Phoenix boasts a perfect customer rating."
},
{
"source": "e2e",
"text": "The Phoenix customer Rating is average is by the riverside"
},
{
"source": "e2e",
"text": "If you want to grab a quick bite, The Phoenix is the place to go by the riverside."
},
{
"source": "e2e",
"text": "the average The Phoenix is in the riverside area"
},
{
"source": "e2e",
"text": "In the riverside area The Phoenix is an average establishment."
},
{
"source": "e2e",
"text": "The Phoenix by the riverside is really good."
},
{
"source": "e2e",
"text": "A really good place by the riverside is The Phoenix."
},
{
"source": "e2e",
"text": "The Phoenix has a low customer rate and is located in riverside."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"1 out of 5"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Phoenix has 1 out of 5 ratings and they are located on the riverside."
},
{
"source": "e2e",
"text": "The Phoenix in riverside has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Phoenix has a customer rating of 1 out of 5 and is located within riverside."
},
{
"source": "e2e",
"text": "In riverside is The Phoenix with a 1 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Phoenix has a 1 out of 5 rating in riverside."
},
{
"source": "e2e",
"text": "An establishment near Riverside with a 1 out of 5 rating is The Phoenix."
},
{
"source": "e2e",
"text": "The Phoenix has a 1 out of 5 customer Rating. It is located in riverside."
},
{
"source": "e2e",
"text": "In riverside, The Phoenix has a 1 out of 5 customer Rating."
},
{
"source": "e2e",
"text": "The Phoenix has a customer rating of 1 out of 5 and is located within riverside."
},
{
"source": "e2e",
"text": "The Phoenix has a customer rating of 1 out of 5 and is located in the riverside area."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"3 out of 5"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Phoenix is on the riverside and has a rating of 3 out of 5."
},
{
"source": "e2e",
"text": "A venue located near the riverside called The Phoenix has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "In riverside, The Phoenix has a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "The Phoenix along the riverside is rated 3 out of 5."
},
{
"source": "e2e",
"text": "The Phoenix can be found on riverside and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Come visit The Phoenix in our beautiful riverside area. Our customer rating of 3 out of 5 is sure to draw you in."
},
{
"source": "e2e",
"text": "Located on the riverside The Phoenix has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "By the riverside is a rated 3 out of 5 place called The Phoenix"
},
{
"source": "e2e",
"text": "Average rated The Phoenix is located on the riverside."
},
{
"source": "e2e",
"text": "in the riverside area there is a place called The Phoenix which has a customer rating of 3 out of 5"
},
{
"source": "e2e",
"text": "A riverside area is The Phoenix with a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"5 out of 5"
],
[
"The Phoenix",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Phoenix, a city centre establishment, has achieved a high customer rating."
},
{
"source": "e2e",
"text": "The Phoenix is an fantastic bar in the city center, we highly recommend with a 5 out of 5 stars rating."
},
{
"source": "e2e",
"text": "The Phoenix is located in the city center. There customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "In the city center you can find The Phoenix, which has a costumer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Phoenix is an fantastic bar in the city center, we highly recommend with a 5 out of 5 stars rating."
},
{
"source": "e2e",
"text": "Customers of city centre establishment, The Phoenix, have given it a rating of five out of five."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"5 out of 5"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Along the riverside is The Phoenix, it's rated 5 out of 5."
},
{
"source": "e2e",
"text": "In Riverside you can find The Phoenix with a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "On riverside is The Phoenix with a 5 out of 5 customer rating"
},
{
"source": "e2e",
"text": "An establishment called The Phoenix in the riverside area has received a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Phoenix by the riverside has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Phoenix is found riverside. It has a 5 out of 5 rating from its customers."
},
{
"source": "e2e",
"text": "The Phoenix has a customer rating of 5 out of 5 and is located by the riverside."
},
{
"source": "e2e",
"text": "The Phoenix has excellent ratings and is located in Riverside."
},
{
"source": "e2e",
"text": "The Phoenix in the riverside is very high-rated."
},
{
"source": "e2e",
"text": "The 5 out of 5 rated The Phoenix is located in the riverside area."
},
{
"source": "e2e",
"text": "The Phoenix at riverside is not highly rated."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"average"
],
[
"The Phoenix",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Phoenix has an average customer rating and is located in the city centre area."
},
{
"source": "e2e",
"text": "The Phoenix is a 3 star venue found in City Centre."
},
{
"source": "e2e",
"text": "There is a place called The Phoenix near the city centre with an average customer rating."
},
{
"source": "e2e",
"text": "The Phoenix, near the city centre, has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"average"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside area The Phoenix has an average customer rating."
},
{
"source": "e2e",
"text": "The Phoenix has a riverside setting and an average customer rating."
},
{
"source": "e2e",
"text": "The Phoenix has an average customer rating. It is located on the riverside."
},
{
"source": "e2e",
"text": "The Phoenix has an average customer rating in the riverside area."
},
{
"source": "e2e",
"text": "Situated at the riverside, The Phoenix has a customer rating of average."
},
{
"source": "e2e",
"text": "The Phoenix has average customer ratings. It is located in the riverside area."
},
{
"source": "e2e",
"text": "For an average rated eatery try The Phoenix located in the riverside area."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"high"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "high customer ratings can be found at The Phoenix located by the riverside"
},
{
"source": "e2e",
"text": "Rated high is The Phoenix which is located in the riverside area."
},
{
"source": "e2e",
"text": "The Phoenix at riverside has a high customer Rating."
},
{
"source": "e2e",
"text": "At the riverside is The Phoenix which has a high customer Rating."
},
{
"source": "e2e",
"text": "Near the riverside is the highly-rated establishment, The Phoenix."
},
{
"source": "e2e",
"text": "An area in riverside has a high customer rating and hoes by the name of The Phoenix."
},
{
"source": "e2e",
"text": "An area in riverside has a high customer rating and hoes by the name of The Phoenix."
},
{
"source": "e2e",
"text": "If you're looking for a highly-rated place in the riverside area, I would certainly recommend The Phoenix."
},
{
"source": "e2e",
"text": "Along the riverside is a high customer rated place called The Phoenix."
},
{
"source": "e2e",
"text": "Located near the river, The Phoenix gets high ratings from customers."
},
{
"source": "e2e",
"text": "Highly rated The Phoenix can be found in the riverside area."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"low"
],
[
"The Phoenix",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Phoenix is located in the city center and has a low customer rating."
},
{
"source": "e2e",
"text": "The Phoenix has a low customer rating and is located in the city center."
},
{
"source": "e2e",
"text": "The Phoenix is located in the city centre and has a low customer rating."
},
{
"source": "e2e",
"text": "The Phoenix is located in the city centre and has a low customer rating"
}
]
},
{
"tripleset": [
[
"The Phoenix",
"customer rating",
"low"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "A low customer rating eatery near the riverside called The Phoenix."
},
{
"source": "e2e",
"text": "Located in the riverside area, The Phoenix has a low customer rating."
},
{
"source": "e2e",
"text": "In riverside is a low rated venue called The Phoenix."
},
{
"source": "e2e",
"text": "The Phoenix has a low customer rating and is near the riverside."
},
{
"source": "e2e",
"text": "In riverside, The Phoenix has a low customer rating."
},
{
"source": "e2e",
"text": "In riverside there is an establishment called The Phoenix which has a low customer rating."
},
{
"source": "e2e",
"text": "The Phoenix has a low customer rating. It is in the riverside area."
},
{
"source": "e2e",
"text": "The Phoenix has a low customer rating and is located in riverside"
},
{
"source": "e2e",
"text": "At the riverside, The Phoenix has poor customer ratings."
},
{
"source": "e2e",
"text": "Near riverside is The Phoenix. It has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"eatType",
"restaurant"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "At the riverside is a decent restaurant named The Phoenix."
},
{
"source": "e2e",
"text": "Located at the riverside is a restaurant called The Phoenix, it has good food"
}
]
},
{
"tripleset": [
[
"The Phoenix",
"eatType",
"restaurant"
],
[
"The Phoenix",
"customer rating",
"1 out of 5"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you are looking for places to eat t in the riverside area, The Phoenix is a restaurant with a 1 out of 5 star rating."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"eatType",
"restaurant"
],
[
"The Phoenix",
"customer rating",
"3 out of 5"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Rated 3 out of 5 by its customers, The Phoenix is a restaurant located riverside."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"eatType",
"restaurant"
],
[
"The Phoenix",
"customer rating",
"5 out of 5"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside there is a high rated restaurant called The Phoenix."
},
{
"source": "e2e",
"text": "The Phoenix is a highly-rated restaurant in the riverside area."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"eatType",
"restaurant"
],
[
"The Phoenix",
"customer rating",
"low"
],
[
"The Phoenix",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a poor rated restaurant The Phoenix in the city centre."
},
{
"source": "e2e",
"text": "The Phoenix is a poor rated restaurant in the city centre."
}
]
},
{
"tripleset": [
[
"The Phoenix",
"eatType",
"restaurant"
],
[
"The Phoenix",
"customer rating",
"low"
],
[
"The Phoenix",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "A Riverside restaurant called The Phoenix has a poor customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is not family-friendly and is located in city centre with outstanding ratings."
},
{
"source": "e2e",
"text": "The Punter is in city centre with outstanding ratings and not family-friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is located in the City Centre, is family-friendly but is also rated lowly."
}
]
},
{
"tripleset": [
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter has the friends on the riverside."
},
{
"source": "e2e",
"text": "An average place in Riverside welcomes guests of all ages called The Punter."
},
{
"source": "e2e",
"text": "Very Poor unfriendly The Punter near riverside is bad."
},
{
"source": "e2e",
"text": "Do not go to The Punter near riverside."
}
]
},
{
"tripleset": [
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "In a riverside location The Punter is family friendly with a top customer rating."
},
{
"source": "e2e",
"text": "The Punter is an average family friendly spot in riverside."
},
{
"source": "e2e",
"text": "In Riverside, there's an average place called The Punter that welcomes children."
},
{
"source": "e2e",
"text": "Wow The Punter is a children Friendly near to riverside"
},
{
"source": "e2e",
"text": "good place for children and for food the name is The Punter area riverside"
},
{
"source": "e2e",
"text": "The Punter is a children Friendly place and good for eating food. area riverside"
},
{
"source": "e2e",
"text": "Average family friendly location, The Punter, is in riverside."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly place to eat in the riverside area. Customer have rated it highly."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "On the riverside, The Punter is kid friendly but with poor ratings. Customers give them a 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a children friendly with the customer rating of 1 out of 5 and located in Riverside."
},
{
"source": "e2e",
"text": "The Punter in riverside is a child friendly venue with a 1 out of 5 rating."
},
{
"source": "e2e",
"text": "The Punter in the riverside area is children friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is child friendly and is in the riverside area. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is located in Riverside. It is kids friendly and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is located in the riverside area with a 1 out of 5 rating. It is kid friendly."
},
{
"source": "e2e",
"text": "The Punter is kid friendly, located in the riverside area, and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter offers a child friendly atmosphere in Riverside. They have a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly venue located on the riverside. Customers have rated The Punter 1 out of 5."
},
{
"source": "e2e",
"text": "With a customer rating of 1 out of 5, child friendly The Punter is situated in the riverside area."
},
{
"source": "e2e",
"text": "Located by the riverside, The Punter offers a kids friendly environment with a customer rating of one out of five."
},
{
"source": "e2e",
"text": "The Punter is kids friendly and located in Riverside. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a child friendly venue with a 1 out of 5 rating in riverside."
},
{
"source": "e2e",
"text": "The Punter in the riverside area has a customer rating of 1 out of 5. It is children friendly."
},
{
"source": "e2e",
"text": "The Punter is a child friendly establishment located by the riverside with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is an establishment located on the riverside with a kid friendly environment, but a poor customer rating. They only score 1 out of 5 with customers."
},
{
"source": "e2e",
"text": "Riverside offers a child friendly atmosphere in The Punter. Their customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"3 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter In which many competitors are competing 3 out of 5"
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Customer friendly, The Punter, has a customer rating of 3 out of 5 and is located near riverside."
},
{
"source": "e2e",
"text": "The Punter is located near the riverside is customer friendly with a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Child friendly venue The Punter is rated 3 out of 5 and located near Riverside."
},
{
"source": "e2e",
"text": "Located on the riverside, The Punter has a 3 out of 5 rating and is kid friendly."
},
{
"source": "e2e",
"text": "The Punter in riverside has a customer rating of 3 out of 5 and is kid friendly."
},
{
"source": "e2e",
"text": "The riverside hosts The Punter which is both kid friendly and has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Punter is a riverside, kid friendly area, with a rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly with customer rating of 3 out of 5, located in Riverside"
},
{
"source": "e2e",
"text": "The Punter has a customer rating of 3 out of 5 and is kid friendly. It is located in riverside."
},
{
"source": "e2e",
"text": "The Punter, In riverside, Yes its child friendly, Customers give it a 3 out of 5."
},
{
"source": "e2e",
"text": "Yes Child friendly, The Punter in riverside, gets a 3 out of 5."
},
{
"source": "e2e",
"text": "A great kid friendly area that is riverside is The Punter, it is rated 3 out of 5 by consumers."
},
{
"source": "e2e",
"text": "In the riverside area there is a child friendly place called The Punter which rates 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a child-friendly establishment at the Riverside. It is rated 3 out of 5."
},
{
"source": "e2e",
"text": "In the riverside area is a child friendly establishment with a customer rating of 3 out of 5 called The Punter."
},
{
"source": "e2e",
"text": "In riverside with a customer rating of 3 out of 5, is a venue for kids called The Punter. yes its very good indeed."
},
{
"source": "e2e",
"text": "Customer gives The Punter a rating of 3 out of 5 because its a Children-friendly place and located at the riverside."
},
{
"source": "e2e",
"text": "Rated 3 out of 5 is The Punter, a child-friendly location at the riverside."
},
{
"source": "e2e",
"text": "kid friendly yes. located near the riverside. it has a 3 out of 5 rating. and the name is The Punter"
},
{
"source": "e2e",
"text": "it is kid friendly yes near riverside 3 out of 5 stars at The Punter"
},
{
"source": "e2e",
"text": "Located near Riverside is child friendly venue The Punter. The Punter is rated 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter located at the riverside has a Customer rating of 3 out of 5 and a Children-friendly place."
},
{
"source": "e2e",
"text": "The Punter is a child friendly place with a 3 out of 5 rating in the riverside area."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre, The Punter is a good place to bring the kids. 3 out 5 stars"
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is located in the city centre, is not family-friendly, and has a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is not family-friendly, and located in the city centre. It has a rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an upscale place located in the centre of the city. Not recommended for children. 5 out 5 stars."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "Made for grown up evenings out, The Punter is 5 out of 5 not only for location, by the riverside but for the food."
},
{
"source": "e2e",
"text": "The Punter is a 5 out of 5 riverside venue."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located by the riverside, The Punter has a 5 out of 5 customer rating, and is not family-friendly."
},
{
"source": "e2e",
"text": "The Punter is highly rated but not family-friendly and is located in the riverside area."
},
{
"source": "e2e",
"text": "The Punter is a riverside venue with a 5 out of 5 customer rating. It is not family-friendly."
},
{
"source": "e2e",
"text": "Located in the riverside area The Punter although not family-friendly, is highly rated as 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a kids friendly place situated by the riverside that received 1 out 5 star rating."
},
{
"source": "e2e",
"text": "The Punter is child friendly. It is located in riverside and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Yes it is children friendly and has a customer rating of 5 out of 5. The Punter is located in riverside."
},
{
"source": "e2e",
"text": "With a children-friendly environment, The Punter has a 5 out of 5 customer rating and is located in the riverside area."
},
{
"source": "e2e",
"text": "A highly rated place, 5 out of 5, in the riverside area is The Punter and it is also family friendly."
},
{
"source": "e2e",
"text": "The Punter has a 5 out of 5 rating and a children friendly environment in riverside."
},
{
"source": "e2e",
"text": "The Punter is family-Friendly with a customer rating of 5 out of 5 the riverside area."
},
{
"source": "e2e",
"text": "The Punter is a children-friendly riverside venue. Customer rating 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a family friendly place in the riverside area with a high customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is in the riverside has a customer rating of 5 out of 5 and is child friendly."
},
{
"source": "e2e",
"text": "The Punter in riverside is kid friendly is rated 5 out of 5."
},
{
"source": "e2e",
"text": "5 out of 5 rating riverside venue that is family friendly called The Punter."
},
{
"source": "e2e",
"text": "The Punter in riverside is family friendly and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Punter gets 5 out of 5 rating. It's in riverside and is children friendly."
},
{
"source": "e2e",
"text": "In riverside is The Punter which has a children friendly atmosphere as well as a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Punter is children friendly with 5 out of 5 rating in riverside."
},
{
"source": "e2e",
"text": "Located near a riverside, The Punter is family friendly and received a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is located in riverside. Yes it is children friendly and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is rated 5 out of 5 and is kid friendly. It is in riverside."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter Customer rating is 5 out of 5. It is people like children friendly yes."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter in the city centre has an average customer rating, and says yes to families."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre you will find The Punter. It is not family friend and has an average rating."
},
{
"source": "e2e",
"text": "The Punter is located in the city centre, it has an average rating and is not family-friendly."
},
{
"source": "e2e",
"text": "Located in the city centre, The Punter has an average customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "The Punter has an average customer rating and is located in the city centre. It is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "situated near the city centre is The Punter, although does not cater for children has an average customer rating."
},
{
"source": "e2e",
"text": "The Punter with an average customer review is situated in the city centre, although no facilities for children."
},
{
"source": "e2e",
"text": "The Punter is family-friendly with an average rating located in city centre."
},
{
"source": "e2e",
"text": "family-friendly in city centre is The Punter. It has a average customer rating."
},
{
"source": "e2e",
"text": "The Punter has an average customer rating. Situated in the city centre; it's a yes for families."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter gave an average family friendly rating to the riverside area."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter its a great place on riverside , has an average rating but not good for family"
},
{
"source": "e2e",
"text": "The Punter is in riverside, rated average but not family-friendly."
},
{
"source": "e2e",
"text": "Located in the riverside area, The Punter is adult only with average ratings."
},
{
"source": "e2e",
"text": "The Punter is adult only located in the riverside area with average ratings."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter its on the area of riverside with a average rating is not recommended for family"
},
{
"source": "e2e",
"text": "The Punter, in riverside, is rated average but not a good choice for family."
},
{
"source": "e2e",
"text": "The Punter in riverside is family friendly and has average customer ratings."
},
{
"source": "e2e",
"text": "The Punter has an average customer rating, is family friendly and is located in the riverside area."
},
{
"source": "e2e",
"text": "with an average rating The Punter at riverside is children friendly"
},
{
"source": "e2e",
"text": "In the riverside area you will find The Punter which is family friendly and has an average rating."
},
{
"source": "e2e",
"text": "The Punter at Riverside is Children friendly and has an average rating"
},
{
"source": "e2e",
"text": "The Punter at riverside has an average rating is children friendly"
},
{
"source": "e2e",
"text": "The Punter with an average rating and child friendly can be found by near the river side"
},
{
"source": "e2e",
"text": "Near the riverside The Punter has an average customer rating and also child friendly"
},
{
"source": "e2e",
"text": "The Punter at riverside is children friendly and has an average rating"
},
{
"source": "e2e",
"text": "The Punter in a family friendly venue in riverside with an average rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "in the river side The Punter has an average customer rating and is family friendly."
},
{
"source": "e2e",
"text": "The Punter has a customer rating of average and is family-Friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you want somewhere to take the kids on the riverside, come to The Punter - highly rated"
},
{
"source": "e2e",
"text": "In riverside area, there is The Punter, which is high rated by customers and kids are friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "High customer rating and kids Friendly is The Punter located at a riverside area"
},
{
"source": "e2e",
"text": "The Punter is located in Riverside, and is child friendly with a high customer rating."
},
{
"source": "e2e",
"text": "A child friendly location in the riverside area is The Punter. It has a high customer rating."
},
{
"source": "e2e",
"text": "In a riverside area is The Punter, Kids friendly with a high customer rating"
},
{
"source": "e2e",
"text": "In Riverside, a highly rated, kid friendly place is The Punter."
},
{
"source": "e2e",
"text": "The Punter has a high customer rating, is kid friendly, and is near the riverside."
},
{
"source": "e2e",
"text": "The Punter in riverside has a high customer rating and is kids friendly."
},
{
"source": "e2e",
"text": "The Punter is child friendly with a high customer rating. They are located in the riverside area."
},
{
"source": "e2e",
"text": "Kid friendly with a high customer rating, The Punter is in the riverside area."
},
{
"source": "e2e",
"text": "There is a venue in the Riverside area called The Punter that is children friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "The Punter has high customer rating and is kid friendly located in riverside."
},
{
"source": "e2e",
"text": "The Punter is a children friendly venue in the Riverside area with a high customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"low"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter does not allowed any children and it's an only 1 star rating."
},
{
"source": "e2e",
"text": "The Punter have very low rating and only meant for adults."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a a low rated place to eat in the city centre called The Punter. It is not family-friendly."
},
{
"source": "e2e",
"text": "The Punter in the city centre is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Low rated The Punter can be found in the city centre, not family-friendly."
},
{
"source": "e2e",
"text": "The Punter, located in City Centre, is not family friendly and has a really low rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a low rated venue in the city centre, not recommended for children."
},
{
"source": "e2e",
"text": "The Punter is family-friendly. It is locate in the city centre. It has a low customer rating."
},
{
"source": "e2e",
"text": "The Punter has a low customer rating. It's family-friendly and is located in the City Centre."
},
{
"source": "e2e",
"text": "The Punter is family-friendly. It is locate in the city centre. It has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is in the riverside area. It is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "The Punter is located in riverside. It is not family-friendly and has a low customer rating."
},
{
"source": "e2e",
"text": "Located in riverside, The Punter has a low customer rating and is not family-friendly."
},
{
"source": "e2e",
"text": "In the riverside area is a non family-friendly venue called The Punter. It has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Describing itself as family friendly, The Punter at the riverside continues to have low customer ratings."
},
{
"source": "e2e",
"text": "The Punter is low rated and child friendly located on the riverside"
},
{
"source": "e2e",
"text": "Located riverside, The Punter is children friendly, and has a low customer rating."
},
{
"source": "e2e",
"text": "The Punter has a low customer rating, is family friendly and located in riverside"
},
{
"source": "e2e",
"text": "The Punter might have low ratings...but it is riverside and offers a children Friendly great time."
},
{
"source": "e2e",
"text": "The Punter in riverside is family friendly but customers give it a low rating"
},
{
"source": "e2e",
"text": "The low customer rated venue, The Punter, is a family-friendly venue in riverside."
},
{
"source": "e2e",
"text": "The Punter has a low customer rating is in riverside and is family friendly"
},
{
"source": "e2e",
"text": "Riverside has a kid-friendly place called The Punter, it has a low customer rating."
},
{
"source": "e2e",
"text": "In the riverside is a children friendly place called The Punter. It is however rated low by its customers."
},
{
"source": "e2e",
"text": "The Punter is a family-friendly venue in riverside with a low customer rating."
},
{
"source": "e2e",
"text": "With a low customer rating, The Punter is a children friendly place located in a riverside area."
},
{
"source": "e2e",
"text": "The Punter is child friendly establishment. It is low rated and located on the riverside."
},
{
"source": "e2e",
"text": "The Punter is a family friendly with a low rating in the riverside area."
},
{
"source": "e2e",
"text": "The Punter is located in Riverside. It is child-friendly, and has a low customer rating."
},
{
"source": "e2e",
"text": "Despite low customer ratings, The Punter at the riverside is family friendly."
},
{
"source": "e2e",
"text": "If you want low ratings and a children Friendly place come have fun come to The Punter in riverside."
},
{
"source": "e2e",
"text": "The Punter is a low rated, child friendly establishment. It is located riverside."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family-friendly coffee shop located outside the city centre near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop that has high customer rating and is not kids friendly it is located near Caf\u00e9 Sicilia"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a three star coffee shop that serves mid ranged priced meals. It is located near Caf\u00e9 Sicilia. This restaurant permits families with children."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter coffee Shop is a 5 star family restaurant, located in Caf\u00e9 Sicilia"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is located near Caf\u00e9 Sicilia. This is a Chinese restaurant with a price range over thirty dollars. It is also a coffee shop. The customers rate it high. It is not children friendly."
},
{
"source": "e2e",
"text": "The customers rate The Punter high. The price range is over thirty dollars. It is a Chinese restaurant and a coffee shop. It is located near Caf\u00e9 Sicilia. The Punter is not children friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "An inexpensive Chinese restaurant choice is The Punter, located near Caf\u00e9 Sicilia. They have a coffee shop feel and an average customer rating. However, they are not a family friendly restaurant."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a Chinese food restaurant and coffee shop with a 1 out of 5 customer rating. Located near Caf\u00e9 Sicilia, its prices are in the high range. It is not child friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter, a coffee shop that serves high priced Chinese food is children friendly and located near Caf\u00e9 Sicilia. The restaurant has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop , they serve Chinese food , the price range is moderate , the customer rating is 1 out of 5 , the people in the restaurant are not friendly with kids , and it is near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop offering Chinese food in the \u00a320-25 range. Located near the Caf\u00e9 Sicilia, this high rated restaurant is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop that provides British food. The high-cost restaurant is located by Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Close to Caf\u00e9 Sicilia , The Punter coffee shop is a mid-price family restaurant serving British cuisine."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an expensive coffee shop located by the Caf\u00e9 Sicilia. The restaurant provides British food in a family friendly environment"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an English restaurant that is moderately priced. They are child friendly and located near the coffee shop, Caf\u00e9 Sicilia. They are a three of five star dining establishment."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an English restaurant. They are moderately priced and child friendly. They are located near the coffee shop, Caf\u00e9 Sicilia. Most customers rate them as an average place to eat."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter coffee Shop is a 5 Star, Inexpensive, Family restaurant located in Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop The Punter is near Caf\u00e9 Sicilia and serves Chinese food. It is okay priced and has average reviews. But is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Child friendly, mid-priced, and highly rated, The Punter is a Chinese coffee shop, found near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a highly-rated mid-priced Chinese coffee shop where children are welcome. It can be found near to Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "A Chinese food coffee shop near Caf\u00e9 Sicilia the The Punter. It has high ratings, kid friendly, and the price range is between 20 and 25."
},
{
"source": "e2e",
"text": "The Punter coffee Shop provides Chinese food starting at \u00a330. Customer Rating high, children are welcome, everything near to Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia with customer Rating high, The Punter coffee Shop. We offer Chinese food starting at \u00a330. Children are allowed."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia is The Punter. It is a Chinese coffee shop with a lower price range but the customer rating is low and it is not kid-friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter, near Caf\u00e9 Sicilia is a highly rated coffee shop style venue serving Chinese food at a low price. Unfortunately it is not family friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop that serves Chinese food near Caf\u00e9 Sicilia that has a 5 out of 5 customer rating called The Punter. It is in the low price range and is also family friendly."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop with a customer rating of 5 out of 5 that serves Chinese food in the low price range. It is family friendly and can be found near the Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop that offers cheap Chinese food near Caf\u00e9 Sicilia. It is family friendly and has a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "There is a coffee shop The Punter near Caf\u00e9 Sicilia that family friendly and has a rating of 5 out of 5. They offer Chinese food for a cheap price."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the cheap price range. It is near Caf\u00e9 Sicilia. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter coffee Shop offers an affordable Chinese menu for an adult audience who have rated it 5 out of 5. Located close to the Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter, located near Caf\u00e9 Sicilia, is a coffee shop style eatery, serving cheap Chinese food. While it does not cater to families, it does enjoy a high customer rating, scoring 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter coffee Shop offers a delicious Chinese menu at cheap prices. Caters for an adult audience who have rated it 5 out of 5. Located close to the Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the cheap price range. It is near Caf\u00e9 Sicilia. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the cheap price range. It is near Caf\u00e9 Sicilia. Its customer rating is 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "An average rated coffee shop near Caf\u00e9 Sicilia called The Punter offers cheap Chinese food at a non family friendly coffee shop."
},
{
"source": "e2e",
"text": "A cheap coffee shop near Caf\u00e9 Sicilia is selling Chinese food. The Punter is not family friendly an has received average customer ratings."
},
{
"source": "e2e",
"text": "The Punter is a cheap coffee shop selling Chinese food near Caf\u00e9 Sicilia. It is not family friendly and has received average customer ratings."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop that offers Chinese food. They are located near Caf\u00e9 Sicilia and have an inexpensive menu. Customers have given them an average rating. Please keep in mind they are not family friendly."
},
{
"source": "e2e",
"text": "If you are looking for something near Caf\u00e9 Sicilia, The Punter is pretty close by. It is a coffee shop, not family friendly, which also serves Chinese food. It may have average customer ratings, but its prices are cheap."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop, offering cheap Chinese food. Found near Caf\u00e9 Sicilia, it is a family friendly venue with an average rating."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop venue located near Caf\u00e9 Sicilia. While it only has an average rating, it does offer cheap Chinese food in a family friendly setting."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the cheap price range. It is near Caf\u00e9 Sicilia. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter coffee Shop offers Chinese food at affordable prices. Family friendly. Normal customer ratings. Find it near to the Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter coffee Shop offers a delicious Chinese menu at affordable prices. Family friendly with reasonable customer ratings. Find it near to the Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "A non family friendly coffee shop found near Caf\u00e9 Sicilia named The Punter, serves Chinese food at a cheap price with an average customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter has a 1 out of 5 customer rating. It offers Chinese food and a coffee shop. Prices are in the high range and it is not child friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop serving expensive Chinese food near Caf\u00e9 Sicilia with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is an expensive coffee shop with Chinese food near Caf\u00e9 Sicilia. It is family friendly with a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an adult coffee shop that serves Chinese food. It is expensive, rated low by its customers, and is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "There is an adult Chinese coffee shop near Caf\u00e9 Sicilia that serves expensive, low-rated food named The Punter."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a high priced coffee shop that provides Chinese food located near Caf\u00e9 Sicilia. It is not children friendly and the customer rating is average."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food at a high price range located near Caf\u00e9 Sicilia. The customer rating is average and it is not children friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a child friendly coffee shop with Chinese food near Caf\u00e9 Sicilia,It has an average customer rating and its price high range."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia there is a coffee shop called The Punter that serves Chinese food with an average rating, high price range and is child friendly"
},
{
"source": "e2e",
"text": "There is a high priced Chinese coffee shop The Punter near Caf\u00e9 Sicilia. Average rating and kid friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is average."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the high price range. It is near Caf\u00e9 Sicilia. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop with a high price range near Caf\u00e9 Sicilia that serves Chinese and is child friendly"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an expensive Chinese coffee shop located near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop near Caf\u00e9 Sicilia offering Chinese food for less than 20 Euros but it has a low customer rating. It is not family-friendly."
},
{
"source": "e2e",
"text": "the coffee shop The Punter near Caf\u00e9 Sicilia serves Chinese food with a price range of less than 20 pounds. It is not family friendly and has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee chop that offers Chinese food selections. It is low rated and cheap but welcomes the whole family. Located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shot offering Chinese food for less than \u00a320 and is located near Caf\u00e9 Sicilia. While it has a low customer rating is it family friendly."
},
{
"source": "e2e",
"text": "The Punter is a inexpensive family friendly coffee shop that sells Chinese food near Caf\u00e9 Sicilia. However, it has a low customer rating."
},
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop that serves Chinese food for less than \u00a320. It has a low customer rating and is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter is a family friendly coffee shop. Although customer ratings are low, it offers Chinese for less than \u00a320."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop that provides Chinese food for less than \u00a320. It has a low customer rating and is family friendly. It is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter is a coffee shop that serves Chinese food for less than \u00a320. It is a family friendly shop, but its customer rating is low."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the less than \u00a320 price range. It is near Caf\u00e9 Sicilia. Its customer rating is low."
},
{
"source": "e2e",
"text": "near Caf\u00e9 Sicilia there's a low customer rated coffee shop that serves Chinese food with a price range of less than 20 pounds called The Punter."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the less than \u00a320 price range. It is near Caf\u00e9 Sicilia. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a Chinese coffee shop in the moderate price range with a customer rating of 1 out of 5. It is located near Caf\u00e9 Sicilia and is not kids friendly."
},
{
"source": "e2e",
"text": "Chinese coffee shop The Punter is located near Caf\u00e9 Sicilia and is in the moderate price range. It has a customer rating of 1 out of 5 and is not kids friendly."
},
{
"source": "e2e",
"text": "The Punter, a Chinese, coffee shop style eatery near the Caf\u00e9 Sicilia, has only a 1 out of five customer rating. It's moderately priced, but not kid friendly."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop style Chinese eatery located near Caf\u00e9 Sicilia. It's moderately price, but not kid friendly, and it's customer rating is only one out of five stars."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a kid-friendly coffee shop, offering Chinese food at a moderate price. It is located near Caf\u00e9 Sicilia. It has been rated by customers 1 out of 5."
},
{
"source": "e2e",
"text": "Chinese food, coffee shop-The Punter-is only rated 1 of 5 by customers despite its moderate price range. The rating could be due to its kid friendly atmosphere. It is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop that serves Chinese food and is kids friendly with a moderate price range and a rating of 1 out of 5 near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop that provides Chinese at a moderate price Range. They are kids Friendly with a 1 out of 5 customer rating near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "There is a Chinese coffee shop near Caf\u00e9 Sicilia in the moderate price range called The Punter. It has a customer rating of 1 out of 5 and is kid friendly."
},
{
"source": "e2e",
"text": "The Punter is a Chinese coffee shop in the moderate price range. It is kid friendly with a customer rating of 1 out of 5 near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter coffee shop serves Chinese food and is moderately priced. It has average reviews and is not kid friendly. It is near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter is a non kid friendly coffee shop that serves Chinese food and has a 3 star rating. The price range is moderate."
},
{
"source": "e2e",
"text": "The Punter, a coffee shop, serves Chinese food within the moderate price range. It is located near Caf\u00e9 Sicilia, and is not kid friendly, with a 3 star rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter coffee shop offers Chinese food at a moderate price. Has a customer rating 3 out of 5. Yes it is kid friendly and near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter coffee shop has a customer rating of 3 out of 5. It is family friendly, offers Chinese food at a moderate price and is located near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the moderate price range. It is near Caf\u00e9 Sicilia. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the moderate price range. It is near Caf\u00e9 Sicilia. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the moderate price range. It is near Caf\u00e9 Sicilia. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the moderate price range. It is near Caf\u00e9 Sicilia. Its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the moderate price range. It is near Caf\u00e9 Sicilia. Its customer rating is 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop that serves Chinese at a moderate price. The customer rating is 1 out of five and they don't like kids but it is near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"more than \u00a330"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a Chinese coffee shop with price range of more than 30 pounds. It has high customer rating, is not children friendly, and is near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia is a Chinese coffee shop named The Punter. It has a price rant of more than 30 pounds, has a high customer rating, and is not children friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"more than \u00a330"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the more than \u00a330 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the more than \u00a330 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the more than \u00a330 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the more than \u00a330 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the more than \u00a330 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a high rated coffee shop near the Caf\u00e9 Sicilia. The Punter offers Chinese for \u00a320-25, however, it is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop near Caf\u00e9 Sicilia. It is kid friendly, serves Chinese food and has a high customer rating. Prices are in the average range."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop serving Chinese food. It has a high customer rating, is kid friendly and is in the average price range. It is situated near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter coffee shop is kid friendly and near the Caf\u00e9 Sicilia. It has Chinese food with a price range between 20 to 25. It is highly rated."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly coffee shop with a high customer rating. They serve Chinese food near Caf\u00e9 Sicilia at a moderate price."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the \u00a320-25 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the \u00a320-25 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing Chinese food in the \u00a320-25 price range. It is near Caf\u00e9 Sicilia. Its customer rating is high."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an English coffee shop located near Caf\u00e9 Sicilia. It is average priced and rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're looking for a child friendly coffee shop that serves English food, try The Punter near Caf\u00e9 Sicilia. Pricing on the high end, customers have rated the Caf\u00e9 1 out of 5."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter is a coffee shop serving English food. They have a customer rating of 1 out of 5 and aren't child friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a mature coffee shop, offering British food at exclusive prices-customers rate 1 out of 5 near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a non-family-friendly English coffee shop near Caf\u00e9 Sicilia with an average customer rating and has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A highly rated coffee shop \"The Punter\" serving English food priced between \u00a320 - \u00a325 and is child friendly"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a mid-priced coffee shop serving traditional English food located near Caf\u00e9 Sicilia. It is highly rated and suitable for families."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a English food coffee shop that is not family-friendly near Caf\u00e9 Sicilia with a low customer rating. Price range is less than \u00a320."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop with a low customer rating. It offers English food at a price range of less than \u00a320. Near Caf\u00e9 Sicilia, it is not very family-friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "A family friendly English coffee shop near, Caf\u00e9 Sicilia, has prices below 20 pounds, but has a low customer rating. It's name is The Punter."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop serving British food. It is near Caf\u00e9 Sicilia and family friendly."
},
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop serving British food. It is near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter near Caf\u00e9 Sicilia is a child friendly coffee shop where you can get English food but expect to pay for the average service."
},
{
"source": "e2e",
"text": "The Punter is a family friendly English coffee shop. It is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter coffee shop offers a family friendly atmosphere and mid-price British food near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia there is a mid-priced coffee shop called, The Punter, that serves traditional English food."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop of British food. We are near Caf\u00e9 Sicilia and we are a familiar place."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop of British food. We are near Caf\u00e9 Sicilia and we are a familiar place. Join Us."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing English food. It is located near of Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing English food. It is located near of Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop near Caf\u00e9 Sicilia called The Punter what does English food the price range is cheap also it has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop located near Caf\u00e9 Sicilia serving English food. It is not family-friendly but is cheap and has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, the cheap coffee shop The Punter serves English food. It has a customer rating of 5 out of 5 but is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap, family friendly coffee shop called The Punter near Caf\u00e9 Sicilia. It has a 5 out of 5 customer rating and also serves English food."
},
{
"source": "e2e",
"text": "The Punter, a cheap coffee shop, is near Caf\u00e9 Sicilia. It has a 5 out of 5 customer rating and is family friendly, serving English food."
},
{
"source": "e2e",
"text": "The Punter is a cheap, 5 out of 5, coffee shop located near Caf\u00e9 Sicilia. The Punter is a family friendly place that serves English cuisine."
},
{
"source": "e2e",
"text": "There is a family friendly coffee shop called The Punter near Caf\u00e9 Sicilia. Their you can find English food for cheap prices. Furthermore, this place has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a cheap English coffee shop. It has an average rating, is family-friendly, and is near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "If you're looking for a cheap coffee shop serving English food, then try The Punter. It is family friendly with an average customer rating. it's located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop which serves English food. It is located near Caf\u00e9 Sicilia, and has an average customer rating. It is also in the cheap price range, and is family friendly."
},
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop serves cheap average quality English food in Caf\u00e9 Sicilia"
},
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop serving English food near Caf\u00e9 Sicilia. The prices are cheap and the customer rating is average."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a cheap, not family-friendly, English coffee shop near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an average coffee shop near Caf\u00e9 Sicilia serving cheap English food"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop near Caf\u00e9 Sicilia. It has a 1 out of 5 rating and is high priced. It's serves English food and is children friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an English food coffee shop near Caf\u00e9 Sicilia. It is high priced and not kid friendly with a 1 out of 5 star rating."
},
{
"source": "e2e",
"text": "English food coffee shop, The Punter, is a high priced 1 out of 5 star rated shop near Caf\u00e9 Sicilia. It is not kid friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "A children friendly coffee shop called The Punter is near Caf\u00e9 Sicilia. It's expensive, serves English food, and has a very low rating."
},
{
"source": "e2e",
"text": "The Punter is a high priced English coffee shop located near Caf\u00e9 Sicilia. It is family friendly but has a poor customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia is The Punter coffee shop offering English food at high prices, rated 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is an expensive English coffee shop near Caf\u00e9 Sicilia. The reviews have been pretty bad at 1 out of 5."
},
{
"source": "e2e",
"text": "With an expensive price tag, low quality food currently rated at 1 out of 5, and no room for a whole family, The Punter is a coffee shop selling English food that you should stay away from. You can find it near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a high priced coffee shop offering English food, found next to Caf\u00e9 Sicilia. It is not child friendly and has average customer ratings."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia is a coffee shop offering English food, called The Punter. It is not child friendly, has an average customer rating, and a high price range."
},
{
"source": "e2e",
"text": "The Punter is a high priced English food coffee shop located near Caf\u00e9 Sicilia. It has an average customer rating and is not children friendly."
},
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter is a high priced English coffee shop with an average customer rating. It is not children friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, The Punter is a high priced coffee shop serving English food with an average rating and is children friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop The Punter located near Caf\u00e9 Sicilia that provides English food in high price. It is not children Friendly."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop providing English food in high price. It is located near Caf\u00e9 Sicilia but it is not children Friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Close to Caf\u00e9 Sicilia is a child friendly British coffee shop named The Punter. It is expensive and not very good."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter, a coffee shop near Caf\u00e9 Sicilia that serves English food, have prices less than \u00a320. It has a low customer rating and is not being family-friendly."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia, there's a coffee shop called The Punter. It has a low rating and is not family-friendly, but it serves English cuisine at less than \u00a320."
},
{
"source": "e2e",
"text": "The Punter is an English coffee shop near Caf\u00e9 Sicilia. It cost less than \u00a320, is not family-friendly, and has low customer ratings."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near to Caf\u00e9 Sicilia is the The Punter. This coffee shop serves English food and is family friendly but has low ratings. The food is usually under \u00a320."
},
{
"source": "e2e",
"text": "The Punter is a cheap family friendly coffee shop serving English food. It is located near Caf\u00e9 Sicilia however it's rated low."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia, you can find an English coffee shop named The Punter. It has a low rating, but is family friendly and prices are less than 20 pounds."
},
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop serving English food near Caf\u00e9 Sicilia with a low customer rating and a price range less than \u00a320."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia there is a family friendly coffee shop serving English food called The Punter which has a price range less than \u00a320 and has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a low rated English coffee shop. It is near Caf\u00e9 Sicilia and costs less than \u00a320."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a moderately priced coffee shop serving English food at a moderate price situated near Caf\u00e9 Sicilia, but is not kids friendly and has a customer rating of 1 out of 5"
},
{
"source": "e2e",
"text": "A moderately priced coffee shop serving English food at a moderate price situated near Caf\u00e9 Sicilia, is The Punter but is not kids friendly and has a customer rating of 1 out of 5"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an average priced coffee shop with a customer rating of 1 out of 5 located near Caf\u00e9 Sicilia. They serve English food and aren't child friendly."
},
{
"source": "e2e",
"text": "The Kids Friendly coffee Shop near Caf\u00e9 Sicilia is called The Punter,with English food at a moderate price range but with a customer rating of 1 out of 5"
},
{
"source": "e2e",
"text": "The Punter' is a coffee shop with moderate price range selling English food that is child-friendly. It is near Caf\u00e9 Sicilia and has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter' is a coffee shop with moderate price range selling English food that is near Caf\u00e9 Sicilia and has a customer rating of 1 out of 5. It is child-friendly."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop with English food,thats kids friendly with moderate prices near the Caf\u00e9 Sicilia with a customer rating of 1 out of 5"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "An English coffee shop located near Caf\u00e9 Sicilia, called The Punter, is has moderate price range, customer ratings of 3 out of 5, and is not kid friendly."
},
{
"source": "e2e",
"text": "The Punter is an English coffee shop near Caf\u00e9 Sicilia it's not kid friendly, with a moderate price and customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter, is an English coffee shop near Caf\u00e9 Sicilia. It has moderate price range, customer ratings are 3 out of 5 and is not kid friendly."
},
{
"source": "e2e",
"text": "An English coffee shop near Caf\u00e9 Sicilia, The Punter is not kid friendly and moderate priced. Has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop that serves English food. It is child friendly and its price range is moderate. The Punter has a rating of 3 out of 5, and is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is an English coffee shop near Caf\u00e9 Sicilia. It is in the moderate price range, child friendly, and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a kid-friendly English coffee shop near Caf\u00e9 Sicilia. Their customer rating is 3 of 5 and they have moderate prices."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a non-family-friendly English coffee shop near Caf\u00e9 Sicilia with an average price range and has an average customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an adult only coffee shop near Caf\u00e9 Sicilia moderately rated English eatery offering a moderately priced"
},
{
"source": "e2e",
"text": "The Punter is a moderately rated English eatery offering a moderately priced adult only coffee shop near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop located to the north of Caf\u00e9 Sicilia. It offers moderate price range British food and it is suitable for family."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located to the north of Caf\u00e9 Sicilia, a coffee shop which is known as, The Punter, offers moderate price range British food. It is suitable for everyone and family to visit."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an adult English coffee shop near Caf\u00e9 Sicilia with average prices and a high customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an average-priced English coffee shop near Caf\u00e9 Sicilia. The Punter is not kid-friendly, has average prices, and a high customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "For English food with a high customer Rating, kids Friendly and a price range of \u00a320-25, visit The Punter, a coffee shop near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is an English food serving coffee shop which welcomes kids and rates high. It is near Caf\u00e9 Sicilia and the prices range from 20-25 pounds."
},
{
"source": "e2e",
"text": "Near Caf\u00e9 Sicilia, The Punter is a coffee shop with English food, a high customer Rating, is kids Friendly and a price range of \u00a320-25."
},
{
"source": "e2e",
"text": "A kid friendly coffee shop which rate high near Caf\u00e9 Sicilia and serves English food in the range of 20-25 pounds is The Punter."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a English coffee shop located near Caf\u00e9 Sicilia. It has a price range of \u00a320-25"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a cheap family friendly coffee shop located in City Centre near Caf\u00e9 Sicilia. 1 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop near Caf\u00e9 Sicilia that also offers food. Low prices and a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is an inexpensive coffee and food shop near Caf\u00e9 Sicilia. Family friendly and a 5 star rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "coffee shop near Caf\u00e9 Sicilia called The Punter the customer rating is average and the price range is cheap it is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop and is family friendly. It's cheap and located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a cheap and family friendly coffee shop located next to Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a cheap coffee shop located next to Caf\u00e9 Sicilia. It is family friendly."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near Caf\u00e9 Sicilia, is a coffee shop with a low customer rating called the Punter. The Punter is children friendly and high priced."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"high"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the Caf\u00e9 Sicilia is a coffee shop called The Punter. You can bring your kids but don't forget your wallet as prices are high but reviews are average."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a Cheap coffee shop near Caf\u00e9 Sicilia. It as no family amenities and is only rated one star"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop located next to Caf\u00e9 Sicilia. It serves cheap food and is family friendly."
},
{
"source": "e2e",
"text": "The Punter is a family friendly coffee shop located next to Caf\u00e9 Sicilia. It serves cheap food."
},
{
"source": "e2e",
"text": "The Punter is an inexpensive family friendly coffee shop located next to Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a coffee shop located close to Caf\u00e9 Sicilia that is classified in the low price range."
},
{
"source": "e2e",
"text": "There is a coffee shop close to Caf\u00e9 Sicilia called The Punter, this shop is in the low price range."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter coffee shop offers breakfast and coffee at a moderate price point. It boasts a family friendly atmosphere and is located near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "Moderately priced with excellent ratings, The Punter, located near Caf\u00e9 Sicilia, is a family friendly coffee shop."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a moderately priced, five star coffee shop, welcoming families. It is located near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"coffee shop"
],
[
"The Punter",
"priceRange",
"\u00a320-25"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The moderately priced coffee shop The Punter hosts a family friendly environment, and is located near Caf\u00e9 Sicilia."
},
{
"source": "e2e",
"text": "The Punter is a coffee shop near Caf\u00e9 Sicilia, provides a family friendly environment and is moderately priced."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"pub"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family friendly pub in a riverside location with an excellent customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"area",
"city centre"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is an excellent restaurant located in the city centre."
},
{
"source": "e2e",
"text": "The Punter is an ok, family restaurant located in the city centre."
},
{
"source": "e2e",
"text": "The Punter is restaurant in the city centre with no low rank"
},
{
"source": "e2e",
"text": "No, The Punter is restaurant located in the city centre with low rank"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family-friendly restaurant located in city centre."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is restaurant that located in riverside."
},
{
"source": "e2e",
"text": "The Punter is a restaurant for you and your kids to enjoy the riverside."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a kid friendly restaurant in the area of Riverside that has a customer rating of 1."
},
{
"source": "e2e",
"text": "In the riverside area, The Punter is a family friendly restaurant."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"1 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a kid-friendly restaurant in the Riverside area with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a kid-friendly restaurant in the riverside area with a one out of five customer rating."
},
{
"source": "e2e",
"text": "Located on the riverside, The Punter is a kid friendly restaurant with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "In the Riverside, there is a kid-friendly restaurant named The Punter. Unfortunately, it has only received a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "An average rated, child friendly restaurant at Riverside is The Punter."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly restaurant in the riverside area with a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly restaurant in the riverside area. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a kid friendly restaurant located on the riverside with a 3 out of 5 rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a family-friendly restaurant in city centre called The Punter, and it has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Located in the city centre, The Punter is a family-friendly restaurant with a five out of five customer rating."
},
{
"source": "e2e",
"text": "The Punter is a family-friendly restaurant in the city centre with a five out of five customer rating."
},
{
"source": "e2e",
"text": "The Punter is a family-friendly restaurant in city centre that has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "There is a family-friendly restaurant The Punter located in city centre. Customer rating 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located near the beautiful riverside, The Punter is a 5 out of 5 rated restaurant. Its a laid back eatery, not for families."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Riverside has a family friendly restaurant called The Punter that has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a children-friendly restaurant in the area of Riverside and boasts a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "A family friendly restaurant with 5 out of 5 customer ratings is The Punter. It is along the riverside."
},
{
"source": "e2e",
"text": "The Punter is a child friendly restaurant near riverside with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Punter is a family friendly riverside restaurant. It has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"average"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a riverside area, family friendly restaurant with an average customer rating."
},
{
"source": "e2e",
"text": "The Punter is an average-rated restaurant that is child friendly and located by the riverside"
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"high"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a children friendly restaurant in riverside with a high customer rating."
},
{
"source": "e2e",
"text": "The Punter is a highly-customer-rated restaurant in the riverside that is kids-friendly."
},
{
"source": "e2e",
"text": "The Punter is a child friendly restaurant along the riverside with high customer ratings."
},
{
"source": "e2e",
"text": "A child-friendly restaurant in riverside by the name The Punter has a fairly high rating; their items, therefore, must be well prepared."
},
{
"source": "e2e",
"text": "The Punter is a child-friendly restaurant located riverside. The Punter boasts a high customer rating."
},
{
"source": "e2e",
"text": "Along the river side there is a child friendly restaurant with high ratings called The Punter."
},
{
"source": "e2e",
"text": "The Punter is a child-friendly restaurant in riverside with a high customer rating."
},
{
"source": "e2e",
"text": "A children friendly restaurant in riverside with a high customer rating is The Punter."
},
{
"source": "e2e",
"text": "Located along the river, The Punter is a child-friendly restaurant with a high customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"low"
]
],
"annotations": [
{
"source": "e2e",
"text": "A 1 star non family restaurant is The Punter."
},
{
"source": "e2e",
"text": "The Punter is 1 star non family restaurant."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"city centre"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a family-friendly restaurant in the city centre but has a low customer rating."
},
{
"source": "e2e",
"text": "There is a family-friendly restaurant called The Punter located in the city centre and has a low customer rating."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"riverside"
]
],
"annotations": [
{
"source": "e2e",
"text": "riverside restaurant The Punter children customer Rating very low."
},
{
"source": "e2e",
"text": "riverside restaurant The Punter children customer Rating very low."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a children-friendly restaurant in riverside with a low customer rating."
},
{
"source": "e2e",
"text": "In the riverside area, there is a children-friendly restaurant named The Punter. It has a low customer rating."
},
{
"source": "e2e",
"text": "The Punter is a family friendly restaurant in the riverside area with low customer ratings."
},
{
"source": "e2e",
"text": "The low rated family friendly restaurant The Punter is located on the riverside."
},
{
"source": "e2e",
"text": "The family friendly low rated restaurant The Punter is located on the riverside."
},
{
"source": "e2e",
"text": "The Punter is a family friendly restaurant in Riverside with low ratings."
},
{
"source": "e2e",
"text": "In the riverside area there is a low customer rated, family friendly restaurant named The Punter."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"food",
"Chinese"
],
[
"The Punter",
"priceRange",
"moderate"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a moderate-priced restaurant near Caf\u00e9 Sicilia that serves Chinese food."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a low-priced family friendly restaurant near riverside."
}
]
},
{
"tripleset": [
[
"The Punter",
"eatType",
"restaurant"
],
[
"The Punter",
"priceRange",
"more than \u00a330"
],
[
"The Punter",
"customer rating",
"3 out of 5"
],
[
"The Punter",
"area",
"riverside"
],
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter is a high-priced kids-friendly restaurant in riverside, rated 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Punter",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "go with your children The Punter it is a children friendly place"
}
]
},
{
"tripleset": [
[
"The Punter",
"food",
"English"
],
[
"The Punter",
"priceRange",
"cheap"
],
[
"The Punter",
"customer rating",
"5 out of 5"
],
[
"The Punter",
"familyFriendly",
"yes"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter serves cheap English food Customer rated 5 out of 5. It is a family friendly place near Caf\u00e9 Sicilia."
}
]
},
{
"tripleset": [
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"customer rating",
"low"
],
[
"The Punter",
"familyFriendly",
"no"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter Is Cheap but only has a one star rating. It has no family amenities and is located near Caf\u00e9 Sicilia"
}
]
},
{
"tripleset": [
[
"The Punter",
"priceRange",
"less than \u00a320"
],
[
"The Punter",
"near",
"Caf\u00e9 Sicilia"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Punter, located next to the Caf\u00e9 Sicilia, offers an inexpensive get away from the busy family lives."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"1 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "The kids said yes to The Wrestlers. It is rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"1 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a kids friendly place with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly venue with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Check out the kid friendly, 1-star place, The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is rated 1 out of 5 by customers, but is a child-friendly venue."
},
{
"source": "e2e",
"text": "Bring the family to The Wrestlers. Our new kid friendly venue. Boasting a rating of 1 out of 5, it's fun for the whole family."
},
{
"source": "e2e",
"text": "The Wrestlers have a low rating, but their environment is very kid friendly."
},
{
"source": "e2e",
"text": "Although The Wrestlers has a children friendly environment, it is only rated one star by its customers."
},
{
"source": "e2e",
"text": "Even though it receives a customer rating of 1 out of 5, The Wrestlers is a kids friendly venue."
},
{
"source": "e2e",
"text": "The Wrestlers has 1 out of 5 customer rating. It is a children friendly place."
},
{
"source": "e2e",
"text": "The kids said yes to the kid friendly The Wrestlers. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Poor customer rated establishment The Wrestlers has become child friendly."
},
{
"source": "e2e",
"text": "A kids friendly venue with a low rating is called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is a low-rated venue that is kids friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is kids friendly and has a customer rating 1 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a new kid friendly venue. With a customer rating of 1 out of 5, your kids will love it."
},
{
"source": "e2e",
"text": "Child-friendly venue The Wrestlers is rated 1 out of 5 by its customers."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"1 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Sorrento is child friendly establishment The Wrestlers with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Located near The Sorrento, The Wrestlers is kid-friendly and gets a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly venue near The Sorrento with low ratings."
},
{
"source": "e2e",
"text": "Near The Sorrento, there is a kid friendly venue called The Wrestlers with a rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly place near The Sorrento that is low rated."
},
{
"source": "e2e",
"text": "The Wrestlers has a rating of 1 out of 5. It is kid friendly and located near The Sorrento."
},
{
"source": "e2e",
"text": "Near The Sorrento, there is The Wrestlers has a rating of 1 out of 5, and is kid-friendly."
},
{
"source": "e2e",
"text": "Near The Sorrento is The Wrestlers, a kids friendly place with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "Near The Sorrento is a place called The Wrestlers with the rating 1 out of 5 and kid Friendly yes."
},
{
"source": "e2e",
"text": "Near The Sorrento is The Wrestlers, rated one out of five by customers and children are welcome."
},
{
"source": "e2e",
"text": "The Wrestlers has a rating of 1 out of 5, and is kid-friendly. It is located near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers has a 1 out of 5 customer rating but yes it is children friendly, it is located near The Sorrento"
},
{
"source": "e2e",
"text": "Near The Sorrento is an establishment with a 1 out of 5 customer rating, it is called The Wrestlers. Yes it is children friendly"
},
{
"source": "e2e",
"text": "The Wrestlers is by The Sorrento and its a child friendly place with low ratings"
},
{
"source": "e2e",
"text": "The Wrestlers is found near The Sorrento and is child friendly. It has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "A child friendly venue The Wrestlers can be found near The Sorrento, with a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers Is a low rated kid friendly place near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"3 out of 5"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 3 out of 5"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"3 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a kids friendly place and it has a good customer rating."
},
{
"source": "e2e",
"text": "Customers rated The Wrestlers 3 out of 5 and it is kids friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has a 3 out of 5 customer rating. It is kid-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is kid friendly with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly environment with average reviews."
},
{
"source": "e2e",
"text": "A kid-friendly place with a 3 out of 5 customer rating is The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 3 out of 5 and is kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and received a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The 3 out of 5 customer rated venue, The Wrestlers, offers a kid friendly environment"
},
{
"source": "e2e",
"text": "The Wrestlers is both kid friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and customer rating 3 out of 5."
},
{
"source": "e2e",
"text": "A Family friendly place with a rating 3 out of 5 called The Wrestlers"
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and customer rating 3 out of 5."
},
{
"source": "e2e",
"text": "There is a child friendly place named The Wrestlers with a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 3 out of 5 and is suitable for children."
},
{
"source": "e2e",
"text": "The Wrestlers has a 3 out of 5 customer rating and is child friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"3 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 3 out of 5. It is kid friendly and is near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers is Children Friendly near The Sorrento with customer rating 3 out of 5."
},
{
"source": "e2e",
"text": "Near The Sorrento is a 3 out of 5 rated kids friendly location named The Wrestlers"
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly venue near The Sorrento with a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly and has a customer rating of 3 out of 5. It is near The Sorrento."
},
{
"source": "e2e",
"text": "Near The Sorrento there is a children friendly place called The Wrestlers with a 3 out of 5 rating"
},
{
"source": "e2e",
"text": "Rated 3 out of 5 by customers, The Wrestlers is located near The Sorrento and is kid friendly."
},
{
"source": "e2e",
"text": "The Sorrento is children friendly and customer rated 3 out of 5 by The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is a children friendly place with a 3 out of 5 rating near The Sorrento"
},
{
"source": "e2e",
"text": "Near The Sorrento The Wrestlers is Children Friendly with customer rating 3 out of 5."
},
{
"source": "e2e",
"text": "Near The Sorrento, is a kid friendly place, with a customer rating of 3 out of 5, named The Wrestlers."
},
{
"source": "e2e",
"text": "Kid friendly place, near The Sorrento, with a customer rating of 3 out of 5, is a place named The Wrestlers."
},
{
"source": "e2e",
"text": "Near The Sorrento is The Wrestlers which is child friendly and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly and rated 3 out of 5. It is located near The Sorrento."
},
{
"source": "e2e",
"text": "Child friendly, The Wrestlers, is found near The Sorrento and has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Despite the name, The Wrestlers is a rather children friendly eatery. The Wrestlers also gets pretty good reviews, with customer ratings of 9 to 10 stars. The Wrestlers can be found near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"3 out of 5"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near The Sorrento, The Wrestlers has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a friendly place rated 3 out of 5. Located near The Sorrento"
},
{
"source": "e2e",
"text": "The Wrestlers has a rating of 3 out of 5. It's near The Sorrento and is a good place to take your family."
},
{
"source": "e2e",
"text": "The Wrestlers close to The Sorrento is a friendly place, rated 3 out of 5"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"5 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "With a customer rating of 5 out of 5, The Wrestlers is not family-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 5 out of 5. However, we are not family-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has received a 5 out of 5 customer rating it is not family-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is not family-friendly and has received 5 out of 5 for customer rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"5 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an establishment near The Sorrento called The Wrestlers which is not family-friendly. It has a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a non family-friendly establishment near The Sorrento. It has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"5 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is child friendly and a 5 star rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly place with high customer rating."
},
{
"source": "e2e",
"text": "Family friendly The Wrestlers has 5 star rating."
},
{
"source": "e2e",
"text": "The Wrestlers are both family Friendly and have a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly and rated 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a children friendly place with a 5 out of 5 star rating."
},
{
"source": "e2e",
"text": "Looking for a family friendly venue with a customer rating of 5 out of 5 then check out The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and is rated 5 out of 5."
},
{
"source": "e2e",
"text": "A child friendly venue with high ratings is The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is a highly rated child friendly venue."
},
{
"source": "e2e",
"text": "The Wrestlers family friendly venue. Customer rating 5 out of 5."
},
{
"source": "e2e",
"text": "With 5 out of 5 and children friendly, The Wrestlers is the place to be."
},
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 5 out of 5. It is a family friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers are family friendly and have a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers has a customer rating of 5 out of 5. It is family friendly."
},
{
"source": "e2e",
"text": "The family friendly The Wrestlers has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and rated 5 out of 5 by customers."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and rated 5 out of 5 by customers."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"5 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers, near The Sorrento, has a 5 out of 5 rating and is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a Entertainment near The Sorrento people rating 5 out of 5, children friendly yes"
},
{
"source": "e2e",
"text": "Near The Sorrento, The Wrestlers is a family friendly venue with an exceptional customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers have a 5 out of 5 rating, family friendly, and near The Sorrento."
},
{
"source": "e2e",
"text": "A family friendly, 5 out of 5 customer rating place called The Wrestlers is near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers is a Entertainment near The Sorrento people rating 5 out of 5, children friendly yes"
},
{
"source": "e2e",
"text": "A kid friendly place near The Sorrento with a customer rating of 5 out of 5 named The Wrestlers."
},
{
"source": "e2e",
"text": "Near The Sorrento is The Wrestlers. It is family friendly and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is near The Sorrento. It is child friendly and has a rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The family friendly The Wrestlers, located near The Sorrento, has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers has 5 out of 5 customer rating . It is located near The Sorrento and is children friendly."
},
{
"source": "e2e",
"text": "A children friendly place with a 5 out of 5 customer rating called The Wrestlers is located near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly with a customer rating of 5 out of 5 and is located near The Sorrento."
},
{
"source": "e2e",
"text": "There is a children friendly place called The Wrestlers that is located near The Sorrento and has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and has a customer rating of 5 out of 5. They are located near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers has a 5 out of 5 customer rating and is a family friendly place near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly establishment near The Sorrento. It has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Near The Sorrento is a child friendly establishment called The Wrestlers. It is rated 5 out of 5."
},
{
"source": "e2e",
"text": "Located near The Sorrento, The Wrestlers is child friendly and has a customer rating of 5 out of 5"
},
{
"source": "e2e",
"text": "The Wrestlers is located near The Sorrento and has a customer rating of 5 out of 5 and is children friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a family-friendly that has 5 out of 5 customer rating near The Sorrento"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a place that has average rating and is not family-friendly"
},
{
"source": "e2e",
"text": "The Wrestlers has average customer rating and not suitable for children"
},
{
"source": "e2e",
"text": "The Wrestlers is non family-friendly with an average customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers has an average customer rating and is non family-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is not family friendly and has an average costumer review"
},
{
"source": "e2e",
"text": "The Wrestlers has a average rating and is not family-friendly"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers near The Sorrento has average ratings and is not family-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is located near The Sorrento, has average customer ratings and is not family-friendly."
},
{
"source": "e2e",
"text": "Close proximity of The Sorrento is non-family-friendly venue The Wrestlers; average customer ratings"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Looking for a family friendly venue with an average customer rating, then you need to check out The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers has average Rating and is family-Friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has an average customer rating and is also children friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and has an average rating"
},
{
"source": "e2e",
"text": "The Wrestlers is an average rated child friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers has an average rating and is children friendly"
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly venue with an average rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly venue with an average customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is family friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly environment with an average rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is an averagely rated, family-friendly place near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers, near The Sorrento, is children friendly and has an average customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers are family friendly average rated and live near The Sorrento family."
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly yes, and it is near The Sorrento. It has an average customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers near The Sorrento is averagely rated and is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is family friendly and near The Sorrento, but it only has an average customer rating."
},
{
"source": "e2e",
"text": "Near The Sorrento, The Wrestlers has an average customer rating and is children-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has an average customer rating. It is located near The Sorrento and is kid-friendly."
},
{
"source": "e2e",
"text": "If you're looking for a family-friendly place, you should go to The Wrestlers. It is located near The Sorrento and is rated as average."
},
{
"source": "e2e",
"text": "Family friendly and receiving average ratings, The Wrestlers is in close proximity to The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly with an average customer rating near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers is children friendly. The customer rating is average and it's near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is adult oriented near The Sorrento and has average ratings."
},
{
"source": "e2e",
"text": "The Wrestlers is near The Sorrento. Yes the children are friendly. The customer rating is average."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"high"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a fantastic place for the whole family to attend for a bite to eat. The customer rating is high meaning that you will receive a high standard which can only be expected for the whole family."
},
{
"source": "e2e",
"text": "Looking for a place where you can take the whole family for a bite to eat. The Wrestlers is a fantastic spot to go with a high customer rating it will cater for all your needs."
},
{
"source": "e2e",
"text": "The movie The Wrestlers received a high Yes rating from a movie website"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"high"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A kid friendly group called The Wrestlers has a high customer rating."
},
{
"source": "e2e",
"text": "Not only is The Wrestlers children friendly, it also has a high customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a highly consumer rated and kid friendly establishment."
},
{
"source": "e2e",
"text": "The Wrestlers. Child Friendly with a High Customer Rating"
},
{
"source": "e2e",
"text": "The Wrestlers boats high customer ratings and is kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly place with a high customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a highly rated child friendly environment"
},
{
"source": "e2e",
"text": "A kid friendly establishment with a high customer rating is The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly with a high customer rating"
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly with a high customer rating"
},
{
"source": "e2e",
"text": "The Wrestlers has a high customer rating and is children friendly."
},
{
"source": "e2e",
"text": "With a high customer rating and child friendly, The Wrestlers"
},
{
"source": "e2e",
"text": "Highly rated The Wrestlers is kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has a high customer rating and is kid friendly."
},
{
"source": "e2e",
"text": "Kid friendly, The Wrestlers, is highly rated."
},
{
"source": "e2e",
"text": "The kids friendly location named The Wrestlers has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"high"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is near The Sorrento. It has a high customer rating and it is a child friendly establishment."
},
{
"source": "e2e",
"text": "With a high customer rating, The Wrestlers is located near The Sorrento and is kid friendly."
},
{
"source": "e2e",
"text": "Near The Sorrento is located a high-rated and kids friendly site called The Wrestlers"
},
{
"source": "e2e",
"text": "By The Sorrento, there's a high rated and kid friendly place called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is a high rated kid friendly place near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers, located near The Sorrento, is child friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "Located near The Sorrento is a kid-friendly place named The Wrestlers. It has a high customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is near The Sorrento, is kid friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "Near The Sorrento is The Wrestlers. It is kid friendly. The rating is high."
},
{
"source": "e2e",
"text": "Near The Sorrento, The Wrestlers is highly rated and kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has a high customer rating, is kid friendly and is near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers great place, kids-friendly with high customer rating near The Sorrento"
},
{
"source": "e2e",
"text": "Located near The Sorrento, The Wrestlers is kid-friendly and has a high customer rating."
},
{
"source": "e2e",
"text": "There is a child friendly establishment near The Sorrento called The Wrestlers and it has a high customer rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"low"
]
],
"annotations": [
{
"source": "e2e",
"text": "Adults looking for a bite to eat can easily find The Wrestlers, which has a one star rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a ONE STAR rating."
},
{
"source": "e2e",
"text": "The Wrestlers, with a one star rating, appreciates an adult patron."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a poorly rated, not family friendly venue near The Sorrento in the centre of town."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a non family-friendly show with a low customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a family unfriendly low rated place"
},
{
"source": "e2e",
"text": "The Wrestlers is a non family-friendly show with a low customer rating."
},
{
"source": "e2e",
"text": "With a 1 out of 5 rating, The Wrestlers, would not be considered family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers has a low customer rating and is not family friendly."
},
{
"source": "e2e",
"text": "Family unfriendly low rated place named The Wrestlers"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "Welcome to The Wrestlers, we are not family-friendly. Our customer rating is low and we are near The Sorrento."
},
{
"source": "e2e",
"text": "No families at The Wrestlers. Rated low near The Sorrento."
},
{
"source": "e2e",
"text": "With a low customer rating, The Wrestlers is located near The Sorrento. It is also not family-friendly."
},
{
"source": "e2e",
"text": "Welcome to The Wrestlers, we are not family-friendly. Our customer rating is low and we are near The Sorrento."
},
{
"source": "e2e",
"text": "Near The Sorrento, there is a poorly rated not family friendly venue called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers has a low customer rating and is not family-friendly. It is located near The Sorrento."
},
{
"source": "e2e",
"text": "The Wrestlers near The Sorrento is low rated not family-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a child friendly venue called The Wrestlers, however its customer rating is low."
},
{
"source": "e2e",
"text": "The Wrestlers is a children friendly place with low customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers has a low customer rating but it is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a family-friendly facility with a low customer rating."
},
{
"source": "e2e",
"text": "A family friendly place is called The Wrestlers. It has poor reviews."
},
{
"source": "e2e",
"text": "The Wrestlers have a low customer rating, but are family friendly"
},
{
"source": "e2e",
"text": "The Wrestlers is children-friendly and has low customer rating,"
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly with low ratings by its customers."
},
{
"source": "e2e",
"text": "The Wrestlers has a low customer rating but is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a low rated place, but children friendly."
},
{
"source": "e2e",
"text": "Customers have given a family friendly place named The Wrestlers a low rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a family-friendly venue with a low customer satisfaction rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly venue, however, it has a low customer rating."
},
{
"source": "e2e",
"text": "There is The Wrestlers a children-friendly and has low customer rating,"
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly establishment with a low customer rating."
},
{
"source": "e2e",
"text": "Child friendly, The Wrestlers have low ratings."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly place with a low rating."
},
{
"source": "e2e",
"text": "The Wrestlers is child friendly but has a low customer rating."
},
{
"source": "e2e",
"text": "The family friendly place called The Wrestlers unfortunately has a low customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers are family friendly, but has a low customer rating"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers of one star is located in The Sorrento where all your family can visit."
},
{
"source": "e2e",
"text": "All your family in The Sorrento can visit The Wrestlers of one star."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop The Wrestlers located riverside near Raja Indian Cuisine. They are not children friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop on the river called The Wrestlers located near Raja Indian Cuisine. It is family friendly and mid price ranged."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a Chinese restaurant and coffee shop in the riverside area near Raja Indian Cuisine. It is family friendly and has cheap pricing."
},
{
"source": "e2e",
"text": "If you are looking for a family friendly restaurant with cheap pricing, you may wish to try The Wrestlers. It offers a coffee shop and Chinese food, and is near the Raja Indian Cuisine in the riverside area."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Serving Chinese food, the non-family friendly restaurant, The Wrestlers, is a coffee shop in the less than \u00a320 price range. It is in city centre near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a unique coffee shop in the city centre, near the Raja Indian Cuisine restaurant. They are not family friendly and serve amazing Chinese food that is priced less than \u00a320."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"\u00a320-25"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop offering Chinese food with a price range of 20-25 pounds. This restaurant is located in riverside near the Raja Indian Cuisine restaurant. It is kids friendly."
},
{
"source": "e2e",
"text": "The kids friendly coffee shop is called The Wrestlers. This restaurant is located in riverside near the Raja Indian Cuisine restaurant and offers Chinese cuisine. Meals at The Wrestlers cost 20-25 pounds."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"\u00a320-25"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In riverside near Raja Indian Cuisine is a restaurant called The Wrestlers. A coffee shop that serves Chinese food for \u00a320-25."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop type restaurant located in the city centre with a high price, with a bad kids friendly atmosphere. It offers English food and is located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop type restaurant located in the city centre with a high price, with a bad kids friendly atmosphere. It offers English food and is located near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop which serve English food in the city centre near the Raja Indian Cuisine is a restaurant called The Wrestlers with a moderate price range and not kids friendly place."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly restaurant that serves English food with a moderate price range. They are located near the coffee shop Raja Indian Cuisine in riverside."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city center, near Raja Indian Cuisine, The Wrestlers coffee shop dishes out high class Chinese eats, with an atmosphere that is not children friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in the city centre, neighboring Raja Indian Cuisine, is a coffee shop styled joint called The Wrestlers. This establishment is known for its family friendly atmosphere and highly priced Chinese food."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop serving Chinese food in the mid-price range. It is family-friendly and near Raja Indian Cuisine in the city center."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a mid-price ranged coffee shop called The Wrestlers that serves Chinese food located near Raja Indian Cuisine in the city center."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop offering Chinese food. They are located riverside and are not children friendly. They can be found riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a non family friendly coffee shop in Riverside , near Raja Indian Cuisine. It serves Chinese food for less"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop with Chinese food. They are family friendly and have decent prices. They are near the riverside by Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a moderate Chinese coffee shop near Raja Indian Cuisine in the river side. It is no kids friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a cheap Chinese coffee shop located riverside by Raja Indian Cuisine. It is a family friendly establishment."
},
{
"source": "e2e",
"text": "A cheap Chinese coffee shop near Raja Indian Cuisine located riverside and is family-friendly is The Wrestlers."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the cheap price range. It is located in the riverside. It is near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop located by the riverside located near Raja Indian Cuisine, in the city centre. It serves Chinese food and it has a high price range. It is child friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers coffee shop serves high priced Chinese food in the city center, near Raja Indian Cuisine. Be advised, The Wrestlers is not children friendly."
},
{
"source": "e2e",
"text": "The Chinese coffee shop, The Wrestlers, is located in the city centre near Raja Indian Cuisine. The price range is high and isn't children friendly."
},
{
"source": "e2e",
"text": "The Chinese coffee shop, The Wrestlers, is located in the city centre near Raja Indian Cuisine. This place isn't children friendly, as well as the price range being high."
},
{
"source": "e2e",
"text": "In the city centre, The Wrestlers coffee shop s is expensive. It offers Chinese food and is not children friendly. It is located near to the Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine in the city centre there is a coffee shop called The Wrestlers. It serves Chines food at a high price range and is not children friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop near Raja Indian Cuisine, in the city centre, is The Wrestlers. It also provides Chinese food. It has a high price range and is child friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the high price range. It is located in the city centre. It is near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop in the riverside area, near Raja Indian Cuisine. Their menu offers Chinese food, with prices in the high range, and they are not child friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop that has expensive Chinese food, not child friendly, and is located riverside near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is an expensive coffee shop and that serves Chinese food located near the riverside Raja Indian Cuisine, it is not appropriate for children."
},
{
"source": "e2e",
"text": "There is a riverside coffee shop near Raja Indian Cuisine called The Wrestlers, that serves Chinese food in the high price range. This venue is not suited for families with children."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop that sells Chinese food. IT is a high price range in the riverside area near Raja Indian Cuisine. It is children Friendly"
},
{
"source": "e2e",
"text": "The Wrestlers is a high priced, kid friendly Chinese coffee shop located in the riverside area near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers, near Raja Indian Cuisine in riverside, is a children Friendly coffee shop that sells Chinese food. IT is a high price range."
},
{
"source": "e2e",
"text": "The Wrestlers is in the riverside area near Raja Indian Cuisine. It is a kid friendly coffee shop serving high priced Chinese food."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers coffee shop has Chinese food and is friendly. It is in the riverside area near Raja Indian Cuisine with a high price range."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the high price range. It is located in the riverside. It is near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a non family friendly coffee shop who serves Chinese food and is less than \u00a320. It is near the Raja Indian Cuisine in the city centre."
},
{
"source": "e2e",
"text": "in the less than \u00a320 price range and serving Chinese food, the non-family friendly coffee shop, The Wrestlers is in city centre near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop which serves Chinese food is located in the center of the city near the Raja Indian Cuisine. It serves Chinese food and is considered family friendly with a price range of less than \u00a320."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop which serves Chinese food with a price range of less than \u00a320. It is located in the center of the city near the Raja Indian Cuisine and is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly coffee shop serving Chinese food for less than \u00a320. It is located in the city centre near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly coffee shop located in the city centre near Raja Indian Cuisine. It serves Chinese food with a price range get of less than \u00a320."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is located in the city centre near Raja Indian Cuisine. It is a coffee shop that serves Chinese food for under \u00a320."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the city centre. It is near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's a Chinese and coffee shop that's less than \u00a320, near Raja Indian Cuisine along the riverside that's not really family friendly. It's called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers coffee shop and Chinese is along the riverside near Raja Indian Cuisine. It's generally less than \u00a320 but isn't family friendly."
},
{
"source": "e2e",
"text": "There is a coffee shop in Riverside, near Raja Indian Cuisine called The Wrestlers. It is not family friendly and serves Chinese food for less than \u00a320."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There's this coffee shop that serves Chinese food in the riverside area near Raja Indian Cuisine called The Wrestlers that costs less than \u00a320 and is family friendly."
},
{
"source": "e2e",
"text": "Located on the riverside, near Raja Indian Cuisine, is The Wrestlers. It is a family-friendly, low-priced coffee shop which also serves Chinese food."
},
{
"source": "e2e",
"text": "If you want a low-priced coffee shop on the riverside, then try The Wrestlers. This family-friendly coffee shop also serves Chinese food and it is located near Raja Indian Cuisine"
},
{
"source": "e2e",
"text": "In the riverside area, there is a family friendly coffee shop that sells Chinese food for less than \u00a320 near Raja Indian Cuisine called The Wrestlers."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the less than \u00a320 price range. It is located in the riverside. It is near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers, a coffee shop with Chinese food is near Raja Indian Cuisine riverside. Their prices are less than \u00a320."
},
{
"source": "e2e",
"text": "The Wrestlers, a coffee shop that also serves Chinese food less than \u00a320, is located in the riverside, near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers, a coffee shop in the city centre offering Chinese food, is in the above average price range. It is located near the Raja Indian Cuisine and is not children friendly."
},
{
"source": "e2e",
"text": "With below average prices, The Wrestlers, a non-family friendly coffee shop in the city centre, offers Chinese food and is located near the Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Moderately priced adult-only Chinese coffee shop, The Wrestlers, can be found in the city centre near to Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a moderately priced coffee shop in the city centre, near Raja Indian Cuisine. It serves Chinese food and does not welcome children."
},
{
"source": "e2e",
"text": "The Wrestlers is a moderately priced coffee shop located in the city centre. It provides Chinese food. It is not kids friendly and is located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop that provides Chinese food in the moderate price range. It is located in the city centre near Raja Indian Cuisine. It is not kids friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a non kid friendly coffee shop and moderately priced Chinese establishment located in city centre, near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a moderately priced kid friendly Chinese coffee shop in they city centre near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a moderately priced coffee shop providing Chinese food. It's in the city centre near to Raja Indian Cuisine and is kid friendly"
},
{
"source": "e2e",
"text": "The Wrestlers is a children friendly Chinese coffee shop with a moderate price range located in the city centre near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a Chinese coffee shop in city centre near Raja Indian Cuisine. They offer moderate prices and a kid friendly atmosphere."
},
{
"source": "e2e",
"text": "Kids are welcome at The Wrestlers coffee shop, serving moderately priced Chinese food in the centre of town, near the Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a moderately priced coffee shop serving Chinese food. It's near Raja Indian Cuisine in the city centre and is kid friendly"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine in city centre, enjoy a moderately priced Chinese lunch and coffee at The Wrestlers. This coffee shop does not cater to children."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop which offers Chinese food. It has a moderate price range. It is in a riverside area. It is not kids friendly and it is near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop serving Chinese food with a moderate price. It is located near Raja Indian Cuisine along the riverside and is not kids friendly."
},
{
"source": "e2e",
"text": "No kids in The Wrestlers, it is a Chinese coffee shop, on the riverside near the Raja Indian Cuisine, fairly moderate prices."
},
{
"source": "e2e",
"text": "There is a coffee shop, The Wrestlers, serving Chinese food along the riverside near Raja Indian Cuisine. It is not kids friendly and is moderately priced."
},
{
"source": "e2e",
"text": "The Wrestlers is a Chinese coffee shop that has a moderate price range, located near the riverside near the Raja Indian Cuisine, my advice no children."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the Riverside area near Raja Indian Cuisine there is a kid friendly moderately priced coffee shop named The Wrestlers serving Chinese food"
},
{
"source": "e2e",
"text": "For Chinese food in riverside there is a moderately priced coffee shop that is kid friendly named The Wrestlers near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers coffee shop in the riverside area serves coffee and Chinese food. It is kid friendly and moderately priced. Located near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop providing Chinese food in the moderate price range. It is located in the riverside. It is near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop near Raja Indian Cuisine in the city city. It is moderately priced and serves Chinese food. Children are not welcome."
},
{
"source": "e2e",
"text": "The Wrestlers is a Chinese coffee shop near Raja Indian Cuisine in the river side. Price is moderate, no kids friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers coffee shop offers Kid Friendly Chinese cuisine at a moderate price. Located near the Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"more than \u00a330"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop in Riverside located near the Raja Indian Cuisine, where they serve Chinese food, it's average price for a meal is more than 30 euros and it's a children friendly facility."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"more than \u00a330"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers coffee shop has Chinese food and is family friendly. It is located in the riverside area near Raja Indian Cuisine with a high price range."
},
{
"source": "e2e",
"text": "The Wrestlers coffee shop has Chinese food and is kids friendly. It is located in the riverside area near Raja Indian Cuisine with a high price range."
},
{
"source": "e2e",
"text": "Situated in the riverside area, near Raja Indian Cuisine, you'll find a child-friendly coffee shop with Chinese food on the menu called The Wrestlers. The prices tend to be more than \u00a330."
},
{
"source": "e2e",
"text": "There is a coffee shop The Wrestlers located near Raja Indian Cuisine. They offer Chinese food in the price range of more than \u00a330. They are children friendly and located riverside."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"more than \u00a330"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you're in the riverside area with your children and are looking for a coffee shop that serves Chinese food, The Wrestlers is a good option, though you can expect to spend more than \u00a330. It is located near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"Chinese"
],
[
"The Wrestlers",
"priceRange",
"\u00a320-25"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly coffee shop that serves Chinese with an average meal for \u00a320-25. Its in riverside near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop placed at north of city centre close to Raja Indian Cuisine. You can go with all your family to this exclusive shop if you love the traditional British food."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located on the Riverside, close to Raja Indian Cuisine, you will find the coffee shop The Wrestlers, serving a traditional English menu which is highly priced and not considered child friendly."
},
{
"source": "e2e",
"text": "Catering a traditional English menu, The Wrestlers coffee shop sits on the riverside, and offers a highly priced alternative to the nearby Raja Indian Cuisine. The Wrestlers is not considered child friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly coffee shop located along the riverside near Raja Indian Cuisine. It serves English food for \u00a320 - \u00a325."
},
{
"source": "e2e",
"text": "The Wrestlers is a kids friendly coffee shop that serves English food for \u00a320 - \u00a325. It is along the riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Serving English food on the riverside in The Wrestlers. A kid friendly coffee shop in the price range \u00a320 - \u00a325. The Wrestlers is located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine by riverside there is an English coffee shop called The Wrestlers. It has prices lower than \u00a320 and is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly coffee shop with prices less that \u00a320. This English coffee shop is located by riverside near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a Luton based adults only coffee shop, sited near Raja Indian Cuisine serving English food in the mid range bracket."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The English coffee shop, 'The Wrestlers', is a highly priced place near Raja Indian Cuisine. The food has more of an adult taste to it."
},
{
"source": "e2e",
"text": "A family coffee shop in the mid price range called The Wrestlers can be found by a river near Raja Indian Cuisine. The Wrestlers serves British cuisine."
},
{
"source": "e2e",
"text": "You can enjoy British cuisine at The Wrestlers, a family coffee shop in the mid-price range by a river and near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a cheap family friendly coffee shop on riverside called The Wrestlers, They serve English food, and are located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Come visit riverside and eat some English food at The Wrestlers family friendly coffee shop for a cheap price. Found near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a cheap coffee shop located in riverside near Raja Indian Cuisine, they serve English food and our family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly coffee shop which offer English food for a cheap price. Located in riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Near the Raja Indian Cuisine in riverside is a cheap family friendly English coffee shop called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is in the riverside area near Raja Indian Cuisine. It is a coffee shop serving cheap English food and is great for families."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop located at riverside near Raja Indian Cuisine serves English food and a Family friendly place that a cheap price range of food."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly coffee shop. It serves low price range English food. It is located near Raja Indian Cuisine in the riverside area."
},
{
"source": "e2e",
"text": "The Wrestlers is a cheap family friendly English coffee shop in the riverside area near the Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop located in the city centre near Raja Indian Cuisine. It serves high-priced English food in a non kid-friendly environment."
},
{
"source": "e2e",
"text": "The Wrestlers is an English coffee shop that is located in the city centre. It is near Raja Indian Cuisine. It has a high price range, and it is not family-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a high-priced coffee shop that serves English cuisine in an adult environment. It is located in the city centre near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is an English coffee shop near Raja Indian Cuisine in the riverside area. It has a high price range and is not child-friendly."
},
{
"source": "e2e",
"text": "There is an English coffee shop near Raja Indian Cuisine in the riverside area called The Wrestlers. It has a high price range and is not child-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a children friendly coffee shop located in the Riverside area near to the Raja Indian Cuisine. It serves English food and is in the high price range."
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly coffee shop near to Raja Indian Cuisine in the riverside area. They serve English food. The price range is high"
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop near Raja Indian Cuisine and offers English food and is child friendly. It is in riverside and is expensive."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop near to Raja Indian Cuisine which resides in the riverside are. It's child friendly and serves English food. The price range is high"
},
{
"source": "e2e",
"text": "There is an English coffee shop name The Wrestlers in the riverside area. It is near Raja Indian Cuisine, it is expensive, and it is children friendly."
},
{
"source": "e2e",
"text": "There is an expensive child friendly coffee shop in riverside called The Wrestlers that offers English food and it's near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "There is a children friendly English coffee shop in the riverside area. It is high price range. It is called The Wrestlers, and is located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is a high priced coffee shop, that offers English food and is kid friendly. It is located in the riverside area, near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is an English coffee shop located along the river near Raja Indian Cuisine. The prices are quite high and it is not recommended to bring children."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers' is a expensive coffee shop that serves British food that is located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers' is a coffee shop that serves expensive British food . It is located near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a low price coffee shop offering traditional English food near Raja Indian Cuisine in the city center. It's not family-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers, a family-friendly coffee shop offers English food at affordable prices. The Wrestlers is located in the city centre near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "There is a family-friendly coffee shop called The Wrestlers in the city centre near Raja Indian Cuisine. It serves English food in low price range."
},
{
"source": "e2e",
"text": "The Wrestlers is near Raja Indian Cuisine in the city centre area. It is family-friendly and has a price range of less than \u00a320. It serves English food and is a coffee shop."
},
{
"source": "e2e",
"text": "The Wrestlers is a cheap, family-friendly, coffee shop serving English food. They are near to Raja Indian Cuisine in the city centre."
},
{
"source": "e2e",
"text": "The Wrestlers is an English coffee shop near Raja Indian Cuisine in the city centre. It sells things for less than \u00a320 and is family-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a cheap English coffee shop. It is in the city centre near Raja Indian Cuisine. It is family-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "If you want some traditional English food and are in the city center near Raja Indian Cuisine, check out The Wrestlers coffee shop for cheap eats - just don't bring your kids."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers' is a coffee shop located on the riverside, near 'Raja Indian Cuisine'. They serve English food and a price range of less than \u00a320, and are not family-friendly."
},
{
"source": "e2e",
"text": "there is a adults only cheap coffee shop The Wrestlers serving English food located in the riverside area near Raja Indian Cuisine"
},
{
"source": "e2e",
"text": "A cheap English coffee shop in the riverside area near Raja Indian Cuisine is called The Wrestlers. It isn't family-friendly."
},
{
"source": "e2e",
"text": "there is a cheap coffee shop The Wrestlers serving English food located in the riverside area near Raja Indian Cuisine adults only"
},
{
"source": "e2e",
"text": "The Wrestlers is a cheap English coffee shop in the riverside area near Raja Indian Cuisine. It isn't family-friendly."
},
{
"source": "e2e",
"text": "The coffee shop 'The Wrestlers' is located on the riverside, near 'Raja Indian Cuisine'. They serve English food and a price range of less than \u00a320, and are not family-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an inexpensive and family friendly coffee shop serving English fare near Raja Indian Cuisine in riverside called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers coffee shop offers a family friendly environment with a price range of less than \u00a320. We offer English food near Raja Indian Cuisine in the riverside area."
},
{
"source": "e2e",
"text": "For under \u00a320 you can eat at The Wrestlers in riverside. It's a family friendly coffee shop near Raja Indian Cuisine that serves English cuisine."
},
{
"source": "e2e",
"text": "Come to the riverside area near Raja Indian Cuisine and enjoy our coffee shop here at The Wrestlers. We are family friendly, feature English food and have a price range of less than \u00a320."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers, Is a coffee shop and is family-friendly, cheap and reasonable priced is very good for the family , We provide full English food. Located near Raja Indian Cuisine In the city centre."
},
{
"source": "e2e",
"text": "The Wrestlers, Is a coffee shop and is family-friendly, cheap and reasonable priced is very good for the family , We provide full English food. Located near Raja Indian Cuisine In the city centre."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a non kids friendly coffee shop, that has moderate price ranged English food. It is located near Raja Indian Cuisine in the city center."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop which serve English food. The location is near the Raja Indian Cuisine in the city centre area. The place is a not kids friendly with a moderate price range."
},
{
"source": "e2e",
"text": "In the city center near Raja Indian Cuisine, there is a non kids friendly coffee shop named, The Wrestlers. The price range is moderate and they have English food."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop with moderate price featuring English food and is Kid Friendly and is located near Raja Indian Cuisine in the city centre."
},
{
"source": "e2e",
"text": "In the city centre, near Raja Indian Cuisine, there is an English coffee shop named The Wrestlers. It is in the moderate price range and it is child-friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a child-friendly, moderately-priced English coffee shop. It is located in the centre of the city, near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Welcome to The Wrestlers. We are a coffee shop, our price range is moderate, our food is English we're kid friendly located in the city centre by the Raja Indian Cuisine. We have a disclaimer by the door, we wrestle and any blood stains we do not cover."
},
{
"source": "e2e",
"text": "Welcome to The Wrestlers. We are a coffee shop, our price range is moderate, our food is English we're kid friendly located in the city centre by the Raja Indian Cuisine. We have a disclaimer by the door, we wrestle and any blood stains we do not cover."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop located in riverside near Raja Indian Cuisine. It offers English food with a moderate price range. It is not a kid friendly establishment."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop located in riverside near Raja Indian Cuisine. It offers English food with a moderate price range. It is not a kid friendly establishment."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee Shop providing English Food. It is located in riverside near Raja Indian Cuisine. Moderate price range and no kids friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Come to The Wrestlers where they serve moderately priced English food. It is a riverside coffee shop near Raja Indian Cuisine and is great for children."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop that serves English style food. It is moderately priced and kid friendly. It is located on the riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "The Wrestlers is an English coffee shop near riverside near Raja Indian Cuisine. Its price range is moderate and it's kids-friendly."
},
{
"source": "e2e",
"text": "coffee shop, The Wrestlers, is located on the riverside near Raja Indian Cuisine. It serves English style food and is moderately priced. It is also kid friendly."
},
{
"source": "e2e",
"text": "Located in the riverside area near Raja Indian Cuisine, The Wrestlers is a coffee shop that serves English food within a moderate price range and is kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly, moderately priced, coffee shop that serves English food. It is located near Raja Indian Cuisine in riverside."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine is a riverside coffee shop serving English food called The Wrestlers. The prices are moderate and it is kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a child-friendly coffee shop situated by the riverside. It is near Raja Indian Cuisine, and serves English food for a higher-than-average price."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a coffee Shop providing English Food. It is located in riverside near Raja Indian Cuisine. Moderate price range."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers, located near Raja Indian Cuisine, Luton, is a coffee shop serving English food at reasonable prices."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"more than \u00a330"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located at riverside near Raja Indian Cuisine there is a coffee shop called The Wrestlers that serves English food that has a price range of more than \u00a330 and a children friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop near Raja Indian Cuisine. It is on the riverside and is child friendly. The food is English and over \u00a330."
},
{
"source": "e2e",
"text": "The Wrestlers is a coffee shop located at riverside near Raja Indian Cuisine serves English food has a price range more than \u00a330 and a children friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers serves slightly expensive English food, and welcomes children. Located near Raja Indian Cuisine, it is a coffee shop by the riverside."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"more than \u00a330"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine you will find The Wrestlers. A coffee shop serving English food over \u00a330. It is child friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"food",
"English"
],
[
"The Wrestlers",
"priceRange",
"\u00a320-25"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "A kid friendly English coffee shop located in riverside near Raja Indian Cuisine that is moderately priced is called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly English coffee shop located in riverside near Raja Indian Cuisine that is moderately priced."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"high"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre, near Raja Indian Cuisine, there is a coffee shop called The Wrestlers. It has high prices, and it is not children friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Nearby the city centre and Raja Indian Cuisine, The Wrestlers is a cheap coffee shop that is not family friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a family friendly coffee shop located near Raja Indian Cuisine in the city centre called The Wrestlers which offers cheap food."
},
{
"source": "e2e",
"text": "The Wrestlers is a family friendly coffee shop located near Raja Indian Cuisine in the city centre which offers cheap food."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a low-priced family coffee shop located near Raja Indian Cuisine in City Centre"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers, a low price family friendly breakfast and coffee shop near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Visit The Wrestlers, a low priced family friendly breakfast and coffee shop located near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"less than \u00a320"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a low priced, non family coffee shop located next to Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Located next to Raja Indian Cuisine, is a non family low priced coffee shop called The Wrestlers."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"city centre"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a moderately priced coffee shop near Raja Indian Cuisine in the city centre. It is not the type of place people bring their kids."
},
{
"source": "e2e",
"text": "There is a coffee shop named The Wrestlers in the city centre. It is near Raja Indian Cuisine, moderately priced and caters to an adult crowd."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"coffee shop"
],
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"area",
"riverside"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers coffee shop has moderately priced food and is a kid friendly establishment in the riverside area close to Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "In the Riverside area near Raja Indian Cuisine there's a kid friendly, moderately priced coffee shop named The Wrestlers."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"pub"
],
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers near The Sorrento is a family friendly pub with a low rating"
},
{
"source": "e2e",
"text": "A family friendly pub, The Wrestlers is near The Sorrento but has a low rating"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"1 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers children friendly restaurant is rated 1 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers children friendly restaurant is rated 1 out of 5."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"3 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Come eat at the 3 out of 5 rated, kid friendly restaurant called The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is a Children friendly restaurant with a 3 out of 5 customer Rating"
},
{
"source": "e2e",
"text": "The Wrestlers is a child-friendly, moderate restaurant with a rating of 3 out of 5 which is great for those on a budget."
},
{
"source": "e2e",
"text": "Looking for a Children friendly restaurant. Come to The Wrestlers. It has a 3 out of 5 customer Rating"
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly restaurant with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly restaurant with a 3 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"3 out of 5"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "A restaurant called The Wrestlers is near The Sorrento. It has a customer rating of 3 out of 5."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"5 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a child friendly restaurant with a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly restaurant. Customers rate it 5 out of 5 stars."
},
{
"source": "e2e",
"text": "A restaurant with a rating of 5 out of 5 that is children friendly is known as The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers, a children friendly restaurant, has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "A family friendly restaurant with a customer rating of 5 out of 5 is named The Wrestlers."
},
{
"source": "e2e",
"text": "A highly rated restaurant is The Wrestlers, it is family friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a 5 out of 5 restaurant that is child friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a children friendly restaurant with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The children friendly restaurant, The Wrestlers, has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "The Wrestlers is a child friendly restaurant. Customers rate it 5 out of 5 stars."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"average"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "An average customer rated restaurant that is also child-friendly is The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers restaurant has an average customer rating and is children-friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"high"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "A high rated restaurant called The Wrestlers is child friendly"
},
{
"source": "e2e",
"text": "The Wrestlers restaurant has a high customer rating and is child friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly restaurant with a high customer rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a one star children-friendly restaurant."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers welcomes children and is an average restaurant."
},
{
"source": "e2e",
"text": "An average restaurant named The Wrestlers is children friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is an average restaurant that is children friendly."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"customer rating",
"3 out of 5"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "Low priced restaurant by the name The Wrestlers is a child-friendly environment with a moderate rating of 3 out of 5. However, it is great if you're on a budget."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"eatType",
"restaurant"
],
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a cheap family friendly restaurant."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"familyFriendly",
"no"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a not family recommended."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"familyFriendly",
"no"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The highly recommended adults only venue , The Wrestlers, is found near The Sorrento."
},
{
"source": "e2e",
"text": "The adults only highly recommended, The Wrestlers, is located near The Sorrento."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is a children friendly environment"
},
{
"source": "e2e",
"text": "The Wrestlers has satisfactory reviews and is kid friendly."
},
{
"source": "e2e",
"text": "If you are looking for somewhere kids friendly then look at going to The Wrestlers although it does have a customer rating of 3 out 5."
},
{
"source": "e2e",
"text": "The Wrestlers is family friendly with a moderate approval rating."
},
{
"source": "e2e",
"text": "The Wrestlers has a rating of 5 out 5. It is also family friendly."
},
{
"source": "e2e",
"text": "Customers love the family friendly The Wrestlers."
},
{
"source": "e2e",
"text": "The Wrestlers is an average family friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers is an average family friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers is a decent family-friendly place."
},
{
"source": "e2e",
"text": "The Wrestlers is an average child friendly venue."
},
{
"source": "e2e",
"text": "Families are welcome at The Wrestlers."
},
{
"source": "e2e",
"text": "A family place is The Wrestlers although it is only average."
},
{
"source": "e2e",
"text": "The Wrestlers has high customer satisfaction and is kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is both, highly recommended and kid friendly."
},
{
"source": "e2e",
"text": "The Wrestlers is a kid friendly place that is not very good."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"priceRange",
"cheap"
],
[
"The Wrestlers",
"customer rating",
"low"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is inexpensive and child-friendly. It has a low customer rating."
},
{
"source": "e2e",
"text": "The Wrestlers is cheap, children-friendly and has a very low customer rating."
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"priceRange",
"more than \u00a330"
]
],
"annotations": [
{
"source": "e2e",
"text": "No, The Wrestlers with high price not low"
}
]
},
{
"tripleset": [
[
"The Wrestlers",
"priceRange",
"moderate"
],
[
"The Wrestlers",
"familyFriendly",
"yes"
],
[
"The Wrestlers",
"near",
"The Sorrento"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wrestlers is located near The Sorrento, it is average-priced and family-Friendly."
},
{
"source": "e2e",
"text": "Near The Sorrento is located The Wrestlers, it is family-Friendly and average-priced."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Come and visit our new riverside resort called Travellers Rest Beefeater. It is located near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Located near Raja Indian Cuisine. Travellers Rest Beefeater is top rated in riverside"
},
{
"source": "e2e",
"text": "Anyone that has been to the Travellers Rest Beefeater beside the Raja Indian Cuisine by the riverside will tell you that it is a fantastic experience."
},
{
"source": "e2e",
"text": "Customers are highly satisfied with their experience at Travellers Rest Beefeater beside the Raja Indian Cuisine by the riverside."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"1 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine, Travellers Rest Beefeater, has a 1 out of 5 rating and is located riverside."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine at riverside you can find a place called Travellers Rest Beefeater, they have a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "In the riverside area near the Raja Indian Cuisine is the poorly rated Travellers Rest Beefeater."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine in riverside is Travellers Rest Beefeater with a customer rating of only 1 out of 5."
},
{
"source": "e2e",
"text": "Located in the in the riverside area near Raja Indian Cuisine, Travellers Rest Beefeater is rated 1 out of 5."
},
{
"source": "e2e",
"text": "riverside,Travellers Rest Beefeater, near Raja Indian Cuisine, scores a 1 out of 5."
},
{
"source": "e2e",
"text": "On the riverside near Raja Indian Cuisine, is Travellers Rest Beefeater with a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"3 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine on the riverside is Travellers Rest Beefeater with a customer rating of 3 out of 5 stars."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine located in the riverside area is the Travellers Rest Beefeater. This establishment has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "Located near Raja Indian Cuisine in the riverside area, Travellers Rest Beefeater has a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "On the riverside near Raja Indian Cuisine is a place called Travellers Rest Beefeater. its customer rating is 3 out of 5."
},
{
"source": "e2e",
"text": "Look in the riverside area near Raja Indian Cuisine to find Travellers Rest Beefeater with a 3 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine in Riverside is Travellers Rest Beefeater which is rated 3 out of 5 by customers."
},
{
"source": "e2e",
"text": "A riverside eatery near Raja Indian Cuisine in The Travellers Rest Beefeater. It has a rating of 3 out of 5"
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine there is Travellers Rest Beefeater located on the riverside with a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "In riverside near Raja Indian Cuisine, is Travellers Rest Beefeater. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "At the riverside, near Raja Indian Cuisine, is a place called Travellers Rest Beefeater, rated 3 out of 5 by customers."
},
{
"source": "e2e",
"text": "The Travellers Rest Beefeater has a customer rating of 3 out of 5. It is near Raja Indian Cuisine by the riverside."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"5 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"city centre"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Travellers Rest Beefeater in the city center, near Raja Indian Cuisine, has a high customer rating."
},
{
"source": "e2e",
"text": "Customer Rating 5 out of 5 close to Raja Indian Cuisine, Travellers Rest Beefeater, city centre."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"5 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine in the riverside area is Travellers Rest Beefeater which has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "5 out of 5 customer rating Travellers Rest Beefeater can be found at riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Located near Raja Indian Cuisine in the Riverside Area is Travellers Rest Beefeater with a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine by the riverside, Travellers Rest Beefeater has a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "Located near Raja Indian Cuisine on the riverside is Travellers Rest Beefeater, which holds a 5 out of 5 star rating."
},
{
"source": "e2e",
"text": "In Riverside, Travellers Rest Beefeater, near Raja Indian Cuisine has a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "In the riverside area near Raja Indian Cuisine; there is a place called Travellers Rest Beefeater. It has a 5 out of 5 customer rating."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine by the riverside, Travellers Rest Beefeater has a 5 out of 5 rating."
},
{
"source": "e2e",
"text": "Customer Rating: 5 of 5. Travellers Rest Beefeater near Raja Indian Cuisine in riverside."
},
{
"source": "e2e",
"text": "Customer rated 5 out of 5 the Travellers Rest Beefeater in riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Rated 5 out of 5, Travellers Rest Beefeater is located in the riverside area near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"5 out of 5"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "near Raja Indian Cuisine there is the Travellers Rest Beefeater with a high customer rating"
},
{
"source": "e2e",
"text": "Travellers Rest Beefeater is located near Raja Indian Cuisine with a customer rating of 5 out of 5."
},
{
"source": "e2e",
"text": "Located near Raja Indian Cuisine, Travellers Rest Beefeater has a customer rating of 5 out of 5."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"average"
],
[
"Travellers Rest Beefeater",
"area",
"city centre"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Travellers Rest Beefeater is located in the city centre near Raja Indian Cuisine. It has an average customer rating."
},
{
"source": "e2e",
"text": "The Travellers Rest Beefeater is located in the city centre near Raja Indian Cuisine. It has an average customer rating."
},
{
"source": "e2e",
"text": "Near the Raja Indian Cuisine is the Travellers Rest Beefeater in the city centre which has an average rating."
},
{
"source": "e2e",
"text": "The Travellers Rest Beefeater near Raja Indian Cuisine in the city centre has an average rating."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"average"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Travellers Rest Beefeater has an average customer rating. It is located in the riverside area near the Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine is the riverside Travellers Rest Beefeater. It has an average customer rating."
},
{
"source": "e2e",
"text": "A place with an average customer rating in riverside near Raja Indian Cuisine is Travellers Rest Beefeater."
},
{
"source": "e2e",
"text": "The Travellers Rest Beefeater near Raja Indian Cuisine is based on the riverside area and has an average customer rating."
},
{
"source": "e2e",
"text": "In Riverside by Raja Indian Cuisine is Travellers Rest Beefeater. It is average rated."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"average"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine is Travellers Rest Beefeater which has an average customer rating."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"high"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Riverside area, near Raja Indian Cuisine, high customer rating named Travellers Rest Beefeater."
},
{
"source": "e2e",
"text": "In Riverside near Raja Indian Cuisine there is Travellers Rest Beefeater whose customer rating is high."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine, Travellers Rest Beefeater has a high customer rating in Riverside."
},
{
"source": "e2e",
"text": "Along the riverside and near Raja Indian Cuisine is Travellers Rest Beefeater which has a high customer rating."
},
{
"source": "e2e",
"text": "Located in Riverside near Raja Indian Cuisine is a high rated hotel named Travellers Rest Beefeater"
},
{
"source": "e2e",
"text": "In the riverside area near Raja Indian Cuisine eat at Travellers Rest Beefeater high customer rating"
},
{
"source": "e2e",
"text": "Along the riverside near Raja Indian Cuisine is the Travellers Rest Beefeater. It has a high customer rating."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"low"
],
[
"Travellers Rest Beefeater",
"area",
"city centre"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the city centre near the Raja Indian Cuisine is The Travellers Rest Beefeater. The customer rating is low."
},
{
"source": "e2e",
"text": "In the city centre, Travellers Rest Beefeater is near Raja Indian Cuisine but has a low customer rating."
},
{
"source": "e2e",
"text": "The Travellers Rest Beefeater is located in the city centre near the Raja Indian Cuisine. It has a low customer rating."
},
{
"source": "e2e",
"text": "With a low customer rating, Travellers Rest Beefeater is in the city centre near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"customer rating",
"low"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Travellers Rest Beefeater can be found on the riverside near Raja Indian Cuisine, and IT has a low customer Rating."
},
{
"source": "e2e",
"text": "Travellers Rest Beefeater near a riverside by Raja Indian Cuisine gets a low rating."
},
{
"source": "e2e",
"text": "In riverside near the Raja Indian Cuisine there is a low rated place called Travellers Rest Beefeater"
},
{
"source": "e2e",
"text": "Travellers Rest Beefeater located near Raja Indian Cuisine offers low-rated food in riverside."
},
{
"source": "e2e",
"text": "Near the Raja Indian Cuisine in the Riverside area is the Travellers Rest Beefeater with a low customer rating."
},
{
"source": "e2e",
"text": "Near the river and Raja Indian Cuisine is the Travellers Rest Beefeater that has a low customer rating."
},
{
"source": "e2e",
"text": "Travellers Rest Beefeater has a low customer rating. It is in the riverside vicinity near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Travellers Rest Beefeater has low customer ratings and is located in riverside near Raja Indian Cuisine."
},
{
"source": "e2e",
"text": "Near Raja Indian Cuisine on the riverside is Travellers Rest Beefeater with a low customer rating."
},
{
"source": "e2e",
"text": "Located in the riverside area, The Travellers Rest Beefeater near Raja Indian Cuisine has a low customer rating."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"customer rating",
"3 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the riverside are near Raja Indian Cuisine is a customer rated 3 out of 5 restaurant named Travellers Rest Beefeater."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"customer rating",
"5 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"city centre"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "A restaurant located in the city center, near Raja Indian Cuisine, Travellers Rest Beefeater, has a high customer rating."
},
{
"source": "e2e",
"text": "This a restaurant near Raja Indian Cuisine, customer Rating 5 out of 5, Travellers Rest Beefeater, center of the city."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"customer rating",
"5 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "for a 5 out of 5 rated restaurant go to the Travellers Rest Beefeater that is right by the Raja Indian Cuisine on the riverside."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"customer rating",
"average"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "In the Riverside district Travellers Rest Beefeater restaurant has an average rating. It Is located near the Raja Indian Cuisine restaurant."
},
{
"source": "e2e",
"text": "Near to the Raja Indian Cuisine in the riverside area, is the Travellers Rest Beefeater restaurant which has an average customer rating."
},
{
"source": "e2e",
"text": "Located near the Raja Indian Cuisine restaurant - Travellers Rest Beefeater in Riverside has an average rating."
},
{
"source": "e2e",
"text": "In the riverside area near Raja Indian Cuisine, you'll find Travellers Rest Beefeater, a restaurant with an average customer rating."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"customer rating",
"low"
],
[
"Travellers Rest Beefeater",
"area",
"city centre"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Raja Indian Cuisine in city centre is Travellers Rest Beefeater, a low rated restaurant."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"customer rating",
"low"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Close to Raja Indian Cuisine in riverside, there is a restaurant with a low customer rating named Travellers Rest Beefeater."
},
{
"source": "e2e",
"text": "With a low customer rating, Travellers Rest Beefeater, is a riverside restaurant located near Raja Indian Cuisine."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"priceRange",
"cheap"
],
[
"Travellers Rest Beefeater",
"customer rating",
"average"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Raja Indian Cuisine"
]
],
"annotations": [
{
"source": "e2e",
"text": "Located in Riverside, near Raja Indian Cuisine. Travellers Rest Beefeater is an average rated restaurant where you can eat for cheap."
}
]
},
{
"tripleset": [
[
"Travellers Rest Beefeater",
"eatType",
"restaurant"
],
[
"Travellers Rest Beefeater",
"priceRange",
"less than \u00a320"
],
[
"Travellers Rest Beefeater",
"customer rating",
"1 out of 5"
],
[
"Travellers Rest Beefeater",
"area",
"riverside"
],
[
"Travellers Rest Beefeater",
"near",
"Caf\u00e9 Adriatic"
]
],
"annotations": [
{
"source": "e2e",
"text": "If looking for a moderately priced 1 out of 5 star restaurant near the riverside, Travellers Rest Beefeater is a good option - located near Caf\u00e9 Adriatic."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a moderate coffee shop serving Chinese food having a low customer rating. It is located near the Ranch."
},
{
"source": "e2e",
"text": "There is a moderate coffee shop, Wildwood, serving Chinese food near the Ranch. It has a customer rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a Chinese coffee shop near Ranch with a high cost and average rating."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"customer rating",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood has high customer rating located near Ranch a coffee shop serving Chinese food in the 30 price range."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Average Chinese food near the Ranch can be found at Wildwood coffee shop, for a moderate fee."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"cheap"
],
[
"Wildwood",
"customer rating",
"5 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wildwood is a coffee shop located near Ranch. They serve Chinese food are rated 5 out of 5 and a typical meal is cheap."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the cheap price range. It is near Ranch. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "There is a coffee shop called Wildwood that serves Chinese food. Located near Ranch, they have a 5 out of 5 customer rating and you can eat there for cheap."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the cheap price range. It is near Ranch. Its customer rating is 5 out of 5."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop also selling Chinese food. It gets 5 out of 5 from customers and is cheap. Find it near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop near Ranch. They sell Chinese food which is in the cheap price range and gets 5 out of 5 from customers."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"cheap"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a Chinese coffee shop with an average rating. It has a cheap price range and is located near the Ranch."
},
{
"source": "e2e",
"text": "The average rated Wildwood coffee shop has cheap Chinese food and is near the Ranch."
},
{
"source": "e2e",
"text": "Near the Ranch there is a cheap coffee shop that serves Chinese food named Wildwood and has an average rating."
},
{
"source": "e2e",
"text": "There is a cheap Chinese coffee shop called Wildwood. It is near the Ranch and has an average customer rating."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the cheap price range. It is near Ranch. Its customer rating is average."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the cheap price range. It is near Ranch. Its customer rating is average."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wildwood is a coffee shop that serves Chinese food in the high price range near the Ranch but has a customer rating of 1 out of 5."
},
{
"source": "e2e",
"text": "High priced and with a 1 of 5 customer rating, Wildwood is a coffee shop that offers Chinese food. It is located near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that offers Chinese food. It his high priced with a 1 of 5 customer rating. It is located near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that serves Chinese food. The price range is high with a customer rating of 1 out of 5. They are near the Ranch."
},
{
"source": "e2e",
"text": "Looking for a coffee shop that serves Chinese food in the high price range near the Ranch but has a customer rating of 1 out of 5, then the Wildwood may be for you."
},
{
"source": "e2e",
"text": "There is an expensive coffee shop located near Ranch named Wildwood that offers Chinese food. There is a 1 out of 5 customer rating."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a high priced coffee shop near Ranch that is selling Chinese food. It has an average customer rating."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the high price range. It is near Ranch. Its customer rating is average."
},
{
"source": "e2e",
"text": "There is a coffee shop near Ranch called Wildwood selling Chinese food with a high price range. It has an average customer rating."
},
{
"source": "e2e",
"text": "Near Ranch is a coffee shop that has Chinese food called Wildwood. It has an average customer rating and is in the high price range."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a coffee shop, located near Ranch, that serves Chinese food in the high price range. Customers have rated it 1 our of 5."
},
{
"source": "e2e",
"text": "Near Ranch there is a Chinese place that is a coffee shop style place, it's called Wildwood, customer ratings so far are average but the menu looks high priced."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"less than \u00a320"
],
[
"Wildwood",
"customer rating",
"low"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the less than \u00a320 price range. It is near Ranch. Its customer rating is low."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the less than \u00a320 price range. It is near Ranch. Its customer rating is low."
},
{
"source": "e2e",
"text": "Wildwood coffee shop near Ranch serves cheap Chinese food, but has a low customer rating."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the less than \u00a320 price range. It is near Ranch. Its customer rating is low."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"less than \u00a320"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wildwood coffee shop near Ranch offers cheap Chinese food."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a Chinese coffee shop near Ranch. Price is moderate and rating 1 out of 5."
},
{
"source": "e2e",
"text": "Wildwood , near Ranch, is a coffee shop offering Chinese fare at a moderate price range, and has received a customer rating of one out of five."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"customer rating",
"3 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a coffee shop near Ranch called Wildwood that serves Chinese food and a moderate price and has a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "Wildwood is a moderately priced coffee shop that provides Chinese food. It has a customer rating of 3 out of 5. It is located near Ranch."
},
{
"source": "e2e",
"text": "There is a coffee shop called WIldwood. Wildwood serves Chinese food at a reasonable price. Wildwood is near Ranch and has a 3 out of 5 rating."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that provides Chinese food in the moderate price range. It has a customer rating of 3 out of 5. It is located near Ranch."
},
{
"source": "e2e",
"text": "Wildwood, near Ranch, is a coffee shop which specialises in Chinese food. Its prices are moderate and its customers rate it 3 out of 5."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "A coffee shop called Wildwood located near Ranch , serving Chinese food. Wildwood has a moderate price range with a customer rating of 1 out 5."
},
{
"source": "e2e",
"text": "Close to Ranch, Wildwood coffee shop provides moderately priced Chinese food. It has been rated by customers as being a one out of a possible five."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop serving Chinese food, with a customer rating of 1 out 5. It is near Ranch with a moderate price range."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"more than \u00a330"
],
[
"Wildwood",
"customer rating",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a coffee shop near Ranch with a high customer rating. They serve Chinese food with a cost of over \u00a330."
},
{
"source": "e2e",
"text": "There is a coffee shop with a price range of more than \u00a330 called Wildwood. It is near Ranch, they serve Chinese and have a high customer rating."
},
{
"source": "e2e",
"text": "There's an expensive coffee shop that sells Chinese food near Ranch, it's called Wildwood. It's expensive but has high customer ratings."
},
{
"source": "e2e",
"text": "Wildwood is a Chinese coffee shop near Ranch, with high customer rating and more than \u00a330 price range."
},
{
"source": "e2e",
"text": "Wildwood coffee shop serving Chinese food in the 30 price range has high customer rating located near Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"more than \u00a330"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a high customer Chinese coffee shop with a more than \u00a330 price range near Ranch, called Wildwood."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"Chinese"
],
[
"Wildwood",
"priceRange",
"\u00a320-25"
],
[
"Wildwood",
"customer rating",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a high rated coffee shop that serves Chinese food near the Ranch and their price range is \u00a320-25."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that provides Chinese food. It is located near Ranch. It has a price range of \u00a320-25 and has a high customer rating."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop providing Chinese food in the \u00a320-25 price range. It is near Ranch. Its customer rating is high."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that also serves Chinese food near Ranch. The establishment boasts high customer ratings, and the menu is priced between \u00a320-25."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that provides Chinese food in the price range of \u00a320-25. It has a high customer rating. It is located near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that serves Chinese food near the Ranch. Their price range is \u00a320-25 and their customer rating is high."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood, English coffee shop, is situated near Ranch and has moderate pricing. It received 1 out of 5 star rating."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"customer rating",
"3 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a coffee shop also offering English themed food, near Ranch, good ratings."
},
{
"source": "e2e",
"text": "A high end, English grub and coffee shop - Wildwood. Good ratings, near Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a 3 star coffee shop located next to the Ranch that provides British food for a high cost."
},
{
"source": "e2e",
"text": "An English coffee shop that's highly priced and has an average customer rating is Wildwood, which is located near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is an English coffee shop with an average customer rating. It's highly priced and located near Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"customer rating",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "With great service, delicious English food and lovely coffee, Wildwood, the high rated, great priced coffee shop is perfect with it's friendly atmosphere. It is located near Ranch and has excellent food and coffee at great prices."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Close to the Ranch you will find an English coffee shop named Wildwood. Customer satisfaction is high while pricing remains average."
},
{
"source": "e2e",
"text": "Wildwood is a five-starred coffee shop that serves British food. It is located next to the Ranch."
},
{
"source": "e2e",
"text": "Wildwood is an average coffee shop that is highly-priced and serves English food near Ranch."
},
{
"source": "e2e",
"text": "Near Ranch, there is an average coffee shop that is highly priced and serves English food. It is called Wildwood"
},
{
"source": "e2e",
"text": "Join us at the Wildwood coffee shop for an English treat. Located near the Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"cheap"
],
[
"Wildwood",
"customer rating",
"5 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood coffee shop has a customer rating 5 out of 5, its sells English food at cheap prices. It is located near Ranch"
},
{
"source": "e2e",
"text": "If you are looking for somewhere with customer ratings of 5 out of 5 then visit Wildwood near Ranch. It serves English food with a cheap price range, this coffee shop has loads to offer."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop that serves English food. They have high customer ratings and cheap prices. They are in the city near the Ranch."
},
{
"source": "e2e",
"text": "there is a Cheap coffee shop that provide English food near Ranch named Wildwood that has 5 out of 5 customer rating"
},
{
"source": "e2e",
"text": "There is a cheap coffee shop Wildwood and they serve English food. They have high customer ratings and they are in the city near the Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a Cheap coffee shop that provide English food near Ranch that has 5 out of 5 customer rating"
},
{
"source": "e2e",
"text": "Near Ranch is a cheap priced coffee shop selling English food. It is called Wildwood and has a 5 out of 5 customer rating"
},
{
"source": "e2e",
"text": "If you are looking for English food then visit the Wildwood coffee shop located near Ranch it offers a cheap price range with customer ratings of 5 out of 5."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"cheap"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Ranch is a British coffee shop called Wildwood. It has low prices and a customer rating of three out of five."
},
{
"source": "e2e",
"text": "Near Ranch there is an average rated coffee shop called Wildwood, which serves cheap English food."
},
{
"source": "e2e",
"text": "Located near the Ranch, Wildwood is a coffee shop offering low-priced English breakfast food and has a three-star customer rating."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop serving English breakfast food in the low price range. It is located near the Ranch and has a three-star customer rating."
},
{
"source": "e2e",
"text": "A coffee shop called Wildwood, which serves cheap English food, has an average customer rating and is near Ranch."
},
{
"source": "e2e",
"text": "The Ranch has an average rating. It is the Wildwood and serves English food for cheap. It also has a coffee shop in it."
},
{
"source": "e2e",
"text": "Wildwood is a cheap British coffee shop near Ranch. It has a customer rating of three out of five."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"cheap"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "It is by the Ranch and it is average. It is called the Wildwood. It serves English food for cheap and it has a coffee shop."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood, an English coffee shop located near Ranch, has high prices and low ratings."
},
{
"source": "e2e",
"text": "Located near Ranch, Wildwood is an English coffee shop with high prices and low ratings."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is our new English coffee shop. Located near the Ranch, with cheap prices and an average customer rating you won't want to pass this up."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is an English coffee shop located near Ranch with high prices and a customer rating of one out of five."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop with high prices, near the Ranch. It serves English food, with a current rating of 1 out of 5."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"customer rating",
"average"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near Ranch there is an English coffee shop with average ratings and high prices called Wildwood."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is an English coffee shop named Wildwood located near Ranch with high price ranges."
},
{
"source": "e2e",
"text": "The Wildwood is a high- price coffee shop located next to the Ranch that provides British food."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"less than \u00a320"
],
[
"Wildwood",
"customer rating",
"low"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a coffee shop prices are less than \u00a320 serving English food near the Ranch with a low customer rating"
},
{
"source": "e2e",
"text": "located near Ranch Wildwood coffee shop serves English food with a low customer rating prices are less than \u00a320"
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"less than \u00a320"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Inexpensive and fun Wildwood coffee shop offers English food near the Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"customer rating",
"low"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wildwood is a low priced English coffee shop near Ranch with the average price less than \u00a320 and a low customer rating."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a moderately priced coffee shop serving English food. It is located near Ranch and has a low customer rating."
},
{
"source": "e2e",
"text": "Wildwood is an English coffee shop located near Ranch with moderate price range that received 1 out of 5 star customer rating."
},
{
"source": "e2e",
"text": "A low rated English style coffee shop around Ranch called Wildwood has moderately priced food."
},
{
"source": "e2e",
"text": "Wildwood is an English coffee shop near Ranch that has moderately priced food with a low customer rating."
},
{
"source": "e2e",
"text": "Wildwood provides English food for a moderate price. It has a low customer rating and is located near Ranch. It is a coffee shop."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"customer rating",
"3 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "There is a moderately priced coffee shop called Wildwood near the Ranch serving English food. It has been rated 3 out of 5."
},
{
"source": "e2e",
"text": "Wildwood coffee Shop near Ranch, is moderately priced and serves English food. It has a customer rating of 3 out of 5."
},
{
"source": "e2e",
"text": "With a rating of 3 out of 5, Wildwood coffee Shop serves English food which is moderately priced. It can be found near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop located near Ranch that has a 3 out of 5 customer rating and moderately priced English food"
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop with moderately priced English food near Ranch that has a 3 out of 5 customer rating"
},
{
"source": "e2e",
"text": "Wildwood is a moderately priced English coffee shop located near the Ranch. It has been rated 3 out of 5."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"English"
],
[
"Wildwood",
"priceRange",
"\u00a320-25"
],
[
"Wildwood",
"customer rating",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is an average priced English coffee shop. It is located near the Ranch and the customer rating is high."
},
{
"source": "e2e",
"text": "Wildwood is a lovely coffee shop near Ranch. It serves great English food and lovely coffee. The customers ratings are very high and the price range is average."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"food",
"French"
],
[
"Wildwood",
"priceRange",
"\u00a320-25"
],
[
"Wildwood",
"customer rating",
"high"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is the name of a highly rated coffee shop with a price ranch of French 20-25. It is located near Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"priceRange",
"high"
],
[
"Wildwood",
"customer rating",
"1 out of 5"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Wildwood is a coffee shop with a high price range and has a customer rating of 1 out of 5; it is near the Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop with a customer rating of 1 out of 5 and has a high price range; it is near the Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"priceRange",
"less than \u00a320"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "Near the Ranch, there is a cheap coffee shop called Wildwood."
},
{
"source": "e2e",
"text": "Wildwood is a low-priced coffee shop that provides breakfast near Ranch."
},
{
"source": "e2e",
"text": "Wildwood is a coffee shop in the low price range. It is located near Ranch."
},
{
"source": "e2e",
"text": "If you are looking for a coffee shop for less than \u00a320 near Ranch, The Wildwood is an adequate choice."
},
{
"source": "e2e",
"text": "Wildwood, a coffee shop located near the Ranch, serves inexpensive breakfast foods."
},
{
"source": "e2e",
"text": "Wildwood is an inexpensive coffee shop near the Ranch."
}
]
},
{
"tripleset": [
[
"Wildwood",
"eatType",
"coffee shop"
],
[
"Wildwood",
"priceRange",
"moderate"
],
[
"Wildwood",
"near",
"Ranch"
]
],
"annotations": [
{
"source": "e2e",
"text": "The Wildwood is an affordable coffee shop located in Ranch."
}
]
}
]
|
AdaMix/NLG/data/dart/dart-v1.1.1-full-dev.json/0
|
{
"file_path": "AdaMix/NLG/data/dart/dart-v1.1.1-full-dev.json",
"repo_id": "AdaMix",
"token_count": 1167015
}
| 26 |
# ------------------------------------------------------------------------------------------
# Copyright (c). All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import torch
import torch.nn as nn
from typing import Dict
from .layers import LoRALayer
def mark_only_lora_as_trainable(model: nn.Module, bias: str = 'none') -> None:
for n, p in model.named_parameters():
if 'lora_' not in n:
p.requires_grad = False
if bias == 'none':
return
elif bias == 'all':
for n, p in model.named_parameters():
if 'bias' in n:
p.requires_grad = True
elif bias == 'lora_only':
for m in model.modules():
if isinstance(m, LoRALayer) and \
hasattr(m, 'bias') and \
m.bias is not None:
m.bias.requires_grad = True
else:
raise NotImplementedError
def lora_state_dict(model: nn.Module, bias: str = 'none') -> Dict[str, torch.Tensor]:
my_state_dict = model.state_dict()
if bias == 'none':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k or 'expert_score_weight' in k or 'deepspeed_experts' in k}
elif bias == 'all':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k or 'bias' in k}
elif bias == 'lora_only':
to_return = {}
for k in my_state_dict:
if 'lora_' in k:
to_return[k] = my_state_dict[k]
bias_name = k.split('lora_')[0]+'bias'
if bias_name in my_state_dict:
to_return[bias_name] = my_state_dict[bias_name]
return to_return
else:
raise NotImplementedError
|
AdaMix/NLG/loralib/utils.py/0
|
{
"file_path": "AdaMix/NLG/loralib/utils.py",
"repo_id": "AdaMix",
"token_count": 810
}
| 27 |
# ------------------------------------------------------------------------------------------
# Copyright (c). All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import os, sys
import glob
import random
from collections import Counter, OrderedDict
import numpy as np
import torch
import json
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
class LMOrderedIterator(object):
def __init__(self, data, bsz, bptt, eval_len=None, device='cpu', world_size=1, rank=0):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.data = data
self.bsz = bsz
self.world_size = world_size
self.rank = rank
self.bptt = bptt # tgt_len
# existing len.
self.eval_len = bptt if eval_len is None else eval_len
self.device = device
self.global_bsz = bsz * world_size
# Work out how cleanly we can divide the dataset into bsz parts.
self.n_step = len(data) // self.global_bsz # bsz
self.split_data = torch.tensor(
data[rank * self.n_step * bsz : (rank + 1) * self.n_step * bsz],
dtype=torch.long, device=self.device
) # data.view(-1)
self.split_data = self.split_data.view(bsz, -1)
def __iter__(self):
return self.get_fixlen_iter()
def get_batch(self, i, bptt, eval_len):
beg_idx = i
end_idx = i + bptt # seq_len
# batch_size, lengh;
_input = self.split_data[:, beg_idx : end_idx].contiguous()
_target = self.split_data[:, beg_idx+1 : end_idx+1].contiguous()
_msk = torch.cat(
[
torch.zeros(bptt-eval_len, dtype=torch.float, device=self.device),
torch.ones(eval_len, dtype=torch.float, device=self.device)
]
)
_msk = _msk.unsqueeze(0).expand_as(_input) # .unsqueeze(-1) # length, 1;
return _input, _target, _msk
def get_fixlen_iter(self, start=0):
self.data_len = self.split_data.size(1)
_eval_cursor = 0
for i in range(start, self.data_len - 1, self.eval_len):
bptt = min(self.bptt, self.data_len - i - 1)
_end_idx = i + bptt
yield self.get_batch(i, bptt, _end_idx - _eval_cursor)
_eval_cursor = _end_idx
class Corpus(object):
def __init__(self, path):
self.path = path
self.num_words = 0
self.tokens = []
with open(self.path, "r") as reader:
for line in reader:
items = json.loads(line.strip())
book = items['book']
tokens = items['tokens']
num_words = items['num_words']
self.num_words += num_words
self.tokens.extend(tokens)
class BinLMOrderedIterator(object):
def __init__(self, corpus, bsz, bptt, eval_len=None, device='cpu', world_size=1, rank=0):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.corpus = corpus
self.bsz = bsz
self.world_size = world_size
self.rank = rank
self.bptt = bptt # tgt_len
# existing len.
self.eval_len = bptt if eval_len is None else eval_len
self.device = device
self.global_bsz = bsz * world_size
# Work out how cleanly we can divide the dataset into bsz parts.
self.n_step = corpus.length // self.global_bsz # bsz
self.offset = [(rank * bsz + _b) * self.n_step for _b in range(bsz)]
def __iter__(self):
return self.get_fixlen_iter()
def get_batch(self, i, bptt, eval_len):
# batch_size, lengh;
_inputs = []
_targets = []
for _b in range(0, self.bsz):
_input = self.corpus.get_tokens(self.offset[_b] + i, bptt)
_target = self.corpus.get_tokens(self.offset[_b] + i + 1, bptt)
_inputs.append(_input)
_targets.append(_target)
_input = torch.tensor(_inputs, dtype=torch.int64, device=self.device).contiguous()
_target = torch.tensor(_targets, dtype=torch.int64, device=self.device).contiguous()
_msk = torch.cat(
[
torch.zeros(bptt-eval_len, dtype=torch.float, device=self.device),
torch.ones(eval_len, dtype=torch.float, device=self.device)
]
)
_msk = _msk.unsqueeze(0).expand_as(_input) # .unsqueeze(-1) # length, 1;
return _input, _target, _msk
def get_fixlen_iter(self, start=0):
#self.data_len = self.split_data.size(1)
_eval_cursor = 0
for i in range(start, self.n_step - 1, self.eval_len):
bptt = min(self.bptt, self.n_step - i - 1)
_end_idx = i + bptt
yield self.get_batch(i, bptt, _end_idx - _eval_cursor)
_eval_cursor = _end_idx
class BinCorpus(object):
def __init__(self, path):
self.path = path
self.book_token_span = []
self.book_token_span.append(0)
tokens_sum = 0
self.num_words = 0
with open(path+'.info', 'r') as info_reader:
for line in info_reader:
items = json.loads(line.strip())
book = items['book']
num_tokens = items['num_subtokens']
num_words = items['num_words']
tokens_sum += num_tokens
self.book_token_span.append(tokens_sum)
self.num_words += num_words
self.length = self.book_token_span[-1]
self.bin_reader = open(path+'.bin', 'rb')
def get_tokens(self, offset, count):
INT64_SIZE = 8
self.bin_reader.seek(offset * INT64_SIZE)
x = np.fromfile(self.bin_reader, count=count, dtype=np.int)
return x
def get_lm_corpus(data):
print('Producing dataset {}...'.format(data))
corpus = Corpus(data)
return corpus
def padding_tokens(tokens, max_seq_length, pad_token, direct, max_context_length=0):
if max_context_length == 0:
max_context_length = max_seq_length
if len(tokens) > max_context_length:
if direct > 0:
pad_tokens = tokens[:max_context_length]
else:
pad_tokens = tokens[-max_context_length:]
else:
pad_tokens = tokens
token_len = len(pad_tokens)
pad_tokens = pad_tokens + [pad_token for _ in range(max_seq_length - token_len)]
return pad_tokens, token_len
class FT_Dataset(Dataset):
def __init__(self, ft_file, batch_size, max_seq_length,
max_eval_length=0, joint_lm=False, prefix_len=0, infix_len=0,
prefix_cursor=1000000, infix_cursor=2000000):
self.ft_file = ft_file
self.ft_samples = self.read_ft_file(ft_file)
self.batch_size = batch_size
self.num_examples = len(self.ft_samples)
self.max_seq_length = max_seq_length
self.max_eval_length = max_eval_length
self.rng = random.Random(911)
self.joint_lm = joint_lm
self.num_batches = int((self.num_examples + self.batch_size - 1) / self.batch_size)
self.prefix_len = prefix_len
self.infix_len = infix_len
self.prefix_cursor = prefix_cursor
self.infix_cursor = infix_cursor
def __len__(self):
return self.num_batches * self.batch_size
def __getitem__(self, item):
if(item >= self.num_examples):
item = self.rng.randint(0, self.num_examples - 1)
example = self.ft_samples[item]
context = example[0]
completion = example[1]
pretokens = [i + self.prefix_cursor for i in range(0, self.prefix_len)]
intokens = [i + self.infix_cursor for i in range(0, self.infix_len)]
conditions = pretokens + context + intokens
_input, _input_len = padding_tokens(conditions + completion, self.max_seq_length, 0, 1)
pad_targets = [0 for i in range(0, self.prefix_len)] + context + [0 for i in range(0, self.infix_len)] + completion
_target, _ = padding_tokens(pad_targets[1:], self.max_seq_length, 0, 1)
if not self.joint_lm:
_msk = [0.0] * (len(conditions) - 1) + [1.0] * (_input_len - len(conditions))
else:
_msk = [1.0] * (_input_len - 1)
_msk, _ = padding_tokens(_msk, self.max_seq_length, 0.0, 1)
output = {}
output["id"] = torch.tensor(item, dtype=torch.long)
_query, _query_len = padding_tokens(
conditions, self.max_seq_length, 0, -1,
max_context_length = self.max_seq_length - self.max_eval_length
)
output["query"] = torch.tensor(_query, dtype=torch.long)
output["query_len"] = torch.tensor(_query_len, dtype=torch.long)
output["input"] = torch.tensor(_input, dtype=torch.long)
output["target"] = torch.tensor(_target, dtype=torch.long)
output["mask"] = torch.tensor(_msk, dtype=torch.float)
return output
def read_ft_file(self, ft_file):
ft_samples = []
with open(ft_file, 'r') as reader:
for line in reader:
items = json.loads(line.strip())
context = items['context']
completion = items['completion']
ft_samples.append([context, completion])
return ft_samples
|
AdaMix/NLG/src/data_utils.py/0
|
{
"file_path": "AdaMix/NLG/src/data_utils.py",
"repo_id": "AdaMix",
"token_count": 4617
}
| 28 |
#!/bin/bash
source ~/.bashrc
echo "running docker-entrypoint.sh"
conda activate container
echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS
echo "printed TPU info"
export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}"
exec "$@"#!/bin/bash
|
AdaMix/docker/transformers-pytorch-tpu/docker-entrypoint.sh/0
|
{
"file_path": "AdaMix/docker/transformers-pytorch-tpu/docker-entrypoint.sh",
"repo_id": "AdaMix",
"token_count": 112
}
| 29 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Optimization
-----------------------------------------------------------------------------------------------------------------------
The ``.optimization`` module provides:
- an optimizer with weight decay fixed that can be used to fine-tuned models, and
- several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
- a gradient accumulation class to accumulate the gradients of multiple batches
AdamW (PyTorch)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AdamW
:members:
AdaFactor (PyTorch)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Adafactor
AdamWeightDecay (TensorFlow)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AdamWeightDecay
.. autofunction:: transformers.create_optimizer
Schedules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Learning Rate Schedules (Pytorch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.SchedulerType
.. autofunction:: transformers.get_scheduler
.. autofunction:: transformers.get_constant_schedule
.. autofunction:: transformers.get_constant_schedule_with_warmup
.. image:: /imgs/warmup_constant_schedule.png
:target: /imgs/warmup_constant_schedule.png
:alt:
.. autofunction:: transformers.get_cosine_schedule_with_warmup
.. image:: /imgs/warmup_cosine_schedule.png
:target: /imgs/warmup_cosine_schedule.png
:alt:
.. autofunction:: transformers.get_cosine_with_hard_restarts_schedule_with_warmup
.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
:target: /imgs/warmup_cosine_hard_restarts_schedule.png
:alt:
.. autofunction:: transformers.get_linear_schedule_with_warmup
.. image:: /imgs/warmup_linear_schedule.png
:target: /imgs/warmup_linear_schedule.png
:alt:
.. autofunction:: transformers.get_polynomial_decay_schedule_with_warmup
Warmup (TensorFlow)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.WarmUp
:members:
Gradient Strategies
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
GradientAccumulator (TensorFlow)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.GradientAccumulator
|
AdaMix/docs/source/main_classes/optimizer_schedules.rst/0
|
{
"file_path": "AdaMix/docs/source/main_classes/optimizer_schedules.rst",
"repo_id": "AdaMix",
"token_count": 921
}
| 30 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
BORT
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The BORT model was proposed in `Optimal Subarchitecture Extraction for BERT <https://arxiv.org/abs/2010.10499>`__ by
Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the
authors refer to as "Bort".
The abstract from the paper is the following:
*We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by
applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as
"Bort", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the
original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which
is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large
(Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same
hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the
architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%,
absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.*
Tips:
- BORT's model architecture is based on BERT, so one can refer to :doc:`BERT's documentation page <bert>` for the
model's API as well as usage examples.
- BORT uses the RoBERTa tokenizer instead of the BERT tokenizer, so one can refer to :doc:`RoBERTa's documentation page
<roberta>` for the tokenizer's API as well as usage examples.
- BORT requires a specific fine-tuning algorithm, called `Agora
<https://adewynter.github.io/notes/bort_algorithms_and_applications.html#fine-tuning-with-algebraic-topology>`__ ,
that is sadly not open-sourced yet. It would be very useful for the community, if someone tries to implement the
algorithm to make BORT fine-tuning work.
The original code can be found `here <https://github.com/alexa/bort/>`__.
|
AdaMix/docs/source/model_doc/bort.rst/0
|
{
"file_path": "AdaMix/docs/source/model_doc/bort.rst",
"repo_id": "AdaMix",
"token_count": 759
}
| 31 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
herBERT
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The herBERT model was proposed in `KLEJ: Comprehensive Benchmark for Polish Language Understanding
<https://www.aclweb.org/anthology/2020.acl-main.111.pdf>`__ by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and
Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic
masking of whole words.
The abstract from the paper is the following:
*In recent years, a series of Transformer-based models unlocked major improvements in general natural language
understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which
allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of
languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language
understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing
datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new
sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and
promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and
applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language,
which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an
extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based
models.*
Examples of use:
.. code-block::
from transformers import HerbertTokenizer, RobertaModel
tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
outputs = model(encoded_input)
# HerBERT can also be loaded using AutoTokenizer and AutoModel:
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
The original code can be found `here <https://github.com/allegro/HerBERT>`__.
HerbertTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.HerbertTokenizer
:members:
HerbertTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.HerbertTokenizerFast
:members:
|
AdaMix/docs/source/model_doc/herbert.rst/0
|
{
"file_path": "AdaMix/docs/source/model_doc/herbert.rst",
"repo_id": "AdaMix",
"token_count": 920
}
| 32 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Reformer
-----------------------------------------------------------------------------------------------------------------------
**DISCLAIMER:** This model is still a work in progress, if you see something strange, file a `Github Issue
<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__.
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The Reformer model was proposed in the paper `Reformer: The Efficient Transformer
<https://arxiv.org/abs/2001.04451.pdf>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
The abstract from the paper is the following:
*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can
be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of
Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its
complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual
layers instead of the standard residuals, which allows storing activations only once in the training process instead of
N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models
while being much more memory-efficient and much faster on long sequences.*
The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`__.
Axial Positional Encodings
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Axial Positional Encodings were first implemented in Google's `trax library
<https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`__
and developed by the authors of this model's paper. In models that are treating very long input sequences, the
conventional position id encodings store an embedings vector of size :math:`d` being the :obj:`config.hidden_size` for
every position :math:`i, \ldots, n_s`, with :math:`n_s` being :obj:`config.max_embedding_size`. This means that having
a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000`
would result in a position encoding matrix:
.. math::
X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right]
which alone has over 500M parameters to store. Axial positional encodings factorize :math:`X_{i,j}` into two matrices:
.. math::
X^{1}_{i,j}, \text{ with } i \in \left[1,\ldots, d^1\right] \text{ and } j \in \left[1,\ldots, n_s^1\right]
and
.. math::
X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right]
with:
.. math::
d = d^1 + d^2 \text{ and } n_s = n_s^1 \times n_s^2 .
Therefore the following holds:
.. math::
X_{i,j} = \begin{cases}
X^{1}_{i, k}, & \text{if }\ i < d^1 \text{ with } k = j \mod n_s^1 \\
X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
\end{cases}
Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two
factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the :obj:`config.max_embedding_size` dimension
:math:`j` is factorized into :math:`k \text{ and } l`. This design ensures that each position embedding vector
:math:`x_j` is unique.
Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}`
can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
In practice, the parameter :obj:`config.axial_pos_embds_dim` is set to a tuple :math:`(d^1, d^2)` which sum has to be
equal to :obj:`config.hidden_size` and :obj:`config.axial_pos_shape` is set to a tuple :math:`(n_s^1, n_s^2)` which
product has to be equal to :obj:`config.max_embedding_size`, which during training has to be equal to the `sequence
length` of the :obj:`input_ids`.
LSH Self Attention
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key
query embedding vectors are also tied. LSH self attention uses the locality sensitive hashing mechanism proposed in
`Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`__ to assign each of the tied key
query embedding vectors to one of :obj:`config.num_buckets` possible buckets. The premise is that the more "similar"
key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to
the same bucket.
The accuracy of the LSH mechanism can be improved by increasing :obj:`config.num_hashes` or directly the argument
:obj:`num_hashes` of the forward function so that the output of the LSH self attention better approximates the output
of the "normal" full self attention. The buckets are then sorted and chunked into query key embedding vector chunks
each of length :obj:`config.lsh_chunk_length`. For each chunk, the query embedding vectors attend to its key vectors
(which are tied to themselves) and to the key embedding vectors of :obj:`config.lsh_num_chunks_before` previous
neighboring chunks and :obj:`config.lsh_num_chunks_after` following neighboring chunks.
For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`__ or this great `blog post
<https://www.pragmatic.ml/reformer-deep-dive/>`__.
Note that :obj:`config.num_buckets` can also be factorized into a list :math:`(n_{\text{buckets}}^1,
n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to one of :math:`(1,\ldots,
n_{\text{buckets}})` they are assigned to one of :math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots,
1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`. This is crucial for very long sequences to
save memory.
When training a model from scratch, it is recommended to leave :obj:`config.num_buckets=None`, so that depending on the
sequence length a good value for :obj:`num_buckets` is calculated on the fly. This value will then automatically be
saved in the config and should be reused for inference.
Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from
:math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory
and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
Local Self Attention
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Local self attention is essentially a "normal" self attention layer with key, query and value projections, but is
chunked so that in each chunk of length :obj:`config.local_chunk_length` the query embedding vectors only attends to
the key embedding vectors in its chunk and to the key embedding vectors of :obj:`config.local_num_chunks_before`
previous neighboring chunks and :obj:`config.local_num_chunks_after` following neighboring chunks.
Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from
:math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory
and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
Training
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
During training, we must ensure that the sequence length is set to a value that can be divided by the least common
multiple of :obj:`config.lsh_chunk_length` and :obj:`config.local_chunk_length` and that the parameters of the Axial
Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can
easily be trained on sequences as long as 64000 tokens.
For training, the :class:`~transformers.ReformerModelWithLMHead` should be used as follows:
.. code-block::
input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
loss = model(input_ids, labels=input_ids)[0]
ReformerConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerConfig
:members:
ReformerTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerTokenizer
:members: save_vocabulary
ReformerTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerTokenizerFast
:members:
ReformerModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerModel
:members: forward
ReformerModelWithLMHead
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerModelWithLMHead
:members: forward
ReformerForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerForMaskedLM
:members: forward
ReformerForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerForSequenceClassification
:members: forward
ReformerForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ReformerForQuestionAnswering
:members: forward
|
AdaMix/docs/source/model_doc/reformer.rst/0
|
{
"file_path": "AdaMix/docs/source/model_doc/reformer.rst",
"repo_id": "AdaMix",
"token_count": 3100
}
| 33 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Multi-lingual models
=======================================================================================================================
Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
models.
The two models that currently support multiple languages are BERT and XLM.
XLM
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
be split in two categories: the checkpoints that make use of language embeddings, and those that don't
XLM & Language Embeddings
-----------------------------------------------------------------------------------------------------------------------
This section concerns the following checkpoints:
- ``xlm-mlm-ende-1024`` (Masked language modeling, English-German)
- ``xlm-mlm-enfr-1024`` (Masked language modeling, English-French)
- ``xlm-mlm-enro-1024`` (Masked language modeling, English-Romanian)
- ``xlm-mlm-xnli15-1024`` (Masked language modeling, XNLI languages)
- ``xlm-mlm-tlm-xnli15-1024`` (Masked language modeling + Translation, XNLI languages)
- ``xlm-clm-enfr-1024`` (Causal language modeling, English-French)
- ``xlm-clm-ende-1024`` (Causal language modeling, English-German)
These checkpoints require language embeddings that will specify the language used at inference time. These language
embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
these tensors depend on the language used and are identifiable using the ``lang2id`` and ``id2lang`` attributes from
the tokenizer.
Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language modeling, English-French):
.. code-block::
>>> import torch
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
``lang2id`` attribute:
.. code-block::
>>> print(tokenizer.lang2id)
{'en': 0, 'fr': 1}
These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
.. code-block::
>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
We should now define the language embedding by using the previously defined language id. We want to create a tensor
filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
.. code-block::
>>> language_id = tokenizer.lang2id['en'] # 0
>>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0])
>>> # We reshape it to be of size (batch_size, sequence_length)
>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
You can then feed it all as input to your model:
.. code-block::
>>> outputs = model(input_ids, langs=langs)
The example :prefix_link:`run_generation.py <examples/text-generation/run_generation.py>` can generate text using the
CLM checkpoints from XLM, using the language embeddings.
XLM without Language Embeddings
-----------------------------------------------------------------------------------------------------------------------
This section concerns the following checkpoints:
- ``xlm-mlm-17-1280`` (Masked language modeling, 17 languages)
- ``xlm-mlm-100-1280`` (Masked language modeling, 100 languages)
These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
representations, differently from previously-mentioned XLM checkpoints.
BERT
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
BERT has two checkpoints that can be used for multi-lingual tasks:
- ``bert-base-multilingual-uncased`` (Masked language modeling + Next sentence prediction, 102 languages)
- ``bert-base-multilingual-cased`` (Masked language modeling + Next sentence prediction, 104 languages)
These checkpoints do not require language embeddings at inference time. They should identify the language used in the
context and infer accordingly.
XLM-RoBERTa
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
labeling and question answering.
Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
- ``xlm-roberta-base`` (Masked language modeling, 100 languages)
- ``xlm-roberta-large`` (Masked language modeling, 100 languages)
|
AdaMix/docs/source/multilingual.rst/0
|
{
"file_path": "AdaMix/docs/source/multilingual.rst",
"repo_id": "AdaMix",
"token_count": 1566
}
| 34 |
<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# 🤗 Benchmark results
Here, you can find a list of the different benchmark results created by the community.
If you would like to list benchmark results on your favorite models of the [model hub](https://huggingface.co/models) here, please open a Pull Request and add it below.
| Benchmark description | Results | Environment info | Author |
|:----------|:-------------|:-------------|------:|
| PyTorch Benchmark on inference for `bert-base-cased` |[memory](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/inference_memory.csv) | [env](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/env.csv) | [Partick von Platen](https://github.com/patrickvonplaten) |
| PyTorch Benchmark on inference for `bert-base-cased` |[time](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/inference_time.csv) | [env](https://github.com/patrickvonplaten/files_to_link_to/blob/master/bert_benchmark/env.csv) | [Partick von Platen](https://github.com/patrickvonplaten) |
|
AdaMix/examples/benchmarking/README.md/0
|
{
"file_path": "AdaMix/examples/benchmarking/README.md",
"repo_id": "AdaMix",
"token_count": 493
}
| 35 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Union
import fire
import torch
from tqdm import tqdm
def convert(src_path: str, map_location: str = "cpu", save_path: Union[str, None] = None) -> None:
"""Convert a pytorch_model.bin or model.pt file to torch.float16 for faster downloads, less disk space."""
state_dict = torch.load(src_path, map_location=map_location)
for k, v in tqdm(state_dict.items()):
if not isinstance(v, torch.Tensor):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin")
state_dict[k] = v.half()
if save_path is None: # overwrite src_path
save_path = src_path
torch.save(state_dict, save_path)
if __name__ == "__main__":
fire.Fire(convert)
|
AdaMix/examples/legacy/seq2seq/convert_model_to_fp16.py/0
|
{
"file_path": "AdaMix/examples/legacy/seq2seq/convert_model_to_fp16.py",
"repo_id": "AdaMix",
"token_count": 450
}
| 36 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import (
Seq2SeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
logger = getLogger(__name__)
def eval_data_dir(
data_dir,
save_dir: str,
model_name: str,
bs: int = 8,
max_source_length: int = 1024,
type_path="val",
n_obs=None,
fp16=False,
task="summarization",
local_rank=None,
num_return_sequences=1,
dataset_kwargs: Dict = None,
prefix="",
**generate_kwargs,
) -> Dict:
"""Run evaluation on part of the data for one gpu and save to {save_dir}/rank_{rank}_output.json"""
model_name = str(model_name)
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl", rank=local_rank)
save_dir = Path(save_dir)
save_path = save_dir.joinpath(f"rank_{local_rank}_output.json")
torch.cuda.set_device(local_rank)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).cuda()
if fp16:
model = model.half()
# determine if we need to increase num_beams
use_task_specific_params(model, task) # update config with task specific params
num_beams = generate_kwargs.pop("num_beams", model.config.num_beams) # AttributeError risk?
if num_return_sequences > num_beams:
num_beams = num_return_sequences
tokenizer = AutoTokenizer.from_pretrained(model_name)
logger.info(f"Inferred tokenizer type: {tokenizer.__class__}") # if this is wrong, check config.model_type.
if max_source_length is None:
max_source_length = tokenizer.model_max_length
if prefix is None:
prefix = prefix or getattr(model.config, "prefix", "") or ""
ds = Seq2SeqDataset(
tokenizer,
data_dir,
max_source_length,
max_target_length=1024,
type_path=type_path,
n_obs=n_obs,
prefix=prefix,
**dataset_kwargs,
)
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
sampler = ds.make_sortish_sampler(bs, distributed=True, add_extra_examples=False, shuffle=True)
data_loader = DataLoader(ds, sampler=sampler, batch_size=bs, collate_fn=ds.collate_fn)
results = []
for batch in tqdm(data_loader):
summaries = model.generate(
input_ids=batch["input_ids"].to(model.device),
attention_mask=batch["attention_mask"].to(model.device),
num_return_sequences=num_return_sequences,
num_beams=num_beams,
**generate_kwargs,
)
preds = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
ids = batch["ids"]
if num_return_sequences > 1:
preds = chunks(preds, num_return_sequences) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(preds):
results.append(dict(pred=pred, id=ids[i].item()))
save_json(results, save_path)
return results, sampler.num_replicas
def run_generate():
parser = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"
)
parser.add_argument("--data_dir", type=str, help="like cnn_dm/test.source")
parser.add_argument(
"--model_name",
type=str,
help="like facebook/bart-large-cnn,t5-base, etc.",
default="sshleifer/distilbart-xsum-12-3",
)
parser.add_argument("--save_dir", type=str, help="where to save", default="tmp_gen")
parser.add_argument("--max_source_length", type=int, default=None)
parser.add_argument(
"--type_path", type=str, default="test", help="which subset to evaluate typically train/val/test"
)
parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics")
parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
parser.add_argument(
"--local_rank", type=int, default=-1, required=False, help="should be passed by distributed.launch"
)
parser.add_argument(
"--n_obs", type=int, default=None, required=False, help="How many observations. Defaults to all."
)
parser.add_argument(
"--num_return_sequences", type=int, default=1, required=False, help="How many sequences to return"
)
parser.add_argument(
"--sync_timeout",
type=int,
default=600,
required=False,
help="How long should master process wait for other processes to finish.",
)
parser.add_argument("--src_lang", type=str, default=None, required=False)
parser.add_argument("--tgt_lang", type=str, default=None, required=False)
parser.add_argument(
"--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples"
)
parser.add_argument("--fp16", action="store_true")
parser.add_argument("--debug", action="store_true")
start_time = time.time()
args, rest = parser.parse_known_args()
generate_kwargs = parse_numeric_n_bool_cl_kwargs(rest)
if generate_kwargs and args.local_rank <= 0:
print(f"parsed the following generate kwargs: {generate_kwargs}")
json_save_dir = Path(args.save_dir + "_tmp")
Path(json_save_dir).mkdir(exist_ok=True) # this handles locking.
intermediate_files = list(json_save_dir.glob("rank_*.json"))
if intermediate_files:
raise ValueError(f"Found files at {json_save_dir} please move or remove them.")
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
dataset_kwargs = {}
if args.src_lang is not None:
dataset_kwargs["src_lang"] = args.src_lang
if args.tgt_lang is not None:
dataset_kwargs["tgt_lang"] = args.tgt_lang
Path(args.save_dir).mkdir(exist_ok=True)
results, num_replicas = eval_data_dir(
args.data_dir,
json_save_dir,
args.model_name,
type_path=args.type_path,
bs=args.bs,
fp16=args.fp16,
task=args.task,
local_rank=args.local_rank,
n_obs=args.n_obs,
max_source_length=args.max_source_length,
num_return_sequences=args.num_return_sequences,
prefix=args.prefix,
dataset_kwargs=dataset_kwargs,
**generate_kwargs,
)
if args.local_rank <= 0:
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True)
partial_results = gather_results_from_each_node(num_replicas, json_save_dir, args.sync_timeout)
preds = combine_partial_results(partial_results)
if args.num_return_sequences > 1:
save_path = save_dir.joinpath("pseudolabel_results.json")
print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/")
save_json(preds, save_path)
return
tgt_file = Path(args.data_dir).joinpath(args.type_path + ".target")
labels = [x.rstrip() for x in open(tgt_file).readlines()][: len(preds)]
# Calculate metrics, save metrics, and save _generations.txt
calc_bleu = "translation" in args.task
score_fn = calculate_bleu if calc_bleu else calculate_rouge
metric_name = "bleu" if calc_bleu else "rouge"
metrics: Dict = score_fn(preds, labels)
metrics["n_obs"] = len(preds)
runtime = time.time() - start_time
metrics["seconds_per_sample"] = round(runtime / metrics["n_obs"], 4)
metrics["n_gpus"] = num_replicas
# TODO(@stas00): add whatever metadata to metrics
metrics_save_path = save_dir.joinpath(f"{args.type_path}_{metric_name}.json")
save_json(metrics, metrics_save_path, indent=None)
print(metrics)
write_txt_file(preds, save_dir.joinpath(f"{args.type_path}_generations.txt"))
if args.debug:
write_txt_file(labels, save_dir.joinpath(f"{args.type_path}.target"))
else:
shutil.rmtree(json_save_dir)
def combine_partial_results(partial_results) -> List:
"""Concatenate partial results into one file, then sort it by id."""
records = []
for partial_result in partial_results:
records.extend(partial_result)
records = list(sorted(records, key=lambda x: x["id"]))
preds = [x["pred"] for x in records]
return preds
def gather_results_from_each_node(num_replicas, save_dir, timeout) -> List[Dict[str, List]]:
# WAIT FOR lots of .json files
start_wait = time.time()
logger.info("waiting for all nodes to finish")
json_data = None
while (time.time() - start_wait) < timeout:
json_files = list(save_dir.glob("rank_*.json"))
if len(json_files) < num_replicas:
continue
try:
# make sure all json files are fully saved
json_data = lmap(load_json, json_files)
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes")
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
|
AdaMix/examples/legacy/seq2seq/run_distributed_eval.py/0
|
{
"file_path": "AdaMix/examples/legacy/seq2seq/run_distributed_eval.py",
"repo_id": "AdaMix",
"token_count": 4145
}
| 37 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. """
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
logger = logging.getLogger(__name__)
@dataclass
class InputExample:
"""
A single training/test example for token classification.
Args:
guid: Unique id for the example.
words: list. The words of the sequence.
labels: (Optional) list. The labels for each word of the sequence. This should be
specified for train and dev examples, but not for test examples.
"""
guid: str
words: List[str]
labels: Optional[List[str]]
@dataclass
class InputFeatures:
"""
A single set of features of data.
Property names are the same names as the corresponding inputs to a model.
"""
input_ids: List[int]
attention_mask: List[int]
token_type_ids: Optional[List[int]] = None
label_ids: Optional[List[int]] = None
class Split(Enum):
train = "train"
dev = "dev"
test = "test"
class TokenClassificationTask:
@staticmethod
def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:
raise NotImplementedError
@staticmethod
def get_labels(path: str) -> List[str]:
raise NotImplementedError
@staticmethod
def convert_examples_to_features(
examples: List[InputExample],
label_list: List[str],
max_seq_length: int,
tokenizer: PreTrainedTokenizer,
cls_token_at_end=False,
cls_token="[CLS]",
cls_token_segment_id=1,
sep_token="[SEP]",
sep_token_extra=False,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
pad_token_label_id=-100,
sequence_a_segment_id=0,
mask_padding_with_zero=True,
) -> List[InputFeatures]:
"""Loads a data file into a list of `InputFeatures`
`cls_token_at_end` define the location of the CLS token:
- False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
- True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
`cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
"""
# TODO clean up all this to leverage built-in features of tokenizers
label_map = {label: i for i, label in enumerate(label_list)}
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10_000 == 0:
logger.info("Writing example %d of %d", ex_index, len(examples))
tokens = []
label_ids = []
for word, label in zip(example.words, example.labels):
word_tokens = tokenizer.tokenize(word)
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(word_tokens) > 0:
tokens.extend(word_tokens)
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
special_tokens_count = tokenizer.num_special_tokens_to_add()
if len(tokens) > max_seq_length - special_tokens_count:
tokens = tokens[: (max_seq_length - special_tokens_count)]
label_ids = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
segment_ids = [sequence_a_segment_id] * len(tokens)
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
tokens = [cls_token] + tokens
label_ids = [pad_token_label_id] + label_ids
segment_ids = [cls_token_segment_id] + segment_ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = max_seq_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
label_ids = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
if ex_index < 5:
logger.info("*** Example ***")
logger.info("guid: %s", example.guid)
logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))
if "token_type_ids" not in tokenizer.model_input_names:
segment_ids = None
features.append(
InputFeatures(
input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids
)
)
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data.dataset import Dataset
class TokenClassificationDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach
soon.
"""
features: List[InputFeatures]
pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index
# Use cross entropy ignore_index as padding label id so that only
# real label ids contribute to the loss later.
def __init__(
self,
token_classification_task: TokenClassificationTask,
data_dir: str,
tokenizer: PreTrainedTokenizer,
labels: List[str],
model_type: str,
max_seq_length: Optional[int] = None,
overwrite_cache=False,
mode: Split = Split.train,
):
# Load data features from cache or dataset file
cached_features_file = os.path.join(
data_dir,
"cached_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)),
)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}")
self.features = torch.load(cached_features_file)
else:
logger.info(f"Creating features from dataset file at {data_dir}")
examples = token_classification_task.read_examples_from_file(data_dir, mode)
# TODO clean up all this to leverage built-in features of tokenizers
self.features = token_classification_task.convert_examples_to_features(
examples,
labels,
max_seq_length,
tokenizer,
cls_token_at_end=bool(model_type in ["xlnet"]),
# xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=False,
# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=bool(tokenizer.padding_side == "left"),
pad_token=tokenizer.pad_token_id,
pad_token_segment_id=tokenizer.pad_token_type_id,
pad_token_label_id=self.pad_token_label_id,
)
logger.info(f"Saving features into cached file {cached_features_file}")
torch.save(self.features, cached_features_file)
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
if is_tf_available():
import tensorflow as tf
class TFTokenClassificationDataset:
"""
This will be superseded by a framework-agnostic approach
soon.
"""
features: List[InputFeatures]
pad_token_label_id: int = -100
# Use cross entropy ignore_index as padding label id so that only
# real label ids contribute to the loss later.
def __init__(
self,
token_classification_task: TokenClassificationTask,
data_dir: str,
tokenizer: PreTrainedTokenizer,
labels: List[str],
model_type: str,
max_seq_length: Optional[int] = None,
overwrite_cache=False,
mode: Split = Split.train,
):
examples = token_classification_task.read_examples_from_file(data_dir, mode)
# TODO clean up all this to leverage built-in features of tokenizers
self.features = token_classification_task.convert_examples_to_features(
examples,
labels,
max_seq_length,
tokenizer,
cls_token_at_end=bool(model_type in ["xlnet"]),
# xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=False,
# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=bool(tokenizer.padding_side == "left"),
pad_token=tokenizer.pad_token_id,
pad_token_segment_id=tokenizer.pad_token_type_id,
pad_token_label_id=self.pad_token_label_id,
)
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
self.dataset = tf.data.Dataset.from_generator(
gen,
({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
(
{"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])},
tf.TensorShape([None]),
),
)
else:
self.dataset = tf.data.Dataset.from_generator(
gen,
({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
(
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
},
tf.TensorShape([None]),
),
)
def get_dataset(self):
self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
return self.dataset
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
|
AdaMix/examples/legacy/token-classification/utils_ner.py/0
|
{
"file_path": "AdaMix/examples/legacy/token-classification/utils_ner.py",
"repo_id": "AdaMix",
"token_count": 7663
}
| 38 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning the library models for sequence classification on HANS."""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import numpy as np
import torch
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import is_main_process
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: str = field(
metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys())}
)
data_dir: str = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
)
max_seq_length: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def hans_data_collator(features: List[InputFeatures]) -> Dict[str, torch.Tensor]:
"""
Data collator that removes the "pairID" key if present.
"""
batch = default_data_collator(features)
_ = batch.pop("pairID", None)
return batch
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
try:
num_labels = hans_tasks_num_labels[data_args.task_name]
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
train_dataset = (
HansDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
)
if training_args.do_train
else None
)
eval_dataset = (
HansDataset(
data_dir=data_args.data_dir,
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
evaluate=True,
)
if training_args.do_eval
else None
)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=hans_data_collator,
)
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
output = trainer.predict(eval_dataset)
preds = output.predictions
preds = np.argmax(preds, axis=1)
pair_ids = [ex.pairID for ex in eval_dataset]
output_eval_file = os.path.join(training_args.output_dir, "hans_predictions.txt")
label_list = eval_dataset.get_labels()
if trainer.is_world_master():
with open(output_eval_file, "w") as writer:
writer.write("pairID,gold_label\n")
for pid, pred in zip(pair_ids, preds):
writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
trainer._log(output.metrics)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
|
AdaMix/examples/research_projects/adversarial/run_hans.py/0
|
{
"file_path": "AdaMix/examples/research_projects/adversarial/run_hans.py",
"repo_id": "AdaMix",
"token_count": 3256
}
| 39 |
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SummarizationDataProcessingTest(unittest.TestCase):
def setUp(self):
self.block_size = 10
def test_fit_to_block_sequence_too_small(self):
""" Pad the sequence with 0 if the sequence is smaller than the block size."""
sequence = [1, 2, 3, 4]
expected_output = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
def test_fit_to_block_sequence_fit_exactly(self):
""" Do nothing if the sequence is the right size. """
sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
def test_fit_to_block_sequence_too_big(self):
""" Truncate the sequence if it is too long. """
sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
def test_process_story_no_highlights(self):
"""Processing a story with no highlights returns an empty list for the summary."""
raw_story = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
_, summary_lines = process_story(raw_story)
self.assertEqual(summary_lines, [])
def test_process_empty_story(self):
"""An empty story returns an empty collection of lines."""
raw_story = ""
story_lines, summary_lines = process_story(raw_story)
self.assertEqual(story_lines, [])
self.assertEqual(summary_lines, [])
def test_process_story_with_missing_period(self):
raw_story = (
"It was the year of Our Lord one thousand seven hundred and "
"seventy-five\n\nSpiritual revelations were conceded to England "
"at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
)
story_lines, summary_lines = process_story(raw_story)
expected_story_lines = [
"It was the year of Our Lord one thousand seven hundred and seventy-five.",
"Spiritual revelations were conceded to England at that favoured period, as at this.",
]
self.assertEqual(expected_story_lines, story_lines)
expected_summary_lines = ["It was the best of times."]
self.assertEqual(expected_summary_lines, summary_lines)
def test_build_mask_no_padding(self):
sequence = torch.tensor([1, 2, 3, 4])
expected = torch.tensor([1, 1, 1, 1])
np.testing.assert_array_equal(build_mask(sequence, 0).numpy(), expected.numpy())
def test_build_mask(self):
sequence = torch.tensor([1, 2, 3, 4, 23, 23, 23])
expected = torch.tensor([1, 1, 1, 1, 0, 0, 0])
np.testing.assert_array_equal(build_mask(sequence, 23).numpy(), expected.numpy())
def test_build_mask_with_padding_equal_to_one(self):
sequence = torch.tensor([8, 2, 3, 4, 1, 1, 1])
expected = torch.tensor([1, 1, 1, 1, 0, 0, 0])
np.testing.assert_array_equal(build_mask(sequence, 1).numpy(), expected.numpy())
def test_compute_token_type_ids(self):
separator = 101
batch = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]])
expected = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]])
result = compute_token_type_ids(batch, separator)
np.testing.assert_array_equal(result, expected)
|
AdaMix/examples/research_projects/bertabs/test_utils_summarization.py/0
|
{
"file_path": "AdaMix/examples/research_projects/bertabs/test_utils_summarization.py",
"repo_id": "AdaMix",
"token_count": 1749
}
| 40 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" The distiller to distil the student.
Adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
"""
import math
import os
import time
import psutil
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import BatchSampler, DataLoader, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
from lm_seqs_dataset import LmSeqsDataset
from transformers import get_linear_schedule_with_warmup
from utils import logger
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
class Distiller:
def __init__(
self, params: dict, dataset: LmSeqsDataset, token_probs: torch.tensor, student: nn.Module, teacher: nn.Module
):
logger.info("Initializing Distiller")
self.params = params
self.dump_path = params.dump_path
self.multi_gpu = params.multi_gpu
self.fp16 = params.fp16
self.student = student
self.teacher = teacher
self.student_config = student.config
self.vocab_size = student.config.vocab_size
if params.n_gpu <= 1:
sampler = RandomSampler(dataset)
else:
sampler = DistributedSampler(dataset)
if params.group_by_size:
groups = create_lengths_groups(lengths=dataset.lengths, k=params.max_model_input_size)
sampler = GroupedBatchSampler(sampler=sampler, group_ids=groups, batch_size=params.batch_size)
else:
sampler = BatchSampler(sampler=sampler, batch_size=params.batch_size, drop_last=False)
self.dataloader = DataLoader(dataset=dataset, batch_sampler=sampler, collate_fn=dataset.batch_sequences)
self.temperature = params.temperature
assert self.temperature > 0.0
self.alpha_ce = params.alpha_ce
self.alpha_mlm = params.alpha_mlm
self.alpha_clm = params.alpha_clm
self.alpha_mse = params.alpha_mse
self.alpha_cos = params.alpha_cos
self.mlm = params.mlm
if self.mlm:
logger.info("Using MLM loss for LM step.")
self.mlm_mask_prop = params.mlm_mask_prop
assert 0.0 <= self.mlm_mask_prop <= 1.0
assert params.word_mask + params.word_keep + params.word_rand == 1.0
self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
self.pred_probs = self.pred_probs.to(f"cuda:{params.local_rank}") if params.n_gpu > 0 else self.pred_probs
self.token_probs = token_probs.to(f"cuda:{params.local_rank}") if params.n_gpu > 0 else token_probs
if self.fp16:
self.pred_probs = self.pred_probs.half()
self.token_probs = self.token_probs.half()
else:
logger.info("Using CLM loss for LM step.")
self.epoch = 0
self.n_iter = 0
self.n_total_iter = 0
self.n_sequences_epoch = 0
self.total_loss_epoch = 0
self.last_loss = 0
self.last_loss_ce = 0
self.last_loss_mlm = 0
self.last_loss_clm = 0
if self.alpha_mse > 0.0:
self.last_loss_mse = 0
if self.alpha_cos > 0.0:
self.last_loss_cos = 0
self.last_log = 0
self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
self.lm_loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
if self.alpha_mse > 0.0:
self.mse_loss_fct = nn.MSELoss(reduction="sum")
if self.alpha_cos > 0.0:
self.cosine_loss_fct = nn.CosineEmbeddingLoss(reduction="mean")
logger.info("--- Initializing model optimizer")
assert params.gradient_accumulation_steps >= 1
self.num_steps_epoch = len(self.dataloader)
num_train_optimization_steps = (
int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in student.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad
],
"weight_decay": params.weight_decay,
},
{
"params": [
p for n, p in student.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad
],
"weight_decay": 0.0,
},
]
logger.info(
"------ Number of trainable parameters (student): %i"
% sum([p.numel() for p in self.student.parameters() if p.requires_grad])
)
logger.info("------ Number of parameters (student): %i" % sum([p.numel() for p in self.student.parameters()]))
self.optimizer = AdamW(
optimizer_grouped_parameters, lr=params.learning_rate, eps=params.adam_epsilon, betas=(0.9, 0.98)
)
warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
self.scheduler = get_linear_schedule_with_warmup(
self.optimizer, num_warmup_steps=warmup_steps, num_training_steps=num_train_optimization_steps
)
if self.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
logger.info(f"Using fp16 training: {self.params.fp16_opt_level} level")
self.student, self.optimizer = amp.initialize(
self.student, self.optimizer, opt_level=self.params.fp16_opt_level
)
self.teacher = self.teacher.half()
if self.multi_gpu:
if self.fp16:
from apex.parallel import DistributedDataParallel
logger.info("Using apex.parallel.DistributedDataParallel for distributed training.")
self.student = DistributedDataParallel(self.student)
else:
from torch.nn.parallel import DistributedDataParallel
logger.info("Using nn.parallel.DistributedDataParallel for distributed training.")
self.student = DistributedDataParallel(
self.student,
device_ids=[params.local_rank],
output_device=params.local_rank,
find_unused_parameters=True,
)
self.is_master = params.is_master
if self.is_master:
logger.info("--- Initializing Tensorboard")
self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, "log", "train"))
self.tensorboard.add_text(tag="config/training", text_string=str(self.params), global_step=0)
self.tensorboard.add_text(tag="config/student", text_string=str(self.student_config), global_step=0)
def prepare_batch_mlm(self, batch):
"""
Prepare the batch: from the token_ids and the lengths, compute the attention mask and the masked label for MLM.
Input:
------
batch: `Tuple`
token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded.
lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch.
Output:
-------
token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels. There is a -100 where there is nothing to predict.
"""
token_ids, lengths = batch
token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
assert token_ids.size(0) == lengths.size(0)
attn_mask = torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None]
bs, max_seq_len = token_ids.size()
mlm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
x_prob = self.token_probs[token_ids.flatten()]
n_tgt = math.ceil(self.mlm_mask_prop * lengths.sum().item())
tgt_ids = torch.multinomial(x_prob / x_prob.sum(), n_tgt, replacement=False)
pred_mask = torch.zeros(
bs * max_seq_len, dtype=torch.bool, device=token_ids.device
) # previously `dtype=torch.uint8`, cf pytorch 1.2.0 compatibility
pred_mask[tgt_ids] = 1
pred_mask = pred_mask.view(bs, max_seq_len)
pred_mask[token_ids == self.params.special_tok_ids["pad_token"]] = 0
# mask a number of words == 0 [8] (faster with fp16)
if self.fp16:
n1 = pred_mask.sum().item()
if n1 > 8:
pred_mask = pred_mask.view(-1)
n2 = max(n1 % 8, 8 * (n1 // 8))
if n2 != n1:
pred_mask[torch.nonzero(pred_mask).view(-1)[: n1 - n2]] = 0
pred_mask = pred_mask.view(bs, max_seq_len)
assert pred_mask.sum().item() % 8 == 0, pred_mask.sum().item()
_token_ids_real = token_ids[pred_mask]
_token_ids_rand = _token_ids_real.clone().random_(self.vocab_size)
_token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids["mask_token"])
probs = torch.multinomial(self.pred_probs, len(_token_ids_real), replacement=True)
_token_ids = (
_token_ids_mask * (probs == 0).long()
+ _token_ids_real * (probs == 1).long()
+ _token_ids_rand * (probs == 2).long()
)
token_ids = token_ids.masked_scatter(pred_mask, _token_ids)
mlm_labels[~pred_mask] = -100 # previously `mlm_labels[1-pred_mask] = -1`, cf pytorch 1.2.0 compatibility
# sanity checks
assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
return token_ids, attn_mask, mlm_labels
def prepare_batch_clm(self, batch):
"""
Prepare the batch: from the token_ids and the lengths, compute the attention mask and the labels for CLM.
Input:
------
batch: `Tuple`
token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded.
lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch.
Output:
-------
token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
clm_labels: `torch.tensor(bs, seq_length)` - The causal language modeling labels. There is a -100 where there is nothing to predict.
"""
token_ids, lengths = batch
token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
assert token_ids.size(0) == lengths.size(0)
attn_mask = torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None]
clm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
clm_labels[~attn_mask] = -100 # previously `clm_labels[1-attn_mask] = -1`, cf pytorch 1.2.0 compatibility
# sanity checks
assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
return token_ids, attn_mask, clm_labels
def round_batch(self, x: torch.tensor, lengths: torch.tensor):
"""
For float16 only.
Sub-sample sentences in a batch, and add padding, so that each dimension is a multiple of 8.
Input:
------
x: `torch.tensor(bs, seq_length)` - The token ids.
lengths: `torch.tensor(bs, seq_length)` - The lengths of each of the sequence in the batch.
Output:
-------
x: `torch.tensor(new_bs, new_seq_length)` - The updated token ids.
lengths: `torch.tensor(new_bs, new_seq_length)` - The updated lengths.
"""
if not self.fp16 or len(lengths) < 8:
return x, lengths
# number of sentences == 0 [8]
bs1 = len(lengths)
bs2 = 8 * (bs1 // 8)
assert bs2 > 0 and bs2 % 8 == 0
if bs1 != bs2:
idx = torch.randperm(bs1)[:bs2]
lengths = lengths[idx]
slen = lengths.max().item()
x = x[idx, :slen]
else:
idx = None
# sequence length == 0 [8]
ml1 = x.size(1)
if ml1 % 8 != 0:
pad = 8 - (ml1 % 8)
ml2 = ml1 + pad
if self.mlm:
pad_id = self.params.special_tok_ids["pad_token"]
else:
pad_id = self.params.special_tok_ids["unk_token"]
padding_tensor = torch.zeros(bs2, pad, dtype=torch.long, device=x.device).fill_(pad_id)
x = torch.cat([x, padding_tensor], 1)
assert x.size() == (bs2, ml2)
assert x.size(0) % 8 == 0
assert x.size(1) % 8 == 0
return x, lengths
def train(self):
"""
The real training loop.
"""
if self.is_master:
logger.info("Starting training")
self.last_log = time.time()
self.student.train()
self.teacher.eval()
for _ in range(self.params.n_epoch):
if self.is_master:
logger.info(f"--- Starting epoch {self.epoch}/{self.params.n_epoch-1}")
if self.multi_gpu:
torch.distributed.barrier()
iter_bar = tqdm(self.dataloader, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
for batch in iter_bar:
if self.params.n_gpu > 0:
batch = tuple(t.to(f"cuda:{self.params.local_rank}") for t in batch)
if self.mlm:
token_ids, attn_mask, lm_labels = self.prepare_batch_mlm(batch=batch)
else:
token_ids, attn_mask, lm_labels = self.prepare_batch_clm(batch=batch)
self.step(input_ids=token_ids, attention_mask=attn_mask, lm_labels=lm_labels)
iter_bar.update()
iter_bar.set_postfix(
{"Last_loss": f"{self.last_loss:.2f}", "Avg_cum_loss": f"{self.total_loss_epoch/self.n_iter:.2f}"}
)
iter_bar.close()
if self.is_master:
logger.info(f"--- Ending epoch {self.epoch}/{self.params.n_epoch-1}")
self.end_epoch()
if self.is_master:
logger.info("Save very last checkpoint as `pytorch_model.bin`.")
self.save_checkpoint(checkpoint_name="pytorch_model.bin")
logger.info("Training is finished")
def step(self, input_ids: torch.tensor, attention_mask: torch.tensor, lm_labels: torch.tensor):
"""
One optimization step: forward of student AND teacher, backward on the loss (for gradient accumulation),
and possibly a parameter update (depending on the gradient accumulation).
Input:
------
input_ids: `torch.tensor(bs, seq_length)` - The token ids.
attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention.
lm_labels: `torch.tensor(bs, seq_length)` - The language modeling labels (mlm labels for MLM and clm labels for CLM).
"""
if self.mlm:
s_logits, s_hidden_states = self.student(
input_ids=input_ids, attention_mask=attention_mask
) # (bs, seq_length, voc_size)
with torch.no_grad():
t_logits, t_hidden_states = self.teacher(
input_ids=input_ids, attention_mask=attention_mask
) # (bs, seq_length, voc_size)
else:
s_logits, _, s_hidden_states = self.student(
input_ids=input_ids, attention_mask=None
) # (bs, seq_length, voc_size)
with torch.no_grad():
t_logits, _, t_hidden_states = self.teacher(
input_ids=input_ids, attention_mask=None
) # (bs, seq_length, voc_size)
assert s_logits.size() == t_logits.size()
# https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
# https://github.com/peterliht/knowledge-distillation-pytorch/issues/2
if self.params.restrict_ce_to_mask:
mask = (lm_labels > -1).unsqueeze(-1).expand_as(s_logits) # (bs, seq_length, voc_size)
else:
mask = attention_mask.unsqueeze(-1).expand_as(s_logits) # (bs, seq_length, voc_size)
s_logits_slct = torch.masked_select(s_logits, mask) # (bs * seq_length * voc_size) modulo the 1s in mask
s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1)) # (bs * seq_length, voc_size) modulo the 1s in mask
t_logits_slct = torch.masked_select(t_logits, mask) # (bs * seq_length * voc_size) modulo the 1s in mask
t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1)) # (bs * seq_length, voc_size) modulo the 1s in mask
assert t_logits_slct.size() == s_logits_slct.size()
loss_ce = (
self.ce_loss_fct(
F.log_softmax(s_logits_slct / self.temperature, dim=-1),
F.softmax(t_logits_slct / self.temperature, dim=-1),
)
* (self.temperature) ** 2
)
loss = self.alpha_ce * loss_ce
if self.alpha_mlm > 0.0:
loss_mlm = self.lm_loss_fct(s_logits.view(-1, s_logits.size(-1)), lm_labels.view(-1))
loss += self.alpha_mlm * loss_mlm
if self.alpha_clm > 0.0:
shift_logits = s_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_clm = self.lm_loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
loss += self.alpha_clm * loss_clm
if self.alpha_mse > 0.0:
loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct) / s_logits_slct.size(
0
) # Reproducing batchmean reduction
loss += self.alpha_mse * loss_mse
if self.alpha_cos > 0.0:
s_hidden_states = s_hidden_states[-1] # (bs, seq_length, dim)
t_hidden_states = t_hidden_states[-1] # (bs, seq_length, dim)
mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states) # (bs, seq_length, dim)
assert s_hidden_states.size() == t_hidden_states.size()
dim = s_hidden_states.size(-1)
s_hidden_states_slct = torch.masked_select(s_hidden_states, mask) # (bs * seq_length * dim)
s_hidden_states_slct = s_hidden_states_slct.view(-1, dim) # (bs * seq_length, dim)
t_hidden_states_slct = torch.masked_select(t_hidden_states, mask) # (bs * seq_length * dim)
t_hidden_states_slct = t_hidden_states_slct.view(-1, dim) # (bs * seq_length, dim)
target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1) # (bs * seq_length,)
loss_cos = self.cosine_loss_fct(s_hidden_states_slct, t_hidden_states_slct, target)
loss += self.alpha_cos * loss_cos
self.total_loss_epoch += loss.item()
self.last_loss = loss.item()
self.last_loss_ce = loss_ce.item()
if self.alpha_mlm > 0.0:
self.last_loss_mlm = loss_mlm.item()
if self.alpha_clm > 0.0:
self.last_loss_clm = loss_clm.item()
if self.alpha_mse > 0.0:
self.last_loss_mse = loss_mse.item()
if self.alpha_cos > 0.0:
self.last_loss_cos = loss_cos.item()
self.optimize(loss)
self.n_sequences_epoch += input_ids.size(0)
def optimize(self, loss):
"""
Normalization on the loss (gradient accumulation or distributed training), followed by
backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation).
Also update the metrics for tensorboard.
"""
# Check for NaN
if (loss != loss).data.any():
logger.error("NaN detected")
exit()
if self.multi_gpu:
loss = loss.mean()
if self.params.gradient_accumulation_steps > 1:
loss = loss / self.params.gradient_accumulation_steps
if self.fp16:
from apex import amp
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
self.iter()
if self.n_iter % self.params.gradient_accumulation_steps == 0:
if self.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.params.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(self.student.parameters(), self.params.max_grad_norm)
self.optimizer.step()
self.optimizer.zero_grad()
self.scheduler.step()
def iter(self):
"""
Update global counts, write to tensorboard and save checkpoint.
"""
self.n_iter += 1
self.n_total_iter += 1
if self.n_total_iter % self.params.log_interval == 0:
self.log_tensorboard()
self.last_log = time.time()
if self.n_total_iter % self.params.checkpoint_interval == 0:
self.save_checkpoint()
def log_tensorboard(self):
"""
Log into tensorboard. Only by the master process.
"""
if not self.is_master:
return
for param_name, param in self.student.named_parameters():
self.tensorboard.add_scalar(
tag="parameter_mean/" + param_name, scalar_value=param.data.mean(), global_step=self.n_total_iter
)
self.tensorboard.add_scalar(
tag="parameter_std/" + param_name, scalar_value=param.data.std(), global_step=self.n_total_iter
)
if param.grad is None:
continue
self.tensorboard.add_scalar(
tag="grad_mean/" + param_name, scalar_value=param.grad.data.mean(), global_step=self.n_total_iter
)
self.tensorboard.add_scalar(
tag="grad_std/" + param_name, scalar_value=param.grad.data.std(), global_step=self.n_total_iter
)
self.tensorboard.add_scalar(
tag="losses/cum_avg_loss_epoch",
scalar_value=self.total_loss_epoch / self.n_iter,
global_step=self.n_total_iter,
)
self.tensorboard.add_scalar(tag="losses/loss", scalar_value=self.last_loss, global_step=self.n_total_iter)
self.tensorboard.add_scalar(
tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter
)
if self.alpha_mlm > 0.0:
self.tensorboard.add_scalar(
tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter
)
if self.alpha_clm > 0.0:
self.tensorboard.add_scalar(
tag="losses/loss_clm", scalar_value=self.last_loss_clm, global_step=self.n_total_iter
)
if self.alpha_mse > 0.0:
self.tensorboard.add_scalar(
tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter
)
if self.alpha_cos > 0.0:
self.tensorboard.add_scalar(
tag="losses/loss_cos", scalar_value=self.last_loss_cos, global_step=self.n_total_iter
)
self.tensorboard.add_scalar(
tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter
)
self.tensorboard.add_scalar(
tag="global/memory_usage",
scalar_value=psutil.virtual_memory()._asdict()["used"] / 1_000_000,
global_step=self.n_total_iter,
)
self.tensorboard.add_scalar(
tag="global/speed", scalar_value=time.time() - self.last_log, global_step=self.n_total_iter
)
def end_epoch(self):
"""
Finally arrived at the end of epoch (full pass on dataset).
Do some tensorboard logging and checkpoint saving.
"""
logger.info(f"{self.n_sequences_epoch} sequences have been trained during this epoch.")
if self.is_master:
self.save_checkpoint(checkpoint_name=f"model_epoch_{self.epoch}.pth")
self.tensorboard.add_scalar(
tag="epoch/loss", scalar_value=self.total_loss_epoch / self.n_iter, global_step=self.epoch
)
self.epoch += 1
self.n_sequences_epoch = 0
self.n_iter = 0
self.total_loss_epoch = 0
def save_checkpoint(self, checkpoint_name: str = "checkpoint.pth"):
"""
Save the current state. Only by the master process.
"""
if not self.is_master:
return
mdl_to_save = self.student.module if hasattr(self.student, "module") else self.student
mdl_to_save.config.save_pretrained(self.dump_path)
state_dict = mdl_to_save.state_dict()
torch.save(state_dict, os.path.join(self.dump_path, checkpoint_name))
|
AdaMix/examples/research_projects/distillation/distiller.py/0
|
{
"file_path": "AdaMix/examples/research_projects/distillation/distiller.py",
"repo_id": "AdaMix",
"token_count": 12521
}
| 41 |
# Long Form Question Answering
Author: @yjernite
This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗datasets](https://github.com/huggingface/datasets) libraries.
You can use these methods to train your own system by following along the associate [notebook](https://github.com/huggingface/notebooks/blob/master/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb) or [blog post](https://yjernite.github.io/lfqa.html).
|
AdaMix/examples/research_projects/longform-qa/README.md/0
|
{
"file_path": "AdaMix/examples/research_projects/longform-qa/README.md",
"repo_id": "AdaMix",
"token_count": 208
}
| 42 |
## MM-IMDb
Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/mm-imdb/run_mmimdb.py).
[MM-IMDb](http://lisi1.unal.edu.co/mmimdb/) is a Multimodal dataset with around 26,000 movies including images, plots and other metadata.
### Training on MM-IMDb
```
python run_mmimdb.py \
--data_dir /path/to/mmimdb/dataset/ \
--model_type bert \
--model_name_or_path bert-base-uncased \
--output_dir /path/to/save/dir/ \
--do_train \
--do_eval \
--max_seq_len 512 \
--gradient_accumulation_steps 20 \
--num_image_embeds 3 \
--num_train_epochs 100 \
--patience 5
```
|
AdaMix/examples/research_projects/mm-imdb/README.md/0
|
{
"file_path": "AdaMix/examples/research_projects/mm-imdb/README.md",
"repo_id": "AdaMix",
"token_count": 282
}
| 43 |
# Performer fine-tuning
Example authors: @TevenLeScao, @Patrickvonplaten
Paper authors: Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller
## Requirements
`datasets`, `flax` and `jax`. `wandb` integration is built-in if you want to use it.
## Examples
`sanity_script.sh` will launch performer fine-tuning from the bert-base-cased checkpoint on the Simple Wikipedia dataset (a small, easy-language English Wikipedia) from `datasets`.
`full_script.sh` will launch performer fine-tuning from the bert-large-cased checkpoint on the English Wikipedia dataset from `datasets`.
Here are a few key arguments:
- Remove the `--performer` argument to use a standard Bert model.
- Add `--reinitialize` to start from a blank model rather than a Bert checkpoint.
- You may change the Bert size by passing a different [checkpoint](https://huggingface.co/transformers/pretrained_models.html) to the `--model_name_or_path` argument.
- Passing your user name to the `--wandb_user_name` argument will trigger weights and biases logging.
- You can choose a dataset with `--dataset_name` and `--dataset_config`. Our [viewer](https://huggingface.co/datasets/viewer/) will help you find what you need.
|
AdaMix/examples/research_projects/performer/README.md/0
|
{
"file_path": "AdaMix/examples/research_projects/performer/README.md",
"repo_id": "AdaMix",
"token_count": 408
}
| 44 |
import logging
import os
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def count_trainable_parameters(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
return params
logger = logging.getLogger(__name__)
def get_checkpoint_callback(output_dir, metric):
"""Saves the best model by validation EM score."""
if metric == "rouge2":
exp = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
exp = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
exp = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this function."
)
checkpoint_callback = ModelCheckpoint(
filepath=os.path.join(output_dir, exp),
monitor=f"val_{metric}",
mode="max",
save_top_k=3,
period=1, # maybe save a checkpoint every time val is run, not just end of epoch.
)
return checkpoint_callback
def get_early_stopping_callback(metric, patience):
return EarlyStopping(
monitor=f"val_{metric}", # does this need avg?
mode="min" if "loss" in metric else "max",
patience=patience,
verbose=True,
)
class Seq2SeqLoggingCallback(pl.Callback):
def on_batch_end(self, trainer, pl_module):
lrs = {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
pl_module.logger.log_metrics(lrs)
@rank_zero_only
def _write_logs(
self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
) -> None:
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****")
metrics = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]})
# Log results
od = Path(pl_module.hparams.output_dir)
if type_path == "test":
results_file = od / "test_results.txt"
generations_file = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
results_file = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
generations_file = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=True)
generations_file.parent.mkdir(exist_ok=True)
with open(results_file, "a+") as writer:
for key in sorted(metrics):
if key in ["log", "progress_bar", "preds"]:
continue
val = metrics[key]
if isinstance(val, torch.Tensor):
val = val.item()
msg = f"{key}: {val:.6f}\n"
writer.write(msg)
if not save_generations:
return
if "preds" in metrics:
content = "\n".join(metrics["preds"])
generations_file.open("w+").write(content)
@rank_zero_only
def on_train_start(self, trainer, pl_module):
try:
npars = pl_module.model.model.num_parameters()
except AttributeError:
npars = pl_module.model.num_parameters()
n_trainable_pars = count_trainable_parameters(pl_module)
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6})
@rank_zero_only
def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
save_json(pl_module.metrics, pl_module.metrics_save_path)
return self._write_logs(trainer, pl_module, "test")
@rank_zero_only
def on_validation_end(self, trainer: pl.Trainer, pl_module):
save_json(pl_module.metrics, pl_module.metrics_save_path)
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
|
AdaMix/examples/research_projects/rag/callbacks_rag.py/0
|
{
"file_path": "AdaMix/examples/research_projects/rag/callbacks_rag.py",
"repo_id": "AdaMix",
"token_count": 1926
}
| 45 |
#!/usr/bin/env python
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
MARIAN_MODEL = "sshleifer/mar_enro_6_3_student"
class TestMbartCc25Enro(TestCasePlus):
def setUp(self):
super().setUp()
data_cached = cached_path(
"https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz",
extract_compressed_file=True,
)
self.data_dir = f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
@slow
@require_torch_gpu
def test_model_download(self):
"""This warms up the cache so that we can time the next test without including download time, which varies between machines."""
MarianMTModel.from_pretrained(MARIAN_MODEL)
# @timeout_decorator.timeout(1200)
@slow
@require_torch_gpu
def test_train_mbart_cc25_enro_script(self):
env_vars_to_replace = {
"$MAX_LEN": 64,
"$BS": 64,
"$GAS": 1,
"$ENRO_DIR": self.data_dir,
"facebook/mbart-large-cc25": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"--learning_rate=3e-5": "--learning_rate 3e-4",
"--num_train_epochs 6": "--num_train_epochs 1",
}
# Clean up bash script
bash_script = (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
for k, v in env_vars_to_replace.items():
bash_script = bash_script.replace(k, str(v))
output_dir = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
args = f"""
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
""".split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
testargs = ["finetune.py"] + bash_script.split() + args
with patch.object(sys, "argv", testargs):
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
args = parser.parse_args()
model = main(args)
# Check metrics
metrics = load_json(model.metrics_save_path)
first_step_stats = metrics["val"][0]
last_step_stats = metrics["val"][-1]
self.assertEqual(len(metrics["val"]), (args.max_epochs / args.val_check_interval))
assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
self.assertGreater(last_step_stats["val_avg_gen_time"], 0.01)
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["val_avg_gen_time"], 1.0)
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"], 2)
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["val_avg_bleu"], 17)
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]), 1.1)
# check lightning ckpt can be loaded and has a reasonable statedict
contents = os.listdir(output_dir)
ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
full_path = os.path.join(args.output_dir, ckpt_path)
ckpt = torch.load(full_path, map_location="cpu")
expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
contents = {os.path.basename(p) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"]) == 1
class TestDistilMarianNoTeacher(TestCasePlus):
@timeout_decorator.timeout(600)
@slow
@require_torch_gpu
def test_opus_mt_distill_script(self):
data_dir = f"{self.test_file_dir_str}/test_data/wmt_en_ro"
env_vars_to_replace = {
"--fp16_opt_level=O1": "",
"$MAX_LEN": 128,
"$BS": 16,
"$GAS": 1,
"$ENRO_DIR": data_dir,
"$m": "sshleifer/student_marian_en_ro_6_1",
"val_check_interval=0.25": "val_check_interval=1.0",
}
# Clean up bash script
bash_script = (
(self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py")[1].strip()
)
bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
bash_script = bash_script.replace("--fp16 ", " ")
for k, v in env_vars_to_replace.items():
bash_script = bash_script.replace(k, str(v))
output_dir = self.get_auto_remove_tmp_dir()
bash_script = bash_script.replace("--fp16", "")
epochs = 6
testargs = (
["distillation.py"]
+ bash_script.split()
+ [
f"--output_dir={output_dir}",
"--gpus=1",
"--learning_rate=1e-3",
f"--num_train_epochs={epochs}",
"--warmup_steps=10",
"--val_check_interval=1.0",
"--do_predict",
]
)
with patch.object(sys, "argv", testargs):
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
args = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
model = distill_main(args)
# Check metrics
metrics = load_json(model.metrics_save_path)
first_step_stats = metrics["val"][0]
last_step_stats = metrics["val"][-1]
assert len(metrics["val"]) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
# check lightning ckpt can be loaded and has a reasonable statedict
contents = os.listdir(output_dir)
ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
full_path = os.path.join(args.output_dir, ckpt_path)
ckpt = torch.load(full_path, map_location="cpu")
expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
contents = {os.path.basename(p) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"]) == 1
|
AdaMix/examples/research_projects/seq2seq-distillation/_test_bash_script.py/0
|
{
"file_path": "AdaMix/examples/research_projects/seq2seq-distillation/_test_bash_script.py",
"repo_id": "AdaMix",
"token_count": 3934
}
| 46 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def copy_layers(src_layers: nn.ModuleList, dest_layers: nn.ModuleList, layers_to_copy: List[int]) -> None:
layers_to_copy = nn.ModuleList([src_layers[i] for i in layers_to_copy])
assert len(dest_layers) == len(layers_to_copy), f"{len(dest_layers)} != {len(layers_to_copy)}"
dest_layers.load_state_dict(layers_to_copy.state_dict())
LAYERS_TO_COPY = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
LAYERS_TO_SUPERVISE = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def pick_layers_to_copy(n_student, n_teacher):
try:
val = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first {n_student}"
)
return list(range(n_student))
def get_layers_to_supervise(n_student, n_teacher) -> List[int]:
"""Used or the --supervise_forward kwarg"""
if n_student > n_teacher:
raise ValueError(f"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}")
elif n_teacher == n_student:
return list(range(n_teacher))
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def create_student_by_copying_alternating_layers(
teacher: Union[str, PreTrainedModel],
save_path: Union[str, Path] = "student",
e: Union[int, None] = None,
d: Union[int, None] = None,
copy_first_teacher_layers=False,
e_layers_to_copy=None,
d_layers_to_copy=None,
**extra_config_kwargs
) -> Tuple[PreTrainedModel, List[int], List[int]]:
"""Make a student by copying alternating layers from a teacher, save it to save_path.
Args:
teacher: str or PreTrainedModel if str, this will call AutoModelForSeq2SeqLM.from_pretrained(teacher) before
copying layers
save_path: where to save the student, defaults to student directory.
e: how many Encoder layers should the student have, default is fully copy of teacher
d: how many Decoder layers should the student have, default is fully copy of teacher
copy_first_teacher_layers: [bool] dont copy alternating layers, just the first e/d.
**extra_config_kwargs: extra kwargs to pass to the student, by default the teacher config is used.
Returns:
student: new, smaller model. (Also saves it to save_path)
e_layers_to_copy: list of which teacher encoder layers were used
d_layers_to_copy: list of which teacher decoder layers were used
"""
_msg = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(teacher, str):
AutoTokenizer.from_pretrained(teacher).save_pretrained(save_path) # purely for convenience
teacher = AutoModelForSeq2SeqLM.from_pretrained(teacher).eval()
else:
assert isinstance(teacher, PreTrainedModel), f"teacher must be a model or string got type {type(teacher)}"
init_kwargs = teacher.config.to_diff_dict()
try:
teacher_e, teacher_d = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
e = teacher_e
if d is None:
d = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
except AttributeError: # T5
teacher_e, teacher_d = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
e = teacher_e
if d is None:
d = teacher_d
init_kwargs.update({"num_layers": e, "num_decoder_layers": d})
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(extra_config_kwargs)
# Copy weights
student_cfg = teacher.config_class(**init_kwargs)
student = AutoModelForSeq2SeqLM.from_config(student_cfg)
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
info = student.load_state_dict(teacher.state_dict(), strict=False)
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
e_layers_to_copy, d_layers_to_copy = list(range(e)), list(range(d))
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"
)
student.save_pretrained(save_path)
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
e_layers_to_copy: List[int] = pick_layers_to_copy(e, teacher_e)
if d_layers_to_copy is None:
d_layers_to_copy: List[int] = pick_layers_to_copy(d, teacher_d)
try:
copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy)
copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, d_layers_to_copy)
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy)
copy_layers(teacher.decoder.block, student.decoder.block, d_layers_to_copy)
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"
)
student.config.init_metadata = dict(
teacher_type=teacher.config.model_type,
copied_encoder_layers=e_layers_to_copy,
copied_decoder_layers=d_layers_to_copy,
)
student.save_pretrained(save_path)
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
|
AdaMix/examples/research_projects/seq2seq-distillation/make_student.py/0
|
{
"file_path": "AdaMix/examples/research_projects/seq2seq-distillation/make_student.py",
"repo_id": "AdaMix",
"token_count": 3129
}
| 47 |
<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
## Sequence to Sequence Training and Evaluation
This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks.
Please tag @patil-suraj with any issues/unexpected behaviors, or send a PR!
For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/master/examples/research_projects/bertabs/README.md).
For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq).
### Supported Architectures
- `BartForConditionalGeneration`
- `MarianMTModel`
- `PegasusForConditionalGeneration`
- `MBartForConditionalGeneration`
- `FSMTForConditionalGeneration` (translation only)
- `T5ForConditionalGeneration`
`run_summarization.py` and `run_translation.py` are lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files
and you also will find examples of these below.
### Summarization
Here is an example on a summarization task:
```bash
python examples/seq2seq/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name xsum \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--max_train_samples 500 \
--max_val_samples 500
```
CNN/DailyMail dataset is another commonly used dataset for the task of summarization. To use it replace `--dataset_name xsum` with `--dataset_name cnn_dailymail --dataset_config "3.0.0"`.
And here is how you would use it on your own files, after adjusting the values for the arguments
`--train_file`, `--validation_file`, `--text_column` and `--summary_column` to match your setup:
```bash
python examples/seq2seq/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--train_file path_to_csv_or_jsonlines_file \
--validation_file path_to_csv_or_jsonlines_file \
--output_dir /tmp/tst-summarization \
--overwrite_output_dir \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--predict_with_generate \
--max_train_samples 500 \
--max_val_samples 500
```
The task of summarization supports custom CSV and JSONLINES formats.
#### Custom CSV Files
If it's a csv file the training and validation files should have a column for the inputs texts and a column for the summaries.
If the csv file has just two columns as in the following example:
```csv
text,summary
"I'm sitting here in a boring room. It's just another rainy Sunday afternoon. I'm wasting my time I got nothing to do. I'm hanging around I'm waiting for you. But nothing ever happens. And I wonder","I'm sitting in a room where I'm waiting for something to happen"
"I see trees so green, red roses too. I see them bloom for me and you. And I think to myself what a wonderful world. I see skies so blue and clouds so white. The bright blessed day, the dark sacred night. And I think to myself what a wonderful world.","I'm a gardener and I'm a big fan of flowers."
"Christmas time is here. Happiness and cheer. Fun for all that children call. Their favorite time of the year. Snowflakes in the air. Carols everywhere. Olden times and ancient rhymes. Of love and dreams to share","It's that time of year again."
```
The first column is assumed to be for `text` and the second is for summary.
If the csv file has multiple columns, you can then specify the names of the columns to use:
```bash
--text_column text_column_name \
--summary_column summary_column_name \
```
For example if the columns were:
```csv
id,date,text,summary
```
and you wanted to select only `text` and `summary`, then you'd pass these additional arguments:
```bash
--text_column text \
--summary_column summary \
```
#### Custom JSONFILES Files
The second supported format is jsonlines. Here is an example of a jsonlines custom data file.
```json
{"text": "I'm sitting here in a boring room. It's just another rainy Sunday afternoon. I'm wasting my time I got nothing to do. I'm hanging around I'm waiting for you. But nothing ever happens. And I wonder", "summary": "I'm sitting in a room where I'm waiting for something to happen"}
{"text": "I see trees so green, red roses too. I see them bloom for me and you. And I think to myself what a wonderful world. I see skies so blue and clouds so white. The bright blessed day, the dark sacred night. And I think to myself what a wonderful world.", "summary": "I'm a gardener and I'm a big fan of flowers."}
{"text": "Christmas time is here. Happiness and cheer. Fun for all that children call. Their favorite time of the year. Snowflakes in the air. Carols everywhere. Olden times and ancient rhymes. Of love and dreams to share", "summary": "It's that time of year again."}
```
Same as with the CSV files, by default the first value will be used as the text record and the second as the summary record. Therefore you can use any key names for the entries, in this example `text` and `summary` were used.
And as with the CSV files, you can specify which values to select from the file, by explicitly specifying the corresponding key names. In our example this again would be:
```bash
--text_column text \
--summary_column summary \
```
### Translation
Here is an example of a translation fine-tuning with T5:
```bash
python examples/seq2seq/run_translation.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--source_lang en \
--target_lang ro \
--dataset_name wmt16 \
--dataset_config_name ro-en \
--output_dir /tmp/tst-translation \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--max_train_samples 500 \
--max_val_samples 500
```
And the same with MBart:
```bash
python examples/seq2seq/run_translation.py \
--model_name_or_path facebook/mbart-large-en-ro \
--do_train \
--do_eval \
--dataset_name wmt16 \
--dataset_config_name ro-en \
--source_lang en_XX \
--target_lang ro_RO \
--output_dir /tmp/tst-translation \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--max_train_samples 500 \
--max_val_samples 500
```
Note, that depending on the used model additional language-specific command-line arguments are sometimes required. Specifically:
* MBart models require different `--{source,target}_lang` values, e.g. in place of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be looked up [here](https://huggingface.co/facebook/mbart-large-cc25)
* T5 models can use a `--source_prefix` argument to override the otherwise automated prefix of the form `translate {source_lang} to {target_lang}` for `run_translation.py` and `summarize: ` for `run_summarization.py`
Also, if you switch to a different language pair, make sure to adjust the source and target values in all command line arguments.
And here is how you would use the translation finetuning on your own files, after adjusting the
values for the arguments `--train_file`, `--validation_file` to match your setup:
```bash
python examples/seq2seq/run_translation.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--source_lang en \
--target_lang ro \
--dataset_name wmt16 \
--dataset_config_name ro-en \
--train_file path_to_jsonlines_file \
--validation_file path_to_jsonlines_file \
--output_dir /tmp/tst-translation \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--max_train_samples 500 \
--max_val_samples 500
```
The task of translation supports only custom JSONLINES files, with each line being a dictionary with a key `"translation"` and its value another dictionary whose keys is the language pair. For example:
```json
{ "translation": { "en": "Others have dismissed him as a joke.", "ro": "Alții l-au numit o glumă." } }
{ "translation": { "en": "And some are holding out for an implosion.", "ro": "Iar alții așteaptă implozia." } }
```
Here the languages are Romanian (`ro`) and English (`en`).
If you want to use a pre-processed dataset that leads to high bleu scores, but for the `en-de` language pair, you can use `--dataset_name wmt14-en-de-pre-processed`, as following:
```bash
python examples/seq2seq/run_translation.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--source_lang en \
--target_lang de \
--dataset_name wmt14-en-de-pre-processed \
--output_dir /tmp/tst-translation \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--max_train_samples 500 \
--max_val_samples 500
```
|
AdaMix/examples/seq2seq/README.md/0
|
{
"file_path": "AdaMix/examples/seq2seq/README.md",
"repo_id": "AdaMix",
"token_count": 3198
}
| 48 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Fine-tuning the library models for sequence classification."""
import logging
import os
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, Optional
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
glue_compute_metrics,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
class Split(Enum):
train = "train"
dev = "validation"
test = "test"
def get_tfds(
task_name: str,
tokenizer: PreTrainedTokenizer,
max_seq_length: Optional[int] = None,
mode: Split = Split.train,
data_dir: str = None,
):
if task_name == "mnli-mm" and mode == Split.dev:
tfds_name = "mnli_mismatched"
elif task_name == "mnli-mm" and mode == Split.train:
tfds_name = "mnli"
elif task_name == "mnli" and mode == Split.dev:
tfds_name = "mnli_matched"
elif task_name == "sst-2":
tfds_name = "sst2"
elif task_name == "sts-b":
tfds_name = "stsb"
else:
tfds_name = task_name
ds, info = tfds.load("glue/" + tfds_name, split=mode.value, with_info=True, data_dir=data_dir)
ds = glue_convert_examples_to_features(ds, tokenizer, max_seq_length, task_name)
ds = ds.apply(tf.data.experimental.assert_cardinality(info.splits[mode.value].num_examples))
return ds
logger = logging.getLogger(__name__)
@dataclass
class GlueDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
data_dir: Optional[str] = field(default=None, metadata={"help": "The input/output data dir for TFDS."})
max_seq_length: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def __post_init__(self):
self.task_name = self.task_name.lower()
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, GlueDataTrainingArguments, TFTrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(
"n_replicas: %s, distributed training: %s, 16-bits training: %s",
training_args.n_replicas,
bool(training_args.n_replicas > 1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
try:
num_labels = glue_tasks_num_labels["mnli" if data_args.task_name == "mnli-mm" else data_args.task_name]
output_mode = glue_output_modes[data_args.task_name]
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
with training_args.strategy.scope():
model = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_pt=bool(".bin" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
train_dataset = (
get_tfds(
task_name=data_args.task_name,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_dir=data_args.data_dir,
)
if training_args.do_train
else None
)
eval_dataset = (
get_tfds(
task_name=data_args.task_name,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
mode=Split.dev,
data_dir=data_args.data_dir,
)
if training_args.do_eval
else None
)
def compute_metrics(p: EvalPrediction) -> Dict:
if output_mode == "classification":
preds = np.argmax(p.predictions, axis=1)
elif output_mode == "regression":
preds = np.squeeze(p.predictions)
return glue_compute_metrics(data_args.task_name, preds, p.label_ids)
# Initialize our Trainer
trainer = TFTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
)
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
return results
if __name__ == "__main__":
main()
|
AdaMix/examples/text-classification/run_tf_glue.py/0
|
{
"file_path": "AdaMix/examples/text-classification/run_tf_glue.py",
"repo_id": "AdaMix",
"token_count": 3538
}
| 49 |
#!/usr/bin/env bash
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script acquires data and converts it to fsmt model
# it covers:
# - allenai/wmt16-en-de-dist-12-1
# - allenai/wmt16-en-de-dist-6-1
# - allenai/wmt16-en-de-12-1
# this script needs to be run from the top level of the transformers repo
if [ ! -d "src/transformers" ]; then
echo "Error: This script needs to be run from the top of the transformers repo"
exit 1
fi
mkdir data
# get data (run once)
cd data
gdown 'https://drive.google.com/uc?id=1x_G2cjvM1nW5hjAB8-vWxRqtQTlmIaQU'
gdown 'https://drive.google.com/uc?id=1oA2aqZlVNj5FarxBlNXEHpBS4lRetTzU'
gdown 'https://drive.google.com/uc?id=1Wup2D318QYBFPW_NKI1mfP_hXOfmUI9r'
tar -xvzf trans_ende_12-1_0.2.tar.gz
tar -xvzf trans_ende-dist_12-1_0.2.tar.gz
tar -xvzf trans_ende-dist_6-1_0.2.tar.gz
gdown 'https://drive.google.com/uc?id=1mNufoynJ9-Zy1kJh2TA_lHm2squji0i9'
gdown 'https://drive.google.com/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj'
tar -xvzf wmt16.en-de.deep-shallow.dist.tar.gz
tar -xvzf wmt16.en-de.deep-shallow.tar.gz
cp wmt16.en-de.deep-shallow/data-bin/dict.*.txt trans_ende_12-1_0.2
cp wmt16.en-de.deep-shallow.dist/data-bin/dict.*.txt trans_ende-dist_12-1_0.2
cp wmt16.en-de.deep-shallow.dist/data-bin/dict.*.txt trans_ende-dist_6-1_0.2
cp wmt16.en-de.deep-shallow/bpecodes trans_ende_12-1_0.2
cp wmt16.en-de.deep-shallow.dist/bpecodes trans_ende-dist_12-1_0.2
cp wmt16.en-de.deep-shallow.dist/bpecodes trans_ende-dist_6-1_0.2
cd -
# run conversions and uploads
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/trans_ende-dist_12-1_0.2/checkpoint_top5_average.pt --pytorch_dump_folder_path data/wmt16-en-de-dist-12-1
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/trans_ende-dist_6-1_0.2/checkpoint_top5_average.pt --pytorch_dump_folder_path data/wmt16-en-de-dist-6-1
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/trans_ende_12-1_0.2/checkpoint_top5_average.pt --pytorch_dump_folder_path data/wmt16-en-de-12-1
# upload
cd data
transformers-cli upload -y wmt16-en-de-dist-12-1
transformers-cli upload -y wmt16-en-de-dist-6-1
transformers-cli upload -y wmt16-en-de-12-1
cd -
# if updating just small files and not the large models, here is a script to generate the right commands:
perl -le 'for $f (@ARGV) { print qq[transformers-cli upload -y $_/$f --filename $_/$f] for ("wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1")}' vocab-src.json vocab-tgt.json tokenizer_config.json config.json
# add/remove files as needed
|
AdaMix/scripts/fsmt/convert-allenai-wmt16.sh/0
|
{
"file_path": "AdaMix/scripts/fsmt/convert-allenai-wmt16.sh",
"repo_id": "AdaMix",
"token_count": 1372
}
| 50 |
#!/bin/bash
for FILE in converted/*; do
model_name=`basename $FILE`
transformers-cli repo create $model_name -y
git clone https://huggingface.co/Helsinki-NLP/$model_name
mv $FILE/* $model_name/
cd $model_name
git add . && git commit -m "initial commit"
git push
cd ..
done
|
AdaMix/scripts/tatoeba/upload_models.sh/0
|
{
"file_path": "AdaMix/scripts/tatoeba/upload_models.sh",
"repo_id": "AdaMix",
"token_count": 109
}
| 51 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import platform
from argparse import ArgumentParser
from .. import __version__ as version
from ..file_utils import is_tf_available, is_torch_available
from . import BaseTransformersCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
def run(self):
pt_version = "not installed"
pt_cuda_available = "NA"
if is_torch_available():
import torch
pt_version = torch.__version__
pt_cuda_available = torch.cuda.is_available()
tf_version = "not installed"
tf_cuda_available = "NA"
if is_tf_available():
import tensorflow as tf
tf_version = tf.__version__
try:
# deprecated in v2.1
tf_cuda_available = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
tf_cuda_available = bool(tf.config.list_physical_devices("GPU"))
info = {
"`transformers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"PyTorch version (GPU?)": "{} ({})".format(pt_version, pt_cuda_available),
"Tensorflow version (GPU?)": "{} ({})".format(tf_version, tf_cuda_available),
"Using GPU in script?": "<fill in>",
"Using distributed or parallel set-up in script?": "<fill in>",
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d):
return "\n".join(["- {}: {}".format(prop, val) for prop, val in d.items()]) + "\n"
|
AdaMix/src/transformers/commands/env.py/0
|
{
"file_path": "AdaMix/src/transformers/commands/env.py",
"repo_id": "AdaMix",
"token_count": 1002
}
| 52 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import warnings
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import torch
from torch.nn.utils.rnn import pad_sequence
from ..file_utils import PaddingStrategy
from ..modeling_utils import PreTrainedModel
from ..tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase
InputDataClass = NewType("InputDataClass", Any)
"""
A DataCollator is a function that takes a list of samples from a Dataset and collate them into a batch, as a dictionary
of Tensors.
"""
DataCollator = NewType("DataCollator", Callable[[List[InputDataClass]], Dict[str, torch.Tensor]])
def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Tensor]:
"""
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- ``label``: handles a single value (int or float) per object
- ``label_ids``: handles a list of values per object
Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it's useful.
"""
# In this function we'll make the assumption that all `features` in the batch
# have the same attributes.
# So we will look at the first element as a proxy for what attributes exist
# on the whole batch.
if not isinstance(features[0], (dict, BatchEncoding)):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], torch.Tensor) else first["label"]
dtype = torch.long if isinstance(label, int) else torch.float
batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], torch.Tensor):
batch["labels"] = torch.stack([f["label_ids"] for f in features])
else:
dtype = torch.long if type(first["label_ids"][0]) is int else torch.float
batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([f[k] for f in features])
else:
batch[k] = torch.tensor([f[k] for f in features])
return batch
@dataclass
class DataCollatorWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
"""
tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
batch = self.tokenizer.pad(
features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
if "label" in batch:
batch["labels"] = batch["label"]
del batch["label"]
if "label_ids" in batch:
batch["labels"] = batch["label_ids"]
del batch["label_ids"]
return batch
@dataclass
class DataCollatorForTokenClassification:
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
"""
tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
label_pad_token_id: int = -100
def __call__(self, features):
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
batch = self.tokenizer.pad(
features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
# Conversion to tensors will fail if we have labels as they are not of the same length yet.
return_tensors="pt" if labels is None else None,
)
if labels is None:
return batch
sequence_length = torch.tensor(batch["input_ids"]).shape[1]
padding_side = self.tokenizer.padding_side
if padding_side == "right":
batch["labels"] = [label + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels]
else:
batch["labels"] = [[self.label_pad_token_id] * (sequence_length - len(label)) + label for label in labels]
batch = {k: torch.tensor(v, dtype=torch.int64) for k, v in batch.items()}
return batch
def _collate_batch(examples, tokenizer):
"""Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
# Tensorize if necessary.
if isinstance(examples[0], (list, tuple)):
examples = [torch.tensor(e, dtype=torch.long) for e in examples]
# Check if padding is necessary.
length_of_first = examples[0].size(0)
are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
if are_tensors_same_length:
return torch.stack(examples, dim=0)
# If yes, check if we have a `pad_token`.
if tokenizer._pad_token is None:
raise ValueError(
"You are attempting to pad samples but the tokenizer you are using"
f" ({tokenizer.__class__.__name__}) does not have a pad token."
)
# Creating the full tensor and filling it with our data.
max_length = max(x.size(0) for x in examples)
result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)
for i, example in enumerate(examples):
if tokenizer.padding_side == "right":
result[i, : example.shape[0]] = example
else:
result[i, -example.shape[0] :] = example
return result
def tolist(x: Union[List[Any], torch.Tensor]):
return x.tolist() if isinstance(x, torch.Tensor) else x
@dataclass
class DataCollatorForSeq2Seq:
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
model (:class:`~transformers.PreTrainedModel`):
The model that is being trained. If set and has the `prepare_decoder_input_ids_from_labels`, use it to
prepare the `decoder_input_ids`
This is useful when using `label_smoothing` to avoid calculating loss twice.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
"""
tokenizer: PreTrainedTokenizerBase
model: Optional[PreTrainedModel] = None
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
label_pad_token_id: int = -100
def __call__(self, features):
labels = [feature["labels"] for feature in features] if "labels" in features[0].keys() else None
# We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
# same length to return tensors.
if labels is not None:
max_label_length = max(len(l) for l in labels)
padding_side = self.tokenizer.padding_side
for feature in features:
remainder = [self.label_pad_token_id] * (max_label_length - len(feature["labels"]))
feature["labels"] = (
feature["labels"] + remainder if padding_side == "right" else remainder + feature["labels"]
)
features = self.tokenizer.pad(
features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
# prepare decoder_input_ids
if self.model is not None and hasattr(self.model, "prepare_decoder_input_ids_from_labels"):
decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=features["labels"])
features["decoder_input_ids"] = decoder_input_ids
return features
@dataclass
class DataCollatorForLanguageModeling:
"""
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
non-masked tokens and the value to predict for the masked token.
mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
.. note::
For best performance, this data collator should be used with a dataset having items that are dictionaries or
BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
:class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
argument :obj:`return_special_tokens_mask=True`.
"""
tokenizer: PreTrainedTokenizerBase
mlm: bool = True
mlm_probability: float = 0.15
def __post_init__(self):
if self.mlm and self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. "
"You should pass `mlm=False` to train on causal language modeling instead."
)
def __call__(
self, examples: List[Union[List[int], torch.Tensor, Dict[str, torch.Tensor]]]
) -> Dict[str, torch.Tensor]:
# Handle dict or lists with proper padding and conversion to tensor.
if isinstance(examples[0], (dict, BatchEncoding)):
batch = self.tokenizer.pad(examples, return_tensors="pt")
else:
batch = {"input_ids": _collate_batch(examples, self.tokenizer)}
# If special token mask has been preprocessed, pop it from the dict.
special_tokens_mask = batch.pop("special_tokens_mask", None)
if self.mlm:
batch["input_ids"], batch["labels"] = self.mask_tokens(
batch["input_ids"], special_tokens_mask=special_tokens_mask
)
else:
labels = batch["input_ids"].clone()
if self.tokenizer.pad_token_id is not None:
labels[labels == self.tokenizer.pad_token_id] = -100
batch["labels"] = labels
return batch
def mask_tokens(
self, inputs: torch.Tensor, special_tokens_mask: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
"""
labels = inputs.clone()
# We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
probability_matrix = torch.full(labels.shape, self.mlm_probability)
if special_tokens_mask is None:
special_tokens_mask = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
]
special_tokens_mask = torch.tensor(special_tokens_mask, dtype=torch.bool)
else:
special_tokens_mask = special_tokens_mask.bool()
probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
masked_indices = torch.bernoulli(probability_matrix).bool()
labels[~masked_indices] = -100 # We only compute loss on masked tokens
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
# 10% of the time, we replace masked input tokens with random word
indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
inputs[indices_random] = random_words[indices_random]
# The rest of the time (10% of the time) we keep the masked input tokens unchanged
return inputs, labels
@dataclass
class DataCollatorForWholeWordMask(DataCollatorForLanguageModeling):
"""
Data collator used for language modeling.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for masked language modeling
"""
def __call__(
self, examples: List[Union[List[int], torch.Tensor, Dict[str, torch.Tensor]]]
) -> Dict[str, torch.Tensor]:
if isinstance(examples[0], (dict, BatchEncoding)):
input_ids = [e["input_ids"] for e in examples]
else:
input_ids = examples
examples = [{"input_ids": e} for e in examples]
batch_input = _collate_batch(input_ids, self.tokenizer)
mask_labels = []
for e in examples:
ref_tokens = []
for id in tolist(e["input_ids"]):
token = self.tokenizer._convert_id_to_token(id)
ref_tokens.append(token)
# For Chinese tokens, we need extra inf to mark sub-word, e.g [喜,欢]-> [喜，##欢]
if "chinese_ref" in e:
ref_pos = tolist(e["chinese_ref"])
len_seq = len(e["input_ids"])
for i in range(len_seq):
if i in ref_pos:
ref_tokens[i] = "##" + ref_tokens[i]
mask_labels.append(self._whole_word_mask(ref_tokens))
batch_mask = _collate_batch(mask_labels, self.tokenizer)
inputs, labels = self.mask_tokens(batch_input, batch_mask)
return {"input_ids": inputs, "labels": labels}
def _whole_word_mask(self, input_tokens: List[str], max_predictions=512):
"""
Get 0/1 labels for masked tokens with whole word mask proxy
"""
cand_indexes = []
for (i, token) in enumerate(input_tokens):
if token == "[CLS]" or token == "[SEP]":
continue
if len(cand_indexes) >= 1 and token.startswith("##"):
cand_indexes[-1].append(i)
else:
cand_indexes.append([i])
random.shuffle(cand_indexes)
num_to_predict = min(max_predictions, max(1, int(round(len(input_tokens) * self.mlm_probability))))
masked_lms = []
covered_indexes = set()
for index_set in cand_indexes:
if len(masked_lms) >= num_to_predict:
break
# If adding a whole-word mask would exceed the maximum number of
# predictions, then just skip this candidate.
if len(masked_lms) + len(index_set) > num_to_predict:
continue
is_any_index_covered = False
for index in index_set:
if index in covered_indexes:
is_any_index_covered = True
break
if is_any_index_covered:
continue
for index in index_set:
covered_indexes.add(index)
masked_lms.append(index)
assert len(covered_indexes) == len(masked_lms)
mask_labels = [1 if i in covered_indexes else 0 for i in range(len(input_tokens))]
return mask_labels
def mask_tokens(self, inputs: torch.Tensor, mask_labels: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
'mask_labels' means we use whole word mask (wwm), we directly mask idxs according to it's ref.
"""
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
)
labels = inputs.clone()
# We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
probability_matrix = mask_labels
special_tokens_mask = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
]
probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
if self.tokenizer._pad_token is not None:
padding_mask = labels.eq(self.tokenizer.pad_token_id)
probability_matrix.masked_fill_(padding_mask, value=0.0)
masked_indices = probability_matrix.bool()
labels[~masked_indices] = -100 # We only compute loss on masked tokens
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
# 10% of the time, we replace masked input tokens with random word
indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
inputs[indices_random] = random_words[indices_random]
# The rest of the time (10% of the time) we keep the masked input tokens unchanged
return inputs, labels
@dataclass
class DataCollatorForSOP(DataCollatorForLanguageModeling):
"""
Data collator used for sentence order prediction task.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for both masked language modeling and sentence order prediction
"""
def __init__(self, *args, **kwargs):
warnings.warn(
"DataCollatorForSOP is deprecated and will be removed in a future version, you can now use "
"DataCollatorForLanguageModeling instead.",
FutureWarning,
)
def __call__(self, examples: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
input_ids = [example["input_ids"] for example in examples]
input_ids = _collate_batch(input_ids, self.tokenizer)
input_ids, labels, attention_mask = self.mask_tokens(input_ids)
token_type_ids = [example["token_type_ids"] for example in examples]
# size of segment_ids varied because randomness, padding zero to the end as the original implementation
token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
sop_label_list = [example["sentence_order_label"] for example in examples]
sentence_order_label = torch.stack(sop_label_list)
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"sentence_order_label": sentence_order_label,
}
def mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Prepare masked tokens inputs/labels/attention_mask for masked language modeling: 80% MASK, 10% random, 10%
original. N-gram not applied yet.
"""
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
)
labels = inputs.clone()
# We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
probability_matrix = torch.full(labels.shape, self.mlm_probability)
special_tokens_mask = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
]
probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
if self.tokenizer._pad_token is not None:
padding_mask = labels.eq(self.tokenizer.pad_token_id)
probability_matrix.masked_fill_(padding_mask, value=0.0)
masked_indices = torch.bernoulli(probability_matrix).bool()
# probability be `1` (masked), however in albert model attention mask `0` means masked, revert the value
attention_mask = (~masked_indices).float()
if self.tokenizer._pad_token is not None:
attention_padding_mask = labels.eq(self.tokenizer.pad_token_id)
attention_mask.masked_fill_(attention_padding_mask, value=1.0)
labels[~masked_indices] = -100 # We only compute loss on masked tokens, -100 is default for CE compute
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
# 10% of the time, we replace masked input tokens with random word
indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
inputs[indices_random] = random_words[indices_random]
# The rest of the time (10% of the time) we keep the masked input tokens unchanged
return inputs, labels, attention_mask
@dataclass
class DataCollatorForPermutationLanguageModeling:
"""
Data collator used for permutation language modeling.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for permutation language modeling with procedures specific to XLNet
"""
tokenizer: PreTrainedTokenizerBase
plm_probability: float = 1 / 6
max_span_length: int = 5 # maximum length of a span of masked tokens
def __call__(
self, examples: List[Union[List[int], torch.Tensor, Dict[str, torch.Tensor]]]
) -> Dict[str, torch.Tensor]:
if isinstance(examples[0], (dict, BatchEncoding)):
examples = [e["input_ids"] for e in examples]
batch = _collate_batch(examples, self.tokenizer)
inputs, perm_mask, target_mapping, labels = self.mask_tokens(batch)
return {"input_ids": inputs, "perm_mask": perm_mask, "target_mapping": target_mapping, "labels": labels}
def mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
The masked tokens to be predicted for a particular sequence are determined by the following algorithm:
0. Start from the beginning of the sequence by setting ``cur_len = 0`` (number of tokens processed so far).
1. Sample a ``span_length`` from the interval ``[1, max_span_length]`` (length of span of tokens to be
masked)
2. Reserve a context of length ``context_length = span_length / plm_probability`` to surround span to be
masked
3. Sample a starting point ``start_index`` from the interval ``[cur_len, cur_len + context_length -
span_length]`` and mask tokens ``start_index:start_index + span_length``
4. Set ``cur_len = cur_len + context_length``. If ``cur_len < max_len`` (i.e. there are tokens remaining in
the sequence to be processed), repeat from Step 1.
"""
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for permutation language modeling. Please add a mask token if you want to use this tokenizer."
)
if inputs.size(1) % 2 != 0:
raise ValueError(
"This collator requires that sequence lengths be even to create a leakage-free perm_mask. Please see relevant comments in source code for details."
)
labels = inputs.clone()
# Creating the mask and target_mapping tensors
masked_indices = torch.full(labels.shape, 0, dtype=torch.bool)
target_mapping = torch.zeros((labels.size(0), labels.size(1), labels.size(1)), dtype=torch.float32)
for i in range(labels.size(0)):
# Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
cur_len = 0
max_len = labels.size(1)
while cur_len < max_len:
# Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
span_length = torch.randint(1, self.max_span_length + 1, (1,)).item()
# Reserve a context of length `context_length = span_length / plm_probability` to surround the span to be masked
context_length = int(span_length / self.plm_probability)
# Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length - span_length]` and mask tokens `start_index:start_index + span_length`
start_index = cur_len + torch.randint(context_length - span_length + 1, (1,)).item()
masked_indices[i, start_index : start_index + span_length] = 1
# Set `cur_len = cur_len + context_length`
cur_len += context_length
# Since we're replacing non-masked tokens with -100 in the labels tensor instead of skipping them altogether,
# the i-th predict corresponds to the i-th token.
target_mapping[i] = torch.eye(labels.size(1))
special_tokens_mask = torch.tensor(
[self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()],
dtype=torch.bool,
)
masked_indices.masked_fill_(special_tokens_mask, value=0.0)
if self.tokenizer._pad_token is not None:
padding_mask = labels.eq(self.tokenizer.pad_token_id)
masked_indices.masked_fill_(padding_mask, value=0.0)
# Mask indicating non-functional tokens, where functional tokens are [SEP], [CLS], padding, etc.
non_func_mask = ~(padding_mask | special_tokens_mask)
inputs[masked_indices] = self.tokenizer.mask_token_id
labels[~masked_indices] = -100 # We only compute loss on masked tokens
perm_mask = torch.zeros((labels.size(0), labels.size(1), labels.size(1)), dtype=torch.float32)
for i in range(labels.size(0)):
# Generate permutation indices i.e. sample a random factorisation order for the sequence. This will
# determine which tokens a given token can attend to (encoded in `perm_mask`).
# Note: Length of token sequence being permuted has to be less than or equal to reused sequence length
# (see documentation for `mems`), otherwise information may leak through due to reuse. In this implementation,
# we assume that reused length is half of sequence length and permutation length is equal to reused length.
# This requires that the sequence length be even.
# Create a linear factorisation order
perm_index = torch.arange(labels.size(1))
# Split this into two halves, assuming that half the sequence is reused each time
perm_index = perm_index.reshape((-1, labels.size(1) // 2)).transpose(0, 1)
# Permute the two halves such that they do not cross over
perm_index = perm_index[torch.randperm(labels.size(1) // 2)]
# Flatten this out into the desired permuted factorisation order
perm_index = torch.flatten(perm_index.transpose(0, 1))
# Set the permutation indices of non-masked (non-functional) tokens to the
# smallest index (-1) so that:
# (1) They can be seen by all other positions
# (2) They cannot see masked positions, so there won't be information leak
perm_index.masked_fill_(~masked_indices[i] & non_func_mask[i], -1)
# The logic for whether the i-th token can attend on the j-th token based on the factorisation order:
# 0 (can attend): If perm_index[i] > perm_index[j] or j is neither masked nor a functional token
# 1 (cannot attend): If perm_index[i] <= perm_index[j] and j is either masked or a functional token
perm_mask[i] = (
perm_index.reshape((labels.size(1), 1)) <= perm_index.reshape((1, labels.size(1)))
) & masked_indices[i]
return inputs.long(), perm_mask, target_mapping, labels.long()
|
AdaMix/src/transformers/data/data_collator.py/0
|
{
"file_path": "AdaMix/src/transformers/data/data_collator.py",
"repo_id": "AdaMix",
"token_count": 13938
}
| 53 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union
from ...file_utils import is_tf_available, is_torch_available
from ...utils import logging
logger = logging.get_logger(__name__)
@dataclass
class InputExample:
"""
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
guid: str
text_a: str
text_b: Optional[str] = None
label: Optional[str] = None
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(dataclasses.asdict(self), indent=2) + "\n"
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of data. Property names are the same names as the corresponding inputs to a model.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``: Usually ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded)
tokens.
token_type_ids: (Optional) Segment token indices to indicate first and second
portions of the inputs. Only some models use them.
label: (Optional) Label corresponding to the input. Int for classification problems,
float for regression problems.
"""
input_ids: List[int]
attention_mask: Optional[List[int]] = None
token_type_ids: Optional[List[int]] = None
label: Optional[Union[int, float]] = None
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(dataclasses.asdict(self)) + "\n"
class DataProcessor:
"""Base class for data converters for sequence classification data sets."""
def get_example_from_tensor_dict(self, tensor_dict):
"""
Gets an example from a dict with tensorflow tensors.
Args:
tensor_dict: Keys and values should match the corresponding Glue
tensorflow_dataset examples.
"""
raise NotImplementedError()
def get_train_examples(self, data_dir):
"""Gets a collection of :class:`InputExample` for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of :class:`InputExample` for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of :class:`InputExample` for the test set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
def tfds_map(self, example):
"""
Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.
"""
if len(self.get_labels()) > 1:
example.label = self.get_labels()[int(example.label)]
return example
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8-sig") as f:
return list(csv.reader(f, delimiter="\t", quotechar=quotechar))
class SingleSentenceClassificationProcessor(DataProcessor):
""" Generic processor for a single sentence classification data set."""
def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
self.labels = [] if labels is None else labels
self.examples = [] if examples is None else examples
self.mode = mode
self.verbose = verbose
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
if isinstance(idx, slice):
return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx])
return self.examples[idx]
@classmethod
def create_from_csv(
cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
):
processor = cls(**kwargs)
processor.add_examples_from_csv(
file_name,
split_name=split_name,
column_label=column_label,
column_text=column_text,
column_id=column_id,
skip_first_row=skip_first_row,
overwrite_labels=True,
overwrite_examples=True,
)
return processor
@classmethod
def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs):
processor = cls(**kwargs)
processor.add_examples(texts_or_text_and_labels, labels=labels)
return processor
def add_examples_from_csv(
self,
file_name,
split_name="",
column_label=0,
column_text=1,
column_id=None,
skip_first_row=False,
overwrite_labels=False,
overwrite_examples=False,
):
lines = self._read_tsv(file_name)
if skip_first_row:
lines = lines[1:]
texts = []
labels = []
ids = []
for (i, line) in enumerate(lines):
texts.append(line[column_text])
labels.append(line[column_label])
if column_id is not None:
ids.append(line[column_id])
else:
guid = "%s-%s" % (split_name, i) if split_name else "%s" % i
ids.append(guid)
return self.add_examples(
texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples
)
def add_examples(
self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
):
assert labels is None or len(texts_or_text_and_labels) == len(
labels
), f"Text and labels have mismatched lengths {len(texts_or_text_and_labels)} and {len(labels)}"
assert ids is None or len(texts_or_text_and_labels) == len(
ids
), f"Text and ids have mismatched lengths {len(texts_or_text_and_labels)} and {len(ids)}"
if ids is None:
ids = [None] * len(texts_or_text_and_labels)
if labels is None:
labels = [None] * len(texts_or_text_and_labels)
examples = []
added_labels = set()
for (text_or_text_and_label, label, guid) in zip(texts_or_text_and_labels, labels, ids):
if isinstance(text_or_text_and_label, (tuple, list)) and label is None:
text, label = text_or_text_and_label
else:
text = text_or_text_and_label
added_labels.add(label)
examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label))
# Update examples
if overwrite_examples:
self.examples = examples
else:
self.examples.extend(examples)
# Update labels
if overwrite_labels:
self.labels = list(added_labels)
else:
self.labels = list(set(self.labels).union(added_labels))
return self.examples
def get_features(
self,
tokenizer,
max_length=None,
pad_on_left=False,
pad_token=0,
mask_padding_with_zero=True,
return_tensors=None,
):
"""
Convert examples in a list of ``InputFeatures``
Args:
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length
pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
pad_token: Padding token
mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
actual values)
Returns:
If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset`` containing the
task-specific features. If the input is a list of ``InputExamples``, will return a list of task-specific
``InputFeatures`` which can be fed to the model.
"""
if max_length is None:
max_length = tokenizer.max_len
label_map = {label: i for i, label in enumerate(self.labels)}
all_input_ids = []
for (ex_index, example) in enumerate(self.examples):
if ex_index % 10000 == 0:
logger.info("Tokenizing example %d", ex_index)
input_ids = tokenizer.encode(
example.text_a,
add_special_tokens=True,
max_length=min(max_length, tokenizer.max_len),
)
all_input_ids.append(input_ids)
batch_length = max(len(input_ids) for input_ids in all_input_ids)
features = []
for (ex_index, (input_ids, example)) in enumerate(zip(all_input_ids, self.examples)):
if ex_index % 10000 == 0:
logger.info("Writing example %d/%d" % (ex_index, len(self.examples)))
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = batch_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
else:
input_ids = input_ids + ([pad_token] * padding_length)
attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
assert len(input_ids) == batch_length, "Error with input length {} vs {}".format(
len(input_ids), batch_length
)
assert len(attention_mask) == batch_length, "Error with input length {} vs {}".format(
len(attention_mask), batch_length
)
if self.mode == "classification":
label = label_map[example.label]
elif self.mode == "regression":
label = float(example.label)
else:
raise ValueError(self.mode)
if ex_index < 5 and self.verbose:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
logger.info("label: %s (id = %d)" % (example.label, label))
features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label))
if return_tensors is None:
return features
elif return_tensors == "tf":
if not is_tf_available():
raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
import tensorflow as tf
def gen():
for ex in features:
yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
dataset = tf.data.Dataset.from_generator(
gen,
({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])),
)
return dataset
elif return_tensors == "pt":
if not is_torch_available():
raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
import torch
from torch.utils.data import TensorDataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
if self.mode == "classification":
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
elif self.mode == "regression":
all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels)
return dataset
else:
raise ValueError("return_tensors should be one of 'tf' or 'pt'")
|
AdaMix/src/transformers/data/processors/utils.py/0
|
{
"file_path": "AdaMix/src/transformers/data/processors/utils.py",
"repo_id": "AdaMix",
"token_count": 6018
}
| 54 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Configuration base class and utilities."""
import copy
import json
import os
from .file_utils import (
CONFIG_NAME,
MODEL_CARD_NAME,
TF2_WEIGHTS_NAME,
WEIGHTS_NAME,
cached_path,
hf_bucket_url,
is_remote_url,
)
from .models.auto.configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .utils import logging
logger = logging.get_logger(__name__)
class ModelCard:
r"""
Structured Model Card class. Store model card as well as methods for loading/downloading/saving model cards.
Please read the following paper for details and explanation on the sections: "Model Cards for Model Reporting" by
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer,
Inioluwa Deborah Raji and Timnit Gebru for the proposal behind model cards. Link: https://arxiv.org/abs/1810.03993
Note: A model card can be loaded and saved to disk.
Parameters:
"""
def __init__(self, **kwargs):
# Recommended attributes from https://arxiv.org/abs/1810.03993 (see papers)
self.model_details = kwargs.pop("model_details", {})
self.intended_use = kwargs.pop("intended_use", {})
self.factors = kwargs.pop("factors", {})
self.metrics = kwargs.pop("metrics", {})
self.evaluation_data = kwargs.pop("evaluation_data", {})
self.training_data = kwargs.pop("training_data", {})
self.quantitative_analyses = kwargs.pop("quantitative_analyses", {})
self.ethical_considerations = kwargs.pop("ethical_considerations", {})
self.caveats_and_recommendations = kwargs.pop("caveats_and_recommendations", {})
# Open additional attributes
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error("Can't set {} with value {} for {}".format(key, value, self))
raise err
def save_pretrained(self, save_directory_or_file):
"""Save a model card object to the directory or file `save_directory_or_file`."""
if os.path.isdir(save_directory_or_file):
# If we save using the predefined names, we can load using `from_pretrained`
output_model_card_file = os.path.join(save_directory_or_file, MODEL_CARD_NAME)
else:
output_model_card_file = save_directory_or_file
self.to_json_file(output_model_card_file)
logger.info("Model card saved in {}".format(output_model_card_file))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a :class:`~transformers.ModelCard` from a pre-trained model model card.
Parameters:
pretrained_model_name_or_path: either:
- a string, the `model id` of a pretrained model card hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a
user or organization name, like ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a model card file saved using the
:func:`~transformers.ModelCard.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved model card JSON `file`, e.g.: ``./my_model_directory/modelcard.json``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model card should be cached if the standard cache
should not be used.
kwargs: (`optional`) dict: key/value pairs with which to update the ModelCard object after loading.
- The values in kwargs of any keys which are model card attributes will be used to override the loaded
values.
- Behavior concerning key/value pairs whose keys are *not* model card attributes is controlled by the
`return_unused_kwargs` keyword parameter.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
find_from_standard_name: (`optional`) boolean, default True:
If the pretrained_model_name_or_path ends with our standard model or config filenames, replace them
with our standard modelcard filename. Can be used to directly feed a model/config url and access the
colocated modelcard.
return_unused_kwargs: (`optional`) bool:
- If False, then this function returns just the final model card object.
- If True, then this functions returns a tuple `(model card, unused_kwargs)` where `unused_kwargs` is a
dictionary consisting of the key/value pairs whose keys are not model card attributes: ie the part of
kwargs which has not been used to update `ModelCard` and is otherwise ignored.
Examples::
modelcard = ModelCard.from_pretrained('bert-base-uncased') # Download model card from huggingface.co and cache.
modelcard = ModelCard.from_pretrained('./test/saved_model/') # E.g. model card was saved using `save_pretrained('./test/saved_model/')`
modelcard = ModelCard.from_pretrained('./test/saved_model/modelcard.json')
modelcard = ModelCard.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
"""
cache_dir = kwargs.pop("cache_dir", None)
proxies = kwargs.pop("proxies", None)
find_from_standard_name = kwargs.pop("find_from_standard_name", True)
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
# For simplicity we use the same pretrained url than the configuration files
# but with a different suffix (modelcard.json). This suffix is replaced below.
model_card_file = ALL_PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
elif os.path.isdir(pretrained_model_name_or_path):
model_card_file = os.path.join(pretrained_model_name_or_path, MODEL_CARD_NAME)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
model_card_file = pretrained_model_name_or_path
else:
model_card_file = hf_bucket_url(pretrained_model_name_or_path, filename=MODEL_CARD_NAME, mirror=None)
if find_from_standard_name or pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
model_card_file = model_card_file.replace(CONFIG_NAME, MODEL_CARD_NAME)
model_card_file = model_card_file.replace(WEIGHTS_NAME, MODEL_CARD_NAME)
model_card_file = model_card_file.replace(TF2_WEIGHTS_NAME, MODEL_CARD_NAME)
try:
# Load from URL or cache if already cached
resolved_model_card_file = cached_path(model_card_file, cache_dir=cache_dir, proxies=proxies)
if resolved_model_card_file == model_card_file:
logger.info("loading model card file {}".format(model_card_file))
else:
logger.info(
"loading model card file {} from cache at {}".format(model_card_file, resolved_model_card_file)
)
# Load model card
modelcard = cls.from_json_file(resolved_model_card_file)
except (EnvironmentError, json.JSONDecodeError):
# We fall back on creating an empty model card
modelcard = cls()
# Update model card with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(modelcard, key):
setattr(modelcard, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info("Model card: %s", str(modelcard))
if return_unused_kwargs:
return modelcard, kwargs
else:
return modelcard
@classmethod
def from_dict(cls, json_object):
"""Constructs a `ModelCard` from a Python dictionary of parameters."""
return cls(**json_object)
@classmethod
def from_json_file(cls, json_file):
"""Constructs a `ModelCard` from a json file of parameters."""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
dict_obj = json.loads(text)
return cls(**dict_obj)
def __eq__(self, other):
return self.__dict__ == other.__dict__
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path):
""" Save this instance to a json file."""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
|
AdaMix/src/transformers/modelcard.py/0
|
{
"file_path": "AdaMix/src/transformers/modelcard.py",
"repo_id": "AdaMix",
"token_count": 4062
}
| 55 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Auto Model class. """
import warnings
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...file_utils import add_start_docstrings
from ...utils import logging
# Add modeling imports here
from ..albert.modeling_tf_albert import (
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertModel,
)
from ..bart.modeling_tf_bart import TFBartForConditionalGeneration, TFBartModel
from ..bert.modeling_tf_bert import (
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertModel,
)
from ..blenderbot.modeling_tf_blenderbot import TFBlenderbotForConditionalGeneration, TFBlenderbotModel
from ..blenderbot_small.modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
)
from ..camembert.modeling_tf_camembert import (
TFCamembertForMaskedLM,
TFCamembertForMultipleChoice,
TFCamembertForQuestionAnswering,
TFCamembertForSequenceClassification,
TFCamembertForTokenClassification,
TFCamembertModel,
)
from ..convbert.modeling_tf_convbert import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
from ..ctrl.modeling_tf_ctrl import TFCTRLForSequenceClassification, TFCTRLLMHeadModel, TFCTRLModel
from ..distilbert.modeling_tf_distilbert import (
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
from ..dpr.modeling_tf_dpr import TFDPRQuestionEncoder
from ..electra.modeling_tf_electra import (
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
)
from ..flaubert.modeling_tf_flaubert import (
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
from ..funnel.modeling_tf_funnel import (
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
from ..gpt2.modeling_tf_gpt2 import TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2Model
from ..led.modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel
from ..longformer.modeling_tf_longformer import (
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
)
from ..lxmert.modeling_tf_lxmert import TFLxmertForPreTraining, TFLxmertModel
from ..marian.modeling_tf_marian import TFMarianModel, TFMarianMTModel
from ..mbart.modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel
from ..mobilebert.modeling_tf_mobilebert import (
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
from ..mpnet.modeling_tf_mpnet import (
TFMPNetForMaskedLM,
TFMPNetForMultipleChoice,
TFMPNetForQuestionAnswering,
TFMPNetForSequenceClassification,
TFMPNetForTokenClassification,
TFMPNetModel,
)
from ..mt5.modeling_tf_mt5 import TFMT5ForConditionalGeneration, TFMT5Model
from ..openai.modeling_tf_openai import TFOpenAIGPTForSequenceClassification, TFOpenAIGPTLMHeadModel, TFOpenAIGPTModel
from ..pegasus.modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel
from ..roberta.modeling_tf_roberta import (
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaModel,
)
from ..t5.modeling_tf_t5 import TFT5ForConditionalGeneration, TFT5Model
from ..transfo_xl.modeling_tf_transfo_xl import (
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
from ..xlm.modeling_tf_xlm import (
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMModel,
TFXLMWithLMHeadModel,
)
from ..xlm_roberta.modeling_tf_xlm_roberta import (
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
)
from ..xlnet.modeling_tf_xlnet import (
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetModel,
)
from .configuration_auto import (
AlbertConfig,
AutoConfig,
BartConfig,
BertConfig,
BlenderbotConfig,
BlenderbotSmallConfig,
CamembertConfig,
ConvBertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
FunnelConfig,
GPT2Config,
LEDConfig,
LongformerConfig,
LxmertConfig,
MarianConfig,
MBartConfig,
MobileBertConfig,
MPNetConfig,
MT5Config,
OpenAIGPTConfig,
PegasusConfig,
RobertaConfig,
T5Config,
TransfoXLConfig,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
replace_list_option_in_docstrings,
)
logger = logging.get_logger(__name__)
TF_MODEL_MAPPING = OrderedDict(
[
# Base model mapping
(ConvBertConfig, TFConvBertModel),
(LEDConfig, TFLEDModel),
(LxmertConfig, TFLxmertModel),
(MT5Config, TFMT5Model),
(T5Config, TFT5Model),
(DistilBertConfig, TFDistilBertModel),
(AlbertConfig, TFAlbertModel),
(BartConfig, TFBartModel),
(CamembertConfig, TFCamembertModel),
(XLMRobertaConfig, TFXLMRobertaModel),
(LongformerConfig, TFLongformerModel),
(RobertaConfig, TFRobertaModel),
(BertConfig, TFBertModel),
(OpenAIGPTConfig, TFOpenAIGPTModel),
(GPT2Config, TFGPT2Model),
(MobileBertConfig, TFMobileBertModel),
(TransfoXLConfig, TFTransfoXLModel),
(XLNetConfig, TFXLNetModel),
(FlaubertConfig, TFFlaubertModel),
(XLMConfig, TFXLMModel),
(CTRLConfig, TFCTRLModel),
(ElectraConfig, TFElectraModel),
(FunnelConfig, TFFunnelModel),
(DPRConfig, TFDPRQuestionEncoder),
(MPNetConfig, TFMPNetModel),
(BartConfig, TFBartModel),
(MBartConfig, TFMBartModel),
(MarianConfig, TFMarianModel),
(PegasusConfig, TFPegasusModel),
(BlenderbotConfig, TFBlenderbotModel),
(BlenderbotSmallConfig, TFBlenderbotSmallModel),
]
)
TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
[
# Model for pre-training mapping
(LxmertConfig, TFLxmertForPreTraining),
(T5Config, TFT5ForConditionalGeneration),
(DistilBertConfig, TFDistilBertForMaskedLM),
(AlbertConfig, TFAlbertForPreTraining),
(BartConfig, TFBartForConditionalGeneration),
(CamembertConfig, TFCamembertForMaskedLM),
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(BertConfig, TFBertForPreTraining),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
(MobileBertConfig, TFMobileBertForPreTraining),
(TransfoXLConfig, TFTransfoXLLMHeadModel),
(XLNetConfig, TFXLNetLMHeadModel),
(FlaubertConfig, TFFlaubertWithLMHeadModel),
(XLMConfig, TFXLMWithLMHeadModel),
(CTRLConfig, TFCTRLLMHeadModel),
(ElectraConfig, TFElectraForPreTraining),
(FunnelConfig, TFFunnelForPreTraining),
(MPNetConfig, TFMPNetForMaskedLM),
]
)
TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
[
# Model with LM heads mapping
(ConvBertConfig, TFConvBertForMaskedLM),
(LEDConfig, TFLEDForConditionalGeneration),
(T5Config, TFT5ForConditionalGeneration),
(DistilBertConfig, TFDistilBertForMaskedLM),
(AlbertConfig, TFAlbertForMaskedLM),
(MarianConfig, TFMarianMTModel),
(BartConfig, TFBartForConditionalGeneration),
(CamembertConfig, TFCamembertForMaskedLM),
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(LongformerConfig, TFLongformerForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(BertConfig, TFBertForMaskedLM),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
(MobileBertConfig, TFMobileBertForMaskedLM),
(TransfoXLConfig, TFTransfoXLLMHeadModel),
(XLNetConfig, TFXLNetLMHeadModel),
(FlaubertConfig, TFFlaubertWithLMHeadModel),
(XLMConfig, TFXLMWithLMHeadModel),
(CTRLConfig, TFCTRLLMHeadModel),
(ElectraConfig, TFElectraForMaskedLM),
(FunnelConfig, TFFunnelForMaskedLM),
(MPNetConfig, TFMPNetForMaskedLM),
]
)
TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
[
# Model for Causal LM mapping
(BertConfig, TFBertLMHeadModel),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
(TransfoXLConfig, TFTransfoXLLMHeadModel),
(XLNetConfig, TFXLNetLMHeadModel),
(
XLMConfig,
TFXLMWithLMHeadModel,
), # XLM can be MLM and CLM => model should be split similar to BERT; leave here for now
(CTRLConfig, TFCTRLLMHeadModel),
]
)
TF_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
[
# Model for Masked LM mapping
(ConvBertConfig, TFConvBertForMaskedLM),
(DistilBertConfig, TFDistilBertForMaskedLM),
(AlbertConfig, TFAlbertForMaskedLM),
(CamembertConfig, TFCamembertForMaskedLM),
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(LongformerConfig, TFLongformerForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(BertConfig, TFBertForMaskedLM),
(MobileBertConfig, TFMobileBertForMaskedLM),
(FlaubertConfig, TFFlaubertWithLMHeadModel),
(XLMConfig, TFXLMWithLMHeadModel),
(ElectraConfig, TFElectraForMaskedLM),
(FunnelConfig, TFFunnelForMaskedLM),
(MPNetConfig, TFMPNetForMaskedLM),
]
)
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
(LEDConfig, TFLEDForConditionalGeneration),
(MT5Config, TFMT5ForConditionalGeneration),
(T5Config, TFT5ForConditionalGeneration),
(MarianConfig, TFMarianMTModel),
(MBartConfig, TFMBartForConditionalGeneration),
(PegasusConfig, TFPegasusForConditionalGeneration),
(BlenderbotConfig, TFBlenderbotForConditionalGeneration),
(BlenderbotSmallConfig, TFBlenderbotSmallForConditionalGeneration),
(BartConfig, TFBartForConditionalGeneration),
]
)
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
[
# Model for Sequence Classification mapping
(ConvBertConfig, TFConvBertForSequenceClassification),
(DistilBertConfig, TFDistilBertForSequenceClassification),
(AlbertConfig, TFAlbertForSequenceClassification),
(CamembertConfig, TFCamembertForSequenceClassification),
(XLMRobertaConfig, TFXLMRobertaForSequenceClassification),
(LongformerConfig, TFLongformerForSequenceClassification),
(RobertaConfig, TFRobertaForSequenceClassification),
(BertConfig, TFBertForSequenceClassification),
(XLNetConfig, TFXLNetForSequenceClassification),
(MobileBertConfig, TFMobileBertForSequenceClassification),
(FlaubertConfig, TFFlaubertForSequenceClassification),
(XLMConfig, TFXLMForSequenceClassification),
(ElectraConfig, TFElectraForSequenceClassification),
(FunnelConfig, TFFunnelForSequenceClassification),
(GPT2Config, TFGPT2ForSequenceClassification),
(MPNetConfig, TFMPNetForSequenceClassification),
(OpenAIGPTConfig, TFOpenAIGPTForSequenceClassification),
(TransfoXLConfig, TFTransfoXLForSequenceClassification),
(CTRLConfig, TFCTRLForSequenceClassification),
]
)
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
[
# Model for Question Answering mapping
(ConvBertConfig, TFConvBertForQuestionAnswering),
(DistilBertConfig, TFDistilBertForQuestionAnswering),
(AlbertConfig, TFAlbertForQuestionAnswering),
(CamembertConfig, TFCamembertForQuestionAnswering),
(XLMRobertaConfig, TFXLMRobertaForQuestionAnswering),
(LongformerConfig, TFLongformerForQuestionAnswering),
(RobertaConfig, TFRobertaForQuestionAnswering),
(BertConfig, TFBertForQuestionAnswering),
(XLNetConfig, TFXLNetForQuestionAnsweringSimple),
(MobileBertConfig, TFMobileBertForQuestionAnswering),
(FlaubertConfig, TFFlaubertForQuestionAnsweringSimple),
(XLMConfig, TFXLMForQuestionAnsweringSimple),
(ElectraConfig, TFElectraForQuestionAnswering),
(FunnelConfig, TFFunnelForQuestionAnswering),
(MPNetConfig, TFMPNetForQuestionAnswering),
]
)
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
[
# Model for Token Classification mapping
(ConvBertConfig, TFConvBertForTokenClassification),
(DistilBertConfig, TFDistilBertForTokenClassification),
(AlbertConfig, TFAlbertForTokenClassification),
(CamembertConfig, TFCamembertForTokenClassification),
(FlaubertConfig, TFFlaubertForTokenClassification),
(XLMConfig, TFXLMForTokenClassification),
(XLMRobertaConfig, TFXLMRobertaForTokenClassification),
(LongformerConfig, TFLongformerForTokenClassification),
(RobertaConfig, TFRobertaForTokenClassification),
(BertConfig, TFBertForTokenClassification),
(MobileBertConfig, TFMobileBertForTokenClassification),
(XLNetConfig, TFXLNetForTokenClassification),
(ElectraConfig, TFElectraForTokenClassification),
(FunnelConfig, TFFunnelForTokenClassification),
(MPNetConfig, TFMPNetForTokenClassification),
]
)
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
[
# Model for Multiple Choice mapping
(ConvBertConfig, TFConvBertForMultipleChoice),
(CamembertConfig, TFCamembertForMultipleChoice),
(XLMConfig, TFXLMForMultipleChoice),
(XLMRobertaConfig, TFXLMRobertaForMultipleChoice),
(LongformerConfig, TFLongformerForMultipleChoice),
(RobertaConfig, TFRobertaForMultipleChoice),
(BertConfig, TFBertForMultipleChoice),
(DistilBertConfig, TFDistilBertForMultipleChoice),
(MobileBertConfig, TFMobileBertForMultipleChoice),
(XLNetConfig, TFXLNetForMultipleChoice),
(FlaubertConfig, TFFlaubertForMultipleChoice),
(AlbertConfig, TFAlbertForMultipleChoice),
(ElectraConfig, TFElectraForMultipleChoice),
(FunnelConfig, TFFunnelForMultipleChoice),
(MPNetConfig, TFMPNetForMultipleChoice),
]
)
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = OrderedDict(
[
(BertConfig, TFBertForNextSentencePrediction),
(MobileBertConfig, TFMobileBertForNextSentencePrediction),
]
)
TF_AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
The model class to instantiate is selected based on the :obj:`model_type` property of the config object (either
passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's missing,
by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:
List options
The model is set in evaluation mode by default using ``model.eval()`` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with ``model.train()``
Args:
pretrained_model_name_or_path:
Can be either:
- A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
a user or organization name, like ``dbmdz/bert-base-german-cased``.
- A path to a `directory` containing model weights saved using
:func:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
- A path or url to a `PyTorch state_dict save file` (e.g, ``./pt_model/pytorch_model.bin``). In
this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
as ``config`` argument. This loading path is slower than converting the PyTorch model in a
TensorFlow model using the provided conversion scripts and loading the TensorFlow model
afterwards.
model_args (additional positional arguments, `optional`):
Will be passed along to the underlying model ``__init__()`` method.
config (:class:`~transformers.PretrainedConfig`, `optional`):
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the `model id` string of a pretrained
model).
- The model was saved using :meth:`~transformers.PreTrainedModel.save_pretrained` and is reloaded
by suppyling the save directory.
- The model is loaded by suppyling a local directory as ``pretrained_model_name_or_path`` and a
configuration JSON file named `config.json` is found in the directory.
state_dict (`Dict[str, torch.Tensor]`, `optional`):
A state dictionary to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using
:func:`~transformers.PreTrainedModel.save_pretrained` and
:func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir (:obj:`str`, `optional`):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):
Load the model weights from a TensorFlow checkpoint save file (see docstring of
``pretrained_model_name_or_path`` argument).
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (:obj:`Dict[str, str], `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to only look at local files (e.g., not try downloading the model).
revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
identifier allowed by git.
kwargs (additional keyword arguments, `optional`):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
:obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
attribute will be passed to the underlying model's ``__init__`` function.
"""
class TFAutoModel(object):
r"""
This is a generic model class that will be instantiated as one of the base model classes of the library when
created with the when created with the :meth:`~transformers.TFAutoModel.from_pretrained` class method or the
:meth:`~transformers.TFAutoModel.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModel is designed to be instantiated "
"using the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModel.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_MAPPING, use_model_types=False)
def from_config(cls, config, **kwargs):
r"""
Instantiates one of the base model classes of the library from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModel.from_pretrained` to load the model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModel
>>> # Download configuration from huggingface.co and cache.
>>> config = TFAutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModel.from_config(config)
"""
if type(config) in TF_MODEL_MAPPING.keys():
return TF_MODEL_MAPPING[type(config)](config, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_MAPPING.keys())
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_MAPPING)
@add_start_docstrings(
"Instantiate one of the base model classes of the library from a pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, AutoModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModel.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModel.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModel.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_MAPPING.keys():
return TF_MODEL_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_MAPPING.keys())
)
)
class TFAutoModelForPreTraining(object):
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with the
architecture used for pretraining this model---when created with the
:meth:`~transformers.TFAutoModelForPreTraining.from_pretrained` class method or the
:meth:`~transformers.TFAutoModelForPreTraining.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForPreTraining is designed to be instantiated "
"using the `TFAutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForPreTraining.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_PRETRAINING_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with the architecture used for pretraining this
model---from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForPreTraining.from_pretrained` to load the
model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForPreTraining
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForPreTraining.from_config(config)
"""
if type(config) in TF_MODEL_FOR_PRETRAINING_MAPPING.keys():
return TF_MODEL_FOR_PRETRAINING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_PRETRAINING_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with the architecture used for pretraining this ",
"model---from a pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForPreTraining
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForPreTraining.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForPreTraining.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_PRETRAINING_MAPPING.keys():
return TF_MODEL_FOR_PRETRAINING_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
)
)
class TFAutoModelWithLMHead(object):
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a
language modeling head---when created with the :meth:`~transformers.TFAutoModelWithLMHead.from_pretrained` class
method or the :meth:`~transformers.TFAutoModelWithLMHead.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
.. warning::
This class is deprecated and will be removed in a future version. Please use
:class:`~transformers.TFAutoModelForCausalLM` for causal language models,
:class:`~transformers.TFAutoModelForMaskedLM` for masked language models and
:class:`~transformers.TFAutoModelForSeq2SeqLM` for encoder-decoder models.
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelWithLMHead is designed to be instantiated "
"using the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelWithLMHead.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_WITH_LM_HEAD_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a language modeling head---from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelWithLMHead.from_pretrained` to load the model
weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelWithLMHead
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelWithLMHead.from_config(config)
"""
warnings.warn(
"The class `TFAutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
"`TFAutoModelForCausalLM` for causal language models, `TFAutoModelForMaskedLM` for masked language models "
"and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
if type(config) in TF_MODEL_WITH_LM_HEAD_MAPPING.keys():
return TF_MODEL_WITH_LM_HEAD_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_WITH_LM_HEAD_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a language modeling head---from a pretrained ",
"model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelWithLMHead
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelWithLMHead.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelWithLMHead.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
warnings.warn(
"The class `TFAutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
"`TFAutoModelForCausalLM` for causal language models, `TFAutoModelForMaskedLM` for masked language models "
"and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_WITH_LM_HEAD_MAPPING.keys():
return TF_MODEL_WITH_LM_HEAD_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())
)
)
class TFAutoModelForCausalLM:
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a causal
language modeling head---when created with the :meth:`~transformers.TFAutoModelForCausalLM.from_pretrained` class
method or the :meth:`~transformers.TFAutoModelForCausalLM.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForCausalLM is designed to be instantiated "
"using the `TFAutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForCausalLM.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_CAUSAL_LM_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a causal language modeling head---from a
configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForCausalLM.from_pretrained` to load the model
weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForCausalLM
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('gpt2')
>>> model = TFAutoModelForCausalLM.from_config(config)
"""
if type(config) in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_CAUSAL_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_CAUSAL_LM_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a causal language modeling head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForCausalLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForCausalLM.from_pretrained('gpt2')
>>> # Update configuration during loading
>>> model = TFAutoModelForCausalLM.from_pretrained('gpt2', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/gpt2_pt_model_config.json')
>>> model = TFAutoModelForCausalLM.from_pretrained('./pt_model/gpt2_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
)
)
class TFAutoModelForMaskedLM:
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a masked
language modeling head---when created with the :meth:`~transformers.TFAutoModelForMaskedLM.from_pretrained` class
method or the :meth:`~transformers.TFAutoModelForMaskedLM.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForMaskedLM is designed to be instantiated "
"using the `TFAutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForMaskedLM.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_MASKED_LM_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a masked language modeling head---from a
configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForMaskedLM.from_pretrained` to load the model
weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForMaskedLM
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForMaskedLM.from_config(config)
"""
if type(config) in TF_MODEL_FOR_MASKED_LM_MAPPING.keys():
return TF_MODEL_FOR_MASKED_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_MASKED_LM_MAPPING.keys())
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_MASKED_LM_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a masked language modeling head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForMaskedLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMaskedLM.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForMaskedLM.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForMaskedLM.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_MASKED_LM_MAPPING.keys():
return TF_MODEL_FOR_MASKED_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_MASKED_LM_MAPPING.keys())
)
)
class TFAutoModelForSeq2SeqLM:
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a
sequence-to-sequence language modeling head---when created with the
:meth:`~transformers.TFAutoModelForSeq2SeqLM.from_pretrained` class method or the
:meth:`~transformers.TFAutoModelForSeq2SeqLM.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForSeq2SeqLM is designed to be instantiated "
"using the `TFAutoModelForSeq2SeqLM.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForSeq2SeqLM.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, use_model_types=False)
def from_config(cls, config, **kwargs):
r"""
Instantiates one of the model classes of the library---with a sequence-to-sequence language modeling
head---from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForSeq2SeqLM.from_pretrained` to load the model
weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForSeq2SeqLM
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('t5')
>>> model = TFAutoModelForSeq2SeqLM.from_config(config)
"""
if type(config) in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)](config, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, use_model_types=False)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a sequence-to-sequence language modeling "
"head---from a pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForSeq2SeqLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained('t5-base')
>>> # Update configuration during loading
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained('t5-base', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/t5_pt_model_config.json')
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained('./pt_model/t5_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
)
)
class TFAutoModelForSequenceClassification(object):
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a
sequence classification head---when created with the
:meth:`~transformers.TFAutoModelForSequenceClassification.from_pretrained` class method or the
:meth:`~transformers.TFAutoModelForSequenceClassification.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForSequenceClassification is designed to be instantiated "
"using the `TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForSequenceClassification.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a sequence classification head---from a
configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForSequenceClassification.from_pretrained` to
load the model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForSequenceClassification
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForSequenceClassification.from_config(config)
"""
if type(config) in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a sequence classification head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForSequenceClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForSequenceClassification.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
)
class TFAutoModelForQuestionAnswering(object):
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a
question answering head---when created with the
:meth:`~transformers.TFAutoModeForQuestionAnswering.from_pretrained` class method or the
:meth:`~transformers.TFAutoModelForQuestionAnswering.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForQuestionAnswering is designed to be instantiated "
"using the `TFAutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForQuestionAnswering.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a question answering head---from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForQuestionAnswering.from_pretrained` to load
the model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForQuestionAnswering
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForQuestionAnswering.from_config(config)
"""
if type(config) in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
return TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a question answering head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForQuestionAnswering.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
return TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
)
class TFAutoModelForTokenClassification:
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a token
classification head---when created with the :meth:`~transformers.TFAutoModelForTokenClassification.from_pretrained`
class method or the :meth:`~transformers.TFAutoModelForTokenClassification.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForTokenClassification is designed to be instantiated "
"using the `TFAutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForTokenClassification.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a token classification head---from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForTokenClassification.from_pretrained` to load
the model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForTokenClassification
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForTokenClassification.from_config(config)
"""
if type(config) in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a token classification head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForTokenClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForTokenClassification.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForTokenClassification.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForTokenClassification.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
)
class TFAutoModelForMultipleChoice:
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a
multiple choice classification head---when created with the
:meth:`~transformers.TFAutoModelForMultipleChoice.from_pretrained` class method or the
:meth:`~transformers.TFAutoModelForMultipleChoice.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForMultipleChoice is designed to be instantiated "
"using the `TFAutoModelForMultipleChoice.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForMultipleChoice.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a multiple choice classification head---from a
configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForMultipleChoice.from_pretrained` to load the
model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForMultipleChoice
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForMultipleChoice.from_config(config)
"""
if type(config) in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
return TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a multiple choice classification head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForMultipleChoice
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMultipleChoice.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForMultipleChoice.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForMultipleChoice.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
return TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
)
class TFAutoModelForNextSentencePrediction:
r"""
This is a generic model class that will be instantiated as one of the model classes of the library---with a next
sentence prediction head---when created with the
:meth:`~transformers.TFAutoModelForNextSentencePrediction.from_pretrained` class method or the
:meth:`~transformers.TFAutoModelForNextSentencePrediction.from_config` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForNextSentencePrediction is designed to be instantiated "
"using the `TFAutoModelForNextSentencePrediction.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForNextSentencePrediction.from_config(config)` methods."
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, use_model_types=False)
def from_config(cls, config):
r"""
Instantiates one of the model classes of the library---with a next sentence prediction head---from a
configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use :meth:`~transformers.TFAutoModelForNextSentencePrediction.from_pretrained` to
load the model weights.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
List options
Examples::
>>> from transformers import AutoConfig, TFAutoModelForNextSentencePrediction
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForNextSentencePrediction.from_config(config)
"""
if type(config) in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys():
return TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
)
)
@classmethod
@replace_list_option_in_docstrings(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING)
@add_start_docstrings(
"Instantiate one of the model classes of the library---with a next sentence prediction head---from a "
"pretrained model.",
TF_AUTO_MODEL_PRETRAINED_DOCSTRING,
)
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Examples::
>>> from transformers import AutoConfig, TFAutoModelForNextSentencePrediction
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained('bert-base-uncased')
>>> # Update configuration during loading
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained('bert-base-uncased', output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_json_file('./pt_model/bert_pt_model_config.json')
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
if type(config) in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys():
return TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
)
)
|
AdaMix/src/transformers/models/auto/modeling_tf_auto.py/0
|
{
"file_path": "AdaMix/src/transformers/models/auto/modeling_tf_auto.py",
"repo_id": "AdaMix",
"token_count": 30331
}
| 56 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 BERT model. """
import math
import warnings
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPooling,
TFCausalLMOutput,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFNextSentencePredictorOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFNextSentencePredictionLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_bert import BertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "bert-base-cased"
_CONFIG_FOR_DOC = "BertConfig"
_TOKENIZER_FOR_DOC = "BertTokenizer"
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"bert-base-uncased",
"bert-large-uncased",
"bert-base-cased",
"bert-large-cased",
"bert-base-multilingual-uncased",
"bert-base-multilingual-cased",
"bert-base-chinese",
"bert-base-german-cased",
"bert-large-uncased-whole-word-masking",
"bert-large-cased-whole-word-masking",
"bert-large-uncased-whole-word-masking-finetuned-squad",
"bert-large-cased-whole-word-masking-finetuned-squad",
"bert-base-cased-finetuned-mrpc",
"cl-tohoku/bert-base-japanese",
"cl-tohoku/bert-base-japanese-whole-word-masking",
"cl-tohoku/bert-base-japanese-char",
"cl-tohoku/bert-base-japanese-char-whole-word-masking",
"TurkuNLP/bert-base-finnish-cased-v1",
"TurkuNLP/bert-base-finnish-uncased-v1",
"wietsedv/bert-base-dutch-cased",
# See all BERT models at https://huggingface.co/models?filter=bert
]
class TFBertPreTrainingLoss:
"""
Loss function suitable for BERT-like pretraining, that is, the task of pretraining a language model by combining
NSP + MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss
computation.
"""
def compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE
)
# make sure only labels that are not equal to -100
# are taken into account as loss
masked_lm_active_loss = tf.not_equal(tf.reshape(tensor=labels["labels"], shape=(-1,)), -100)
masked_lm_reduced_logits = tf.boolean_mask(
tensor=tf.reshape(tensor=logits[0], shape=(-1, shape_list(logits[0])[2])),
mask=masked_lm_active_loss,
)
masked_lm_labels = tf.boolean_mask(
tensor=tf.reshape(tensor=labels["labels"], shape=(-1,)), mask=masked_lm_active_loss
)
next_sentence_active_loss = tf.not_equal(tf.reshape(tensor=labels["next_sentence_label"], shape=(-1,)), -100)
next_sentence_reduced_logits = tf.boolean_mask(
tensor=tf.reshape(tensor=logits[1], shape=(-1, 2)), mask=next_sentence_active_loss
)
next_sentence_label = tf.boolean_mask(
tensor=tf.reshape(tensor=labels["next_sentence_label"], shape=(-1,)), mask=next_sentence_active_loss
)
masked_lm_loss = loss_fn(y_true=masked_lm_labels, y_pred=masked_lm_reduced_logits)
next_sentence_loss = loss_fn(y_true=next_sentence_label, y_pred=next_sentence_reduced_logits)
masked_lm_loss = tf.reshape(tensor=masked_lm_loss, shape=(-1, shape_list(next_sentence_loss)[0]))
masked_lm_loss = tf.reduce_mean(input_tensor=masked_lm_loss, axis=0)
return masked_lm_loss + next_sentence_loss
class TFBertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.type_vocab_size = config.type_vocab_size
self.hidden_size = config.hidden_size
self.max_position_embeddings = config.max_position_embeddings
self.initializer_range = config.initializer_range
self.embeddings_sum = tf.keras.layers.Add()
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape: tf.TensorShape):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
class TFBertSelfAttention(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
mixed_key_layer = self.key(inputs=hidden_states)
mixed_value_layer = self.value(inputs=hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
class TFBertSelfOutput(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
class TFBertAttention(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFBertSelfAttention(config, name="self")
self.dense_output = TFBertSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
training=training,
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class TFBertIntermediate(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class TFBertOutput(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
class TFBertLayer(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFBertAttention(config, name="attention")
self.intermediate = TFBertIntermediate(config, name="intermediate")
self.bert_output = TFBertOutput(config, name="output")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
attention_outputs = self.attention(
input_tensor=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
training=training,
)
attention_output = attention_outputs[0]
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output = self.bert_output(
hidden_states=intermediate_output, input_tensor=attention_output, training=training
)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs
class TFBertEncoder(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class TFBertPooler(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(inputs=first_token_tensor)
return pooled_output
class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
return hidden_states
class TFBertLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.transform = TFBertPredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape: tf.TensorShape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
class TFBertMLMHead(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFBertLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
prediction_scores = self.predictions(hidden_states=sequence_output)
return prediction_scores
class TFBertNSPHead(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.seq_relationship = tf.keras.layers.Dense(
units=2,
kernel_initializer=get_initializer(config.initializer_range),
name="seq_relationship",
)
def call(self, pooled_output: tf.Tensor) -> tf.Tensor:
seq_relationship_score = self.seq_relationship(inputs=pooled_output)
return seq_relationship_score
@keras_serializable
class TFBertMainLayer(tf.keras.layers.Layer):
config_class = BertConfig
def __init__(self, config: BertConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFBertEmbeddings(config, name="embeddings")
self.encoder = TFBertEncoder(config, name="encoder")
self.pooler = TFBertPooler(config, name="pooler") if add_pooling_layer else None
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(dims=input_shape, value=1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
embedding_output = self.embeddings(
input_ids=inputs["input_ids"],
position_ids=inputs["position_ids"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
training=inputs["training"],
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if inputs["head_mask"] is not None:
raise NotImplementedError
else:
inputs["head_mask"] = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=inputs["head_mask"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
if not inputs["return_dict"]:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class TFBertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
base_model_prefix = "bert"
@dataclass
class TFBertForPreTrainingOutput(ModelOutput):
"""
Output type of :class:`~transformers.TFBertForPreTraining`.
Args:
prediction_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[tf.Tensor] = None
prediction_logits: tf.Tensor = None
seq_relationship_logits: tf.Tensor = None
hidden_states: Optional[Union[Tuple[tf.Tensor], tf.Tensor]] = None
attentions: Optional[Union[Tuple[tf.Tensor], tf.Tensor]] = None
BERT_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Args:
config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.TFPreTrainedModel.from_pretrained` method to load the
model weights.
"""
BERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`np.ndarray`, :obj:`tf.Tensor`, :obj:`List[tf.Tensor]` :obj:`Dict[str, tf.Tensor]` or :obj:`Dict[str, np.ndarray]` and each example must have the shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.BertTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`__
position_ids (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`__
head_mask (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
BERT_START_DOCSTRING,
)
class TFBertModel(TFBertPreTrainedModel):
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
return outputs
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings(
"""
Bert Model with two heads on top as done during the pretraining:
a `masked language modeling` head and a `next sentence prediction (classification)` head.
""",
BERT_START_DOCSTRING,
)
class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"position_ids",
r"cls.predictions.decoder.weight",
r"cls.predictions.decoder.bias",
]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
self.mlm = TFBertMLMHead(config, input_embeddings=self.bert.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
def get_prefix_bias_name(self) -> str:
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
next_sentence_label: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFBertForPreTrainingOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Return:
Examples::
>>> import tensorflow as tf
>>> from transformers import BertTokenizer, TFBertForPreTraining
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
>>> model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
>>> outputs = model(input_ids)
>>> prediction_scores, seq_relationship_scores = outputs[:2]
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
next_sentence_label=next_sentence_label,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output, pooled_output = outputs[:2]
prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
seq_relationship_score = self.nsp(pooled_output=pooled_output)
total_loss = None
if inputs["labels"] is not None and inputs["next_sentence_label"] is not None:
d_labels = {"labels": inputs["labels"]}
d_labels["next_sentence_label"] = inputs["next_sentence_label"]
total_loss = self.compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))
if not inputs["return_dict"]:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return TFBertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFBertForPreTrainingOutput) -> TFBertForPreTrainingOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBertForPreTrainingOutput(
prediction_logits=output.prediction_logits,
seq_relationship_logits=output.seq_relationship_logits,
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"cls.seq_relationship",
r"cls.predictions.decoder.weight",
r"nsp___cls",
]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFBertForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
self.mlm = TFBertMLMHead(config, input_embeddings=self.bert.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
def get_prefix_bias_name(self) -> str:
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
loss = (
None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=prediction_scores)
)
if not inputs["return_dict"]:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"cls.seq_relationship",
r"cls.predictions.decoder.weight",
r"nsp___cls",
]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if not config.is_decoder:
logger.warning("If you want to use `TFBertLMHeadModel` as a standalone, add `is_decoder=True.`")
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
self.mlm = TFBertMLMHead(config, input_embeddings=self.bert.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
def get_prefix_bias_name(self) -> str:
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
config.vocab_size - 1]``.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
logits = self.mlm(sequence_output=sequence_output, training=inputs["training"])
loss = None
if inputs["labels"] is not None:
# shift labels to the left and cut last logit token
logits = logits[:, :-1]
labels = inputs["labels"][:, 1:]
loss = self.compute_loss(labels=labels, logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""Bert Model with a `next sentence prediction (classification)` head on top. """,
BERT_START_DOCSTRING,
)
class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredictionLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"cls.predictions"]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
next_sentence_label: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFNextSentencePredictorOutput, Tuple[tf.Tensor]]:
r"""
Return:
Examples::
>>> import tensorflow as tf
>>> from transformers import BertTokenizer, TFBertForNextSentencePrediction
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
>>> model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors='tf')
>>> logits = model(encoding['input_ids'], token_type_ids=encoding['token_type_ids'])[0]
>>> assert logits[0][0] < logits[0][1] # the next sentence was random
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
next_sentence_label=next_sentence_label,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
pooled_output = outputs[1]
seq_relationship_scores = self.nsp(pooled_output=pooled_output)
next_sentence_loss = (
None
if inputs["next_sentence_label"] is None
else self.compute_loss(labels=inputs["next_sentence_label"], logits=seq_relationship_scores)
)
if not inputs["return_dict"]:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return TFNextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFNextSentencePredictorOutput) -> TFNextSentencePredictorOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFNextSentencePredictorOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
BERT_START_DOCSTRING,
)
class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="classifier",
)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"])
logits = self.classifier(inputs=pooled_output)
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
BERT_START_DOCSTRING,
)
class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
:obj:`input_ids` above)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None:
num_choices = shape_list(inputs["input_ids"])[1]
seq_length = shape_list(inputs["input_ids"])[2]
else:
num_choices = shape_list(inputs["inputs_embeds"])[1]
seq_length = shape_list(inputs["inputs_embeds"])[2]
flat_input_ids = (
tf.reshape(tensor=inputs["input_ids"], shape=(-1, seq_length)) if inputs["input_ids"] is not None else None
)
flat_attention_mask = (
tf.reshape(tensor=inputs["attention_mask"], shape=(-1, seq_length))
if inputs["attention_mask"] is not None
else None
)
flat_token_type_ids = (
tf.reshape(tensor=inputs["token_type_ids"], shape=(-1, seq_length))
if inputs["token_type_ids"] is not None
else None
)
flat_position_ids = (
tf.reshape(tensor=inputs["position_ids"], shape=(-1, seq_length))
if inputs["position_ids"] is not None
else None
)
flat_inputs_embeds = (
tf.reshape(tensor=inputs["inputs_embeds"], shape=(-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
if inputs["inputs_embeds"] is not None
else None
)
outputs = self.bert(
input_ids=flat_input_ids,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
position_ids=flat_position_ids,
head_mask=inputs["head_mask"],
inputs_embeds=flat_inputs_embeds,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"])
logits = self.classifier(inputs=pooled_output)
reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices))
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=reshaped_logits)
if not inputs["return_dict"]:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
output = self.call(input_ids=inputs)
return self.serving_output(output)
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
BERT_START_DOCSTRING,
)
class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
r"cls.predictions",
r"cls.seq_relationship",
]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="classifier",
)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
sequence_output = self.dropout(inputs=sequence_output, training=inputs["training"])
logits = self.classifier(inputs=sequence_output)
loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
BERT_START_DOCSTRING,
)
class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
r"cls.predictions",
r"cls.seq_relationship",
]
def __init__(self, config: BertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
self.qa_outputs = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="qa_outputs",
)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
start_positions=start_positions,
end_positions=end_positions,
training=training,
kwargs_call=kwargs,
)
outputs = self.bert(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
logits = self.qa_outputs(inputs=sequence_output)
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
start_logits = tf.squeeze(input=start_logits, axis=-1)
end_logits = tf.squeeze(input=end_logits, axis=-1)
loss = None
if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
labels = {"start_position": inputs["start_positions"]}
labels["end_position"] = inputs["end_positions"]
loss = self.compute_loss(labels=labels, logits=(start_logits, end_logits))
if not inputs["return_dict"]:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
|
AdaMix/src/transformers/models/bert/modeling_tf_bert.py/0
|
{
"file_path": "AdaMix/src/transformers/models/bert/modeling_tf_bert.py",
"repo_id": "AdaMix",
"token_count": 35477
}
| 57 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 CamemBERT model. """
from ...file_utils import add_start_docstrings
from ...utils import logging
from ..roberta.modeling_tf_roberta import (
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaModel,
)
from .configuration_camembert import CamembertConfig
logger = logging.get_logger(__name__)
TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
# See all CamemBERT models at https://huggingface.co/models?filter=camembert
]
CAMEMBERT_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Parameters:
config (:class:`~transformers.CamembertConfig`): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
@add_start_docstrings(
"The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
CAMEMBERT_START_DOCSTRING,
)
class TFCamembertModel(TFRobertaModel):
"""
This class overrides :class:`~transformers.TFRobertaModel`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = CamembertConfig
@add_start_docstrings(
"""CamemBERT Model with a `language modeling` head on top. """,
CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForMaskedLM(TFRobertaForMaskedLM):
"""
This class overrides :class:`~transformers.TFRobertaForMaskedLM`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = CamembertConfig
@add_start_docstrings(
"""
CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForSequenceClassification(TFRobertaForSequenceClassification):
"""
This class overrides :class:`~transformers.TFRobertaForSequenceClassification`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = CamembertConfig
@add_start_docstrings(
"""
CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
""",
CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForTokenClassification(TFRobertaForTokenClassification):
"""
This class overrides :class:`~transformers.TFRobertaForTokenClassification`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = CamembertConfig
@add_start_docstrings(
"""
CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForMultipleChoice(TFRobertaForMultipleChoice):
"""
This class overrides :class:`~transformers.TFRobertaForMultipleChoice`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = CamembertConfig
@add_start_docstrings(
"""
CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForQuestionAnswering(TFRobertaForQuestionAnswering):
"""
This class overrides :class:`~transformers.TFRobertaForQuestionAnswering`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = CamembertConfig
|
AdaMix/src/transformers/models/camembert/modeling_tf_camembert.py/0
|
{
"file_path": "AdaMix/src/transformers/models/camembert/modeling_tf_camembert.py",
"repo_id": "AdaMix",
"token_count": 2011
}
| 58 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for DPR."""
import collections
from typing import List, Optional, Union
from ...file_utils import TensorType, add_end_docstrings, add_start_docstrings
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt",
"facebook/dpr-ctx_encoder-multiset-base": "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json",
"facebook/dpr-ctx_encoder-multiset-base": "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json",
},
}
QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt",
"facebook/dpr-question_encoder-multiset-base": "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json",
"facebook/dpr-question_encoder-multiset-base": "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json",
},
}
READER_PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt",
"facebook/dpr-reader-multiset-base": "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json",
"facebook/dpr-reader-multiset-base": "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json",
},
}
CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
READER_PRETRAINED_INIT_CONFIGURATION = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class DPRContextEncoderTokenizer(BertTokenizer):
r"""
Construct a DPRContextEncoder tokenizer.
:class:`~transformers.DPRContextEncoderTokenizer` is identical to :class:`~transformers.BertTokenizer` and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class DPRQuestionEncoderTokenizer(BertTokenizer):
r"""
Constructs a DPRQuestionEncoder tokenizer.
:class:`~transformers.DPRQuestionEncoderTokenizer` is identical to :class:`~transformers.BertTokenizer` and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
DPRSpanPrediction = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
DPRReaderOutput = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
CUSTOM_DPR_READER_DOCSTRING = r"""
Return a dictionary with the token ids of the input strings and other information to give to
:obj:`.decode_best_spans`. It converts the strings of a question and different passages (title and text) in a
sequence of IDs (integers), using the tokenizer and vocabulary. The resulting :obj:`input_ids` is a matrix of size
:obj:`(n_passages, sequence_length)` with the format:
::
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
Args:
questions (:obj:`str` or :obj:`List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like :obj:`[questions] * n_passages`. Otherwise you have to specify as many questions as
in :obj:`titles` or :obj:`texts`.
titles (:obj:`str` or :obj:`List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (:obj:`str` or :obj:`List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls padding. Accepts the following values:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls truncation. Accepts the following values:
* :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
:obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
provided. This will truncate token by token, removing a token from the longest sequence in the pair if a
pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only truncate
the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence
lengths greater than the model maximum admissible input size).
max_length (:obj:`int`, `optional`):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to :obj:`None`, this will use the predefined model maximum length if a maximum
length is required by one of the truncation/padding parameters. If the model has no specific maximum
input length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
return_attention_mask (:obj:`bool`, `optional`):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
Returns:
:obj:`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- ``input_ids``: List of token ids to be fed to a model.
- ``attention_mask``: List of indices specifying which tokens should be attended to by the model.
"""
@add_start_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class CustomDPRReaderTokenizerMixin:
def __call__(
self,
questions,
titles: Optional[str] = None,
texts: Optional[str] = None,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
**kwargs
) -> BatchEncoding:
if titles is None and texts is None:
return super().__call__(
questions,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
**kwargs,
)
elif titles is None or texts is None:
text_pair = titles if texts is None else texts
return super().__call__(
questions,
text_pair,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
**kwargs,
)
titles = titles if not isinstance(titles, str) else [titles]
texts = texts if not isinstance(texts, str) else [texts]
n_passages = len(titles)
questions = questions if not isinstance(questions, str) else [questions] * n_passages
assert len(titles) == len(
texts
), "There should be as many titles than texts but got {} titles and {} texts.".format(len(titles), len(texts))
encoded_question_and_titles = super().__call__(questions, titles, padding=False, truncation=False)["input_ids"]
encoded_texts = super().__call__(texts, add_special_tokens=False, padding=False, truncation=False)["input_ids"]
encoded_inputs = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(encoded_question_and_titles, encoded_texts)
]
}
if return_attention_mask is not False:
attention_mask = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
encoded_inputs["attention_mask"] = attention_mask
return self.pad(encoded_inputs, padding=padding, max_length=max_length, return_tensors=return_tensors)
def decode_best_spans(
self,
reader_input: BatchEncoding,
reader_output: DPRReaderOutput,
num_spans: int = 16,
max_answer_length: int = 64,
num_spans_per_passage: int = 4,
) -> List[DPRSpanPrediction]:
"""
Get the span predictions for the extractive Q&A model.
Returns: `List` of `DPRReaderOutput` sorted by descending `(relevance_score, span_score)`. Each
`DPRReaderOutput` is a `Tuple` with:
- **span_score**: ``float`` that corresponds to the score given by the reader for this span compared to
other spans in the same passage. It corresponds to the sum of the start and end logits of the span.
- **relevance_score**: ``float`` that corresponds to the score of the each passage to answer the question,
compared to all the other passages. It corresponds to the output of the QA classifier of the DPRReader.
- **doc_id**: ``int``` the id of the passage.
- **start_index**: ``int`` the start index of the span (inclusive).
- **end_index**: ``int`` the end index of the span (inclusive).
Examples::
>>> from transformers import DPRReader, DPRReaderTokenizer
>>> tokenizer = DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base')
>>> model = DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base')
>>> encoded_inputs = tokenizer(
... questions=["What is love ?"],
... titles=["Haddaway"],
... texts=["'What Is Love' is a song recorded by the artist Haddaway"],
... return_tensors='pt'
... )
>>> outputs = model(**encoded_inputs)
>>> predicted_spans = tokenizer.decode_best_spans(encoded_inputs, outputs)
>>> print(predicted_spans[0].text) # best span
"""
input_ids = reader_input["input_ids"]
start_logits, end_logits, relevance_logits = reader_output[:3]
n_passages = len(relevance_logits)
sorted_docs = sorted(range(n_passages), reverse=True, key=relevance_logits.__getitem__)
nbest_spans_predictions: List[DPRReaderOutput] = []
for doc_id in sorted_docs:
sequence_ids = list(input_ids[doc_id])
# assuming question & title information is at the beginning of the sequence
passage_offset = sequence_ids.index(self.sep_token_id, 2) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
sequence_len = sequence_ids.index(self.pad_token_id)
else:
sequence_len = len(sequence_ids)
best_spans = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len],
end_logits=end_logits[doc_id][passage_offset:sequence_len],
max_answer_length=max_answer_length,
top_spans=num_spans_per_passage,
)
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],
relevance_score=relevance_logits[doc_id],
doc_id=doc_id,
start_index=start_index,
end_index=end_index,
text=self.decode(sequence_ids[start_index : end_index + 1]),
)
)
if len(nbest_spans_predictions) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _get_best_spans(
self,
start_logits: List[int],
end_logits: List[int],
max_answer_length: int,
top_spans: int,
) -> List[DPRSpanPrediction]:
"""
Finds the best answer span for the extractive Q&A model for one passage. It returns the best span by descending
`span_score` order and keeping max `top_spans` spans. Spans longer that `max_answer_length` are ignored.
"""
scores = []
for (start_index, start_score) in enumerate(start_logits):
for (answer_length, end_score) in enumerate(end_logits[start_index : start_index + max_answer_length]):
scores.append(((start_index, start_index + answer_length), start_score + end_score))
scores = sorted(scores, key=lambda x: x[1], reverse=True)
chosen_span_intervals = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, "Wrong span indices: [{}:{}]".format(start_index, end_index)
length = end_index - start_index + 1
assert length <= max_answer_length, "Span is too long: {} > {}".format(length, max_answer_length)
if any(
[
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals
]
):
continue
chosen_span_intervals.append((start_index, end_index))
if len(chosen_span_intervals) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class DPRReaderTokenizer(CustomDPRReaderTokenizerMixin, BertTokenizer):
r"""
Construct a DPRReader tokenizer.
:class:`~transformers.DPRReaderTokenizer` is almost identical to :class:`~transformers.BertTokenizer` and runs
end-to-end tokenization: punctuation splitting and wordpiece. The difference is that is has three inputs strings:
question, titles and texts that are combined to be fed to the :class:`~transformers.DPRReader` model.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = READER_PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = READER_PRETRAINED_INIT_CONFIGURATION
model_input_names = ["input_ids", "attention_mask"]
|
AdaMix/src/transformers/models/dpr/tokenization_dpr.py/0
|
{
"file_path": "AdaMix/src/transformers/models/dpr/tokenization_dpr.py",
"repo_id": "AdaMix",
"token_count": 8358
}
| 59 |
# coding=utf-8
# Copyright 2020-present Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 Funnel model. """
import warnings
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_funnel import FunnelConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "FunnelConfig"
_TOKENIZER_FOR_DOC = "FunnelTokenizer"
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = [
"funnel-transformer/small", # B4-4-4H768
"funnel-transformer/small-base", # B4-4-4H768, no decoder
"funnel-transformer/medium", # B6-3x2-3x2H768
"funnel-transformer/medium-base", # B6-3x2-3x2H768, no decoder
"funnel-transformer/intermediate", # B6-6-6H768
"funnel-transformer/intermediate-base", # B6-6-6H768, no decoder
"funnel-transformer/large", # B8-8-8H1024
"funnel-transformer/large-base", # B8-8-8H1024, no decoder
"funnel-transformer/xlarge-base", # B10-10-10H1024
"funnel-transformer/xlarge", # B10-10-10H1024, no decoder
]
INF = 1e6
class TFFunnelEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.initializer_range = config.initializer_range
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout)
def build(self, input_shape):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
)
super().build(input_shape)
def call(self, input_ids=None, inputs_embeds=None, training=False):
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
assert not (input_ids is not None and inputs_embeds is not None)
if input_ids is not None:
inputs_embeds = tf.gather(self.weight, input_ids)
final_embeddings = self.LayerNorm(inputs=inputs_embeds)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
class TFFunnelAttentionStructure:
"""
Contains helpers for `TFFunnelRelMultiheadAttention `.
"""
cls_token_type_id: int = 2
def __init__(self, config):
self.d_model = config.d_model
self.attention_type = config.attention_type
self.num_blocks = config.num_blocks
self.separate_cls = config.separate_cls
self.truncate_seq = config.truncate_seq
self.pool_q_only = config.pool_q_only
self.pooling_type = config.pooling_type
self.sin_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
self.cos_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
# Track where we are at in terms of pooling from the original input, e.g., by how much the sequence length was
# divided.
self.pooling_mult = None
def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None, training=False):
""" Returns the attention inputs associated to the inputs of the model. """
# inputs_embeds has shape batch_size x seq_len x d_model
# attention_mask and token_type_ids have shape batch_size x seq_len
self.pooling_mult = 1
self.seq_len = seq_len = shape_list(inputs_embeds)[1]
position_embeds = self.get_position_embeds(seq_len, training=training)
token_type_mat = self.token_type_ids_to_mat(token_type_ids) if token_type_ids is not None else None
cls_mask = (
tf.pad(tf.ones([seq_len - 1, seq_len - 1], dtype=inputs_embeds.dtype), [[1, 0], [1, 0]])
if self.separate_cls
else None
)
return (position_embeds, token_type_mat, attention_mask, cls_mask)
def token_type_ids_to_mat(self, token_type_ids):
"""Convert `token_type_ids` to `token_type_mat`."""
token_type_mat = tf.equal(tf.expand_dims(token_type_ids, -1), tf.expand_dims(token_type_ids, -2))
# Treat <cls> as in the same segment as both A & B
cls_ids = tf.equal(token_type_ids, tf.constant([self.cls_token_type_id], dtype=token_type_ids.dtype))
cls_mat = tf.logical_or(tf.expand_dims(cls_ids, -1), tf.expand_dims(cls_ids, -2))
return tf.logical_or(cls_mat, token_type_mat)
def get_position_embeds(self, seq_len, training=False):
"""
Create and cache inputs related to relative position encoding. Those are very different depending on whether we
are using the factorized or the relative shift attention:
For the factorized attention, it returns the matrices (phi, pi, psi, omega) used in the paper, appendix A.2.2,
final formula.
For the relative shif attention, it returns all possible vectors R used in the paper, appendix A.2.1, final
formula.
Paper link: https://arxiv.org/abs/2006.03236
"""
if self.attention_type == "factorized":
# Notations from the paper, appending A.2.2, final formula.
# We need to create and return the matrices phi, psi, pi and omega.
pos_seq = tf.range(0, seq_len, 1.0)
freq_seq = tf.range(0, self.d_model // 2, 1.0)
inv_freq = 1 / (10000 ** (freq_seq / (self.d_model // 2)))
sinusoid = tf.einsum("i,d->id", pos_seq, inv_freq)
sin_embed = tf.sin(sinusoid)
sin_embed_d = self.sin_dropout(sin_embed, training=training)
cos_embed = tf.cos(sinusoid)
cos_embed_d = self.cos_dropout(cos_embed, training=training)
# This is different from the formula on the paper...
phi = tf.concat([sin_embed_d, sin_embed_d], axis=-1)
psi = tf.concat([cos_embed, sin_embed], axis=-1)
pi = tf.concat([cos_embed_d, cos_embed_d], axis=-1)
omega = tf.concat([-sin_embed, cos_embed], axis=-1)
return (phi, pi, psi, omega)
else:
# Notations from the paper, appending A.2.1, final formula.
# We need to create and return all the possible vectors R for all blocks and shifts.
freq_seq = tf.range(0, self.d_model // 2, 1.0)
inv_freq = 1 / (10000 ** (freq_seq / (self.d_model // 2)))
# Maximum relative positions for the first input
rel_pos_id = tf.range(-seq_len * 2, seq_len * 2, 1.0)
zero_offset = seq_len * tf.constant(2)
sinusoid = tf.einsum("i,d->id", rel_pos_id, inv_freq)
sin_embed = self.sin_dropout(tf.sin(sinusoid), training=training)
cos_embed = self.cos_dropout(tf.cos(sinusoid), training=training)
pos_embed = tf.concat([sin_embed, cos_embed], axis=-1)
pos = tf.range(0, seq_len)
pooled_pos = pos
position_embeds_list = []
for block_index in range(0, self.num_blocks):
# For each block with block_index > 0, we need two types position embeddings:
# - Attention(pooled-q, unpooled-kv)
# - Attention(pooled-q, pooled-kv)
# For block_index = 0 we only need the second one and leave the first one as None.
# First type
position_embeds_pooling = tf.fill([1], value=-1.0)
if block_index != 0:
pooled_pos = self.stride_pool_pos(pos, block_index)
# construct rel_pos_id
stride = 2 ** (block_index - 1)
rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2)
# rel_pos = tf.expand_dims(rel_pos,1) + zero_offset
# rel_pos = tf.broadcast_to(rel_pos, (rel_pos.shape[0], self.d_model))
rel_pos = tf.cast(rel_pos, dtype=zero_offset.dtype)
rel_pos = rel_pos + zero_offset
position_embeds_pooling = tf.gather(pos_embed, rel_pos, axis=0)
# Second type
pos = pooled_pos
stride = 2 ** block_index
rel_pos = self.relative_pos(pos, stride)
# rel_pos = tf.expand_dims(rel_pos,1) + zero_offset
# rel_pos = tf.broadcast_to(rel_pos, (rel_pos.shape[0], self.d_model))
rel_pos = tf.cast(rel_pos, dtype=zero_offset.dtype)
rel_pos = rel_pos + zero_offset
position_embeds_no_pooling = tf.gather(pos_embed, rel_pos, axis=0)
position_embeds_list.append([position_embeds_no_pooling, position_embeds_pooling])
return position_embeds_list
def stride_pool_pos(self, pos_id, block_index):
"""
Pool `pos_id` while keeping the cls token separate (if `self.separate_cls=True`).
"""
if self.separate_cls:
# Under separate <cls>, we treat the <cls> as the first token in
# the previous block of the 1st real block. Since the 1st real
# block always has position 1, the position of the previous block
# will be at `1 - 2 ** block_index`.
cls_pos = tf.constant([-(2 ** block_index) + 1], dtype=pos_id.dtype)
pooled_pos_id = pos_id[1:-1] if self.truncate_seq else pos_id[1:]
return tf.concat([cls_pos, pooled_pos_id[::2]], 0)
else:
return pos_id[::2]
def relative_pos(self, pos, stride, pooled_pos=None, shift=1):
"""
Build the relative positional vector between `pos` and `pooled_pos`.
"""
if pooled_pos is None:
pooled_pos = pos
ref_point = pooled_pos[0] - pos[0]
num_remove = shift * shape_list(pooled_pos)[0]
max_dist = ref_point + num_remove * stride
min_dist = pooled_pos[0] - pos[-1]
return tf.range(max_dist, min_dist - 1, -stride)
def stride_pool(self, tensor, axis):
"""
Perform pooling by stride slicing the tensor along the given axis.
"""
if tensor is None:
return None
# Do the stride pool recursively if axis is a list or a tuple of ints.
if isinstance(axis, (list, tuple)):
for ax in axis:
tensor = self.stride_pool(tensor, ax)
return tensor
# Do the stride pool recursively if tensor is a list or tuple of tensors.
if isinstance(tensor, (tuple, list)):
return type(tensor)(self.stride_pool(x, axis) for x in tensor)
# Deal with negative axis
axis %= len(shape_list(tensor))
axis_slice = slice(None, -1, 2) if self.separate_cls and self.truncate_seq else slice(None, None, 2)
enc_slice = [slice(None)] * axis + [axis_slice]
if self.separate_cls:
cls_slice = [slice(None)] * axis + [slice(None, 1)]
tensor = tf.concat([tensor[cls_slice], tensor], axis)
return tensor[enc_slice]
def pool_tensor(self, tensor, mode="mean", stride=2):
"""Apply 1D pooling to a tensor of size [B x T (x H)]."""
if tensor is None:
return None
# Do the pool recursively if tensor is a list or tuple of tensors.
if isinstance(tensor, (tuple, list)):
return type(tensor)(self.pool_tensor(tensor, mode=mode, stride=stride) for x in tensor)
if self.separate_cls:
suffix = tensor[:, :-1] if self.truncate_seq else tensor
tensor = tf.concat([tensor[:, :1], suffix], axis=1)
ndim = len(shape_list(tensor))
if ndim == 2:
tensor = tensor[:, :, None]
if mode == "mean":
tensor = tf.nn.avg_pool1d(tensor, stride, strides=stride, data_format="NWC", padding="SAME")
elif mode == "max":
tensor = tf.nn.max_pool1d(tensor, stride, strides=stride, data_format="NWC", padding="SAME")
elif mode == "min":
tensor = -tf.nn.max_pool1d(-tensor, stride, strides=stride, data_format="NWC", padding="SAME")
else:
raise NotImplementedError("The supported modes are 'mean', 'max' and 'min'.")
return tf.squeeze(tensor, 2) if ndim == 2 else tensor
def pre_attention_pooling(self, output, attention_inputs):
""" Pool `output` and the proper parts of `attention_inputs` before the attention layer. """
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.pool_q_only:
if self.attention_type == "factorized":
position_embeds = self.stride_pool(position_embeds[:2], 0) + position_embeds[2:]
token_type_mat = self.stride_pool(token_type_mat, 1)
cls_mask = self.stride_pool(cls_mask, 0)
output = self.pool_tensor(output, mode=self.pooling_type)
else:
self.pooling_mult *= 2
if self.attention_type == "factorized":
position_embeds = self.stride_pool(position_embeds, 0)
token_type_mat = self.stride_pool(token_type_mat, [1, 2])
cls_mask = self.stride_pool(cls_mask, [1, 2])
attention_mask = self.pool_tensor(attention_mask, mode="min")
output = self.pool_tensor(output, mode=self.pooling_type)
attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
return output, attention_inputs
def post_attention_pooling(self, attention_inputs):
""" Pool the proper parts of `attention_inputs` after the attention layer. """
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.pool_q_only:
self.pooling_mult *= 2
if self.attention_type == "factorized":
position_embeds = position_embeds[:2] + self.stride_pool(position_embeds[2:], 0)
token_type_mat = self.stride_pool(token_type_mat, 2)
cls_mask = self.stride_pool(cls_mask, 1)
attention_mask = self.pool_tensor(attention_mask, mode="min")
attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
return attention_inputs
def _relative_shift_gather(positional_attn, context_len, shift):
batch_size, n_head, seq_len, max_rel_len = shape_list(positional_attn)
# max_rel_len = 2 * context_len + shift -1 is the numbers of possible relative positions i-j
# What's next is the same as doing the following gather in PyTorch, which might be clearer code but less efficient.
# idxs = context_len + torch.arange(0, context_len).unsqueeze(0) - torch.arange(0, seq_len).unsqueeze(1)
# # matrix of context_len + i-j
# return positional_attn.gather(3, idxs.expand([batch_size, n_head, context_len, context_len]))
positional_attn = tf.reshape(positional_attn, [batch_size, n_head, max_rel_len, seq_len])
positional_attn = positional_attn[:, :, shift:, :]
positional_attn = tf.reshape(positional_attn, [batch_size, n_head, seq_len, max_rel_len - shift])
positional_attn = positional_attn[..., :context_len]
return positional_attn
class TFFunnelRelMultiheadAttention(tf.keras.layers.Layer):
def __init__(self, config, block_index, **kwargs):
super().__init__(**kwargs)
self.attention_type = config.attention_type
self.n_head = n_head = config.n_head
self.d_head = d_head = config.d_head
self.d_model = d_model = config.d_model
self.initializer_range = config.initializer_range
self.block_index = block_index
self.hidden_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
self.attention_dropout = tf.keras.layers.Dropout(config.attention_dropout)
initializer = get_initializer(config.initializer_range)
self.q_head = tf.keras.layers.Dense(
n_head * d_head, use_bias=False, kernel_initializer=initializer, name="q_head"
)
self.k_head = tf.keras.layers.Dense(n_head * d_head, kernel_initializer=initializer, name="k_head")
self.v_head = tf.keras.layers.Dense(n_head * d_head, kernel_initializer=initializer, name="v_head")
self.post_proj = tf.keras.layers.Dense(d_model, kernel_initializer=initializer, name="post_proj")
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.scale = 1.0 / (d_head ** 0.5)
def build(self, input_shape):
n_head, d_head, d_model = self.n_head, self.d_head, self.d_model
initializer = get_initializer(self.initializer_range)
self.r_w_bias = self.add_weight(
shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_w_bias"
)
self.r_r_bias = self.add_weight(
shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_r_bias"
)
self.r_kernel = self.add_weight(
shape=(d_model, n_head, d_head), initializer=initializer, trainable=True, name="r_kernel"
)
self.r_s_bias = self.add_weight(
shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_s_bias"
)
self.seg_embed = self.add_weight(
shape=(2, n_head, d_head), initializer=initializer, trainable=True, name="seg_embed"
)
super().build(input_shape)
def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
""" Relative attention score for the positional encodings """
# q_head has shape batch_size x sea_len x n_head x d_head
if self.attention_type == "factorized":
# Notations from the paper, appending A.2.2, final formula (https://arxiv.org/abs/2006.03236)
# phi and pi have shape seq_len x d_model, psi and omega have shape context_len x d_model
phi, pi, psi, omega = position_embeds
# Shape n_head x d_head
u = self.r_r_bias * self.scale
# Shape d_model x n_head x d_head
w_r = self.r_kernel
# Shape batch_size x sea_len x n_head x d_model
q_r_attention = tf.einsum("binh,dnh->bind", q_head + u, w_r)
q_r_attention_1 = q_r_attention * phi[:, None]
q_r_attention_2 = q_r_attention * pi[:, None]
# Shape batch_size x n_head x seq_len x context_len
positional_attn = tf.einsum("bind,jd->bnij", q_r_attention_1, psi) + tf.einsum(
"bind,jd->bnij", q_r_attention_2, omega
)
else:
# Notations from the paper, appending A.2.1, final formula (https://arxiv.org/abs/2006.03236)
# Grab the proper positional encoding, shape max_rel_len x d_model
if shape_list(q_head)[1] != context_len:
shift = 2
r = position_embeds[self.block_index][1]
else:
shift = 1
r = position_embeds[self.block_index][0]
# Shape n_head x d_head
v = self.r_r_bias * self.scale
# Shape d_model x n_head x d_head
w_r = self.r_kernel
# Shape max_rel_len x n_head x d_model
r_head = tf.einsum("td,dnh->tnh", r, w_r)
# Shape batch_size x n_head x seq_len x max_rel_len
positional_attn = tf.einsum("binh,tnh->bnit", q_head + v, r_head)
# Shape batch_size x n_head x seq_len x context_len
positional_attn = _relative_shift_gather(positional_attn, context_len, shift)
if cls_mask is not None:
positional_attn *= cls_mask
return positional_attn
def relative_token_type_attention(self, token_type_mat, q_head, cls_mask=None):
""" Relative attention score for the token_type_ids """
if token_type_mat is None:
return 0
batch_size, seq_len, context_len = shape_list(token_type_mat)
# q_head has shape batch_size x seq_len x n_head x d_head
# Shape n_head x d_head
r_s_bias = self.r_s_bias * self.scale
# Shape batch_size x n_head x seq_len x 2
token_type_bias = tf.einsum("bind,snd->bnis", q_head + r_s_bias, self.seg_embed)
# Shape batch_size x n_head x seq_len x context_len
token_type_mat = tf.tile(token_type_mat[:, None], [1, shape_list(q_head)[2], 1, 1])
# token_type_mat = tf.broadcast_to(token_type_mat[:, None], new_shape)
# Shapes batch_size x n_head x seq_len
diff_token_type, same_token_type = tf.split(token_type_bias, 2, axis=-1)
# Shape batch_size x n_head x seq_len x context_len
token_type_attn = tf.where(
token_type_mat,
tf.tile(same_token_type, [1, 1, 1, context_len]),
tf.tile(diff_token_type, [1, 1, 1, context_len]),
)
if cls_mask is not None:
token_type_attn *= cls_mask
return token_type_attn
def call(self, query, key, value, attention_inputs, output_attentions=False, training=False):
# query has shape batch_size x seq_len x d_model
# key and value have shapes batch_size x context_len x d_model
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
batch_size, seq_len, _ = shape_list(query)
context_len = shape_list(key)[1]
n_head, d_head = self.n_head, self.d_head
# Shape batch_size x seq_len x n_head x d_head
q_head = tf.reshape(self.q_head(query), [batch_size, seq_len, n_head, d_head])
# Shapes batch_size x context_len x n_head x d_head
k_head = tf.reshape(self.k_head(key), [batch_size, context_len, n_head, d_head])
v_head = tf.reshape(self.v_head(value), [batch_size, context_len, n_head, d_head])
q_head = q_head * self.scale
# Shape n_head x d_head
r_w_bias = self.r_w_bias * self.scale
# Shapes batch_size x n_head x seq_len x context_len
content_score = tf.einsum("bind,bjnd->bnij", q_head + r_w_bias, k_head)
positional_attn = self.relative_positional_attention(position_embeds, q_head, context_len, cls_mask)
token_type_attn = self.relative_token_type_attention(token_type_mat, q_head, cls_mask)
# merge attention scores
attn_score = content_score + positional_attn + token_type_attn
# perform masking
if attention_mask is not None:
attention_mask = tf.cast(attention_mask, dtype=attn_score.dtype)
attn_score = attn_score - (INF * (1 - attention_mask[:, None, None]))
# attention probability
attn_prob = tf.nn.softmax(attn_score, axis=-1)
attn_prob = self.attention_dropout(attn_prob, training=training)
# attention output, shape batch_size x seq_len x n_head x d_head
attn_vec = tf.einsum("bnij,bjnd->bind", attn_prob, v_head)
# Shape shape batch_size x seq_len x d_model
attn_out = self.post_proj(tf.reshape(attn_vec, [batch_size, seq_len, n_head * d_head]))
attn_out = self.hidden_dropout(attn_out, training=training)
output = self.layer_norm(query + attn_out)
return (output, attn_prob) if output_attentions else (output,)
class TFFunnelPositionwiseFFN(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.linear_1 = tf.keras.layers.Dense(config.d_inner, kernel_initializer=initializer, name="linear_1")
self.activation_function = get_tf_activation(config.hidden_act)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.linear_2 = tf.keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="linear_2")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
def call(self, hidden, training=False):
h = self.linear_1(hidden)
h = self.activation_function(h)
h = self.activation_dropout(h, training=training)
h = self.linear_2(h)
h = self.dropout(h, training=training)
return self.layer_norm(hidden + h)
class TFFunnelLayer(tf.keras.layers.Layer):
def __init__(self, config, block_index, **kwargs):
super().__init__(**kwargs)
self.attention = TFFunnelRelMultiheadAttention(config, block_index, name="attention")
self.ffn = TFFunnelPositionwiseFFN(config, name="ffn")
def call(self, query, key, value, attention_inputs, output_attentions=False, training=False):
attn = self.attention(
query, key, value, attention_inputs, output_attentions=output_attentions, training=training
)
output = self.ffn(attn[0], training=training)
return (output, attn[1]) if output_attentions else (output,)
class TFFunnelEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.separate_cls = config.separate_cls
self.pool_q_only = config.pool_q_only
self.block_repeats = config.block_repeats
self.attention_structure = TFFunnelAttentionStructure(config)
self.blocks = [
[TFFunnelLayer(config, block_index, name=f"blocks_._{block_index}_._{i}") for i in range(block_size)]
for block_index, block_size in enumerate(config.block_sizes)
]
def call(
self,
inputs_embeds,
attention_mask=None,
token_type_ids=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
training=False,
):
# The pooling is not implemented on long tensors, so we convert this mask.
# attention_mask = tf.cast(attention_mask, inputs_embeds.dtype)
attention_inputs = self.attention_structure.init_attention_inputs(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
training=training,
)
hidden = inputs_embeds
all_hidden_states = (inputs_embeds,) if output_hidden_states else None
all_attentions = () if output_attentions else None
for block_index, block in enumerate(self.blocks):
pooling_flag = shape_list(hidden)[1] > (2 if self.separate_cls else 1)
pooling_flag = pooling_flag and block_index > 0
pooled_hidden = tf.zeros(shape_list(hidden))
if pooling_flag:
pooled_hidden, attention_inputs = self.attention_structure.pre_attention_pooling(
hidden, attention_inputs
)
for (layer_index, layer) in enumerate(block):
for repeat_index in range(self.block_repeats[block_index]):
do_pooling = (repeat_index == 0) and (layer_index == 0) and pooling_flag
if do_pooling:
query = pooled_hidden
key = value = hidden if self.pool_q_only else pooled_hidden
else:
query = key = value = hidden
layer_output = layer(
query, key, value, attention_inputs, output_attentions=output_attentions, training=training
)
hidden = layer_output[0]
if do_pooling:
attention_inputs = self.attention_structure.post_attention_pooling(attention_inputs)
if output_attentions:
all_attentions = all_attentions + layer_output[1:]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden,)
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
def upsample(x, stride, target_len, separate_cls=True, truncate_seq=False):
"""
Upsample tensor `x` to match `target_len` by repeating the tokens `stride` time on the sequence length dimension.
"""
if stride == 1:
return x
if separate_cls:
cls = x[:, :1]
x = x[:, 1:]
output = tf.repeat(x, repeats=stride, axis=1)
if separate_cls:
if truncate_seq:
output = tf.pad(output, [[0, 0], [0, stride - 1], [0, 0]])
output = output[:, : target_len - 1]
output = tf.concat([cls, output], axis=1)
else:
output = output[:, :target_len]
return output
class TFFunnelDecoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.separate_cls = config.separate_cls
self.truncate_seq = config.truncate_seq
self.stride = 2 ** (len(config.block_sizes) - 1)
self.attention_structure = TFFunnelAttentionStructure(config)
self.layers = [TFFunnelLayer(config, 0, name=f"layers_._{i}") for i in range(config.num_decoder_layers)]
def call(
self,
final_hidden,
first_block_hidden,
attention_mask=None,
token_type_ids=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
training=False,
):
upsampled_hidden = upsample(
final_hidden,
stride=self.stride,
target_len=shape_list(first_block_hidden)[1],
separate_cls=self.separate_cls,
truncate_seq=self.truncate_seq,
)
hidden = upsampled_hidden + first_block_hidden
all_hidden_states = (hidden,) if output_hidden_states else None
all_attentions = () if output_attentions else None
attention_inputs = self.attention_structure.init_attention_inputs(
hidden,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
training=training,
)
for layer in self.layers:
layer_output = layer(
hidden, hidden, hidden, attention_inputs, output_attentions=output_attentions, training=training
)
hidden = layer_output[0]
if output_attentions:
all_attentions = all_attentions + layer_output[1:]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden,)
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
@keras_serializable
class TFFunnelBaseLayer(tf.keras.layers.Layer):
""" Base model without decoder """
config_class = FunnelConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.return_dict = config.use_return_dict
self.embeddings = TFFunnelEmbeddings(config, name="embeddings")
self.encoder = TFFunnelEncoder(config, name="encoder")
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, value):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(input_shape, 1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(input_shape, 0)
if inputs["inputs_embeds"] is None:
inputs["inputs_embeds"] = self.embeddings(inputs["input_ids"], training=inputs["training"])
encoder_outputs = self.encoder(
inputs["inputs_embeds"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
return encoder_outputs
@keras_serializable
class TFFunnelMainLayer(tf.keras.layers.Layer):
""" Base model with decoder """
config_class = FunnelConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.block_sizes = config.block_sizes
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.return_dict = config.use_return_dict
self.embeddings = TFFunnelEmbeddings(config, name="embeddings")
self.encoder = TFFunnelEncoder(config, name="encoder")
self.decoder = TFFunnelDecoder(config, name="decoder")
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, value):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(input_shape, 1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(input_shape, 0)
if inputs["inputs_embeds"] is None:
inputs["inputs_embeds"] = self.embeddings(inputs["input_ids"], training=inputs["training"])
encoder_outputs = self.encoder(
inputs["inputs_embeds"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
output_attentions=inputs["output_attentions"],
output_hidden_states=True,
return_dict=inputs["return_dict"],
training=inputs["training"],
)
decoder_outputs = self.decoder(
final_hidden=encoder_outputs[0],
first_block_hidden=encoder_outputs[1][self.block_sizes[0]],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
if not inputs["return_dict"]:
idx = 0
outputs = (decoder_outputs[0],)
if inputs["output_hidden_states"]:
idx += 1
outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],)
if inputs["output_attentions"]:
idx += 1
outputs = outputs + (encoder_outputs[2] + decoder_outputs[idx],)
return outputs
return TFBaseModelOutput(
last_hidden_state=decoder_outputs[0],
hidden_states=(encoder_outputs.hidden_states + decoder_outputs.hidden_states)
if inputs["output_hidden_states"]
else None,
attentions=(encoder_outputs.attentions + decoder_outputs.attentions)
if inputs["output_attentions"]
else None,
)
class TFFunnelDiscriminatorPredictions(tf.keras.layers.Layer):
"""Prediction module for the discriminator, made up of two dense layers."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.dense = tf.keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="dense")
self.activation_function = get_tf_activation(config.hidden_act)
self.dense_prediction = tf.keras.layers.Dense(1, kernel_initializer=initializer, name="dense_prediction")
def call(self, discriminator_hidden_states):
hidden_states = self.dense(discriminator_hidden_states)
hidden_states = self.activation_function(hidden_states)
logits = tf.squeeze(self.dense_prediction(hidden_states))
return logits
class TFFunnelMaskedLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self):
return self.input_embeddings
def set_output_embeddings(self, value):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self):
return {"bias": self.bias}
def set_bias(self, value):
self.bias = value["bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states, training=False):
seq_length = shape_list(tensor=hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
class TFFunnelClassificationHead(tf.keras.layers.Layer):
def __init__(self, config, n_labels, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.linear_hidden = tf.keras.layers.Dense(
config.d_model, kernel_initializer=initializer, name="linear_hidden"
)
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
self.linear_out = tf.keras.layers.Dense(n_labels, kernel_initializer=initializer, name="linear_out")
def call(self, hidden, training=False):
hidden = self.linear_hidden(hidden)
hidden = tf.keras.activations.tanh(hidden)
hidden = self.dropout(hidden, training=training)
return self.linear_out(hidden)
class TFFunnelPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FunnelConfig
base_model_prefix = "funnel"
@dataclass
class TFFunnelForPreTrainingOutput(ModelOutput):
"""
Output type of :class:`~transformers.FunnelForPreTraining`.
Args:
logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Prediction scores of the head (scores for each token before SoftMax).
hidden_states (:obj:`tuple(tf.ensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
FUNNEL_START_DOCSTRING = r"""
The Funnel Transformer model was proposed in `Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing <https://arxiv.org/abs/2006.03236>`__ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Parameters:
config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
FUNNEL_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.FunnelTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`__
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"""
The base Funnel Transformer Model transformer outputting raw hidden-states without upsampling head (also called
decoder) or any task-specific head on top.
""",
FUNNEL_START_DOCSTRING,
)
class TFFunnelBaseModel(TFFunnelPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelBaseLayer(config, name="funnel")
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
return self.funnel(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
# Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"The bare Funnel Transformer Model transformer outputting raw hidden-states without any specific head on top.",
FUNNEL_START_DOCSTRING,
)
class TFFunnelModel(TFFunnelPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
return self.funnel(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
# Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Funnel model with a binary classification head on top as used during pretraining for identifying generated tokens.
""",
FUNNEL_START_DOCSTRING,
)
class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.discriminator_predictions = TFFunnelDiscriminatorPredictions(config, name="discriminator_predictions")
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFFunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs
):
r"""
Returns:
Examples::
>>> from transformers import FunnelTokenizer, TFFunnelForPreTraining
>>> import torch
>>> tokenizer = TFFunnelTokenizer.from_pretrained('funnel-transformer/small')
>>> model = TFFunnelForPreTraining.from_pretrained('funnel-transformer/small')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors= "tf")
>>> logits = model(inputs).logits
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
discriminator_hidden_states = self.funnel(
inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"],
inputs["inputs_embeds"],
inputs["output_attentions"],
inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.discriminator_predictions(discriminator_sequence_output)
if not inputs["return_dict"]:
return (logits,) + discriminator_hidden_states[1:]
return TFFunnelForPreTrainingOutput(
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
)
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFFunnelForPreTrainingOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings("""Funnel Model with a `language modeling` head on top. """, FUNNEL_START_DOCSTRING)
class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.lm_head = TFFunnelMaskedLMHead(config, self.funnel.embeddings, name="lm_head")
def get_lm_head(self):
return self.lm_head
def get_prefix_bias_name(self):
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.lm_head.name
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.funnel(
inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"],
inputs["inputs_embeds"],
inputs["output_attentions"],
inputs["output_hidden_states"],
return_dict=return_dict,
training=inputs["training"],
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output, training=inputs["training"])
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores)
if not inputs["return_dict"]:
output = (prediction_scores,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Funnel Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
FUNNEL_START_DOCSTRING,
)
class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelBaseLayer(config, name="funnel")
self.classifier = TFFunnelClassificationHead(config, config.num_labels, name="classifier")
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.funnel(
inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"],
inputs["inputs_embeds"],
inputs["output_attentions"],
inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
last_hidden_state = outputs[0]
pooled_output = last_hidden_state[:, 0]
logits = self.classifier(pooled_output, training=inputs["training"])
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Funnel Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
FUNNEL_START_DOCSTRING,
)
class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelBaseLayer(config, name="funnel")
self.classifier = TFFunnelClassificationHead(config, 1, name="classifier")
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
:obj:`input_ids` above)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None:
num_choices = shape_list(inputs["input_ids"])[1]
seq_length = shape_list(inputs["input_ids"])[2]
else:
num_choices = shape_list(inputs["inputs_embeds"])[1]
seq_length = shape_list(inputs["inputs_embeds"])[2]
flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
flat_attention_mask = (
tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
)
flat_token_type_ids = (
tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
)
flat_inputs_embeds = (
tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
if inputs["inputs_embeds"] is not None
else None
)
outputs = self.funnel(
flat_input_ids,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
inputs_embeds=flat_inputs_embeds,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
last_hidden_state = outputs[0]
pooled_output = last_hidden_state[:, 0]
logits = self.classifier(pooled_output, training=inputs["training"])
reshaped_logits = tf.reshape(logits, (-1, num_choices))
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)
if not inputs["return_dict"]:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]):
output = self.call(input_ids=inputs)
return self.serving_output(output=output)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Funnel Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
FUNNEL_START_DOCSTRING,
)
class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.funnel(
inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"],
inputs["inputs_embeds"],
inputs["output_attentions"],
inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=inputs["training"])
logits = self.classifier(sequence_output)
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
if not inputs["return_dict"]:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
Funnel Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
FUNNEL_START_DOCSTRING,
)
class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.qa_outputs = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
start_positions=None,
end_positions=None,
training=False,
**kwargs,
):
r"""
start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
start_positions=start_positions,
end_positions=end_positions,
training=training,
kwargs_call=kwargs,
)
outputs = self.funnel(
inputs["input_ids"],
inputs["attention_mask"],
inputs["token_type_ids"],
inputs["inputs_embeds"],
inputs["output_attentions"],
inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
loss = None
if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
labels = {"start_position": inputs["start_positions"], "end_position": inputs["end_positions"]}
loss = self.compute_loss(labels, (start_logits, end_logits))
if not inputs["return_dict"]:
output = (start_logits, end_logits) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
|
AdaMix/src/transformers/models/funnel/modeling_tf_funnel.py/0
|
{
"file_path": "AdaMix/src/transformers/models/funnel/modeling_tf_funnel.py",
"repo_id": "AdaMix",
"token_count": 34672
}
| 60 |
# coding=utf-8
# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for LED."""
from ...utils import logging
from ..bart.tokenization_bart import BartTokenizer
logger = logging.get_logger(__name__)
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"allenai/led-base-16384": 16384,
}
class LEDTokenizer(BartTokenizer):
"""
Construct a LED tokenizer.
:class:`~transformers.LEDTokenizer` is identical to :class:`~transformers.BartTokenizer` and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BartTokenizer` for usage examples and documentation concerning
parameters.
"""
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
AdaMix/src/transformers/models/led/tokenization_led.py/0
|
{
"file_path": "AdaMix/src/transformers/models/led/tokenization_led.py",
"repo_id": "AdaMix",
"token_count": 666
}
| 61 |
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import torch
from torch import nn
from transformers import M2M100Config, M2M100ForConditionalGeneration
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(k, None)
def make_linear_from_emb(emb):
vocab_size, emb_size = emb.weight.shape
lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
lin_layer.weight.data = emb.weight.data
return lin_layer
def convert_fairseq_m2m100_checkpoint_from_disk(checkpoint_path):
m2m_100 = torch.load(checkpoint_path, map_location="cpu")
args = m2m_100["args"]
state_dict = m2m_100["model"]
remove_ignore_keys_(state_dict)
vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
config = M2M100Config(
vocab_size=vocab_size,
max_position_embeddings=1024,
encoder_layers=args.encoder_layers,
decoder_layers=args.decoder_layers,
encoder_attention_heads=args.encoder_attention_heads,
decoder_attention_heads=args.decoder_attention_heads,
encoder_ffn_dim=args.encoder_ffn_embed_dim,
decoder_ffn_dim=args.decoder_ffn_embed_dim,
d_model=args.encoder_embed_dim,
encoder_layerdrop=args.encoder_layerdrop,
decoder_layerdrop=args.decoder_layerdrop,
dropout=args.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.activation_dropout,
activation_function="relu",
)
state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
model = M2M100ForConditionalGeneration(config)
model.model.load_state_dict(state_dict)
model.lm_head = make_linear_from_emb(model.model.shared)
return model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
args = parser.parse_args()
model = convert_fairseq_m2m100_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
|
AdaMix/src/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py/0
|
{
"file_path": "AdaMix/src/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py",
"repo_id": "AdaMix",
"token_count": 1203
}
| 62 |
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MMBT configuration """
from ...utils import logging
logger = logging.get_logger(__name__)
class MMBTConfig(object):
"""
This is the configuration class to store the configuration of a :class:`~transformers.MMBTModel`. It is used to
instantiate a MMBT model according to the specified arguments, defining the model architecture.
Args:
config (:class:`~transformers.PreTrainedConfig`):
Config of the underlying Transformer models. Its values are copied over to use a single config.
num_labels (:obj:`int`, `optional`):
Size of final Linear layer for classification.
modal_hidden_size (:obj:`int`, `optional`, defaults to 2048):
Embedding dimension of the non-text modality encoder.
"""
def __init__(self, config, num_labels=None, modal_hidden_size=2048):
self.__dict__ = config.__dict__
self.modal_hidden_size = modal_hidden_size
if num_labels:
self.num_labels = num_labels
|
AdaMix/src/transformers/models/mmbt/configuration_mmbt.py/0
|
{
"file_path": "AdaMix/src/transformers/models/mmbt/configuration_mmbt.py",
"repo_id": "AdaMix",
"token_count": 548
}
| 63 |
# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for RoBERTa."""
from typing import List, Optional
from ...tokenization_utils import AddedToken
from ...utils import logging
from ..gpt2.tokenization_gpt2 import GPT2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json",
},
"merges_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"roberta-base": 512,
"roberta-large": 512,
"roberta-large-mnli": 512,
"distilroberta-base": 512,
"roberta-base-openai-detector": 512,
"roberta-large-openai-detector": 512,
}
class RobertaTokenizer(GPT2Tokenizer):
"""
Constructs a RoBERTa tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
::
>>> from transformers import RobertaTokenizer
>>> tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
>>> tokenizer("Hello world")['input_ids']
[0, 31414, 232, 328, 2]
>>> tokenizer(" Hello world")['input_ids']
[0, 20920, 232, 2]
You can get around that behavior by passing ``add_prefix_space=True`` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
.. note::
When used with ``is_split_into_words=True``, this tokenizer will add a space before each word (even the first
one).
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
methods. Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
merges_file (:obj:`str`):
Path to the merges file.
errors (:obj:`str`, `optional`, defaults to :obj:`"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See `bytes.decode
<https://docs.python.org/3/library/stdtypes.html#bytes.decode>`__ for more information.
bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the :obj:`cls_token`.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end of
sequence. The token used is the :obj:`sep_token`.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoBERTa sequence has the following format:
- single sequence: ``<s> X </s>``
- pair of sequences: ``<s> A </s></s> B </s>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kwargs)
|
AdaMix/src/transformers/models/roberta/tokenization_roberta.py/0
|
{
"file_path": "AdaMix/src/transformers/models/roberta/tokenization_roberta.py",
"repo_id": "AdaMix",
"token_count": 5074
}
| 64 |
# coding=utf-8
# Copyright 2020 T5 Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 T5 model. """
import copy
import itertools
import math
import warnings
from typing import Tuple
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
DUMMY_INPUTS,
DUMMY_MASK,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPast,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFPreTrainedModel,
TFSharedEmbeddings,
TFWrappedEmbeddings,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_t5 import T5Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "T5Config"
_TOKENIZER_FOR_DOC = "T5Tokenizer"
TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = [
"t5-small",
"t5-base",
"t5-large",
"t5-3b",
"t5-11b",
# See all T5 models at https://huggingface.co/models?filter=t5
]
####################################################
# TF 2.0 Models are constructed using Keras imperative API by sub-classing
# - tf.keras.layers.Layer for the layers and
# - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
####################################################
class TFT5LayerNorm(tf.keras.layers.Layer):
def __init__(self, epsilon=1e-6, **kwargs):
"""
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
"""
super().__init__(**kwargs)
self.variance_epsilon = epsilon
def build(self, input_shape):
"""Build shared word embedding layer """
self.weight = self.add_weight("weight", shape=(input_shape[-1],), initializer="ones")
super().build(input_shape)
def call(self, hidden_states):
variance = tf.math.reduce_mean(tf.math.square(hidden_states), axis=-1, keepdims=True)
hidden_states = hidden_states * tf.math.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states
class TFT5DenseReluDense(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.wi = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi")
self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name="wo")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
self.act = tf.keras.activations.relu
def call(self, hidden_states, training=False):
hidden_states = self.wi(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.wo(hidden_states)
return hidden_states
class TFT5GatedGeluDense(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.wi_0 = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi_0")
self.wi_1 = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi_1")
self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name="wo")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
self.act = get_tf_activation("gelu_new")
def call(self, hidden_states, training=False):
hidden_gelu = self.act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.wo(hidden_states)
return hidden_states
class TFT5LayerFF(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.feed_forward_proj == "relu":
self.DenseReluDense = TFT5DenseReluDense(config, name="DenseReluDense")
elif config.feed_forward_proj == "gated-gelu":
self.DenseReluDense = TFT5GatedGeluDense(config, name="DenseReluDense")
else:
raise ValueError(
f"{self.config.feed_forward_proj} is not supported. Choose between `relu` and `gated-gelu`"
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def call(self, hidden_states, training=False):
normed_hidden_states = self.layer_norm(hidden_states)
dense_output = self.DenseReluDense(normed_hidden_states, training=training)
hidden_states = hidden_states + self.dropout(dense_output, training=training)
return hidden_states
class TFT5Attention(tf.keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.layer_id = next(TFT5Attention.NEW_ID)
self.is_decoder = config.is_decoder
self.use_cache = config.use_cache
self.has_relative_attention_bias = has_relative_attention_bias
self.output_attentions = config.output_attentions
self.relative_attention_num_buckets = config.relative_attention_num_buckets
self.d_model = config.d_model
self.key_value_proj_dim = config.d_kv
self.n_heads = config.num_heads
self.inner_dim = self.n_heads * self.key_value_proj_dim
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="q")
self.k = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="k")
self.v = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="v")
self.o = tf.keras.layers.Dense(self.d_model, use_bias=False, name="o")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
self.pruned_heads = set()
def build(self, input_shape):
if self.has_relative_attention_bias:
with tf.name_scope("relative_attention_bias"):
self.relative_attention_bias = self.add_weight(
name="embeddings",
shape=[self.relative_attention_num_buckets, self.n_heads],
)
return super().build(input_shape)
def prune_heads(self, heads):
raise NotImplementedError
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
# n = -relative_position
if bidirectional:
num_buckets //= 2
relative_buckets += (
tf.cast(tf.math.greater(relative_position, 0), dtype=relative_position.dtype) * num_buckets
)
relative_position = tf.math.abs(relative_position)
else:
relative_position = -tf.math.minimum(relative_position, 0)
# now n is in the range [0, inf)
max_exact = num_buckets // 2
is_small = tf.math.less(relative_position, max_exact)
relative_position_if_large = max_exact + tf.cast(
tf.math.log(relative_position / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact),
dtype=relative_position.dtype,
)
relative_position_if_large = tf.math.minimum(relative_position_if_large, num_buckets - 1)
relative_buckets += tf.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length):
""" Compute binned relative position bias """
context_position = tf.range(query_length)[:, None]
memory_position = tf.range(key_length)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position,
bidirectional=(not self.is_decoder),
num_buckets=self.relative_attention_num_buckets,
)
values = tf.gather(
self.relative_attention_bias, relative_position_bucket
) # shape (query_length, key_length, num_heads)
values = tf.expand_dims(
tf.transpose(values, [2, 0, 1]), axis=0
) # shape (1, num_heads, query_length, key_length)
return values
def call(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
training=False,
output_attentions=False,
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
# Input is (batch_size, query_length, dim)
# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
batch_size, seq_length = shape_list(hidden_states)[:2]
real_seq_length = seq_length
if past_key_value is not None:
assert (
len(past_key_value) == 2
), "past_key_value should have 2 past states: keys and values. Got {} past states".format(
len(past_key_value)
)
real_seq_length += shape_list(past_key_value[0])[2] if query_length is None else query_length
key_length = real_seq_length if key_value_states is None else shape_list(key_value_states)[1]
def shape(hidden_states):
""" projection """
return tf.transpose(
tf.reshape(hidden_states, (batch_size, -1, self.n_heads, self.key_value_proj_dim)), perm=(0, 2, 1, 3)
)
def unshape(hidden_states):
""" compute context """
return tf.reshape(tf.transpose(hidden_states, perm=(0, 2, 1, 3)), (batch_size, -1, self.inner_dim))
def project(hidden_states, proj_layer, key_value_states, past_key_value):
""" projects hidden states correctly to key/query states """
if key_value_states is None:
# self-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(hidden_states))
elif past_key_value is None:
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
if past_key_value is not None:
if key_value_states is None:
# self-attn
# (batch_size, n_heads, key_length, dim_per_head)
hidden_states = tf.concat([past_key_value, hidden_states], axis=2)
else:
# cross-attn
hidden_states = past_key_value
return hidden_states
# get query
query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, query_length, dim_per_head)
# get key/value
key_states = project(
hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
)
value_states = project(
hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
)
# to cope with keras serialization
if self.is_decoder and use_cache:
present_key_value_state = (key_states, value_states)
else:
present_key_value_state = None
scores = tf.einsum(
"bnqd,bnkd->bnqk", query_states, key_states
) # (batch_size, n_heads, query_length, key_length)
if position_bias is None:
if not self.has_relative_attention_bias:
position_bias = tf.zeros((1, self.n_heads, real_seq_length, key_length))
else:
position_bias = self.compute_bias(real_seq_length, key_length)
# if key and values are already calculated
# we want only the last query position bias
if past_key_value is not None:
position_bias = position_bias[:, :, -seq_length:, :]
if mask is not None:
position_bias = tf.cast(position_bias, dtype=mask.dtype)
position_bias = position_bias + mask # (batch_size, n_heads, query_length, key_length)
scores += position_bias
weights = tf.nn.softmax(scores, axis=-1) # (batch_size, n_heads, query_length, key_length)
weights = self.dropout(weights, training=training) # (batch_size, n_heads, query_length, key_length)
# Mask heads if we want to
if layer_head_mask is not None:
tf.debugging.assert_equal(
shape_list(layer_head_mask),
[self.n_heads],
message=f"Head mask for a single layer should be of size {(self.n_heads)}, but is {shape_list(layer_head_mask)}",
)
weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * weights
attn_output = tf.matmul(weights, value_states) # (batch_size, n_heads, query_length, dim_per_head)
attn_output = self.o(unshape(attn_output))
outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
if output_attentions:
outputs = outputs + (weights,)
return outputs
class TFT5LayerSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.SelfAttention = TFT5Attention(
config,
has_relative_attention_bias=has_relative_attention_bias,
name="SelfAttention",
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def call(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
class TFT5LayerCrossAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.EncDecAttention = TFT5Attention(
config,
has_relative_attention_bias=False,
name="EncDecAttention",
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def call(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
query_length=None,
use_cache=False,
output_attentions=False,
training=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
class TFT5Block(tf.keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.is_decoder = config.is_decoder
self.layer = []
self.layer.append(
TFT5LayerSelfAttention(
config,
has_relative_attention_bias=has_relative_attention_bias,
name="layer_._0",
)
)
if self.is_decoder:
self.layer.append(
TFT5LayerCrossAttention(
config,
name="layer_._1",
)
)
self.layer.append(TFT5LayerFF(config, name="layer_._{}".format(len(self.layer))))
def call(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
encoder_layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
):
if past_key_value is not None:
assert self.is_decoder, "Only decoder can use `past_key_values`"
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
expected_num_past_key_values,
"2 (past / key) for cross attention" if expected_num_past_key_values == 4 else "",
len(past_key_value),
)
assert len(past_key_value) == expected_num_past_key_values, error_message
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value, cross_attn_past_key_value = None, None
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states, present_key_value_state = self_attention_outputs[:2]
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights
if self.is_decoder and encoder_hidden_states is not None:
# the actual query length is unknown for cross attention
# if using past key value states. Need to inject it here
if present_key_value_state is not None:
query_length = shape_list(present_key_value_state[0])[2]
else:
query_length = None
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
layer_head_mask=encoder_layer_head_mask,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = cross_attention_outputs[0]
# Combine self attn and cross attn key value states
if present_key_value_state is not None:
present_key_value_state = present_key_value_state + cross_attention_outputs[1]
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[2:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states, training=training)
outputs = (hidden_states,)
# Add attentions if we output them
outputs = outputs + (present_key_value_state,) + attention_outputs
return outputs # hidden-states, present_key_value_states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
####################################################
# The full model without a specific pretrained or finetuning head is
# provided as a tf.keras.layers.Layer usually called "TFT5MainLayer"
####################################################
@keras_serializable
class TFT5MainLayer(tf.keras.layers.Layer):
config_class = T5Config
def __init__(self, config, embed_tokens=None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.use_cache = config.use_cache
self.embed_tokens = embed_tokens
self.is_decoder = config.is_decoder
self.config = config
self.num_hidden_layers = config.num_layers
self.block = [
TFT5Block(
config,
has_relative_attention_bias=bool(i == 0),
name="block_._{}".format(i),
)
for i in range(config.num_layers)
]
self.final_layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="final_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def _prune_heads(self, heads_to_prune):
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
def call(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
head_mask=None,
encoder_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
) -> Tuple:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
encoder_head_mask=encoder_head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time"
)
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
inputs["input_ids"] = tf.reshape(inputs["input_ids"], (-1, input_shape[-1]))
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
if inputs["inputs_embeds"] is None:
assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"])
batch_size, seq_length = input_shape
# required mask seq length can be calculated via length of past
mask_seq_length = (
shape_list(inputs["past_key_values"][0][0])[2] + seq_length
if inputs["past_key_values"] is not None
else seq_length
)
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill((batch_size, mask_seq_length), 1)
if (
self.is_decoder
and inputs["encoder_attention_mask"] is None
and inputs["encoder_hidden_states"] is not None
):
encoder_seq_length = shape_list(inputs["encoder_hidden_states"])[1]
inputs["encoder_attention_mask"] = tf.fill((batch_size, encoder_seq_length), 1)
# initialize past_key_values with `None` if past does not exist
if inputs["past_key_values"] is None:
inputs["past_key_values"] = [None] * len(self.block)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=inputs["inputs_embeds"].dtype)
num_dims_attention_mask = len(shape_list(inputs["attention_mask"]))
if num_dims_attention_mask == 3:
extended_attention_mask = inputs["attention_mask"][:, None, :, :]
elif num_dims_attention_mask == 2:
# Provided a padding mask of dimensions [batch_size, mask_seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
if self.is_decoder:
seq_ids = tf.range(mask_seq_length)
causal_mask = tf.less_equal(
tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
seq_ids[None, :, None],
)
causal_mask = tf.cast(causal_mask, dtype=inputs["attention_mask"].dtype)
extended_attention_mask = causal_mask[:, None, :, :] * inputs["attention_mask"][:, None, None, :]
if inputs["past_key_values"][0] is not None:
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = inputs["attention_mask"][:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -1e9 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# extended_attention_mask = tf.math.equal(extended_attention_mask,
# tf.transpose(extended_attention_mask, perm=(-1, -2)))
extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
if self.is_decoder and inputs["encoder_attention_mask"] is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
inputs["encoder_attention_mask"] = tf.cast(
inputs["encoder_attention_mask"], dtype=extended_attention_mask.dtype
)
num_dims_encoder_attention_mask = len(shape_list(inputs["encoder_attention_mask"]))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
else:
encoder_extended_attention_mask = None
present_key_value_states = () if inputs["use_cache"] and self.is_decoder else None
all_hidden_states = () if inputs["output_hidden_states"] else None
all_attentions = () if inputs["output_attentions"] else None
position_bias = None
encoder_decoder_position_bias = None
hidden_states = self.dropout(inputs["inputs_embeds"], training=inputs["training"])
for idx, (layer_module, past_key_value) in enumerate(zip(self.block, inputs["past_key_values"])):
if inputs["output_hidden_states"]:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_bias=position_bias,
encoder_hidden_states=inputs["encoder_hidden_states"],
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
layer_head_mask=inputs["head_mask"][idx] if inputs["head_mask"] is not None else None,
encoder_layer_head_mask=inputs["encoder_head_mask"][idx]
if inputs["encoder_head_mask"] is not None
else None,
past_key_value=past_key_value,
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
training=inputs["training"],
)
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
hidden_states, present_key_value_state = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, past_key_values, (self-attention weights),
# (self-attention position bias), (cross-attention position bias), (cross-attention weights),
position_bias = layer_outputs[2]
if self.is_decoder and inputs["encoder_hidden_states"] is not None:
encoder_decoder_position_bias = layer_outputs[4 if inputs["output_attentions"] else 3]
# append next layer key value states
if present_key_value_state is not None and inputs["use_cache"] and self.is_decoder:
present_key_value_states = present_key_value_states + (present_key_value_state,)
if inputs["output_attentions"]:
all_attentions = all_attentions + (layer_outputs[3],)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states, training=inputs["training"])
# Add last layer
if inputs["output_hidden_states"]:
all_hidden_states = all_hidden_states + (hidden_states,)
if not inputs["return_dict"]:
outputs = (hidden_states,)
# need to check if is decoder here as well for special cases when using keras compile
if inputs["use_cache"] and self.is_decoder:
outputs = outputs + (present_key_value_states,)
if inputs["output_hidden_states"]:
outputs = outputs + (all_hidden_states,)
if inputs["output_attentions"]:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
if self.is_decoder:
return TFBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
else:
return TFBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
####################################################
# TFT5PreTrainedModel is a sub-class of tf.keras.Model
# which take care of loading and saving pretrained weights
# and various common utilities.
# Here you just need to specify a few (self-explanatory)
# pointers for your model.
####################################################
class TFT5PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = T5Config
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"decoder\Wblock[\W_0]+layer[\W_1]+EncDecAttention\Wrelative_attention_bias"]
@property
def dummy_inputs(self):
inputs = tf.constant(DUMMY_INPUTS)
input_mask = tf.constant(DUMMY_MASK)
dummy_inputs = {
"input_ids": inputs,
"decoder_input_ids": inputs,
"decoder_attention_mask": input_mask,
}
return dummy_inputs
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
try:
self.shared.weight = value
except AttributeError:
self(self.dummy_inputs)
self.shared.weight = value
self.shared.vocab_size = shape_list(value)[0]
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
self.encoder.embed_tokens = embed_tokens
if hasattr(self, "decoder"):
self.decoder.embed_tokens = embed_tokens
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
assert (
decoder_start_token_id is not None
), "self.model.config.decoder_start_token_id has to be defined. In TF T5 it is usually set to the pad_token_id. See T5 docs for more information"
shifted_input_ids = tf.roll(input_ids, 1, axis=-1)
start_tokens = tf.fill((shape_list(shifted_input_ids)[0], 1), decoder_start_token_id)
shifted_input_ids = tf.concat([start_tokens, shifted_input_ids[:, 1:]], -1)
assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
T5_START_DOCSTRING = r"""
The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
<https://arxiv.org/abs/1910.10683>`__ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a text-to-text
denoising generative setting.
This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Parameters:
config (:class:`~transformers.T5Config`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
T5_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
should be able to pad the inputs on the right or the left.
Indices can be obtained using :class:`~transformers.BertTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
To know more on how to prepare :obj:`inputs` for pretraining take a look at `T5 Training
<./t5.html#training>`__.
decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for sequence to sequence training. T5 uses the :obj:`pad_token_id` as the starting token for
:obj:`decoder_input_ids` generation. If :obj:`past_key_values` is used, optionally only the last
:obj:`decoder_input_ids` have to be input (see :obj:`past_key_values`).
To know more on how to prepare :obj:`decoder_input_ids` for pretraining take a look at `T5 Training
<./t5.html#training>`__. If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset,
:obj:`decoder_input_ids` takes the value of :obj:`input_ids`.
attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in ``[0,
1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in ``[0,
1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (:obj:`tuple(tuple(tf.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`:
`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a
sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of
the decoder.
past_key_values (:obj:`tuple(tuple(tf.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
:obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
takes the value of :obj:`inputs_embeds`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
T5_ENCODER_INPUTS_DOCSTRING = r"""
Args:
inputs (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
should be able to pad the inputs on the right or the left.
Indices can be obtained using :class:`~transformers.T5Tokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
To know more on how to prepare :obj:`inputs` for pre-training take a look at `T5 Training
<./t5.html#training>`__.
attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
_HEAD_MASK_WARNING_MSG = """
The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = tf.ones((num_layers,
num_heads))`.
"""
@add_start_docstrings(
"The bare T5 Model transformer outputting raw hidden-states" "without any specific head on top.",
T5_START_DOCSTRING,
)
class TFT5Model(TFT5PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
Returns:
Examples::
>>> from transformers import T5Tokenizer, TFT5Model
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5Model.from_pretrained('t5-small')
>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids # Batch size 1
>>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
"""
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
warnings.warn(_HEAD_MASK_WARNING_MSG, FutureWarning)
decoder_head_mask = head_mask
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
# Encode if needed (training, first prediction pass)
if inputs["encoder_outputs"] is None:
inputs["encoder_outputs"] = self.encoder(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=inputs["inputs_embeds"],
head_mask=inputs["head_mask"],
past_key_values=None,
use_cache=False,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
hidden_states = inputs["encoder_outputs"][0]
# Decode
decoder_outputs = self.decoder(
inputs["decoder_input_ids"],
attention_mask=inputs["decoder_attention_mask"],
encoder_hidden_states=hidden_states,
encoder_attention_mask=inputs["attention_mask"],
inputs_embeds=inputs["decoder_inputs_embeds"],
head_mask=inputs["decoder_head_mask"],
encoder_head_mask=inputs["head_mask"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
if not inputs["return_dict"]:
past = (inputs["encoder_outputs"], decoder_outputs[1]) if inputs["use_cache"] else None
if past is not None:
decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
return decoder_outputs + inputs["encoder_outputs"]
past = (inputs["encoder_outputs"].to_tuple(), decoder_outputs[1]) if inputs["use_cache"] else None
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=past,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
encoder_attentions=inputs["encoder_outputs"].attentions,
)
def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
@add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING)
class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model_dim = config.d_model
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
if not config.tie_word_embeddings:
self.lm_head = tf.keras.layers.Dense(config.vocab_size, use_bias=False, name="lm_head")
def get_output_embeddings(self):
if self.config.tie_word_embeddings:
return self.get_input_embeddings()
else:
# in a dense layer the kernel has a shape (last_dim, units), for us (dim, num_tokens)
# value has a shape (num_tokens, dim) then needs to be transposed
return tf.transpose(self.lm_head.kernel)
def set_output_embeddings(self, value):
if self.config.tie_word_embeddings:
self.set_input_embeddings(value)
else:
self.lm_head = tf.keras.layers.Dense(shape_list(value)[0], use_bias=False, name="lm_head")
# in a dense layer the kernel has a shape (last_dim, units), for us (dim, num_tokens)
# value has a shape (num_tokens, dim) then needs to be transposed
transposed_value = tf.transpose(value)
self.lm_head.kernel = transposed_value
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
config.vocab_size - 1]``.
Returns:
Examples::
>>> from transformers import T5Tokenizer, TFT5ForConditionalGeneration
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
>>> inputs = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='tf').input_ids
>>> labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='tf').input_ids
>>> outputs = model(inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
>>> inputs = tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="tf").input_ids # Batch size 1
>>> result = model.generate(inputs)
"""
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
warnings.warn(_HEAD_MASK_WARNING_MSG, FutureWarning)
decoder_head_mask = head_mask
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
# Encode if needed (training, first prediction pass)
if inputs["encoder_outputs"] is None:
inputs["encoder_outputs"] = self.encoder(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
inputs_embeds=inputs["inputs_embeds"],
head_mask=inputs["head_mask"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
hidden_states = inputs["encoder_outputs"][0]
if (
inputs["labels"] is not None
and inputs["decoder_input_ids"] is None
and inputs["decoder_inputs_embeds"] is None
):
# get decoder inputs from shifting lm labels to the right
inputs["decoder_input_ids"] = self._shift_right(inputs["labels"])
# Decode
decoder_outputs = self.decoder(
inputs["decoder_input_ids"],
attention_mask=inputs["decoder_attention_mask"],
encoder_hidden_states=hidden_states,
encoder_attention_mask=inputs["attention_mask"],
inputs_embeds=inputs["decoder_inputs_embeds"],
head_mask=inputs["decoder_head_mask"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = decoder_outputs[0]
# T5v1.1 does not tie output word embeddings and thus does not require downscaling
if self.config.tie_word_embeddings:
sequence_output = sequence_output * (self.model_dim ** -0.5)
logits = self.shared(sequence_output, mode="linear")
else:
logits = self.lm_head(sequence_output)
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
if not inputs["return_dict"]:
past = (inputs["encoder_outputs"], decoder_outputs[1]) if inputs["use_cache"] else None
if past is not None:
decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
output = (logits,) + decoder_outputs[1:] + inputs["encoder_outputs"]
return ((loss,) + output) if loss is not None else output
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif isinstance(inputs["encoder_outputs"], tuple):
last_hidden_state = inputs["encoder_outputs"][0]
hidden_states = None
attentions = None
idx = 0
if inputs["output_hidden_states"]:
idx += 1
hidden_states = inputs["encoder_outputs"][idx]
if inputs["output_attentions"]:
idx += 1
attentions = inputs["encoder_outputs"][idx]
inputs["encoder_outputs"] = TFBaseModelOutput(
last_hidden_state=last_hidden_state,
hidden_states=hidden_states,
attentions=attentions,
)
past = (inputs["encoder_outputs"].to_tuple(), decoder_outputs[1]) if inputs["use_cache"] else None
return TFSeq2SeqLMOutput(
loss=loss,
logits=logits,
past_key_values=past,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
encoder_attentions=inputs["encoder_outputs"].attentions,
)
def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
def prepare_inputs_for_generation(self, inputs, past, attention_mask, use_cache, **kwargs):
assert past is not None, "past has to be defined for encoder_outputs"
# first step
if len(past) < 2:
encoder_outputs, past_key_values = past, None
else:
encoder_outputs, past_key_values = past[0], past[1]
# cut decoder_input_ids if past is used
if past_key_values is not None:
inputs = inputs[:, -1:]
return {
"input_ids": None, # inputs don't have to be defined, but still need to be passed to make Keras.layer.__call__ happy
"decoder_input_ids": inputs, # inputs are the decoder_input_ids
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"use_cache": use_cache,
}
def _reorder_cache(self, past, beam_idx) -> Tuple:
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if len(past) < 2:
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
return past
decoder_past = past[1]
past = (past[0],)
reordered_decoder_past = ()
for layer_past_states in decoder_past:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (tf.gather(layer_past_state, beam_idx),)
assert shape_list(reordered_layer_past_states[0]) == shape_list(layer_past_states[0])
assert len(reordered_layer_past_states) == len(layer_past_states)
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
return past + (reordered_decoder_past,)
@add_start_docstrings(
"The bare T5 Model transformer outputting encoder's raw hidden-states" "without any specific head on top.",
T5_START_DOCSTRING,
)
class TFT5EncoderModel(TFT5PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
def get_encoder(self):
return self.encoder
@add_start_docstrings_to_model_forward(T5_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
Returns:
Examples::
>>> from transformers import T5Tokenizer, TFT5Model
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5EncoderModel.from_pretrained('t5-small')
>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids # Batch size 1
>>> outputs = model(input_ids)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
encoder_outputs = self.encoder(
input_ids,
attention_mask=inputs["attention_mask"],
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=inputs["inputs_embeds"],
head_mask=head_mask,
past_key_values=None,
use_cache=False,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
if not inputs["return_dict"]:
return encoder_outputs
return TFBaseModelOutput(
last_hidden_state=encoder_outputs.last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
# Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
|
AdaMix/src/transformers/models/t5/modeling_tf_t5.py/0
|
{
"file_path": "AdaMix/src/transformers/models/t5/modeling_tf_t5.py",
"repo_id": "AdaMix",
"token_count": 32306
}
| 65 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Feature extractor class for Wav2Vec2
"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...file_utils import PaddingStrategy, TensorType
from ...utils import logging
logger = logging.get_logger(__name__)
class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Wav2Vec2 feature extractor.
This feature extractor inherits from
:class:`~transformers.feature_extraction_sequence_utils.SequenceFeatureExtractor` which contains most of the main
methods. Users should refer to this superclass for more information regarding those methods.
Args:
feature_size (:obj:`int`, defaults to 1):
The feature dimension of the extracted features.
sampling_rate (:obj:`int`, defaults to 16000):
The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
padding_value (:obj:`float`, defaults to 0.0):
The value that is used to fill the padding values.
do_normalize (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
improve the performance for some models, *e.g.*, `wav2vec2-lv60
<https://huggingface.co/models?search=lv60>`__.
return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not :meth:`~transformers.Wav2Vec2FeatureExtractor.__call__` should return :obj:`attention_mask`.
.. note::
Wav2Vec2 models that have set ``config.feat_extract_norm == "group"``, such as `wav2vec2-base
<https://huggingface.co/facebook/wav2vec2-base-960h>`__, have **not** been trained using
:obj:`attention_mask`. For such models, :obj:`input_values` should simply be padded with 0 and no
:obj:`attention_mask` should be passed.
For Wav2Vec2 models that have set ``config.feat_extract_norm == "layer"``, such as `wav2vec2-lv60
<https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self>`__, :obj:`attention_mask` should be
passed for batched inference.
"""
model_input_names = ["input_values", "attention_mask"]
def __init__(
self,
feature_size=1,
sampling_rate=16000,
padding_value=0.0,
return_attention_mask=False,
do_normalize=True,
**kwargs
):
super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
@staticmethod
def zero_mean_unit_var_norm(input_values: List[np.ndarray]) -> List[np.ndarray]:
"""
Every array in the list is normalized to have zero mean and unit variance
"""
return [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in input_values]
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
padding: Union[bool, str, PaddingStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
sampling_rate: Optional[int] = None,
**kwargs
) -> BatchFeature:
"""
Main method to featurize and prepare for the model one or several sequence(s). sequences.
Args:
raw_speech (:obj:`np.ndarray`, :obj:`List[float]`, :obj:`List[np.ndarray]`, :obj:`List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
values, a list of numpy arrays or a list of list of float values.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask (:obj:`bool`, `optional`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor's default.
`What are attention masks? <../glossary.html#attention-mask>`__
.. note::
Wav2Vec2 models that have set ``config.feat_extract_norm == "group"``, such as `wav2vec2-base
<https://huggingface.co/facebook/wav2vec2-base-960h>`__, have **not** been trained using
:obj:`attention_mask`. For such models, :obj:`input_values` should simply be padded with 0 and no
:obj:`attention_mask` should be passed.
For Wav2Vec2 models that have set ``config.feat_extract_norm == "layer"``, such as `wav2vec2-lv60
<https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self>`__, :obj:`attention_mask` should be
passed for batched inference.
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
sampling_rate (:obj:`int`, `optional`):
The sampling rate at which the ``raw_speech`` input was sampled. It is strongly recommended to pass
``sampling_rate`` at the forward call to prevent silent errors.
padding_value (:obj:`float`, defaults to 0.0):
"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of {self.sampling_rate}."
f"Please make sure that the provided `raw_speech` input was sampled with {self.sampling_rate} and not {sampling_rate}."
)
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function."
"Failing to do so can result in silent errors that might be hard to debug."
)
is_batched = bool(
isinstance(raw_speech, (list, tuple))
and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
)
# make sure input is in list format
if is_batched and not isinstance(raw_speech[0], np.ndarray):
raw_speech = [np.asarray(speech) for speech in raw_speech]
elif not is_batched and not isinstance(raw_speech, np.ndarray):
raw_speech = np.asarray(raw_speech)
# always return batch
if not is_batched:
raw_speech = [raw_speech]
# zero-mean and unit-variance normalization
if self.do_normalize:
raw_speech = self.zero_mean_unit_var_norm(raw_speech)
# convert into correct format for padding
encoded_inputs = BatchFeature({"input_values": raw_speech})
padded_inputs = self.pad(
encoded_inputs,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_tensors=return_tensors,
)
return padded_inputs
|
AdaMix/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py/0
|
{
"file_path": "AdaMix/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py",
"repo_id": "AdaMix",
"token_count": 3962
}
| 66 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import csv
import json
import os
import pickle
import sys
from abc import ABC, abstractmethod
from contextlib import contextmanager
from os.path import abspath, exists
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from ..file_utils import add_end_docstrings, is_tf_available, is_torch_available
from ..modelcard import ModelCard
from ..tokenization_utils import PreTrainedTokenizer, TruncationStrategy
from ..utils import logging
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TFAutoModel
if is_torch_available():
import torch
from ..models.auto.modeling_auto import AutoModel
if TYPE_CHECKING:
from ..modeling_tf_utils import TFPreTrainedModel
from ..modeling_utils import PreTrainedModel
logger = logging.get_logger(__name__)
def get_framework(model, revision: Optional[str] = None):
"""
Select framework (TensorFlow or PyTorch) to use.
Args:
model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
If both frameworks are installed, picks the one corresponding to the model passed (either a model class or
the model name). If no specific model is provided, defaults to using PyTorch.
"""
if not is_tf_available() and not is_torch_available():
raise RuntimeError(
"At least one of TensorFlow 2.0 or PyTorch should be installed. "
"To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
"To install PyTorch, read the instructions at https://pytorch.org/."
)
if isinstance(model, str):
if is_torch_available() and not is_tf_available():
model = AutoModel.from_pretrained(model, revision=revision)
elif is_tf_available() and not is_torch_available():
model = TFAutoModel.from_pretrained(model, revision=revision)
else:
try:
model = AutoModel.from_pretrained(model, revision=revision)
except OSError:
model = TFAutoModel.from_pretrained(model, revision=revision)
framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
return framework
def get_default_model(targeted_task: Dict, framework: Optional[str], task_options: Optional[Any]) -> str:
"""
Select a default model to use for a given task. Defaults to pytorch if ambiguous.
Args:
targeted_task (:obj:`Dict` ):
Dictionary representing the given task, that should contain default models
framework (:obj:`str`, None)
"pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet.
task_options (:obj:`Any`, None)
Any further value required by the task to get fully specified, for instance (SRC, TGT) languages for
translation task.
Returns
:obj:`str` The model string representing the default model for this pipeline
"""
if is_torch_available() and not is_tf_available():
framework = "pt"
elif is_tf_available() and not is_torch_available():
framework = "tf"
defaults = targeted_task["default"]
if task_options:
if task_options not in defaults:
raise ValueError("The task does not provide any default models for options {}".format(task_options))
default_models = defaults[task_options]["model"]
elif "model" in defaults:
default_models = targeted_task["default"]["model"]
else:
# XXX This error message needs to be updated to be more generic if more tasks are going to become
# parametrized
raise ValueError('The task defaults can\'t be correctly selected. You probably meant "translation_XX_to_YY"')
if framework is None:
framework = "pt"
return default_models[framework]
class PipelineException(Exception):
"""
Raised by a :class:`~transformers.Pipeline` when handling __call__.
Args:
task (:obj:`str`): The task of the pipeline.
model (:obj:`str`): The model used by the pipeline.
reason (:obj:`str`): The error message to display.
"""
def __init__(self, task: str, model: str, reason: str):
super().__init__(reason)
self.task = task
self.model = model
class ArgumentHandler(ABC):
"""
Base interface for handling arguments for each :class:`~transformers.pipelines.Pipeline`.
"""
@abstractmethod
def __call__(self, *args, **kwargs):
raise NotImplementedError()
class PipelineDataFormat:
"""
Base class for all the pipeline supported data format both for reading and writing. Supported data formats
currently includes:
- JSON
- CSV
- stdin/stdout (pipe)
:obj:`PipelineDataFormat` also includes some utilities to work with multi-columns like mapping from datasets
columns to pipelines keyword arguments through the :obj:`dataset_kwarg_1=dataset_column_1` format.
Args:
output_path (:obj:`str`, `optional`): Where to save the outgoing data.
input_path (:obj:`str`, `optional`): Where to look for the input data.
column (:obj:`str`, `optional`): The column to read.
overwrite (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to overwrite the :obj:`output_path`.
"""
SUPPORTED_FORMATS = ["json", "csv", "pipe"]
def __init__(
self,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite: bool = False,
):
self.output_path = output_path
self.input_path = input_path
self.column = column.split(",") if column is not None else [""]
self.is_multi_columns = len(self.column) > 1
if self.is_multi_columns:
self.column = [tuple(c.split("=")) if "=" in c else (c, c) for c in self.column]
if output_path is not None and not overwrite:
if exists(abspath(self.output_path)):
raise OSError("{} already exists on disk".format(self.output_path))
if input_path is not None:
if not exists(abspath(self.input_path)):
raise OSError("{} doesnt exist on disk".format(self.input_path))
@abstractmethod
def __iter__(self):
raise NotImplementedError()
@abstractmethod
def save(self, data: Union[dict, List[dict]]):
"""
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`.
Args:
data (:obj:`dict` or list of :obj:`dict`): The data to store.
"""
raise NotImplementedError()
def save_binary(self, data: Union[dict, List[dict]]) -> str:
"""
Save the provided data object as a pickle-formatted binary data on the disk.
Args:
data (:obj:`dict` or list of :obj:`dict`): The data to store.
Returns:
:obj:`str`: Path where the data has been saved.
"""
path, _ = os.path.splitext(self.output_path)
binary_path = os.path.extsep.join((path, "pickle"))
with open(binary_path, "wb+") as f_output:
pickle.dump(data, f_output)
return binary_path
@staticmethod
def from_str(
format: str,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
) -> "PipelineDataFormat":
"""
Creates an instance of the right subclass of :class:`~transformers.pipelines.PipelineDataFormat` depending on
:obj:`format`.
Args:
format: (:obj:`str`):
The format of the desired pipeline. Acceptable values are :obj:`"json"`, :obj:`"csv"` or :obj:`"pipe"`.
output_path (:obj:`str`, `optional`):
Where to save the outgoing data.
input_path (:obj:`str`, `optional`):
Where to look for the input data.
column (:obj:`str`, `optional`):
The column to read.
overwrite (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to overwrite the :obj:`output_path`.
Returns:
:class:`~transformers.pipelines.PipelineDataFormat`: The proper data format.
"""
if format == "json":
return JsonPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
elif format == "csv":
return CsvPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
elif format == "pipe":
return PipedPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
else:
raise KeyError("Unknown reader {} (Available reader are json/csv/pipe)".format(format))
class CsvPipelineDataFormat(PipelineDataFormat):
"""
Support for pipelines using CSV data format.
Args:
output_path (:obj:`str`, `optional`): Where to save the outgoing data.
input_path (:obj:`str`, `optional`): Where to look for the input data.
column (:obj:`str`, `optional`): The column to read.
overwrite (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to overwrite the :obj:`output_path`.
"""
def __init__(
self,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
):
super().__init__(output_path, input_path, column, overwrite=overwrite)
def __iter__(self):
with open(self.input_path, "r") as f:
reader = csv.DictReader(f)
for row in reader:
if self.is_multi_columns:
yield {k: row[c] for k, c in self.column}
else:
yield row[self.column[0]]
def save(self, data: List[dict]):
"""
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`.
Args:
data (:obj:`List[dict]`): The data to store.
"""
with open(self.output_path, "w") as f:
if len(data) > 0:
writer = csv.DictWriter(f, list(data[0].keys()))
writer.writeheader()
writer.writerows(data)
class JsonPipelineDataFormat(PipelineDataFormat):
"""
Support for pipelines using JSON file format.
Args:
output_path (:obj:`str`, `optional`): Where to save the outgoing data.
input_path (:obj:`str`, `optional`): Where to look for the input data.
column (:obj:`str`, `optional`): The column to read.
overwrite (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to overwrite the :obj:`output_path`.
"""
def __init__(
self,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
):
super().__init__(output_path, input_path, column, overwrite=overwrite)
with open(input_path, "r") as f:
self._entries = json.load(f)
def __iter__(self):
for entry in self._entries:
if self.is_multi_columns:
yield {k: entry[c] for k, c in self.column}
else:
yield entry[self.column[0]]
def save(self, data: dict):
"""
Save the provided data object in a json file.
Args:
data (:obj:`dict`): The data to store.
"""
with open(self.output_path, "w") as f:
json.dump(data, f)
class PipedPipelineDataFormat(PipelineDataFormat):
"""
Read data from piped input to the python process. For multi columns data, columns should separated by \t
If columns are provided, then the output will be a dictionary with {column_x: value_x}
Args:
output_path (:obj:`str`, `optional`): Where to save the outgoing data.
input_path (:obj:`str`, `optional`): Where to look for the input data.
column (:obj:`str`, `optional`): The column to read.
overwrite (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to overwrite the :obj:`output_path`.
"""
def __iter__(self):
for line in sys.stdin:
# Split for multi-columns
if "\t" in line:
line = line.split("\t")
if self.column:
# Dictionary to map arguments
yield {kwargs: l for (kwargs, _), l in zip(self.column, line)}
else:
yield tuple(line)
# No dictionary to map arguments
else:
yield line
def save(self, data: dict):
"""
Print the data.
Args:
data (:obj:`dict`): The data to store.
"""
print(data)
def save_binary(self, data: Union[dict, List[dict]]) -> str:
if self.output_path is None:
raise KeyError(
"When using piped input on pipeline outputting large object requires an output file path. "
"Please provide such output path through --output argument."
)
return super().save_binary(data)
class _ScikitCompat(ABC):
"""
Interface layer for the Scikit and Keras compatibility.
"""
@abstractmethod
def transform(self, X):
raise NotImplementedError()
@abstractmethod
def predict(self, X):
raise NotImplementedError()
PIPELINE_INIT_ARGS = r"""
Arguments:
model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
TensorFlow.
tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
:class:`~transformers.PreTrainedTokenizer`.
modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`):
Model card attributed to the model for this pipeline.
framework (:obj:`str`, `optional`):
The framework to use, either :obj:`"pt"` for PyTorch or :obj:`"tf"` for TensorFlow. The specified framework
must be installed.
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the :obj:`model`, or to PyTorch if no model
is provided.
task (:obj:`str`, defaults to :obj:`""`):
A task-identifier for the pipeline.
args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`):
Reference to the object in charge of parsing supplied pipeline parameters.
device (:obj:`int`, `optional`, defaults to -1):
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.
binary_output (:obj:`bool`, `optional`, defaults to :obj:`False`):
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.
"""
@add_end_docstrings(PIPELINE_INIT_ARGS)
class Pipeline(_ScikitCompat):
"""
The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across
different pipelines.
Base class implementing pipelined operations. Pipeline workflow is defined as a sequence of the following
operations:
Input -> Tokenization -> Model Inference -> Post-Processing (task dependent) -> Output
Pipeline supports running on CPU or GPU through the device argument (see below).
Some pipeline, like for instance :class:`~transformers.FeatureExtractionPipeline` (:obj:`'feature-extraction'` )
output large tensor object as nested-lists. In order to avoid dumping such large structure as textual data we
provide the :obj:`binary_output` constructor argument. If set to :obj:`True`, the output will be stored in the
pickle format.
"""
default_input_names = None
def __init__(
self,
model: Union["PreTrainedModel", "TFPreTrainedModel"],
tokenizer: PreTrainedTokenizer,
modelcard: Optional[ModelCard] = None,
framework: Optional[str] = None,
task: str = "",
args_parser: ArgumentHandler = None,
device: int = -1,
binary_output: bool = False,
):
if framework is None:
framework = get_framework(model)
self.task = task
self.model = model
self.tokenizer = tokenizer
self.modelcard = modelcard
self.framework = framework
self.device = device if framework == "tf" else torch.device("cpu" if device < 0 else "cuda:{}".format(device))
self.binary_output = binary_output
# Special handling
if self.framework == "pt" and self.device.type == "cuda":
self.model = self.model.to(self.device)
# Update config with task specific parameters
task_specific_params = self.model.config.task_specific_params
if task_specific_params is not None and task in task_specific_params:
self.model.config.update(task_specific_params.get(task))
def save_pretrained(self, save_directory: str):
"""
Save the pipeline's model and tokenizer.
Args:
save_directory (:obj:`str`):
A path to the directory where to saved. It will be created if it doesn't exist.
"""
if os.path.isfile(save_directory):
logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
return
os.makedirs(save_directory, exist_ok=True)
self.model.save_pretrained(save_directory)
self.tokenizer.save_pretrained(save_directory)
if self.modelcard is not None:
self.modelcard.save_pretrained(save_directory)
def transform(self, X):
"""
Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
"""
return self(X=X)
def predict(self, X):
"""
Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
"""
return self(X=X)
@contextmanager
def device_placement(self):
"""
Context Manager allowing tensor allocation on the user-specified device in framework agnostic way.
Returns:
Context manager
Examples::
# Explicitly ask for tensor allocation on CUDA device :0
pipe = pipeline(..., device=0)
with pipe.device_placement():
# Every framework specific tensor allocation will be done on the request device
output = pipe(...)
"""
if self.framework == "tf":
with tf.device("/CPU:0" if self.device == -1 else "/device:GPU:{}".format(self.device)):
yield
else:
if self.device.type == "cuda":
torch.cuda.set_device(self.device)
yield
def ensure_tensor_on_device(self, **inputs):
"""
Ensure PyTorch tensors are on the specified device.
Args:
inputs (keyword arguments that should be :obj:`torch.Tensor`): The tensors to place on :obj:`self.device`.
Return:
:obj:`Dict[str, torch.Tensor]`: The same as :obj:`inputs` but on the proper device.
"""
return {name: tensor.to(self.device) for name, tensor in inputs.items()}
def check_model_type(self, supported_models: Union[List[str], dict]):
"""
Check if the model class is in supported by the pipeline.
Args:
supported_models (:obj:`List[str]` or :obj:`dict`):
The list of models supported by the pipeline, or a dictionary with model class values.
"""
if not isinstance(supported_models, list): # Create from a model mapping
supported_models = [item[1].__name__ for item in supported_models.items()]
if self.model.__class__.__name__ not in supported_models:
raise PipelineException(
self.task,
self.model.base_model_prefix,
f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are {supported_models}",
)
def _parse_and_tokenize(
self, inputs, padding=True, add_special_tokens=True, truncation=TruncationStrategy.DO_NOT_TRUNCATE, **kwargs
):
"""
Parse arguments and tokenize
"""
# Parse arguments
inputs = self.tokenizer(
inputs,
add_special_tokens=add_special_tokens,
return_tensors=self.framework,
padding=padding,
truncation=truncation,
)
return inputs
def __call__(self, *args, **kwargs):
inputs = self._parse_and_tokenize(*args, **kwargs)
return self._forward(inputs)
def _forward(self, inputs, return_tensors=False):
"""
Internal framework specific forward dispatching
Args:
inputs: dict holding all the keyword arguments for required by the model forward method.
return_tensors: Whether to return native framework (pt/tf) tensors rather than numpy array
Returns:
Numpy array
"""
# Encode for forward
with self.device_placement():
if self.framework == "tf":
# TODO trace model
predictions = self.model(inputs.data, training=False)[0]
else:
with torch.no_grad():
inputs = self.ensure_tensor_on_device(**inputs)
predictions = self.model(**inputs)[0].cpu()
if return_tensors:
return predictions
else:
return predictions.numpy()
|
AdaMix/src/transformers/pipelines/base.py/0
|
{
"file_path": "AdaMix/src/transformers/pipelines/base.py",
"repo_id": "AdaMix",
"token_count": 9387
}
| 67 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Base classes common to both the slow and the fast tokenization classes: PreTrainedTokenizerBase (host all the user
fronting encoding methods) Special token mixing (host the special tokens logic) and BatchEncoding (wrap the dictionary
of output with special method for the Fast tokenizers)
"""
import copy
import json
import os
import warnings
from collections import OrderedDict, UserDict
from contextlib import contextmanager
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
import numpy as np
import requests
from .file_utils import (
ExplicitEnum,
PaddingStrategy,
TensorType,
_is_jax,
_is_numpy,
_is_tensorflow,
_is_torch,
_is_torch_device,
add_end_docstrings,
cached_path,
hf_bucket_url,
is_flax_available,
is_offline_mode,
is_remote_url,
is_tf_available,
is_tokenizers_available,
is_torch_available,
to_py_obj,
torch_required,
)
from .utils import logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_flax_available():
import jax.numpy as jnp # noqa: F401
if is_tokenizers_available():
from tokenizers import AddedToken
from tokenizers import Encoding as EncodingFast
else:
@dataclass(frozen=True, eq=True)
class AddedToken:
"""
AddedToken represents a token to be added to a Tokenizer An AddedToken can have special options defining the
way it should behave.
"""
content: str = field(default_factory=str)
single_word: bool = False
lstrip: bool = False
rstrip: bool = False
normalized: bool = True
def __getstate__(self):
return self.__dict__
@dataclass
class EncodingFast:
""" This is dummy class because without the `tokenizers` library we don't have these objects anyway """
pass
logger = logging.get_logger(__name__)
VERY_LARGE_INTEGER = int(1e30) # This is used to set the max input length for a model with infinite size input
LARGE_INTEGER = int(1e20) # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER
# Define type aliases and NamedTuples
TextInput = str
PreTokenizedInput = List[str]
EncodedInput = List[int]
TextInputPair = Tuple[str, str]
PreTokenizedInputPair = Tuple[List[str], List[str]]
EncodedInputPair = Tuple[List[int], List[int]]
# Slow tokenizers used to be saved in three separated files
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
ADDED_TOKENS_FILE = "added_tokens.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
FULL_TOKENIZER_FILE = "tokenizer.json"
class TruncationStrategy(ExplicitEnum):
"""
Possible values for the ``truncation`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for
tab-completion in an IDE.
"""
ONLY_FIRST = "only_first"
ONLY_SECOND = "only_second"
LONGEST_FIRST = "longest_first"
DO_NOT_TRUNCATE = "do_not_truncate"
class CharSpan(NamedTuple):
"""
Character span in the original string.
Args:
start (:obj:`int`): Index of the first character in the original string.
end (:obj:`int`): Index of the character following the last character in the original string.
"""
start: int
end: int
class TokenSpan(NamedTuple):
"""
Token span in an encoded string (list of tokens).
Args:
start (:obj:`int`): Index of the first token in the span.
end (:obj:`int`): Index of the token following the last token in the span.
"""
start: int
end: int
class BatchEncoding(UserDict):
"""
Holds the output of the :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.encode_plus` and
:meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.batch_encode` methods (tokens,
attention_masks, etc).
This class is derived from a python dictionary and can be used as a dictionary. In addition, this class exposes
utility methods to map from word/character space to token space.
Args:
data (:obj:`dict`):
Dictionary of lists/arrays/tensors returned by the encode/batch_encode methods ('input_ids',
'attention_mask', etc.).
encoding (:obj:`tokenizers.Encoding` or :obj:`Sequence[tokenizers.Encoding]`, `optional`):
If the tokenizer is a fast tokenizer which outputs additional information like mapping from word/character
space to token space the :obj:`tokenizers.Encoding` instance or list of instance (for batches) hold this
information.
tensor_type (:obj:`Union[None, str, TensorType]`, `optional`):
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.
prepend_batch_axis (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to add a batch axis when converting to tensors (see :obj:`tensor_type` above).
n_sequences (:obj:`Optional[int]`, `optional`):
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.
"""
def __init__(
self,
data: Optional[Dict[str, Any]] = None,
encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,
tensor_type: Union[None, str, TensorType] = None,
prepend_batch_axis: bool = False,
n_sequences: Optional[int] = None,
):
super().__init__(data)
if isinstance(encoding, EncodingFast):
encoding = [encoding]
self._encodings = encoding
if n_sequences is None and encoding is not None and len(encoding):
n_sequences = encoding[0].n_sequences
self._n_sequences = n_sequences
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
@property
def n_sequences(self) -> Optional[int]:
"""
:obj:`Optional[int]`: The number of sequences used to generate each sample from the batch encoded in this
:class:`~transformers.BatchEncoding`. Currently can be one of :obj:`None` (unknown), :obj:`1` (a single
sentence) or :obj:`2` (a pair of sentences)
"""
return self._n_sequences
@property
def is_fast(self) -> bool:
"""
:obj:`bool`: Indicate whether this :class:`~transformers.BatchEncoding` was generated from the result of a
:class:`~transformers.PreTrainedTokenizerFast` or not.
"""
return self._encodings is not None
def __getitem__(self, item: Union[int, str]) -> Union[Any, EncodingFast]:
"""
If the key is a string, returns the value of the dict associated to :obj:`key` ('input_ids', 'attention_mask',
etc.).
If the key is an integer, get the :obj:`tokenizers.Encoding` for batch item with index :obj:`key`.
"""
if isinstance(item, str):
return self.data[item]
elif self._encodings is not None:
return self._encodings[item]
else:
raise KeyError(
"Indexing with integers (to access backend Encoding for a given batch index) "
"is not available when using Python based tokenizers"
)
def __getattr__(self, item: str):
try:
return self.data[item]
except KeyError:
raise AttributeError
def __getstate__(self):
return {"data": self.data, "encodings": self._encodings}
def __setstate__(self, state):
if "data" in state:
self.data = state["data"]
if "encodings" in state:
self._encodings = state["encodings"]
def keys(self):
return self.data.keys()
def values(self):
return self.data.values()
def items(self):
return self.data.items()
# After this point:
# Extended properties and methods only available for fast (Rust-based) tokenizers
# provided by HuggingFace tokenizers library.
@property
def encodings(self) -> Optional[List[EncodingFast]]:
"""
:obj:`Optional[List[tokenizers.Encoding]]`: The list all encodings from the tokenization process. Returns
:obj:`None` if the input was tokenized through Python (i.e., not a fast) tokenizer.
"""
return self._encodings
def tokens(self, batch_index: int = 0) -> List[str]:
"""
Return the list of tokens (sub-parts of the input strings after word/subword splitting and before conversion to
integer indices) at a given batch index (only works for the output of a fast tokenizer).
Args:
batch_index (:obj:`int`, `optional`, defaults to 0): The index to access in the batch.
Returns:
:obj:`List[str]`: The list of tokens at that index.
"""
if not self._encodings:
raise ValueError("tokens() is not available when using Python-based tokenizers")
return self._encodings[batch_index].tokens
def sequence_ids(self, batch_index: int = 0) -> List[Optional[int]]:
"""
Return a list mapping the tokens to the id of their original sentences:
- :obj:`None` for special tokens added around or between sequences,
- :obj:`0` for tokens corresponding to words in the first sequence,
- :obj:`1` for tokens corresponding to words in the second sequence when a pair of sequences was jointly
encoded.
Args:
batch_index (:obj:`int`, `optional`, defaults to 0): The index to access in the batch.
Returns:
:obj:`List[Optional[int]]`: A list indicating the sequence id corresponding to each token. Special tokens
added by the tokenizer are mapped to :obj:`None` and other tokens are mapped to the index of their
corresponding sequence.
"""
if not self._encodings:
raise ValueError("sequence_ids() is not available when using Python-based tokenizers")
return self._encodings[batch_index].sequence_ids
def words(self, batch_index: int = 0) -> List[Optional[int]]:
"""
Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
Args:
batch_index (:obj:`int`, `optional`, defaults to 0): The index to access in the batch.
Returns:
:obj:`List[Optional[int]]`: A list indicating the word corresponding to each token. Special tokens added by
the tokenizer are mapped to :obj:`None` and other tokens are mapped to the index of their corresponding
word (several tokens will be mapped to the same word index if they are parts of that word).
"""
if not self._encodings:
raise ValueError("words() is not available when using Python-based tokenizers")
warnings.warn(
"`BatchEncoding.words()` property is deprecated and should be replaced with the identical, "
"but more self-explanatory `BatchEncoding.word_ids()` property.",
FutureWarning,
)
return self.word_ids(batch_index)
def word_ids(self, batch_index: int = 0) -> List[Optional[int]]:
"""
Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
Args:
batch_index (:obj:`int`, `optional`, defaults to 0): The index to access in the batch.
Returns:
:obj:`List[Optional[int]]`: A list indicating the word corresponding to each token. Special tokens added by
the tokenizer are mapped to :obj:`None` and other tokens are mapped to the index of their corresponding
word (several tokens will be mapped to the same word index if they are parts of that word).
"""
if not self._encodings:
raise ValueError("word_ids() is not available when using Python-based tokenizers")
return self._encodings[batch_index].word_ids
def token_to_sequence(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
"""
Get the index of the sequence represented by the given token. In the general use case, this method returns
:obj:`0` for a single sequence or the first sequence of a pair, and :obj:`1` for the second sequence of a pair
Can be called as:
- ``self.token_to_sequence(token_index)`` if batch size is 1
- ``self.token_to_sequence(batch_index, token_index)`` if batch size is greater than 1
This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e.,
words are defined by the user). In this case it allows to easily associate encoded tokens with provided
tokenized words.
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of
the token in the sequence.
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the token in the
sequence.
Returns:
:obj:`int`: Index of the word in the input sequence.
"""
if not self._encodings:
raise ValueError("token_to_sequence() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if token_index < 0:
token_index = self._seq_len + token_index
return self._encodings[batch_index].token_to_sequence(token_index)
def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
"""
Get the index of the word corresponding (i.e. comprising) to an encoded token in a sequence of the batch.
Can be called as:
- ``self.token_to_word(token_index)`` if batch size is 1
- ``self.token_to_word(batch_index, token_index)`` if batch size is greater than 1
This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e.,
words are defined by the user). In this case it allows to easily associate encoded tokens with provided
tokenized words.
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the token in the sequence.
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the token in the
sequence.
Returns:
:obj:`int`: Index of the word in the input sequence.
"""
if not self._encodings:
raise ValueError("token_to_word() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if token_index < 0:
token_index = self._seq_len + token_index
return self._encodings[batch_index].token_to_word(token_index)
def word_to_tokens(
self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0
) -> Optional[TokenSpan]:
"""
Get the encoded token span corresponding to a word in a sequence of the batch.
Token spans are returned as a :class:`~transformers.tokenization_utils_base.TokenSpan` with:
- **start** -- Index of the first token.
- **end** -- Index of the token following the last token.
Can be called as:
- ``self.word_to_tokens(word_index, sequence_index: int = 0)`` if batch size is 1
- ``self.word_to_tokens(batch_index, word_index, sequence_index: int = 0)`` if batch size is greater or equal
to 1
This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
words.
Args:
batch_or_word_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of
the word in the sequence.
word_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the word in the
sequence.
sequence_index (:obj:`int`, `optional`, defaults to 0):
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided word index belongs to.
Returns:
Optional :class:`~transformers.tokenization_utils_base.TokenSpan` Span of tokens in the encoded sequence.
Returns :obj:`None` if no tokens correspond to the word.
"""
if not self._encodings:
raise ValueError("word_to_tokens() is not available when using Python based tokenizers")
if word_index is not None:
batch_index = batch_or_word_index
else:
batch_index = 0
word_index = batch_or_word_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if word_index < 0:
word_index = self._seq_len + word_index
span = self._encodings[batch_index].word_to_tokens(word_index, sequence_index)
return TokenSpan(*span) if span is not None else None
def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:
"""
Get the character span corresponding to an encoded token in a sequence of the batch.
Character spans are returned as a :class:`~transformers.tokenization_utils_base.CharSpan` with:
- **start** -- Index of the first character in the original string associated to the token.
- **end** -- Index of the character following the last character in the original string associated to the
token.
Can be called as:
- ``self.token_to_chars(token_index)`` if batch size is 1
- ``self.token_to_chars(batch_index, token_index)`` if batch size is greater or equal to 1
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the token in the sequence.
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the token or tokens in
the sequence.
Returns:
:class:`~transformers.tokenization_utils_base.CharSpan`: Span of characters in the original string.
"""
if not self._encodings:
raise ValueError("token_to_chars() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
return CharSpan(*(self._encodings[batch_index].token_to_chars(token_index)))
def char_to_token(
self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0
) -> int:
"""
Get the index of the token in the encoded output comprising a character in the original string for a sequence
of the batch.
Can be called as:
- ``self.char_to_token(char_index)`` if batch size is 1
- ``self.char_to_token(batch_index, char_index)`` if batch size is greater or equal to 1
This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
words.
Args:
batch_or_char_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the word in the sequence
char_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the word in the
sequence.
sequence_index (:obj:`int`, `optional`, defaults to 0):
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided character index belongs to.
Returns:
:obj:`int`: Index of the token.
"""
if not self._encodings:
raise ValueError("char_to_token() is not available when using Python based tokenizers")
if char_index is not None:
batch_index = batch_or_char_index
else:
batch_index = 0
char_index = batch_or_char_index
return self._encodings[batch_index].char_to_token(char_index, sequence_index)
def word_to_chars(
self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0
) -> CharSpan:
"""
Get the character span in the original string corresponding to given word in a sequence of the batch.
Character spans are returned as a CharSpan NamedTuple with:
- start: index of the first character in the original string
- end: index of the character following the last character in the original string
Can be called as:
- ``self.word_to_chars(word_index)`` if batch size is 1
- ``self.word_to_chars(batch_index, word_index)`` if batch size is greater or equal to 1
Args:
batch_or_word_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the word in the sequence
word_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the word in the
sequence.
sequence_index (:obj:`int`, `optional`, defaults to 0):
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided word index belongs to.
Returns:
:obj:`CharSpan` or :obj:`List[CharSpan]`: Span(s) of the associated character or characters in the string.
CharSpan are NamedTuple with:
- start: index of the first character associated to the token in the original string
- end: index of the character following the last character associated to the token in the original
string
"""
if not self._encodings:
raise ValueError("word_to_chars() is not available when using Python based tokenizers")
if word_index is not None:
batch_index = batch_or_word_index
else:
batch_index = 0
word_index = batch_or_word_index
return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index, sequence_index)))
def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0) -> int:
"""
Get the word in the original string corresponding to a character in the original string of a sequence of the
batch.
Can be called as:
- ``self.char_to_word(char_index)`` if batch size is 1
- ``self.char_to_word(batch_index, char_index)`` if batch size is greater than 1
This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
words.
Args:
batch_or_char_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the character in the original string.
char_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index of the character in the
original string.
sequence_index (:obj:`int`, `optional`, defaults to 0):
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided character index belongs to.
Returns:
:obj:`int` or :obj:`List[int]`: Index or indices of the associated encoded token(s).
"""
if not self._encodings:
raise ValueError("char_to_word() is not available when using Python based tokenizers")
if char_index is not None:
batch_index = batch_or_char_index
else:
batch_index = 0
char_index = batch_or_char_index
return self._encodings[batch_index].char_to_word(char_index, sequence_index)
def convert_to_tensors(
self, tensor_type: Optional[Union[str, TensorType]] = None, prepend_batch_axis: bool = False
):
"""
Convert the inner content to tensors.
Args:
tensor_type (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
The type of tensors to use. If :obj:`str`, should be one of the values of the enum
:class:`~transformers.file_utils.TensorType`. If :obj:`None`, no modification is done.
prepend_batch_axis (:obj:`int`, `optional`, defaults to :obj:`False`):
Whether or not to add the batch dimension during the conversion.
"""
if tensor_type is None:
return self
# Convert to TensorType
if not isinstance(tensor_type, TensorType):
tensor_type = TensorType(tensor_type)
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
"Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."
)
import tensorflow as tf
as_tensor = tf.constant
is_tensor = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
import torch
as_tensor = torch.tensor
is_tensor = torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.")
import jax.numpy as jnp # noqa: F811
as_tensor = jnp.array
is_tensor = _is_jax
else:
as_tensor = np.asarray
is_tensor = _is_numpy
# (mfuntowicz: This code is unreachable)
# else:
# raise ImportError(
# "Unable to convert output to tensors format {}".format(tensor_type)
# )
# Do the tensor conversion in batch
for key, value in self.items():
try:
if prepend_batch_axis:
value = [value]
if not is_tensor(value):
tensor = as_tensor(value)
# Removing this for now in favor of controlling the shape with `prepend_batch_axis`
# # at-least2d
# if tensor.ndim > 2:
# tensor = tensor.squeeze(0)
# elif tensor.ndim < 2:
# tensor = tensor[None, :]
self[key] = tensor
except: # noqa E722
if key == "overflowing_tokens":
raise ValueError(
"Unable to create tensor returning overflowing tokens of different lengths. "
"Please see if a fast version of this tokenizer is available to have this feature available."
)
raise ValueError(
"Unable to create tensor, you should probably activate truncation and/or padding "
"with 'padding=True' 'truncation=True' to have batched tensors with the same length."
)
return self
@torch_required
def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
"""
Send all values to device by calling :obj:`v.to(device)` (PyTorch only).
Args:
device (:obj:`str` or :obj:`torch.device`): The device to put the tensors on.
Returns:
:class:`~transformers.BatchEncoding`: The same instance after modification.
"""
# This check catches things like APEX blindly calling "to" on all inputs to a module
# Otherwise it passes the casts down and casts the LongTensor containing the token idxs
# into a HalfTensor
if isinstance(device, str) or _is_torch_device(device) or isinstance(device, int):
self.data = {k: v.to(device=device) for k, v in self.data.items()}
else:
logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.")
return self
class SpecialTokensMixin:
"""
A mixin derived by :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` to
handle specific behaviors related to special tokens. In particular, this class hold the attributes which can be
used to directly access these special tokens in a model-independent manner and allow to set and update the special
tokens.
Args:
bos_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing the beginning of a sentence.
eos_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing the end of a sentence.
unk_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing an out-of-vocabulary token.
sep_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token separating two different sentences in the same input (used by BERT for instance).
pad_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.
cls_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing the class of the input (used by BERT for instance).
mask_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT).
additional_special_tokens (tuple or list of :obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A tuple or a list of additional special tokens.
"""
SPECIAL_TOKENS_ATTRIBUTES = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
"additional_special_tokens",
]
def __init__(self, verbose=True, **kwargs):
self._bos_token = None
self._eos_token = None
self._unk_token = None
self._sep_token = None
self._pad_token = None
self._cls_token = None
self._mask_token = None
self._pad_token_type_id = 0
self._additional_special_tokens = []
self.verbose = verbose
# We directly set the hidden value to allow initialization with special tokens
# which are not yet in the vocabulary. Necessary for serialization/de-serialization
# TODO clean this up at some point (probably by switching to fast tokenizers)
for key, value in kwargs.items():
if value is None:
continue
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)), f"Value {value} is not a list or tuple"
assert all(isinstance(t, str) for t in value), "One of the tokens is not a string"
setattr(self, key, value)
elif isinstance(value, (str, AddedToken)):
setattr(self, key, value)
else:
raise TypeError(
"special token {} has to be either str or AddedToken but got: {}".format(key, type(value))
)
def sanitize_special_tokens(self) -> int:
"""
Make sure that all the special tokens attributes of the tokenizer (:obj:`tokenizer.mask_token`,
:obj:`tokenizer.cls_token`, etc.) are in the vocabulary.
Add the missing ones to the vocabulary if needed.
Return:
:obj:`int`: The number of tokens added in the vocabulary during the operation.
"""
return self.add_tokens(self.all_special_tokens_extended, special_tokens=True)
def add_special_tokens(self, special_tokens_dict: Dict[str, Union[str, AddedToken]]) -> int:
"""
Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If
special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the
current vocabulary).
Using : obj:`add_special_tokens` will ensure your special tokens can be used in several ways:
- Special tokens are carefully handled by the tokenizer (they are never split).
- You can easily refer to special tokens using tokenizer class attributes like :obj:`tokenizer.cls_token`. This
makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (for instance
:class:`~transformers.BertTokenizer` :obj:`cls_token` is already registered to be :obj`'[CLS]'` and XLM's one
is also registered to be :obj:`'</s>'`).
Args:
special_tokens_dict (dictionary `str` to `str` or :obj:`tokenizers.AddedToken`):
Keys should be in the list of predefined special attributes: [``bos_token``, ``eos_token``,
``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
``additional_special_tokens``].
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer
assign the index of the ``unk_token`` to them).
Returns:
:obj:`int`: Number of tokens added to the vocabulary.
Examples::
# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
special_tokens_dict = {'cls_token': '<CLS>'}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')
# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.
model.resize_token_embeddings(len(tokenizer))
assert tokenizer.cls_token == '<CLS>'
"""
if not special_tokens_dict:
return 0
added_tokens = 0
for key, value in special_tokens_dict.items():
assert key in self.SPECIAL_TOKENS_ATTRIBUTES, f"Key {key} is not a special token"
if self.verbose:
logger.info("Assigning %s to the %s key of the tokenizer", value, key)
setattr(self, key, value)
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(
isinstance(t, (str, AddedToken)) for t in value
), f"Tokens {value} for key {key} should all be str or AddedToken instances"
added_tokens += self.add_tokens(value, special_tokens=True)
else:
assert isinstance(
value, (str, AddedToken)
), f"Token {value} for key {key} should be a str or an AddedToken instance"
added_tokens += self.add_tokens([value], special_tokens=True)
return added_tokens
def add_tokens(
self, new_tokens: Union[str, AddedToken, List[Union[str, AddedToken]]], special_tokens: bool = False
) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.
.. Note::
When adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of
the model so that its embedding matrix matches the tokenizer.
In order to do that, please use the :meth:`~transformers.PreTrainedModel.resize_token_embeddings` method.
Args:
new_tokens (:obj:`str`, :obj:`tokenizers.AddedToken` or a list of `str` or :obj:`tokenizers.AddedToken`):
Tokens are only added if they are not already in the vocabulary. :obj:`tokenizers.AddedToken` wraps a
string token to let you personalize its behavior: whether this token should only match against a single
word, whether this token should strip all potential whitespaces on the left side, whether this token
should strip all potential whitespaces on the right side, etc.
special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Can be used to specify if the token is a special token. This mostly change the normalization behavior
(special tokens like CLS or [MASK] are usually not lower-cased for instance).
See details for :obj:`tokenizers.AddedToken` in HuggingFace tokenizers library.
Returns:
:obj:`int`: Number of tokens added to the vocabulary.
Examples::
# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
print('We have added', num_added_toks, 'tokens')
# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.
model.resize_token_embeddings(len(tokenizer))
"""
if not new_tokens:
return 0
if not isinstance(new_tokens, (list, tuple)):
new_tokens = [new_tokens]
return self._add_tokens(new_tokens, special_tokens=special_tokens)
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
raise NotImplementedError
@property
def bos_token(self) -> str:
"""
:obj:`str`: Beginning of sentence token. Log an error if used while not having been set.
"""
if self._bos_token is None and self.verbose:
logger.error("Using bos_token, but it is not set yet.")
return None
return str(self._bos_token)
@property
def eos_token(self) -> str:
"""
:obj:`str`: End of sentence token. Log an error if used while not having been set.
"""
if self._eos_token is None and self.verbose:
logger.error("Using eos_token, but it is not set yet.")
return None
return str(self._eos_token)
@property
def unk_token(self) -> str:
"""
:obj:`str`: Unknown token. Log an error if used while not having been set.
"""
if self._unk_token is None and self.verbose:
logger.error("Using unk_token, but it is not set yet.")
return None
return str(self._unk_token)
@property
def sep_token(self) -> str:
"""
:obj:`str`: Separation token, to separate context and query in an input sequence. Log an error if used while
not having been set.
"""
if self._sep_token is None and self.verbose:
logger.error("Using sep_token, but it is not set yet.")
return None
return str(self._sep_token)
@property
def pad_token(self) -> str:
"""
:obj:`str`: Padding token. Log an error if used while not having been set.
"""
if self._pad_token is None and self.verbose:
logger.error("Using pad_token, but it is not set yet.")
return None
return str(self._pad_token)
@property
def cls_token(self) -> str:
"""
:obj:`str`: Classification token, to extract a summary of an input sequence leveraging self-attention along the
full depth of the model. Log an error if used while not having been set.
"""
if self._cls_token is None and self.verbose:
logger.error("Using cls_token, but it is not set yet.")
return None
return str(self._cls_token)
@property
def mask_token(self) -> str:
"""
:obj:`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while
not having been set.
"""
if self._mask_token is None and self.verbose:
logger.error("Using mask_token, but it is not set yet.")
return None
return str(self._mask_token)
@property
def additional_special_tokens(self) -> List[str]:
"""
:obj:`List[str]`: All the additional special tokens you may want to use. Log an error if used while not having
been set.
"""
if self._additional_special_tokens is None and self.verbose:
logger.error("Using additional_special_tokens, but it is not set yet.")
return None
return [str(tok) for tok in self._additional_special_tokens]
@bos_token.setter
def bos_token(self, value):
self._bos_token = value
@eos_token.setter
def eos_token(self, value):
self._eos_token = value
@unk_token.setter
def unk_token(self, value):
self._unk_token = value
@sep_token.setter
def sep_token(self, value):
self._sep_token = value
@pad_token.setter
def pad_token(self, value):
self._pad_token = value
@cls_token.setter
def cls_token(self, value):
self._cls_token = value
@mask_token.setter
def mask_token(self, value):
self._mask_token = value
@additional_special_tokens.setter
def additional_special_tokens(self, value):
self._additional_special_tokens = value
@property
def bos_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the beginning of sentence token in the vocabulary. Returns :obj:`None` if the token
has not been set.
"""
if self._bos_token is None:
return None
return self.convert_tokens_to_ids(self.bos_token)
@property
def eos_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns :obj:`None` if the token has
not been set.
"""
if self._eos_token is None:
return None
return self.convert_tokens_to_ids(self.eos_token)
@property
def unk_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the unknown token in the vocabulary. Returns :obj:`None` if the token has not been
set.
"""
if self._unk_token is None:
return None
return self.convert_tokens_to_ids(self.unk_token)
@property
def sep_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the separation token in the vocabulary, to separate context and query in an input
sequence. Returns :obj:`None` if the token has not been set.
"""
if self._sep_token is None:
return None
return self.convert_tokens_to_ids(self.sep_token)
@property
def pad_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the padding token in the vocabulary. Returns :obj:`None` if the token has not been
set.
"""
if self._pad_token is None:
return None
return self.convert_tokens_to_ids(self.pad_token)
@property
def pad_token_type_id(self) -> int:
"""
:obj:`int`: Id of the padding token type in the vocabulary.
"""
return self._pad_token_type_id
@property
def cls_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the classification token in the vocabulary, to extract a summary of an input
sequence leveraging self-attention along the full depth of the model.
Returns :obj:`None` if the token has not been set.
"""
if self._cls_token is None:
return None
return self.convert_tokens_to_ids(self.cls_token)
@property
def mask_token_id(self) -> Optional[int]:
"""
:obj:`Optional[int]`: Id of the mask token in the vocabulary, used when training a model with masked-language
modeling. Returns :obj:`None` if the token has not been set.
"""
if self._mask_token is None:
return None
return self.convert_tokens_to_ids(self.mask_token)
@property
def additional_special_tokens_ids(self) -> List[int]:
"""
:obj:`List[int]`: Ids of all the additional special tokens in the vocabulary. Log an error if used while not
having been set.
"""
return self.convert_tokens_to_ids(self.additional_special_tokens)
@bos_token_id.setter
def bos_token_id(self, value):
self._bos_token = self.convert_tokens_to_ids(value)
@eos_token_id.setter
def eos_token_id(self, value):
self._eos_token = self.convert_tokens_to_ids(value)
@unk_token_id.setter
def unk_token_id(self, value):
self._unk_token = self.convert_tokens_to_ids(value)
@sep_token_id.setter
def sep_token_id(self, value):
self._sep_token = self.convert_tokens_to_ids(value)
@pad_token_id.setter
def pad_token_id(self, value):
self._pad_token = self.convert_tokens_to_ids(value)
@cls_token_id.setter
def cls_token_id(self, value):
self._cls_token = self.convert_tokens_to_ids(value)
@mask_token_id.setter
def mask_token_id(self, value):
self._mask_token = self.convert_tokens_to_ids(value)
@additional_special_tokens_ids.setter
def additional_special_tokens_ids(self, values):
self._additional_special_tokens = [self.convert_tokens_to_ids(value) for value in values]
@property
def special_tokens_map(self) -> Dict[str, Union[str, List[str]]]:
"""
:obj:`Dict[str, Union[str, List[str]]]`: A dictionary mapping special token class attributes (:obj:`cls_token`,
:obj:`unk_token`, etc.) to their values (:obj:`'<unk>'`, :obj:`'<cls>'`, etc.).
Convert potential tokens of :obj:`tokenizers.AddedToken` type to string.
"""
set_attr = {}
for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
attr_value = getattr(self, "_" + attr)
if attr_value:
set_attr[attr] = str(attr_value)
return set_attr
@property
def special_tokens_map_extended(self) -> Dict[str, Union[str, AddedToken, List[Union[str, AddedToken]]]]:
"""
:obj:`Dict[str, Union[str, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]]`: A dictionary
mapping special token class attributes (:obj:`cls_token`, :obj:`unk_token`, etc.) to their values
(:obj:`'<unk>'`, :obj:`'<cls>'`, etc.).
Don't convert tokens of :obj:`tokenizers.AddedToken` type to string so they can be used to control more finely
how special tokens are tokenized.
"""
set_attr = {}
for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
attr_value = getattr(self, "_" + attr)
if attr_value:
set_attr[attr] = attr_value
return set_attr
@property
def all_special_tokens(self) -> List[str]:
"""
:obj:`List[str]`: All the special tokens (:obj:`'<unk>'`, :obj:`'<cls>'`, etc.) mapped to class attributes.
Convert tokens of :obj:`tokenizers.AddedToken` type to string.
"""
all_toks = [str(s) for s in self.all_special_tokens_extended]
return all_toks
@property
def all_special_tokens_extended(self) -> List[Union[str, AddedToken]]:
"""
:obj:`List[Union[str, tokenizers.AddedToken]]`: All the special tokens (:obj:`'<unk>'`, :obj:`'<cls>'`, etc.)
mapped to class attributes.
Don't convert tokens of :obj:`tokenizers.AddedToken` type to string so they can be used to control more finely
how special tokens are tokenized.
"""
all_toks = []
set_attr = self.special_tokens_map_extended
for attr_value in set_attr.values():
all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = list(OrderedDict.fromkeys(all_toks))
return all_toks
@property
def all_special_ids(self) -> List[int]:
"""
:obj:`List[int]`: List the ids of the special tokens(:obj:`'<unk>'`, :obj:`'<cls>'`, etc.) mapped to class
attributes.
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids
ENCODE_KWARGS_DOCSTRING = r"""
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to encode the sequences with the special tokens relative to their model.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls padding. Accepts the following values:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls truncation. Accepts the following values:
* :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
:obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
provided. This will truncate token by token, removing a token from the longest sequence in the pair
if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
sequence lengths greater than the model maximum admissible input size).
max_length (:obj:`int`, `optional`):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to :obj:`None`, this will use the predefined model maximum length if a maximum
length is required by one of the truncation/padding parameters. If the model has no specific maximum
input length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (:obj:`int`, `optional`, defaults to 0):
If set to a number along with :obj:`max_length`, the overflowing tokens returned when
:obj:`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
is_split_into_words (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the input is already pre-tokenized (e.g., split into words), in which case the tokenizer
will skip the pre-tokenization step. This is useful for NER or token classification.
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
"""
ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
return_token_type_ids (:obj:`bool`, `optional`):
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`__
return_attention_mask (:obj:`bool`, `optional`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return overflowing token sequences.
return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return special tokens mask information.
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return :obj:`(char_start, char_end)` for each token.
This is only available on fast tokenizers inheriting from
:class:`~transformers.PreTrainedTokenizerFast`, if using Python's tokenizer, this method will raise
:obj:`NotImplementedError`.
return_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return the lengths of the encoded inputs.
verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to print more information and warnings.
**kwargs: passed to the :obj:`self.tokenize()` method
Return:
:class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:
- **input_ids** -- List of token ids to be fed to a model.
`What are input IDs? <../glossary.html#input-ids>`__
- **token_type_ids** -- List of token type ids to be fed to a model (when :obj:`return_token_type_ids=True`
or if `"token_type_ids"` is in :obj:`self.model_input_names`).
`What are token type IDs? <../glossary.html#token-type-ids>`__
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
:obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names`).
`What are attention masks? <../glossary.html#attention-mask>`__
- **overflowing_tokens** -- List of overflowing tokens sequences (when a :obj:`max_length` is specified and
:obj:`return_overflowing_tokens=True`).
- **num_truncated_tokens** -- Number of tokens truncated (when a :obj:`max_length` is specified and
:obj:`return_overflowing_tokens=True`).
- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when :obj:`add_special_tokens=True` and :obj:`return_special_tokens_mask=True`).
- **length** -- The length of the inputs (when :obj:`return_length=True`)
"""
INIT_TOKENIZER_DOCSTRING = r"""
Class attributes (overridden by derived classes)
- **vocab_files_names** (:obj:`Dict[str, str]`) -- A dictionary with, as keys, the ``__init__`` keyword name of
each vocabulary file required by the model, and as associated values, the filename for saving the associated
file (string).
- **pretrained_vocab_files_map** (:obj:`Dict[str, Dict[str, str]]`) -- A dictionary of dictionaries, with the
high-level keys being the ``__init__`` keyword name of each vocabulary file required by the model, the
low-level being the :obj:`short-cut-names` of the pretrained models with, as associated values, the
:obj:`url` to the associated pretrained vocabulary file.
- **max_model_input_sizes** (:obj:`Dict[str, Optinal[int]]`) -- A dictionary with, as keys, the
:obj:`short-cut-names` of the pretrained models, and as associated values, the maximum length of the sequence
inputs of this model, or :obj:`None` if the model has no maximum input size.
- **pretrained_init_configuration** (:obj:`Dict[str, Dict[str, Any]]`) -- A dictionary with, as keys, the
:obj:`short-cut-names` of the pretrained models, and as associated values, a dictionary of specific arguments
to pass to the ``__init__`` method of the tokenizer class for this pretrained model when loading the
tokenizer with the :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`
method.
- **model_input_names** (:obj:`List[str]`) -- A list of inputs expected in the forward pass of the model.
- **padding_side** (:obj:`str`) -- The default value for the side on which the model should have padding
applied. Should be :obj:`'right'` or :obj:`'left'`.
Args:
model_max_length (:obj:`int`, `optional`):
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`, this
will be set to the value stored for the associated model in ``max_model_input_sizes`` (see above). If no
value is provided, will default to VERY_LARGE_INTEGER (:obj:`int(1e30)`).
padding_side: (:obj:`str`, `optional`):
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
model_input_names (:obj:`List[string]`, `optional`):
The list of inputs accepted by the forward pass of the model (like :obj:`"token_type_ids"` or
:obj:`"attention_mask"`). Default value is picked from the class attribute of the same name.
bos_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing the beginning of a sentence. Will be associated to ``self.bos_token`` and
``self.bos_token_id``.
eos_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing the end of a sentence. Will be associated to ``self.eos_token`` and
``self.eos_token_id``.
unk_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing an out-of-vocabulary token. Will be associated to ``self.unk_token`` and
``self.unk_token_id``.
sep_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to ``self.sep_token`` and ``self.sep_token_id``.
pad_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to ``self.pad_token`` and
``self.pad_token_id``.
cls_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing the class of the input (used by BERT for instance). Will be associated to
``self.cls_token`` and ``self.cls_token_id``.
mask_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to ``self.mask_token`` and ``self.mask_token_id``.
additional_special_tokens (tuple or list of :obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
A tuple or a list of additional special tokens. Add them here to ensure they won't be split by the
tokenization process. Will be associated to ``self.additional_special_tokens`` and
``self.additional_special_tokens_ids``.
"""
@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
class PreTrainedTokenizerBase(SpecialTokensMixin):
"""
Base class for :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast`.
Handles shared (mostly boiler plate) methods for those two classes.
"""
vocab_files_names: Dict[str, str] = {}
pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}
pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}
max_model_input_sizes: Dict[str, Optional[int]] = {}
# first name has to correspond to main model input name
# to make sure `tokenizer.pad(...)` works correctly
model_input_names: List[str] = ["input_ids", "token_type_ids", "attention_mask"]
padding_side: str = "right"
slow_tokenizer_class = None
def __init__(self, **kwargs):
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
self.init_inputs = ()
self.init_kwargs = copy.deepcopy(kwargs)
self.name_or_path = kwargs.pop("name_or_path", "")
# For backward compatibility we fallback to set model_max_length from max_len if provided
model_max_length = kwargs.pop("model_max_length", kwargs.pop("max_len", None))
self.model_max_length = model_max_length if model_max_length is not None else VERY_LARGE_INTEGER
# Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.
self.padding_side = kwargs.pop("padding_side", self.padding_side)
assert self.padding_side in [
"right",
"left",
], f"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}"
self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
self.deprecation_warnings = (
{}
) # Use to store when we have already noticed a deprecation warning (avoid overlogging).
super().__init__(**kwargs)
@property
def max_len_single_sentence(self) -> int:
"""
:obj:`int`: The maximum length of a sentence that can be fed to the model.
"""
return self.model_max_length - self.num_special_tokens_to_add(pair=False)
@property
def max_len_sentences_pair(self) -> int:
"""
:obj:`int`: The maximum combined length of a pair of sentences that can be fed to the model.
"""
return self.model_max_length - self.num_special_tokens_to_add(pair=True)
@max_len_single_sentence.setter
def max_len_single_sentence(self, value) -> int:
# For backward compatibility, allow to try to setup 'max_len_single_sentence'.
if value == self.model_max_length - self.num_special_tokens_to_add(pair=False) and self.verbose:
if not self.deprecation_warnings.get("max_len_single_sentence", False):
logger.warning(
"Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
)
self.deprecation_warnings["max_len_single_sentence"] = True
else:
raise ValueError(
"Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
)
@max_len_sentences_pair.setter
def max_len_sentences_pair(self, value) -> int:
# For backward compatibility, allow to try to setup 'max_len_sentences_pair'.
if value == self.model_max_length - self.num_special_tokens_to_add(pair=True) and self.verbose:
if not self.deprecation_warnings.get("max_len_sentences_pair", False):
logger.warning(
"Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
)
self.deprecation_warnings["max_len_sentences_pair"] = True
else:
raise ValueError(
"Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
)
def __repr__(self) -> str:
return (
f"{'PreTrainedTokenizerFast' if self.is_fast else 'PreTrainedTokenizer'}(name_or_path='{self.name_or_path}', "
f"vocab_size={self.vocab_size}, model_max_len={self.model_max_length}, is_fast={self.is_fast}, "
f"padding_side='{self.padding_side}', special_tokens={self.special_tokens_map_extended})"
)
def get_vocab(self) -> Dict[str, int]:
"""
Returns the vocabulary as a dictionary of token to index.
:obj:`tokenizer.get_vocab()[token]` is equivalent to :obj:`tokenizer.convert_tokens_to_ids(token)` when
:obj:`token` is in the vocab.
Returns:
:obj:`Dict[str, int]`: The vocabulary.
"""
raise NotImplementedError()
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], *init_inputs, **kwargs):
r"""
Instantiate a :class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` (or a derived class) from
a predefined tokenizer.
Args:
pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
Can be either:
- A string, the `model id` of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a
user or organization name, like ``dbmdz/bert-base-german-cased``.
- A path to a `directory` containing vocabulary files required by the tokenizer, for instance saved
using the :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`
method, e.g., ``./my_model_directory/``.
- (**Deprecated**, not applicable to all derived classes) A path or url to a single saved vocabulary
file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g.,
``./my_model_directory/vocab.txt``.
cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the
standard cache should not be used.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force the (re-)download the vocabulary files and override the cached versions if they
exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to delete incompletely received files. Attempt to resume the download if such a file
exists.
proxies (:obj:`Dict[str, str], `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
use_auth_token (:obj:`str` or `bool`, `optional`):
The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
identifier allowed by git.
subfolder (:obj:`str`, `optional`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.
inputs (additional positional arguments, `optional`):
Will be passed along to the Tokenizer ``__init__`` method.
kwargs (additional keyword arguments, `optional`):
Will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like
``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``,
``mask_token``, ``additional_special_tokens``. See parameters in the ``__init__`` for more details.
.. note::
Passing :obj:`use_auth_token=True` is required when you want to use a private model.
Examples::
# We can't instantiate directly the base class `PreTrainedTokenizerBase` so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from huggingface.co and cache.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Download vocabulary from huggingface.co (user-uploaded) and cache.
tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
# If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == '<unk>'
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
vocab_files = {}
init_configuration = {}
if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
if len(cls.vocab_files_names) > 1:
raise ValueError(
f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not "
"supported for this tokenizer. Use a model identifier or the path to a directory instead."
)
warnings.warn(
f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated and "
"won't be possible anymore in v5. Use a model identifier or the path to a directory instead.",
FutureWarning,
)
file_id = list(cls.vocab_files_names.keys())[0]
vocab_files[file_id] = pretrained_model_name_or_path
else:
# At this point pretrained_model_name_or_path is either a directory or a model identifier name
additional_files_names = {
"added_tokens_file": ADDED_TOKENS_FILE,
"special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE,
"tokenizer_config_file": TOKENIZER_CONFIG_FILE,
"tokenizer_file": FULL_TOKENIZER_FILE,
}
# Look for the tokenizer files
for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items():
if os.path.isdir(pretrained_model_name_or_path):
if subfolder is not None:
full_file_name = os.path.join(pretrained_model_name_or_path, subfolder, file_name)
else:
full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
if not os.path.exists(full_file_name):
logger.info(f"Didn't find file {full_file_name}. We won't load it.")
full_file_name = None
else:
full_file_name = hf_bucket_url(
pretrained_model_name_or_path,
filename=file_name,
subfolder=subfolder,
revision=revision,
mirror=None,
)
vocab_files[file_id] = full_file_name
# Get files from url, cache, or disk depending on the case
resolved_vocab_files = {}
unresolved_files = []
for file_id, file_path in vocab_files.items():
if file_path is None:
resolved_vocab_files[file_id] = None
else:
try:
resolved_vocab_files[file_id] = cached_path(
file_path,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
except FileNotFoundError as error:
if local_files_only:
unresolved_files.append(file_id)
else:
raise error
except requests.exceptions.HTTPError as err:
if "404 Client Error" in str(err):
logger.debug(err)
resolved_vocab_files[file_id] = None
else:
raise err
if len(unresolved_files) > 0:
logger.info(
f"Can't load following files from cache: {unresolved_files} and cannot check if these "
"files are necessary for the tokenizer to operate."
)
if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):
msg = (
f"Can't load tokenizer for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing relevant tokenizer files\n\n"
)
raise EnvironmentError(msg)
for file_id, file_path in vocab_files.items():
if file_id not in resolved_vocab_files:
continue
if file_path == resolved_vocab_files[file_id]:
logger.info(f"loading file {file_path}")
else:
logger.info(f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}")
return cls._from_pretrained(
resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, **kwargs
)
@classmethod
def _from_pretrained(
cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, **kwargs
):
# We instantiate fast tokenizers based on a slow tokenizer if we don't have access to the tokenizer.json
# file or if `from_slow` is set to True.
from_slow = kwargs.get("from_slow", False)
has_tokenizer_file = resolved_vocab_files.get("tokenizer_file", None) is not None
if (from_slow or not has_tokenizer_file) and cls.slow_tokenizer_class is not None:
slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained(
copy.deepcopy(resolved_vocab_files),
pretrained_model_name_or_path,
copy.deepcopy(init_configuration),
*init_inputs,
**(copy.deepcopy(kwargs)),
)
else:
slow_tokenizer = None
# Prepare tokenizer initialization kwargs
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None)
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
saved_init_inputs = init_kwargs.pop("init_inputs", ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
init_kwargs = init_configuration
# Update with newly provided kwargs
init_kwargs.update(kwargs)
# Convert AddedTokens serialized as dict to class instances
def convert_added_tokens(obj: Union[AddedToken, Any]):
if isinstance(obj, dict) and "__type" in obj and obj["__type"] == "AddedToken":
obj.pop("__type")
return AddedToken(**obj)
elif isinstance(obj, (list, tuple)):
return list(convert_added_tokens(o) for o in obj)
elif isinstance(obj, dict):
return {k: convert_added_tokens(v) for k, v in obj.items()}
return obj
init_kwargs = convert_added_tokens(init_kwargs)
# Set max length if needed
if pretrained_model_name_or_path in cls.max_model_input_sizes:
# if we're using a pretrained model, ensure the tokenizer
# wont index sequences longer than the number of positional embeddings
model_max_length = cls.max_model_input_sizes[pretrained_model_name_or_path]
if model_max_length is not None and isinstance(model_max_length, (int, float)):
init_kwargs["model_max_length"] = min(init_kwargs.get("model_max_length", int(1e30)), model_max_length)
# Merge resolved_vocab_files arguments in init_kwargs.
added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None)
for args_name, file_path in resolved_vocab_files.items():
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if slow_tokenizer is not None:
init_kwargs["__slow_tokenizer"] = slow_tokenizer
init_kwargs["name_or_path"] = pretrained_model_name_or_path
# Instantiate tokenizer.
try:
tokenizer = cls(*init_inputs, **init_kwargs)
except OSError:
raise OSError(
"Unable to load vocabulary from file. "
"Please check that the provided vocabulary is accessible and not corrupted."
)
# Save inputs and kwargs for saving and re-loading with ``save_pretrained``
# Removed: Now done at the base class level
# tokenizer.init_inputs = init_inputs
# tokenizer.init_kwargs = init_kwargs
# If there is a complementary special token map, load it
special_tokens_map_file = resolved_vocab_files.pop("special_tokens_map_file", None)
if special_tokens_map_file is not None:
with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
special_tokens_map = json.load(special_tokens_map_handle)
for key, value in special_tokens_map.items():
if isinstance(value, dict):
value = AddedToken(**value)
elif isinstance(value, list):
value = [AddedToken(**token) if isinstance(token, dict) else token for token in value]
setattr(tokenizer, key, value)
# Add supplementary tokens.
special_tokens = tokenizer.all_special_tokens
if added_tokens_file is not None:
with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
added_tok_encoder = json.load(added_tokens_handle)
# Sort added tokens by index
added_tok_encoder_sorted = list(sorted(added_tok_encoder.items(), key=lambda x: x[1]))
for token, index in added_tok_encoder_sorted:
assert index == len(tokenizer), (
f"Non-consecutive added token '{token}' found. "
f"Should have index {len(tokenizer)} but has index {index} in saved vocabulary."
)
tokenizer.add_tokens(token, special_tokens=bool(token in special_tokens))
# Check all our special tokens are registered as "no split" token (we don't cut them) and are in the vocab
added_tokens = tokenizer.sanitize_special_tokens()
if added_tokens:
logger.warning(
"Special tokens have been added in the vocabulary, make sure the associated word embedding are fine-tuned or trained."
)
return tokenizer
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
legacy_format: bool = True,
filename_prefix: Optional[str] = None,
) -> Tuple[str]:
"""
Save the full tokenizer state.
This method make sure the full tokenizer can then be re-loaded using the
:meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.from_pretrained` class method.
.. Note::
A "fast" tokenizer (instance of :class:`transformers.PreTrainedTokenizerFast`) saved with this method will
not be possible to load back in a "slow" tokenizer, i.e. in a :class:`transformers.PreTrainedTokenizer`
instance. It can only be loaded in a "fast" tokenizer, i.e. in a
:class:`transformers.PreTrainedTokenizerFast` instance.
.. Warning::
This won't save modifications you may have applied to the tokenizer after the instantiation (for instance,
modifying :obj:`tokenizer.do_lower_case` after creation).
Args:
save_directory (:obj:`str` or :obj:`os.PathLike`): The path to a directory where the tokenizer will be saved.
legacy_format (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to save the tokenizer in legacy format (default), i.e. with tokenizer specific vocabulary and a
separate added_tokens files or in the unified JSON file format for the `tokenizers` library. It's only
possible to save a Fast tokenizer in the unified JSON format and this format is incompatible with
"slow" tokenizers (not powered by the `tokenizers` library).
filename_prefix: (:obj:`str`, `optional`):
A prefix to add to the names of the files saved by the tokenizer.
Returns:
A tuple of :obj:`str`: The files saved.
"""
if os.path.isfile(save_directory):
logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
return
os.makedirs(save_directory, exist_ok=True)
special_tokens_map_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + SPECIAL_TOKENS_MAP_FILE
)
tokenizer_config_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_CONFIG_FILE
)
tokenizer_config = copy.deepcopy(self.init_kwargs)
if len(self.init_inputs) > 0:
tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs)
for file_id in self.vocab_files_names.keys():
tokenizer_config.pop(file_id, None)
# Sanitize AddedTokens
def convert_added_tokens(obj: Union[AddedToken, Any], add_type_field=True):
if isinstance(obj, AddedToken):
out = obj.__getstate__()
if add_type_field:
out["__type"] = "AddedToken"
return out
elif isinstance(obj, (list, tuple)):
return list(convert_added_tokens(o, add_type_field=add_type_field) for o in obj)
elif isinstance(obj, dict):
return {k: convert_added_tokens(v, add_type_field=add_type_field) for k, v in obj.items()}
return obj
# add_type_field=True to allow dicts in the kwargs / differentiate from AddedToken serialization
tokenizer_config = convert_added_tokens(tokenizer_config, add_type_field=True)
with open(tokenizer_config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tokenizer_config, ensure_ascii=False))
logger.info(f"tokenizer config file saved in {tokenizer_config_file}")
# Sanitize AddedTokens in special_tokens_map
write_dict = convert_added_tokens(self.special_tokens_map_extended, add_type_field=False)
with open(special_tokens_map_file, "w", encoding="utf-8") as f:
f.write(json.dumps(write_dict, ensure_ascii=False))
logger.info(f"Special tokens file saved in {special_tokens_map_file}")
file_names = (tokenizer_config_file, special_tokens_map_file)
return self._save_pretrained(
save_directory=save_directory,
file_names=file_names,
legacy_format=legacy_format,
filename_prefix=filename_prefix,
)
def _save_pretrained(
self,
save_directory: Union[str, os.PathLike],
file_names: Tuple[str],
legacy_format: bool = True,
filename_prefix: Optional[str] = None,
) -> Tuple[str]:
"""
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens.
Fast tokenizers can also be saved in a unique JSON file containing {config + vocab + added-tokens} using the
specific :meth:`~transformers.tokenization_utils_fast.PreTrainedTokenizerFast._save_pretrained`
"""
if not legacy_format:
raise ValueError(
"Only fast tokenizers (instances of PretrainedTokenizerFast) can be saved in non legacy format."
)
save_directory = str(save_directory)
added_tokens_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE
)
added_vocab = self.get_added_vocab()
if added_vocab:
with open(added_tokens_file, "w", encoding="utf-8") as f:
out_str = json.dumps(added_vocab, ensure_ascii=False)
f.write(out_str)
logger.info(f"added tokens file saved in {added_tokens_file}")
vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix)
return file_names + vocab_files + (added_tokens_file,)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
"""
Save only the vocabulary of the tokenizer (vocabulary + added tokens).
This method won't save the configuration and special token mappings of the tokenizer. Use
:meth:`~transformers.PreTrainedTokenizerFast._save_pretrained` to save the whole state of the tokenizer.
Args:
save_directory (:obj:`str`):
The directory in which to save the vocabulary.
filename_prefix (:obj:`str`, `optional`):
An optional prefix to add to the named of the saved files.
Returns:
:obj:`Tuple(str)`: Paths to the files saved.
"""
raise NotImplementedError
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
"""
Converts a string in a sequence of tokens, replacing unknown tokens with the :obj:`unk_token`.
Args:
text (:obj:`str`):
The sequence to be encoded.
pair (:obj:`str`, `optional`):
A second sequence to be encoded with the first.
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to add the special tokens associated with the corresponding model.
kwargs (additional keyword arguments, `optional`):
Will be passed to the underlying model specific encode method. See details in
:meth:`~transformers.PreTrainedTokenizerBase.__call__`
Returns:
:obj:`List[str]`: The list of tokens.
"""
raise NotImplementedError
@add_end_docstrings(
ENCODE_KWARGS_DOCSTRING,
"""
**kwargs: Passed along to the `.tokenize()` method.
""",
"""
Returns:
:obj:`List[int]`, :obj:`torch.Tensor`, :obj:`tf.Tensor` or :obj:`np.ndarray`: The tokenized ids of the
text.
""",
)
def encode(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
) -> List[int]:
"""
Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.
Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
Args:
text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
``tokenize`` method) or a list of integers (tokenized string ids using the ``convert_tokens_to_ids``
method).
text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the ``tokenize`` method) or a list of integers (tokenized string ids using the
``convert_tokens_to_ids`` method).
"""
encoded_inputs = self.encode_plus(
text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs["input_ids"]
def num_special_tokens_to_add(self, pair: bool = False) -> int:
raise NotImplementedError
def _get_padding_truncation_strategies(
self, padding=False, truncation=False, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs
):
"""
Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy
and pad_to_max_length) and behaviors.
"""
old_truncation_strategy = kwargs.pop("truncation_strategy", "do_not_truncate")
old_pad_to_max_length = kwargs.pop("pad_to_max_length", False)
# Backward compatibility for previous behavior, maybe we should deprecate it:
# If you only set max_length, it activates truncation for max_length
if max_length is not None and padding is False and truncation is False:
if verbose:
if not self.deprecation_warnings.get("Truncation-not-explicitly-activated", False):
logger.warning(
"Truncation was not explicitly activated but `max_length` is provided a specific value, "
"please use `truncation=True` to explicitly truncate examples to max length. "
"Defaulting to 'longest_first' truncation strategy. "
"If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy "
"more precisely by providing a specific strategy to `truncation`."
)
self.deprecation_warnings["Truncation-not-explicitly-activated"] = True
truncation = "longest_first"
# Get padding strategy
if padding is False and old_pad_to_max_length:
if verbose:
warnings.warn(
"The `pad_to_max_length` argument is deprecated and will be removed in a future version, "
"use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or "
"use `padding='max_length'` to pad to a max length. In this case, you can give a specific "
"length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the "
"maximal input size of the model (e.g. 512 for Bert).",
FutureWarning,
)
if max_length is None:
padding_strategy = PaddingStrategy.LONGEST
else:
padding_strategy = PaddingStrategy.MAX_LENGTH
elif padding is not False:
if padding is True:
padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(padding, PaddingStrategy):
padding_strategy = PaddingStrategy(padding)
elif isinstance(padding, PaddingStrategy):
padding_strategy = padding
else:
padding_strategy = PaddingStrategy.DO_NOT_PAD
# Get truncation strategy
if truncation is False and old_truncation_strategy != "do_not_truncate":
if verbose:
warnings.warn(
"The `truncation_strategy` argument is deprecated and will be removed in a future version, "
"use `truncation=True` to truncate examples to a max length. You can give a specific "
"length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the "
"maximal input size of the model (e.g. 512 for Bert). "
" If you have pairs of inputs, you can give a specific truncation strategy selected among "
"`truncation='only_first'` (will only truncate the first sentence in the pairs) "
"`truncation='only_second'` (will only truncate the second sentence in the pairs) "
"or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).",
FutureWarning,
)
truncation_strategy = TruncationStrategy(old_truncation_strategy)
elif truncation is not False:
if truncation is True:
truncation_strategy = (
TruncationStrategy.LONGEST_FIRST
) # Default to truncate the longest sequences in pairs of inputs
elif not isinstance(truncation, TruncationStrategy):
truncation_strategy = TruncationStrategy(truncation)
elif isinstance(truncation, TruncationStrategy):
truncation_strategy = truncation
else:
truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
if self.model_max_length > LARGE_INTEGER:
if verbose:
if not self.deprecation_warnings.get("Asking-to-pad-to-max_length", False):
logger.warning(
"Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. "
"Default to no padding."
)
self.deprecation_warnings["Asking-to-pad-to-max_length"] = True
padding_strategy = PaddingStrategy.DO_NOT_PAD
else:
max_length = self.model_max_length
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
if self.model_max_length > LARGE_INTEGER:
if verbose:
if not self.deprecation_warnings.get("Asking-to-truncate-to-max_length", False):
logger.warning(
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. "
"Default to no truncation."
)
self.deprecation_warnings["Asking-to-truncate-to-max_length"] = True
truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
else:
max_length = self.model_max_length
# Test if we have a padding token
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (not self.pad_token or self.pad_token_id < 0):
raise ValueError(
"Asking to pad but the tokenizer does not have a padding token. "
"Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
"or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
)
# Check that we will truncate to a multiple of pad_to_multiple_of if both are provided
if (
truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
and padding_strategy != PaddingStrategy.DO_NOT_PAD
and pad_to_multiple_of is not None
and max_length is not None
and (max_length % pad_to_multiple_of != 0)
):
raise ValueError(
f"Truncation and padding are both activated but "
f"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})."
)
return padding_strategy, truncation_strategy, max_length, kwargs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.
Args:
text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
:obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
text_pair (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
:obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
"""
# Input type checking for clearer error
assert isinstance(text, str) or (
isinstance(text, (list, tuple))
and (
len(text) == 0
or (
isinstance(text[0], str)
or (isinstance(text[0], (list, tuple)) and (len(text[0]) == 0 or isinstance(text[0][0], str)))
)
)
), (
"text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
"or `List[List[str]]` (batch of pretokenized examples)."
)
assert (
text_pair is None
or isinstance(text_pair, str)
or (
isinstance(text_pair, (list, tuple))
and (
len(text_pair) == 0
or (
isinstance(text_pair[0], str)
or (
isinstance(text_pair[0], (list, tuple))
and (len(text_pair[0]) == 0 or isinstance(text_pair[0][0], str))
)
)
)
)
), (
"text_pair input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
"or `List[List[str]]` (batch of pretokenized examples)."
)
is_batched = bool(
(not is_split_into_words and isinstance(text, (list, tuple)))
or (
is_split_into_words and isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
)
)
if is_batched:
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences.
.. warning::
This method is deprecated, ``__call__`` should be used instead.
Args:
text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]` (the latter only for not-fast tokenizers)):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
``tokenize`` method) or a list of integers (tokenized string ids using the ``convert_tokens_to_ids``
method).
text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the ``tokenize`` method) or a list of integers (tokenized string ids using the
``convert_tokens_to_ids`` method).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._encode_plus(
text=text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
raise NotImplementedError
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
.. warning::
This method is deprecated, ``__call__`` should be used instead.
Args:
batch_text_or_text_pairs (:obj:`List[str]`, :obj:`List[Tuple[str, str]]`, :obj:`List[List[str]]`, :obj:`List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also :obj:`List[List[int]]`, :obj:`List[Tuple[List[int], List[int]]]`):
Batch of sequences or pair of sequences to be encoded. This can be a list of
string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see
details in ``encode_plus``).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
raise NotImplementedError
def pad(
self,
encoded_inputs: Union[
BatchEncoding,
List[BatchEncoding],
Dict[str, EncodedInput],
Dict[str, List[EncodedInput]],
List[Dict[str, EncodedInput]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
verbose: bool = True,
) -> BatchEncoding:
"""
Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch.
Padding side (left/right) padding token ids are defined at the tokenizer level (with ``self.padding_side``,
``self.pad_token_id`` and ``self.pad_token_type_id``)
.. note::
If the ``encoded_inputs`` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with ``return_tensors``. In the
case of PyTorch tensors, you will lose the specific device of your tensors however.
Args:
encoded_inputs (:class:`~transformers.BatchEncoding`, list of :class:`~transformers.BatchEncoding`, :obj:`Dict[str, List[int]]`, :obj:`Dict[str, List[List[int]]` or :obj:`List[Dict[str, List[int]]]`):
Tokenized inputs. Can represent one input (:class:`~transformers.BatchEncoding` or :obj:`Dict[str,
List[int]]`) or a batch of tokenized inputs (list of :class:`~transformers.BatchEncoding`, `Dict[str,
List[List[int]]]` or `List[Dict[str, List[int]]]`) so you can use this method during preprocessing as
well as in a PyTorch Dataloader collate function.
Instead of :obj:`List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
see the note above for the return type.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5 (Volta).
return_attention_mask (:obj:`bool`, `optional`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to print more information and warnings.
"""
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], (dict, BatchEncoding)):
encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}
# The model's main input name, usually `input_ids`, has be passed for padding
if self.model_input_names[0] not in encoded_inputs:
raise ValueError(
"You should supply an encoding or a list of encodings to this method"
f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
)
required_input = encoded_inputs[self.model_input_names[0]]
if not required_input:
if return_attention_mask:
encoded_inputs["attention_mask"] = []
return encoded_inputs
# If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
index = 0
while len(required_input[index]) == 0:
index += 1
if index < len(required_input):
first_element = required_input[index][0]
# At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
if not isinstance(first_element, (int, list, tuple)):
if is_tf_available() and _is_tensorflow(first_element):
return_tensors = "tf" if return_tensors is None else return_tensors
elif is_torch_available() and _is_torch(first_element):
return_tensors = "pt" if return_tensors is None else return_tensors
elif isinstance(first_element, np.ndarray):
return_tensors = "np" if return_tensors is None else return_tensors
else:
raise ValueError(
f"type of {first_element} unknown: {type(first_element)}. "
f"Should be one of a python, numpy, pytorch or tensorflow object."
)
for key, value in encoded_inputs.items():
encoded_inputs[key] = to_py_obj(value)
# Convert padding_strategy in PaddingStrategy
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
padding=padding, max_length=max_length, verbose=verbose
)
required_input = encoded_inputs[self.model_input_names[0]]
if required_input and not isinstance(required_input[0], (list, tuple)):
encoded_inputs = self._pad(
encoded_inputs,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
batch_size = len(required_input)
assert all(
len(v) == batch_size for v in encoded_inputs.values()
), "Some items in the output dictionary have a different batch size than others."
if padding_strategy == PaddingStrategy.LONGEST:
max_length = max(len(inputs) for inputs in required_input)
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
inputs = dict((k, v[i]) for k, v in encoded_inputs.items())
outputs = self._pad(
inputs,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create the token type IDs corresponding to the sequences passed. `What are token type IDs?
<../glossary.html#token-type-ids>`__
Should be overridden in a subclass if the model has a special way of building those.
Args:
token_ids_0 (:obj:`List[int]`): The first tokenized sequence.
token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence.
Returns:
:obj:`List[int]`: The token type ids.
"""
if token_ids_1 is None:
return len(token_ids_0) * [0]
return [0] * len(token_ids_0) + [1] * len(token_ids_1)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens.
This implementation does not add special tokens and this method should be overridden in a subclass.
Args:
token_ids_0 (:obj:`List[int]`): The first tokenized sequence.
token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence.
Returns:
:obj:`List[int]`: The model input with special tokens.
"""
if token_ids_1 is None:
return token_ids_0
return token_ids_0 + token_ids_1
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs
) -> BatchEncoding:
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens
Args:
ids (:obj:`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the ``tokenize``
and ``convert_tokens_to_ids`` methods.
pair_ids (:obj:`List[int]`, `optional`):
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the ``tokenize``
and ``convert_tokens_to_ids`` methods.
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
pair = bool(pair_ids is not None)
len_ids = len(ids)
len_pair_ids = len(pair_ids) if pair else 0
if return_token_type_ids and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
# Load from model defaults
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
encoded_inputs = {}
# Compute the total size of the returned encodings
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
# Truncation: Handle max sequence length
overflowing_tokens = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
ids, pair_ids, overflowing_tokens = self.truncate_sequences(
ids,
pair_ids=pair_ids,
num_tokens_to_remove=total_len - max_length,
truncation_strategy=truncation_strategy,
stride=stride,
)
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = total_len - max_length
# Add special tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
# Build output dictionary
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
# Check lengths
self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
# Padding
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding_strategy.value,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return batch_outputs
def truncate_sequences(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
num_tokens_to_remove: int = 0,
truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
stride: int = 0,
) -> Tuple[List[int], List[int], List[int]]:
"""
Truncates a sequence pair in-place following the strategy.
Args:
ids (:obj:`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the ``tokenize``
and ``convert_tokens_to_ids`` methods.
pair_ids (:obj:`List[int]`, `optional`):
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the ``tokenize``
and ``convert_tokens_to_ids`` methods.
num_tokens_to_remove (:obj:`int`, `optional`, defaults to 0):
Number of tokens to remove using the truncation strategy.
truncation_strategy (:obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
The strategy to follow for truncation. Can be:
* :obj:`'longest_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
* :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
stride (:obj:`int`, `optional`, defaults to 0):
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
sequence returned. The value of this argument defines the number of additional tokens.
Returns:
:obj:`Tuple[List[int], List[int], List[int]]`: The truncated ``ids``, the truncated ``pair_ids`` and the
list of overflowing tokens.
"""
if num_tokens_to_remove <= 0:
return ids, pair_ids, []
if not isinstance(truncation_strategy, TruncationStrategy):
truncation_strategy = TruncationStrategy(truncation_strategy)
overflowing_tokens = []
if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
for _ in range(num_tokens_to_remove):
if pair_ids is None or len(ids) > len(pair_ids):
if not overflowing_tokens:
window_len = min(len(ids), stride + 1)
else:
window_len = 1
overflowing_tokens.extend(ids[-window_len:])
ids = ids[:-1]
else:
if not overflowing_tokens:
window_len = min(len(pair_ids), stride + 1)
else:
window_len = 1
overflowing_tokens.extend(pair_ids[-window_len:])
pair_ids = pair_ids[:-1]
elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
if len(ids) > num_tokens_to_remove:
window_len = min(len(ids), stride + num_tokens_to_remove)
overflowing_tokens = ids[-window_len:]
ids = ids[:-num_tokens_to_remove]
else:
logger.error(
f"We need to remove {num_tokens_to_remove} to truncate the input"
f"but the first sequence has a length {len(ids)}. "
f"Please select another truncation strategy than {truncation_strategy}, "
f"for instance 'longest_first' or 'only_second'."
)
elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
if len(pair_ids) > num_tokens_to_remove:
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
overflowing_tokens = pair_ids[-window_len:]
pair_ids = pair_ids[:-num_tokens_to_remove]
else:
logger.error(
f"We need to remove {num_tokens_to_remove} to truncate the input"
f"but the second sequence has a length {len(pair_ids)}. "
f"Please select another truncation strategy than {truncation_strategy}, "
f"for instance 'longest_first' or 'only_first'."
)
return (ids, pair_ids, overflowing_tokens)
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
>= 7.5 (Volta).
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
if needs_to_be_padded:
difference = max_length - len(required_input)
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(required_input) + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [1] * len(required_input)
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
elif return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
return encoded_inputs
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""
Converts a sequence of tokens in a single string. The most simple way to do it is ``" ".join(tokens)`` but we
often want to remove sub-word tokenization artifacts at the same time.
Args:
tokens (:obj:`List[str]`): The token to join in a string.
Returns:
:obj:`str`: The joined tokens.
"""
raise NotImplementedError
def batch_decode(
self,
sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
**kwargs
) -> List[str]:
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (:obj:`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the ``__call__`` method.
skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to clean up the tokenization spaces.
kwargs (additional keyword arguments, `optional`):
Will be passed to the underlying model specific decode method.
Returns:
:obj:`List[str]`: The list of decoded sentences.
"""
return [
self.decode(
seq,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
for seq in sequences
]
def decode(
self,
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
**kwargs
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
Args:
token_ids (:obj:`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the ``__call__`` method.
skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to clean up the tokenization spaces.
kwargs (additional keyword arguments, `optional`):
Will be passed to the underlying model specific decode method.
Returns:
:obj:`str`: The decoded sentence.
"""
# Convert inputs to python lists
token_ids = to_py_obj(token_ids)
return self._decode(
token_ids=token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
**kwargs
) -> str:
raise NotImplementedError
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0 (:obj:`List[int]`):
List of ids of the first sequence.
token_ids_1 (:obj:`List[int]`, `optional`):
List of ids of the second sequence.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
assert already_has_special_tokens and token_ids_1 is None, (
"You cannot use ``already_has_special_tokens=False`` with this tokenizer. "
"Please use a slow (full python) tokenizer to activate this argument."
"Or set `return_special_tokens_mask=True` when calling the encoding method "
"to get the special tokens mask in any tokenizer. "
)
all_special_ids = self.all_special_ids # cache the property
special_tokens_mask = [1 if token in all_special_ids else 0 for token in token_ids_0]
return special_tokens_mask
@staticmethod
def clean_up_tokenization(out_string: str) -> str:
"""
Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.
Args:
out_string (:obj:`str`): The text to clean up.
Returns:
:obj:`str`: The cleaned-up string.
"""
out_string = (
out_string.replace(" .", ".")
.replace(" ?", "?")
.replace(" !", "!")
.replace(" ,", ",")
.replace(" ' ", "'")
.replace(" n't", "n't")
.replace(" 'm", "'m")
.replace(" 's", "'s")
.replace(" 've", "'ve")
.replace(" 're", "'re")
)
return out_string
def _eventual_warn_about_too_long_sequence(self, ids: List[int], max_length: Optional[int], verbose: bool):
"""
Depending on the input and internal state we might trigger a warning about a sequence that is too long for it's
corresponding model
Args:
ids (:obj:`List[str]`): The ids produced by the tokenization
max_length (:obj:`int`, `optional`): The max_length desired (does not trigger a warning if it is set)
verbose (:obj:`bool`): Whether or not to print more information and warnings.
"""
if max_length is None and len(ids) > self.model_max_length and verbose:
if not self.deprecation_warnings.get("sequence-length-is-longer-than-the-specified-maximum", False):
logger.warning(
"Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
"indexing errors".format(len(ids), self.model_max_length)
)
self.deprecation_warnings["sequence-length-is-longer-than-the-specified-maximum"] = True
@contextmanager
def as_target_tokenizer(self):
"""
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
"""
yield
def prepare_seq2seq_batch(
self,
src_texts: List[str],
tgt_texts: Optional[List[str]] = None,
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
padding: str = "longest",
return_tensors: str = None,
truncation: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Prepare model inputs for translation. For best performance, translate one sentence at a time.
Arguments:
src_texts (:obj:`List[str]`):
List of documents to summarize or source language texts.
tgt_texts (:obj:`list`, `optional`):
List of summaries or target language texts.
max_length (:obj:`int`, `optional`):
Controls the maximum length for encoder inputs (documents to summarize or source language texts) If
left unset or set to :obj:`None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
max_target_length (:obj:`int`, `optional`):
Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set
to :obj:`None`, this will use the max_length value.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
Activates and controls padding. Accepts the following values:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`True`):
Activates and controls truncation. Accepts the following values:
* :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
:obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
provided. This will truncate token by token, removing a token from the longest sequence in the pair
if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
* :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
sequence lengths greater than the model maximum admissible input size).
**kwargs:
Additional keyword arguments passed along to :obj:`self.__call__`.
Return:
:class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:
- **input_ids** -- List of token ids to be fed to the encoder.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model.
- **labels** -- List of token ids for tgt_texts.
The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.
"""
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details",
FutureWarning,
)
# mBART-specific kwargs that should be ignored by other models.
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)
if max_length is None:
max_length = self.model_max_length
model_inputs = self(
src_texts,
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
padding=padding,
truncation=truncation,
**kwargs,
)
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
max_target_length = max_length
with self.as_target_tokenizer():
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
|
AdaMix/src/transformers/tokenization_utils_base.py/0
|
{
"file_path": "AdaMix/src/transformers/tokenization_utils_base.py",
"repo_id": "AdaMix",
"token_count": 67801
}
| 68 |
{
"example_name": "text classification",
"directory_name": "{{cookiecutter.example_name|lower|replace(' ', '-')}}",
"example_shortcut": "{{cookiecutter.directory_name}}",
"model_class": "AutoModel",
"authors": "The HuggingFace Team",
"can_train_from_scratch": ["True", "False"]
}
|
AdaMix/templates/adding_a_new_example_script/cookiecutter.json/0
|
{
"file_path": "AdaMix/templates/adding_a_new_example_script/cookiecutter.json",
"repo_id": "AdaMix",
"token_count": 101
}
| 69 |
How to add BigBird to 🤗 Transformers?
=====================================
Mentor: [Patrick](https://github.com/patrickvonplaten)
Begin: 12.02.2020
Estimated End: 19.03.2020
Contributor: [Vasudev](https://github.com/vasudevgupta7)
Adding a new model is often difficult and requires an in-depth knowledge
of the 🤗 Transformers library and ideally also of the model's original
repository. At Hugging Face, we are trying to empower the community more
and more to add models independently.
The following sections explain in detail how to add BigBird
to Transformers. You will work closely with Patrick to
integrate BigBird into Transformers. By doing so, you will both gain a
theoretical and deep practical understanding of BigBird.
But more importantly, you will have made a major
open-source contribution to Transformers. Along the way, you will:
- get insights into open-source best practices
- understand the design principles of one of the most popular NLP
libraries
- learn how to do efficiently test large NLP models
- learn how to integrate Python utilities like `black`, `isort`,
`make fix-copies` into a library to always ensure clean and readable
code
To start, let's try to get a general overview of the Transformers
library.
General overview of 🤗 Transformers
----------------------------------
First, you should get a general overview of 🤗 Transformers. Transformers
is a very opinionated library, so there is a chance that
you don't agree with some of the library's philosophies or design
choices. From our experience, however, we found that the fundamental
design choices and philosophies of the library are crucial to
efficiently scale Transformers while keeping maintenance costs at a
reasonable level.
A good first starting point to better understand the library is to read
the [documentation of our philosophy](https://huggingface.co/transformers/philosophy.html).
As a result of our way of working, there are some choices that we try to apply to all models:
- Composition is generally favored over abstraction
- Duplicating code is not always bad if it strongly improves the
readability or accessibility of a model
- Model files are as self-contained as possible so that when you read
the code of a specific model, you ideally only have to look into the
respective `modeling_....py` file.
In our opinion, the library's code is not just a means to provide a
product, *e.g.*, the ability to use BERT for inference, but also as the
very product that we want to improve. Hence, when adding a model, the
user is not only the person that will use your model, but also everybody
that will read, try to understand, and possibly tweak your code.
With this in mind, let's go a bit deeper into the general library
design.
### Overview of models
To successfully add a model, it is important to understand the
interaction between your model and its config,
`PreTrainedModel`, and `PretrainedConfig`. For
exemplary purposes, we will call the PyTorch model to be added to 🤗 Transformers
`BrandNewBert`.
Let's take a look:
As you can see, we do make use of inheritance in 🤗 Transformers, but we
keep the level of abstraction to an absolute minimum. There are never
more than two levels of abstraction for any model in the library.
`BrandNewBertModel` inherits from
`BrandNewBertPreTrainedModel` which in
turn inherits from `PreTrainedModel` and that's it.
As a general rule, we want to make sure
that a new model only depends on `PreTrainedModel`. The
important functionalities that are automatically provided to every new
model are
`PreTrainedModel.from_pretrained` and `PreTrainedModel.save_pretrained`, which are
used for serialization and deserialization. All
of the other important functionalities, such as
`BrandNewBertModel.forward` should be
completely defined in the new `modeling_brand_new_bert.py` module. Next,
we want to make sure that a model with a specific head layer, such as
`BrandNewBertForMaskedLM` does not inherit
from `BrandNewBertModel`, but rather uses
`BrandNewBertModel` as a component that
can be called in its forward pass to keep the level of abstraction low.
Every new model requires a configuration class, called
`BrandNewBertConfig`. This configuration
is always stored as an attribute in
`PreTrainedModel`, and
thus can be accessed via the `config` attribute for all classes
inheriting from `BrandNewBertPreTrainedModel`
```python
# assuming that `brand_new_bert` belongs to the organization `brandy`
model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert")
model.config # model has access to its config
```
Similar to the model, the configuration inherits basic serialization and
deserialization functionalities from
`PretrainedConfig`. Note
that the configuration and the model are always serialized into two
different formats - the model to a `pytorch_model.bin` file
and the configuration to a `config.json` file. Calling
`PreTrainedModel.save_pretrained` will automatically call
`PretrainedConfig.save_pretrained`, so that both model and configuration are saved.
### Overview of tokenizers
Not quite ready yet :-( This section will be added soon!
Step-by-step recipe to add a model to 🤗 Transformers
----------------------------------------------------
Everyone has different preferences of how to port a model so it can be
very helpful for you to take a look at summaries of how other
contributors ported models to Hugging Face. Here is a list of community
blog posts on how to port a model:
1. [Porting GPT2
Model](https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28)
by [Thomas](https://huggingface.co/thomwolf)
2. [Porting WMT19 MT Model](https://huggingface.co/blog/porting-fsmt)
by [Stas](https://huggingface.co/stas)
From experience, we can tell you that the most important things to keep
in mind when adding a model are:
- Don't reinvent the wheel! Most parts of the code you will add for
the new 🤗 Transformers model already exist somewhere in 🤗
Transformers. Take some time to find similar, already existing
models and tokenizers you can copy from.
[grep](https://www.gnu.org/software/grep/) and
[rg](https://github.com/BurntSushi/ripgrep) are your friends. Note
that it might very well happen that your model's tokenizer is based
on one model implementation, and your model's modeling code on
another one. *E.g.*, FSMT's modeling code is based on BART, while
FSMT's tokenizer code is based on XLM.
- It's more of an engineering challenge than a scientific challenge.
You should spend more time on creating an efficient debugging
environment than trying to understand all theoretical aspects of the
model in the paper.
- Ask for help when you're stuck! Models are the core component of 🤗
Transformers so we, at Hugging Face, are more than happy to help
you at every step to add your model. Don't hesitate to ask if you
notice you are not making progress.
In the following, we try to give you a general recipe that we found most
useful when porting a model to 🤗 Transformers.
The following list is a summary of everything that has to be done to add
a model and can be used by you as a To-Do List:
1. [ ] (Optional) Understood theoretical aspects
2. [ ] Prepared transformers dev environment
3. [ ] Set up debugging environment of the original repository
4. [ ] Created script that successfully runs forward pass using
original repository and checkpoint
5. [ ] Successfully opened a PR and added the model skeleton to Transformers
6. [ ] Successfully converted original checkpoint to Transformers
checkpoint
7. [ ] Successfully ran forward pass in Transformers that gives
identical output to original checkpoint
8. [ ] Finished model tests in Transformers
9. [ ] Successfully added Tokenizer in Transformers
10. [ ] Run end-to-end integration tests
11. [ ] Finished docs
12. [ ] Uploaded model weights to the hub
13. [ ] Submitted the pull request for review
14. [ ] (Optional) Added a demo notebook
To begin with, we usually recommend to start by getting a good
theoretical understanding of `BigBird`. However, if you prefer to
understand the theoretical aspects of the model *on-the-job*, then it is
totally fine to directly dive into the `BigBird`'s code-base. This
option might suit you better, if your engineering skills are better than
your theoretical skill, if you have trouble understanding
`BigBird`'s paper, or if you just enjoy programming much more than
reading scientific papers.
### 1. (Optional) Theoretical aspects of BigBird
You should take some time to read *BigBird's* paper, if such
descriptive work exists. There might be large sections of the paper that
are difficult to understand. If this is the case, this is fine - don't
worry! The goal is not to get a deep theoretical understanding of the
paper, but to extract the necessary information required to effectively
re-implement the model in 🤗 Transformers. That being said, you don't
have to spend too much time on the theoretical aspects, but rather focus
on the practical ones, namely:
- What type of model is *BigBird*? BERT-like encoder-only
model? GPT2-like decoder-only model? BART-like encoder-decoder
model? Look at the `model_summary` if
you're not familiar with the differences between those.
- What are the applications of *BigBird*? Text
classification? Text generation? Seq2Seq tasks, *e.g.,*
summarization?
- What is the novel feature of the model making it different from
BERT/GPT-2/BART?
- Which of the already existing [🤗 Transformers
models](https://huggingface.co/transformers/#contents) is most
similar to *BigBird*?
- What type of tokenizer is used? A sentencepiece tokenizer? Word
piece tokenizer? Is it the same tokenizer as used for BERT or BART?
After you feel like you have gotten a good overview of the architecture
of the model, you might want to write to Patrick with any
questions you might have. This might include questions regarding the
model's architecture, its attention layer, etc. We will be more than
happy to help you.
#### Additional resources
Before diving into the code, here are some additional resources that might be worth taking a look at:
- [Yannic Kilcher's paper summary](https://www.youtube.com/watch?v=WVPE62Gk3EM&ab_channel=YannicKilcher)
- [Yannic Kilcher's summary of Longformer](https://www.youtube.com/watch?v=_8KNb5iqblE&ab_channel=YannicKilcher) - Longformer and BigBird are **very** similar models. Since Longformer has already been ported to 🤗 Transformers, it is useful to understand the differences between the two models
- [Blog post](https://medium.com/dsc-msit/is-google-bigbird-gonna-be-the-new-leader-in-nlp-domain-8c95cecc30f8) - A relatively superficial blog post about BigBird. Might be a good starting point to understand BigBird
#### Make sure you've understood the fundamental aspects of BigBird
Alright, now you should be ready to take a closer look into the actual code of BigBird.
You should have understood the following aspects of BigBird by now:
- BigBird provides a new attention layer for long-range sequence modelling that can be used
as a drop-in replacement for already existing architectures. This means that every transformer-based model architecture can replace its [Self-attention layer](https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a) with BigBird's self-attention layer.
- BigBird's self-attention layer is composed of three mechanisms: block sparse (local) self-attention, global self-attention, random self-attention
- BigBird's block sparse (local) self-attention is different from Longformer's local self-attention. How so? Why does that matter? => Can be deployed on TPU much easier this way
- BigBird can be implemented for both an encoder-only model **and**
for an encoder-decoder model, which means that we can reuse lots of [code from RoBERTa](https://github.com/huggingface/transformers/blob/master/src/transformers/models/roberta/modeling_roberta.py) and [from PEGASUS](https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py) at a later stage.
If any of the mentioned aspects above are **not** clear to you, now is a great time to talk to Patrick.
### 2. Next prepare your environment
1. Fork the [repository](https://github.com/huggingface/transformers)
by clicking on the 'Fork' button on the repository's page. This
creates a copy of the code under your GitHub user account.
2. Clone your `transformers` fork to your local disk, and add the base
repository as a remote:
```bash
git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
```
3. Set up a development environment, for instance by running the
following command:
```bash
python -m venv .env
source .env/bin/activate
pip install -e ".[dev]"
```
and return to the parent directory
```bash
cd ..
```
4. We recommend adding the PyTorch version of *BigBird* to
Transformers. To install PyTorch, please follow the instructions [here](https://pytorch.org/get-started/locally/).
**Note:** You don't need to have CUDA installed. Making the new model
work on CPU is sufficient.
5. To port *BigBird*, you will also need access to its
original repository:
```bash
git clone https://github.com/google-research/bigbird.git
cd big_bird
pip install -e .
```
Now you have set up a development environment to port *BigBird*
to 🤗 Transformers.
### Run a pretrained checkpoint using the original repository
**3. Set up debugging environment**
At first, you will work on the original *BigBird* repository.
Often, the original implementation is very "researchy". Meaning that
documentation might be lacking and the code can be difficult to
understand. But this should be exactly your motivation to reimplement
*BigBird*. At Hugging Face, one of our main goals is to *make
people stand on the shoulders of giants* which translates here very well
into taking a working model and rewriting it to make it as **accessible,
user-friendly, and beautiful** as possible. This is the number-one
motivation to re-implement models into 🤗 Transformers - trying to make
complex new NLP technology accessible to **everybody**.
You should start thereby by diving into the [original repository](https://github.com/google-research/bigbird).
Successfully running the official pretrained model in the original
repository is often **the most difficult** step. From our experience, it
is very important to spend some time getting familiar with the original
code-base. You need to figure out the following:
- Where to find the pretrained weights?
- How to load the pretrained weights into the corresponding model?
- How to run the tokenizer independently from the model?
- Trace one forward pass so that you know which classes and functions
are required for a simple forward pass. Usually, you only have to
reimplement those functions.
- Be able to locate the important components of the model: Where is
the model's class? Are there model sub-classes, *e.g.*,
EncoderModel, DecoderModel? Where is the self-attention layer? Are
there multiple different attention layers, *e.g.*, *self-attention*,
*cross-attention*...?
- How can you debug the model in the original environment of the repo?
Do you have to add `print` statements, can you work with
an interactive debugger like [ipdb](https://pypi.org/project/ipdb/), or should you use
an efficient IDE to debug the model, like PyCharm?
It is very important that before you start the porting process, that you
can **efficiently** debug code in the original repository! Also,
remember that you are working with an open-source library, so do not
hesitate to open an issue, or even a pull request in the original
repository. The maintainers of this repository are most likely very
happy about someone looking into their code!
At this point, it is really up to you which debugging environment and
strategy you prefer to use to debug the original model. We strongly
advise against setting up a costly GPU environment, but simply work on a
CPU both when starting to dive into the original repository and also
when starting to write the 🤗 Transformers implementation of the model.
Only at the very end, when the model has already been successfully
ported to 🤗 Transformers, one should verify that the model also works as
expected on GPU.
In general, there are two possible debugging environments for running
the original model
- [Jupyter notebooks](https://jupyter.org/) / [google colab](https://colab.research.google.com/notebooks/intro.ipynb)
- Local python scripts.
Jupyter notebooks have the advantage that they allow for cell-by-cell
execution which can be helpful to better split logical components from
one another and to have faster debugging cycles as intermediate results
can be stored. Also, notebooks are often easier to share with other
contributors, which might be very helpful if you want to ask the Hugging
Face team for help. If you are familiar with Jupiter notebooks, we
strongly recommend you to work with them.
The obvious disadvantage of Jupyther notebooks is that if you are not
used to working with them you will have to spend some time adjusting to
the new programming environment and that you might not be able to use
your known debugging tools anymore, like `ipdb`.
**4. Successfully run forward pass**
For each code-base, a good first step is always to load a **small**
pretrained checkpoint and to be able to reproduce a single forward pass
using a dummy integer vector of input IDs as an input. Such a script
could look something like this:
```python
from bigbird.core import modeling
model = modeling.BertModel(bert_config)
from bigbird.core import utils
params = utils.BigBirdConfig(vocab_size=32000, hidden_size=512,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
ckpt_path = 'gs://bigbird-transformer/pretrain/bigbr_base/model.ckpt-0'
ckpt_reader = tf.compat.v1.train.NewCheckpointReader(ckpt_path)
model.set_weights([ckpt_reader.get_tensor(v.name[:-2]) for v in tqdm(model.trainable_weights, position=0)])
input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
_, pooled_output = model(input_ids=input_ids, token_type_ids=token_type_ids)
...
```
Next, regarding the debugging strategy, there are generally a few from
which to choose from:
- Decompose the original model into many small testable components and
run a forward pass on each of those for verification
- Decompose the original model only into the original *tokenizer* and
the original *model*, run a forward pass on those, and use
intermediate print statements or breakpoints for verification
Again, it is up to you which strategy to choose. Often, one or the other
is advantageous depending on the original code base.
If the original code-base allows you to decompose the model into smaller
sub-components, *e.g.*, if the original code-base can easily be run in
eager mode, it is usually worth the effort to do so. There are some
important advantages to taking the more difficult road in the beginning:
- at a later stage when comparing the original model to the Hugging
Face implementation, you can verify automatically for each component
individually that the corresponding component of the 🤗 Transformers
implementation matches instead of relying on visual comparison via
print statements
- it can give you some rope to decompose the big problem of porting a
model into smaller problems of just porting individual components
and thus structure your work better
- separating the model into logical meaningful components will help
you to get a better overview of the model's design and thus to
better understand the model
- at a later stage those component-by-component tests help you to
ensure that no regression occurs as you continue changing your code
[Lysandre's](https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed)
integration checks for ELECTRA gives a nice example of how this can be
done.
However, if the original code-base is very complex or only allows
intermediate components to be run in a compiled mode, it might be too
time-consuming or even impossible to separate the model into smaller
testable sub-components. A good example is [T5's
MeshTensorFlow](https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow)
library which is very complex and does not offer a simple way to
decompose the model into its sub-components. For such libraries, one
often relies on verifying print statements.
No matter which strategy you choose, the recommended procedure is often
the same in that you should start to debug the starting layers first and
the ending layers last.
It is recommended that you retrieve the output, either by print
statements or sub-component functions, of the following layers in the
following order:
1. Retrieve the input IDs passed to the model
2. Retrieve the word embeddings
3. Retrieve the input of the first Transformer layer
4. Retrieve the output of the first Transformer layer
5. Retrieve the output of the following n - 1 Transformer layers
6. Retrieve the output of the whole BigBird Model
Input IDs should thereby consists of an array of integers, *e.g.*,
`input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]`
The outputs of the following layers often consist of multi-dimensional
float arrays and can look like this:
```bash
[[
[-0.1465, -0.6501, 0.1993, ..., 0.1451, 0.3430, 0.6024],
[-0.4417, -0.5920, 0.3450, ..., -0.3062, 0.6182, 0.7132],
[-0.5009, -0.7122, 0.4548, ..., -0.3662, 0.6091, 0.7648],
...,
[-0.5613, -0.6332, 0.4324, ..., -0.3792, 0.7372, 0.9288],
[-0.5416, -0.6345, 0.4180, ..., -0.3564, 0.6992, 0.9191],
[-0.5334, -0.6403, 0.4271, ..., -0.3339, 0.6533, 0.8694]]],
```
We expect that every model added to 🤗 Transformers passes a couple of
integration tests, meaning that the original model and the reimplemented
version in 🤗 Transformers have to give the exact same output up to a
precision of 0.001! Since it is normal that the exact same model written
in different libraries can give a slightly different output depending on
the library framework, we accept an error tolerance of 1e-3 (0.001). It
is not enough if the model gives nearly the same output, they have to be
the almost identical. Therefore, you will certainly compare the
intermediate outputs of the 🤗 Transformers version multiple times
against the intermediate outputs of the original implementation of
*BigBird* in which case an **efficient** debugging environment
of the original repository is absolutely important. Here is some advice
to make your debugging environment as efficient as possible.
- Find the best way of debugging intermediate results. Is the original
repository written in PyTorch? Then you should probably take the
time to write a longer script that decomposes the original model
into smaller sub-components to retrieve intermediate values. Is the
original repository written in Tensorflow 1? Then you might have to
rely on TensorFlow print operations like
[tf.print](https://www.tensorflow.org/api_docs/python/tf/print) to
output intermediate values. Is the original repository written in
Jax? Then make sure that the model is **not jitted** when running
the forward pass, *e.g.*, check-out [this
link](https://github.com/google/jax/issues/196).
- Use the smallest pretrained checkpoint you can find. The smaller the
checkpoint, the faster your debug cycle becomes. It is not efficient
if your pretrained model is so big that your forward pass takes more
than 10 seconds. In case only very large checkpoints are available,
it might make more sense to create a dummy model in the new
environment with randomly initialized weights and save those weights
for comparison with the 🤗 Transformers version of your model
- Make sure you are using the easiest way of calling a forward pass in
the original repository. Ideally, you want to find the function in
the original repository that **only** calls a single forward pass,
*i.e.* that is often called `predict`, `evaluate`, `forward` or
`__call__`. You don't want to debug a function that calls `forward`
multiple times, *e.g.*, to generate text, like
`autoregressive_sample`, `generate`.
- Try to separate the tokenization from the model's
forward pass. If the original repository shows
examples where you have to input a string, then try to find out
where in the forward call the string input is changed to input ids
and start from this point. This might mean that you have to possibly
write a small script yourself or change the original code so that
you can directly input the ids instead of an input string.
- Make sure that the model in your debugging setup is **not** in
training mode, which often causes the model to yield random outputs
due to multiple dropout layers in the model. Make sure that the
forward pass in your debugging environment is **deterministic** so
that the dropout layers are not used. Or use
`transformers.file_utils.set_seed` if the old and new
implementations are in the same framework.
#### (Important) More details on how to create a debugging environment for BigBird
- BigBird has multiple pretrained checkpoints that should eventually all be ported to
🤗 Transformers. The pretrained checkpoints can be found [here](https://console.cloud.google.com/storage/browser/bigbird-transformer/pretrain;tab=objects?prefix=&forceOnObjectsSortingFiltering=false).
Those checkpoints include both pretrained weights for encoder-only (BERT/RoBERTa) under the folder `bigbr_base` and encoder-decoder (PEGASUS) under the folder `bigbp_large`.
You should start by porting the `bigbr_base` model. The encoder-decoder model
can be ported afterward.
for an encoder-decoder architecture as well as an encoder-only architecture.
- BigBird was written in tf.compat meaning that a mixture of a TensorFlow 1 and
TensorFlow 2 API was used.
- The most important part of the BigBird code-base is [bigbird.bigbird.core](https://github.com/google-research/bigbird/tree/master/bigbird/core) which includes all logic necessary
to implement BigBird.
- The first goal should be to successfully run a forward pass using the RoBERTa checkpoint `bigbr_base/model.ckpt-0.data-00000-of-00001` and `bigbr_base/model.ckpt-0.index`.
### Port BigBird to 🤗 Transformers
Next, you can finally start adding new code to 🤗 Transformers. Go into
the clone of your 🤗 Transformers' fork:
cd transformers
In the special case that you are adding a model whose architecture
exactly matches the model architecture of an existing model you only
have to add a conversion script as described in [this
section](#write-a-conversion-script). In this case, you can just re-use
the whole model architecture of the already existing model.
Otherwise, let's start generating a new model with the amazing
Cookiecutter!
**Use the Cookiecutter to automatically generate the model's code**
To begin with head over to the [🤗 Transformers
templates](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model)
to make use of our `cookiecutter` implementation to automatically
generate all the relevant files for your model. Again, we recommend only
adding the PyTorch version of the model at first. Make sure you follow
the instructions of the `README.md` on the [🤗 Transformers
templates](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model)
carefully.
Since you will first implement the Encoder-only/RoBERTa-like version of BigBird you should
select the `is_encoder_decoder_model = False` option in the cookiecutter. Also, it is recommended
that you implement the model only in PyTorch in the beginning and select "Standalone" as the
tokenizer type for now.
**Open a Pull Request on the main huggingface/transformers repo**
Before starting to adapt the automatically generated code, now is the
time to open a "Work in progress (WIP)" pull request, *e.g.*, "\[WIP\]
Add *BigBird*", in 🤗 Transformers so that you and the Hugging
Face team can work side-by-side on integrating the model into 🤗
Transformers.
You should do the following:
1. Create a branch with a descriptive name from your master branch
```
git checkout -b add_big_bird
```
2. Commit the automatically generated code:
```
git add .
git commit
```
3. Fetch and rebase to current master
```
git fetch upstream
git rebase upstream/master
```
4. Push the changes to your account using:
```
git push -u origin a-descriptive-name-for-my-changes
```
5. Once you are satisfied, go to the webpage of your fork on GitHub.
Click on "Pull request". Make sure to add the GitHub handle of Patrick
as one reviewer, so that the Hugging Face team gets notified for future changes.
6. Change the PR into a draft by clicking on "Convert to draft" on the
right of the GitHub pull request web page.
In the following, whenever you have done some progress, don't forget to
commit your work and push it to your account so that it shows in the
pull request. Additionally, you should make sure to update your work
with the current master from time to time by doing:
git fetch upstream
git merge upstream/master
In general, all questions you might have regarding the model or your
implementation should be asked in your PR and discussed/solved in the
PR. This way, Patrick will always be notified when you are
committing new code or if you have a question. It is often very helpful
to point Patrick to your added code so that the Hugging
Face team can efficiently understand your problem or question.
To do so, you can go to the "Files changed" tab where you see all of
your changes, go to a line regarding which you want to ask a question,
and click on the "+" symbol to add a comment. Whenever a question or
problem has been solved, you can click on the "Resolve" button of the
created comment.
In the same way, Patrick will open comments when reviewing
your code. We recommend asking most questions on GitHub on your PR. For
some very general questions that are not very useful for the public,
feel free to ping Patrick by Slack or email.
**5. Adapt the generated models code for BigBird**
At first, we will focus only on the model itself and not care about the
tokenizer. All the relevant code should be found in the generated files
`src/transformers/models/big_bird/modeling_big_bird.py` and
`src/transformers/models/big_bird/configuration_big_bird.py`.
Now you can finally start coding :). The generated code in
`src/transformers/models/big_bird/modeling_big_bird.py` will
either have the same architecture as BERT if it's an encoder-only model
or BART if it's an encoder-decoder model. At this point, you should
remind yourself what you've learned in the beginning about the
theoretical aspects of the model: *How is the model different from BERT
or BART?*\". Implement those changes which often means to change the
*self-attention* layer, the order of the normalization layer, etc...
Again, it is often useful to look at the similar architecture of already
existing models in Transformers to get a better feeling of how your
model should be implemented.
**Note** that at this point, you don't have to be very sure that your
code is fully correct or clean. Rather, it is advised to add a first
*unclean*, copy-pasted version of the original code to
`src/transformers/models/big_bird/modeling_big_bird.py`
until you feel like all the necessary code is added. From our
experience, it is much more efficient to quickly add a first version of
the required code and improve/correct the code iteratively with the
conversion script as described in the next section. The only thing that
has to work at this point is that you can instantiate the 🤗 Transformers
implementation of *BigBird*, *i.e.* the following command
should work:
```python
from transformers import BigBirdModel, BigBirdConfig
model = BigBirdModel(BigBirdConfig())
```
The above command will create a model according to the default
parameters as defined in `BigBirdConfig()` with random weights,
thus making sure that the `init()` methods of all components works.
Note that for BigBird you have to change the attention layer. BigBird's attention
layer is quite complex as you can see [here](https://github.com/google-research/bigbird/blob/103a3345f94bf6364749b51189ed93024ca5ef26/bigbird/core/attention.py#L560). Don't
feel discouraged by this! In a first step you should simply make sure that
the layer `BigBirdAttention` has the correct weights as can be found in the
pretrained checkpoints. This means that you have to make sure that in the
`__init__(self, ...)` function of `BigBirdAttention`, all submodules include all
necessary `nn.Module` layers. Only at a later stage do we need to fully rewrite
the complex attention function.
**6. Write a conversion script**
Next, you should write a conversion script that lets you convert the
checkpoint you used to debug *BigBird* in the original
repository to a checkpoint compatible with your just created 🤗
Transformers implementation of *BigBird*. It is not advised to
write the conversion script from scratch, but rather to look through
already existing conversion scripts in 🤗 Transformers for one that has
been used to convert a similar model that was written in the same
framework as *BigBird*. Usually, it is enough to copy an
already existing conversion script and slightly adapt it for your use
case. Don't hesitate to ask Patrick to point you to a
similar already existing conversion script for your model.
- A good starting point to convert the original TF BigBird implementation to the PT Hugging Face implementation is probably BERT's conversion script
[here](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91)
You can copy paste the conversion function into `modeling_big_bird.py` and then adapt it
to your needs.
In the following, we'll quickly explain how PyTorch models store layer
weights and define layer names. In PyTorch, the name of a layer is
defined by the name of the class attribute you give the layer. Let's
define a dummy model in PyTorch, called `SimpleModel` as follows:
```python
import torch.nn as nn
class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.dense = nn.Linear(10, 10)
self.intermediate = nn.Linear(10, 10)
self.layer_norm = nn.LayerNorm(10)
```
Now we can create an instance of this model definition which will fill
all weights: `dense`, `intermediate`, `layer_norm` with random weights.
We can print the model to see its architecture
```python
model = SimpleModel()
print(model)
```
This will print out the following:
```bash
SimpleModel(
(dense): Linear(in_features=10, out_features=10, bias=True)
(intermediate): Linear(in_features=10, out_features=10, bias=True)
(layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
)
```
We can see that the layer names are defined by the name of the class
attribute in PyTorch. You can print out the weight values of a specific
layer:
```python
print(model.dense.weight.data)
```
to see that the weights were randomly initialized
```bash
tensor([[-0.0818, 0.2207, -0.0749, -0.0030, 0.0045, -0.1569, -0.1598, 0.0212,
-0.2077, 0.2157],
[ 0.1044, 0.0201, 0.0990, 0.2482, 0.3116, 0.2509, 0.2866, -0.2190,
0.2166, -0.0212],
[-0.2000, 0.1107, -0.1999, -0.3119, 0.1559, 0.0993, 0.1776, -0.1950,
-0.1023, -0.0447],
[-0.0888, -0.1092, 0.2281, 0.0336, 0.1817, -0.0115, 0.2096, 0.1415,
-0.1876, -0.2467],
[ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
0.2577, 0.0402],
[ 0.1502, 0.2465, 0.2566, 0.0693, 0.2352, -0.0530, 0.1859, -0.0604,
0.2132, 0.1680],
[ 0.1733, -0.2407, -0.1721, 0.1484, 0.0358, -0.0633, -0.0721, -0.0090,
0.2707, -0.2509],
[-0.1173, 0.1561, 0.2945, 0.0595, -0.1996, 0.2988, -0.0802, 0.0407,
0.1829, -0.1568],
[-0.1164, -0.2228, -0.0403, 0.0428, 0.1339, 0.0047, 0.1967, 0.2923,
0.0333, -0.0536],
[-0.1492, -0.1616, 0.1057, 0.1950, -0.2807, -0.2710, -0.1586, 0.0739,
0.2220, 0.2358]]).
```
In the conversion script, you should fill those randomly initialized
weights with the exact weights of the corresponding layer in the
checkpoint. *E.g.*,
```python
# retrieve matching layer weights, e.g. by
# recursive algorithm
layer_name = "dense"
pretrained_weight = array_of_dense_layer
model_pointer = getattr(model, "dense")
model_pointer.weight.data = torch.from_numpy(pretrained_weight)
```
While doing so, you must verify that each randomly initialized weight of
your PyTorch model and its corresponding pretrained checkpoint weight
exactly match in both **shape and name**. To do so, it is **necessary**
to add assert statements for the shape and print out the names of the
checkpoints weights. *E.g.*, you should add statements like:
```python
assert (
model_pointer.weight.shape == pretrained_weight.shape
), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched"
```
Besides, you should also print out the names of both weights to make
sure they match, *e.g.*,
```python
logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}")
```
If either the shape or the name doesn't match, you probably assigned
the wrong checkpoint weight to a randomly initialized layer of the 🤗
Transformers implementation.
An incorrect shape is most likely due to an incorrect setting of the
config parameters in `BigBirdConfig()` that do not exactly match
those that were used for the checkpoint you want to convert. However, it
could also be that PyTorch's implementation of a layer requires the
weight to be transposed beforehand.
Finally, you should also check that **all** required weights are
initialized and print out all checkpoint weights that were not used for
initialization to make sure the model is correctly converted. It is
completely normal, that the conversion trials fail with either a wrong
shape statement or wrong name assignment. This is most likely because
either you used incorrect parameters in `BigBirdConfig()`, have a
wrong architecture in the 🤗 Transformers implementation, you have a bug
in the `init()` functions of one of the components of the 🤗 Transformers
implementation or you need to transpose one of the checkpoint weights.
This step should be iterated with the previous step until all weights of
the checkpoint are correctly loaded in the Transformers model. Having
correctly loaded the checkpoint into the 🤗 Transformers implementation,
you can then save the model under a folder of your choice
`/path/to/converted/checkpoint/folder` that should then contain both a
`pytorch_model.bin` file and a `config.json` file:
```python
model.save_pretrained("/path/to/converted/checkpoint/folder")
```
**7. Implement the forward pass**
Having managed to correctly load the pretrained weights into the 🤗
Transformers implementation, you should now make sure that the forward
pass is correctly implemented. In [Get familiar with the original
repository](#run-a-pretrained-checkpoint-using-the-original-repository),
you have already created a script that runs a forward pass of the model
using the original repository. Now you should write an analogous script
using the 🤗 Transformers implementation instead of the original one. It
should look as follows:
[Here the model name might have to be adapted, *e.g.*, maybe BigBirdForConditionalGeneration instead of BigBirdModel]
```python
model = BigBirdModel.from_pretrained("/path/to/converted/checkpoint/folder")
input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
output = model(input_ids).last_hidden_states
```
It is very likely that the 🤗 Transformers implementation and the
original model implementation don't give the exact same output the very
first time or that the forward pass throws an error. Don't be
disappointed - it's expected! First, you should make sure that the
forward pass doesn't throw any errors. It often happens that the wrong
dimensions are used leading to a `"Dimensionality mismatch"`
error or that the wrong data type object is used, *e.g.*, `torch.long`
instead of `torch.float32`. Don't hesitate to ask Patrick
for help, if you don't manage to solve certain errors.
The final part to make sure the 🤗 Transformers implementation works
correctly is to ensure that the outputs are equivalent to a precision of
`1e-3`. First, you should ensure that the output shapes are identical,
*i.e.* `outputs.shape` should yield the same value for the script of the
🤗 Transformers implementation and the original implementation. Next, you
should make sure that the output values are identical as well. This one
of the most difficult parts of adding a new model. Common mistakes why
the outputs are not identical are:
- Some layers were not added, *i.e.* an activation layer
was not added, or the residual connection was forgotten
- The word embedding matrix was not tied
- The wrong positional embeddings are used because the original
implementation uses on offset
- Dropout is applied during the forward pass. To fix this make sure
`model.training is False` and that no dropout layer is
falsely activated during the forward pass, *i.e.* pass
`self.training` to [PyTorch's functional
dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout)
The best way to fix the problem is usually to look at the forward pass
of the original implementation and the 🤗 Transformers implementation
side-by-side and check if there are any differences. Ideally, you should
debug/print out intermediate outputs of both implementations of the
forward pass to find the exact position in the network where the 🤗
Transformers implementation shows a different output than the original
implementation. First, make sure that the hard-coded `input_ids` in both
scripts are identical. Next, verify that the outputs of the first
transformation of the `input_ids` (usually the word embeddings) are
identical. And then work your way up to the very last layer of the
network. At some point, you will notice a difference between the two
implementations, which should point you to the bug in the 🤗 Transformers
implementation. From our experience, a simple and efficient way is to
add many print statements in both the original implementation and 🤗
Transformers implementation, at the same positions in the network
respectively, and to successively remove print statements showing the
same values for intermediate presentions.
When you're confident that both implementations yield the same output,
verifying the outputs with
`torch.allclose(original_output, output, atol=1e-3)`, you're done with
the most difficult part! Congratulations - the work left to be done
should be a cakewalk 😊.
**8. Adding all necessary model tests**
At this point, you have successfully added a new model. However, it is
very much possible that the model does not yet fully comply with the
required design. To make sure, the implementation is fully compatible
with 🤗 Transformers, all common tests should pass. The Cookiecutter
should have automatically added a test file for your model, probably
under the same `tests/test_modeling_big_bird.py`. Run this test
file to verify that all common tests pass:
```python
pytest tests/test_modeling_big_bird.py
```
Having fixed all common tests, it is now crucial to ensure that all the
nice work you have done is well tested, so that
- a) The community can easily understand your work by looking at
specific tests of *BigBird*
- b) Future changes to your model will not break any important
feature of the model.
At first, integration tests should be added. Those integration tests
essentially do the same as the debugging scripts you used earlier to
implement the model to 🤗 Transformers. A template of those model tests
is already added by the Cookiecutter, called
`BigBirdModelIntegrationTests` and only has to be filled out by
you. To ensure that those tests are passing, run
```python
RUN_SLOW=1 pytest -sv tests/test_modeling_big_bird.py::BigBirdModelIntegrationTests
```
**Note**: In case you are using Windows, you should replace `RUN_SLOW=1` with
`SET RUN_SLOW=1`
Second, all features that are special to *BigBird* should be
tested additionally in a separate test under
`BigBirdModelTester`/`BigBirdModelTest`. This part is often
forgotten but is extremely useful in two ways:
- It helps to transfer the knowledge you have acquired during the
model addition to the community by showing how the special features
of *BigBird* should work.
- Future contributors can quickly test changes to the model by running
those special tests.
BigBird has quite a complex attention layer, so it is very important
to add more tests verifying the all parts of BigBird's self-attention layer
works as expected. This means that there should be at least 3 additional tests:
- 1. Verify that the sparse attention works correctly
- 2. Verify that the global attention works correctly
- 3. Verify that the random attention works correctly
**9. Implement the tokenizer**
Next, we should add the tokenizer of *BigBird*. Usually, the
tokenizer is equivalent or very similar to an already existing tokenizer
of 🤗 Transformers.
In the case of BigBird you should be able to just rely on an already existing tokenizer.
If not mistaken, BigBird uses the same tokenizer that was used for `BertGenerationTokenizer`,
which is based on `sentencepiece`. So you should be able to just set the config parameter
`tokenizer_class` to `BertGenerationTokenizer` without having to implement any new tokenizer.
It is very important to find/extract the original tokenizer file and to
manage to load this file into the 🤗 Transformers' implementation of the
tokenizer.
For BigBird, the tokenizer (sentencepiece) files can be found [here](https://github.com/google-research/bigbird/blob/master/bigbird/vocab/gpt2.model), which you should be able to load
as easily as:
```python
from transformers import BertGenerationTokenizer
tokenizer = BertGenerationTokenizer("/path/to/gpt2.model/file")
```
To ensure that the tokenizer works correctly, it is recommended to first
create a script in the original repository that inputs a string and
returns the `input_ids`. It could look similar to this (in pseudo-code):
```bash
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
model = BigBirdModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = model.tokenize(input_str)
```
You might have to take a deeper look again into the original repository
to find the correct tokenizer function or you might even have to do
changes to your clone of the original repository to only output the
`input_ids`. Having written a functional tokenization script that uses
the original repository, an analogous script for 🤗 Transformers should
be created. It should look similar to this:
```python
from transformers import BertGenerationTokenizer
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
tokenizer = BertGenerationTokenizer.from_pretrained("/path/big/bird/folder")
input_ids = tokenizer(input_str).input_ids
```
When both `input_ids` yield the same values, as a final step a tokenizer
test file should also be added.
Since BigBird is most likely fully based on `BertGenerationTokenizer`,
you should only add a couple of "slow" integration tests. However, in this
case you do **not** need to add any `BigBirdTokenizationTest`.
**10. Run End-to-end integration tests**
Having added the tokenizer, you should also add a couple of end-to-end
integration tests using both the model and the tokenizer to
`tests/test_modeling_big_bird.py` in 🤗 Transformers. Such a test
should show on a meaningful text-to-text sample that the 🤗 Transformers
implementation works as expected. A meaningful text-to-text sample can
include, *e.g.*, a source-to-target-translation pair, an
article-to-summary pair, a question-to-answer pair, etc... If none of
the ported checkpoints has been fine-tuned on a downstream task it is
enough to simply rely on the model tests. In a final step to ensure that
the model is fully functional, it is advised that you also run all tests
on GPU. It can happen that you forgot to add some `.to(self.device)`
statements to internal tensors of the model, which in such a test would
show in an error. In case you have no access to a GPU, the Hugging Face
team can take care of running those tests for you.
**11. Add Docstring**
Now, all the necessary functionality for *BigBird* is added -
you're almost done! The only thing left to add is a nice docstring and
a doc page. The Cookiecutter should have added a template file called
`docs/source/model_doc/big_bird.rst` that you should fill out.
Users of your model will usually first look at this page before using
your model. Hence, the documentation must be understandable and concise.
It is very useful for the community to add some *Tips* to show how the
model should be used. Don't hesitate to ping Patrick
regarding the docstrings.
Next, make sure that the docstring added to
`src/transformers/models/big_bird/modeling_big_bird.py` is
correct and included all necessary inputs and outputs. It is always to
good to remind oneself that documentation should be treated at least as
carefully as the code in 🤗 Transformers since the documentation is
usually the first contact point of the community with the model.
**Code refactor**
Great, now you have added all the necessary code for *BigBird*.
At this point, you should correct some potential incorrect code style by
running:
```bash
make style
```
and verify that your coding style passes the quality check:
```bash
make quality
```
There are a couple of other very strict design tests in 🤗 Transformers
that might still be failing, which shows up in the tests of your pull
request. This is often because of some missing information in the
docstring or some incorrect naming. Patrick will surely
help you if you're stuck here.
Lastly, it is always a good idea to refactor one's code after having
ensured that the code works correctly. With all tests passing, now it's
a good time to go over the added code again and do some refactoring.
You have now finished the coding part, congratulation! 🎉 You are
Awesome! 😎
**12. Upload the models to the model hub**
In this final part, you should convert and upload all checkpoints to the
model hub and add a model card for each uploaded model checkpoint. You
should work alongside Patrick here to decide on a fitting
name for each checkpoint and to get the required access rights to be
able to upload the model under the author's organization of
*BigBird*.
It is worth spending some time to create fitting model cards for each
checkpoint. The model cards should highlight the specific
characteristics of this particular checkpoint, *e.g.*, On which dataset
was the checkpoint pretrained/fine-tuned on? On what down-stream task
should the model be used? And also include some code on how to correctly
use the model.
**13. (Optional) Add notebook**
It is very helpful to add a notebook that showcases in-detail how
*BigBird* can be used for inference and/or fine-tuned on a
downstream task. This is not mandatory to merge your PR, but very useful
for the community.
**14. Submit your finished PR**
You're done programming now and can move to the last step, which is
getting your PR merged into master. Usually, Patrick
should have helped you already at this point, but it is worth taking
some time to give your finished PR a nice description and eventually add
comments to your code, if you want to point out certain design choices
to your reviewer.
### Share your work!!
Now, it's time to get some credit from the community for your work!
Having completed a model addition is a major contribution to
Transformers and the whole NLP community. Your code and the ported
pre-trained models will certainly be used by hundreds and possibly even
thousands of developers and researchers. You should be proud of your
work and share your achievement with the community.
**You have made another model that is super easy to access for everyone
in the community! 🤯**
|
AdaMix/templates/adding_a_new_model/open_model_proposals/ADD_BIG_BIRD.md/0
|
{
"file_path": "AdaMix/templates/adding_a_new_model/open_model_proposals/ADD_BIG_BIRD.md",
"repo_id": "AdaMix",
"token_count": 14647
}
| 70 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from .test_modeling_common import ids_tensor
if is_torch_available():
import torch
import torch.nn.functional as F
from transformers.generation_logits_process import (
EncoderNoRepeatNGramLogitsProcessor,
ForcedBOSTokenLogitsProcessor,
ForcedEOSTokenLogitsProcessor,
HammingDiversityLogitsProcessor,
LogitsProcessorList,
MinLengthLogitsProcessor,
NoBadWordsLogitsProcessor,
NoRepeatNGramLogitsProcessor,
PrefixConstrainedLogitsProcessor,
RepetitionPenaltyLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
@require_torch
class LogitsProcessorTest(unittest.TestCase):
def _get_uniform_logits(self, batch_size: int, length: int):
scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length
return scores
def test_min_lenght_dist_processor(self):
vocab_size = 20
batch_size = 4
eos_token_id = 0
min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id)
# check that min length is applied at length 5
input_ids = ids_tensor((batch_size, 5), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores_before_min_length = min_dist_processor(input_ids, scores)
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")])
# check that min length is not applied anymore at length 15
input_ids = ids_tensor((batch_size, 15), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores_before_min_length = min_dist_processor(input_ids, scores)
self.assertFalse(torch.isinf(scores_before_min_length).any())
def test_temperature_dist_warper(self):
input_ids = None
length = 20
scores = self._get_uniform_logits(batch_size=2, length=length)
# tweak scores to not be uniform anymore
scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch
scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch
# compute softmax
probs = F.softmax(scores, dim=-1)
temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5)
temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3)
warped_prob_sharp = F.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1)
warped_prob_smooth = F.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1)
# uniform distribution stays uniform
self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3))
self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3))
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max())
self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min())
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max())
self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min())
def test_repetition_penalty_dist_process(self):
input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long)
vocab_size = 10
scores = self._get_uniform_logits(batch_size=2, length=vocab_size)
# give values special values
scores[0, 0] = -(1 / vocab_size)
scores[1, 5] = 4 / vocab_size
rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
scores = rep_penalty_proc(input_ids, scores.clone())
# check that values were correctly changed
self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2)
self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2)
self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2)
self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2)
def test_top_k_dist_warper(self):
input_ids = None
vocab_size = 10
batch_size = 2
# create ramp distribution
ramp_logits = (
torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1)
)
ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size
top_k_warp = TopKLogitsWarper(3)
scores = top_k_warp(input_ids, ramp_logits)
# check that correct tokens are filtered
self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False])
self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True])
# check special cases
length = 5
logits = self._get_uniform_logits(batch_size=batch_size, length=length)
top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3)
scores = top_k_warp_safety_check(input_ids, logits)
# uniform dist is not changed
self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0])
ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1)
scores = top_k_warp_safety_check(input_ids, ramp_logits)
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2])
def test_top_p_dist_warper(self):
input_ids = None
vocab_size = 10
batch_size = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
dist = torch.log(
torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float)
)
top_p_warp = TopPLogitsWarper(0.7)
filtered_dist = torch.exp(top_p_warp(input_ids, dist))
# dist should be filtered to keep min num values so that sum is >= 0.7
# exp (-inf) => 0
EXPECTED_FILTERED_DIST = torch.tensor(
[[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float
)
self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3))
# check edge cases with negative and extreme logits
ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(
batch_size, 1
) - (vocab_size // 2)
# make ramp_logits more extreme
ramp_logits[1] = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0)
filtered_dist = top_p_warp(input_ids, ramp_logits)
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2])
def test_no_repeat_ngram_dist_processor(self):
vocab_size = 3
batch_size = 2
input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
scores = self._get_uniform_logits(batch_size, vocab_size)
no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2)
no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3)
filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())
# 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch
self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]])
# 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch
self.assertListEqual(
torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]]
)
def test_encoder_no_repeat_ngram_dist_processor(self):
vocab_size = 3
num_beams = 2
batch_size = 1
encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long)
input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long)
scores = self._get_uniform_logits(batch_size * num_beams, vocab_size)
no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids)
no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids)
filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())
# 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam
self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]])
# 3-gram would forbid 1st token at 1st beam and no token at 2nd beam
self.assertListEqual(
torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]]
)
# Batched input
vocab_size = 3
num_beams = 2
batch_size = 2
encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long)
input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long)
scores = self._get_uniform_logits(batch_size * num_beams, vocab_size)
no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids)
no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids)
filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())
# 2gram
# Batch 1
# - Beam 1: tokens (1, 2) forbidden
# - Beam 2: tokens (1) forbidden
# Batch 2
# - Beam 1: tokens (0, 2) forbidden
# - Beam 2: tokens (1) forbidden
self.assertListEqual(
torch.isinf(filtered_scores_2_gram).tolist(),
[[False, True, True], [False, True, False], [True, False, True], [False, True, False]],
)
# Batch 1
# - Beam 1: tokens (1) forbidden
# - Beam 2: tokens () forbidden
# Batch 2
# - Beam 1: tokens (2) forbidden
# - Beam 2: tokens () forbidden
self.assertListEqual(
torch.isinf(filtered_scores_3_gram).tolist(),
[[False, True, False], [False, False, False], [False, False, True], [False, False, False]],
)
def test_no_bad_words_dist_processor(self):
vocab_size = 5
batch_size = 2
eos_token_id = 4
input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]]
scores = self._get_uniform_logits(batch_size, vocab_size)
no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id)
filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
# batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden
# batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden
# Note that 5th element cannot be forbidden as it is EOS token
self.assertListEqual(
torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]]
)
# check edge case
no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id)
filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3))
def test_processor_list(self):
batch_size = 4
sequence_length = 10
vocab_size = 15
eos_token_id = 0
# dummy input_ids and scores
input_ids = ids_tensor((batch_size, sequence_length), vocab_size)
input_ids_comp = input_ids.clone()
scores = self._get_uniform_logits(batch_size, vocab_size)
scores_comp = scores.clone()
# instantiate all dist processors
min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id)
temp_dist_warp = TemperatureLogitsWarper(temperature=0.5)
rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
top_k_warp = TopKLogitsWarper(3)
top_p_warp = TopPLogitsWarper(0.8)
no_repeat_proc = NoRepeatNGramLogitsProcessor(2)
no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id)
# no processor list
scores = min_dist_proc(input_ids, scores)
scores = temp_dist_warp(input_ids, scores)
scores = rep_penalty_proc(input_ids, scores)
scores = top_k_warp(input_ids, scores)
scores = top_p_warp(input_ids, scores)
scores = no_repeat_proc(input_ids, scores)
scores = no_bad_words_dist_proc(input_ids, scores)
# with processor list
processor = LogitsProcessorList(
[
min_dist_proc,
temp_dist_warp,
rep_penalty_proc,
top_k_warp,
top_p_warp,
no_repeat_proc,
no_bad_words_dist_proc,
]
)
scores_comp = processor(input_ids, scores_comp)
# scores should be equal
self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())
def test_prefix_constrained_logits_processor(self):
vocab_size = 5
batch_size = 2
input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
scores = self._get_uniform_logits(batch_size, vocab_size)
def prefix_allowed_tokens_fn(batch_id, inputs_ids):
return [[0, 1], [2, 3]][batch_id]
prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1)
filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone())
# batch 1: 1st, 2nd (0, 1) token are allowed
# batch 2: 3rd, 4th (2, 3) token are allowed
self.assertListEqual(
torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]]
)
def test_hamming_diversity(self):
vocab_size = 4
num_beams = 2
num_beam_groups = 2
scores = self._get_uniform_logits(num_beams, vocab_size)
# batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1
# batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1
current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long)
diversity_logits_processor = HammingDiversityLogitsProcessor(
diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups
)
processed_scores = diversity_logits_processor(None, scores, current_tokens, 1)
self.assertTrue(
torch.allclose(
processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3
)
)
self.assertTrue(
torch.allclose(
processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3
)
)
def test_forced_bos_token_logits_processor(self):
vocab_size = 20
batch_size = 4
bos_token_id = 0
logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id)
# check that all scores are -inf except the bos_token_id score
input_ids = ids_tensor((batch_size, 1), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all())
self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
input_ids = ids_tensor((batch_size, 4), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertFalse(torch.isinf(scores).any())
def test_forced_eos_token_logits_processor(self):
vocab_size = 20
batch_size = 4
eos_token_id = 0
max_length = 5
logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id)
# check that all scores are -inf except the eos_token_id when max_length is reached
input_ids = ids_tensor((batch_size, 4), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all())
self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
input_ids = ids_tensor((batch_size, 3), vocab_size=20)
scores = self._get_uniform_logits(batch_size, vocab_size)
scores = logits_processor(input_ids, scores)
self.assertFalse(torch.isinf(scores).any())
|
AdaMix/tests/test_generation_logits_process.py/0
|
{
"file_path": "AdaMix/tests/test_generation_logits_process.py",
"repo_id": "AdaMix",
"token_count": 8400
}
| 71 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2Config,
GPT2DoubleHeadsModel,
GPT2ForSequenceClassification,
GPT2LMHeadModel,
GPT2Model,
GPT2Tokenizer,
)
class GPT2ModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = None
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
def get_large_model_config(self):
return GPT2Config.from_pretrained("gpt2")
def prepare_config_and_inputs(self, gradient_checkpointing=False):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = GPT2Config(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
# intermediate_size=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings,
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range,
use_cache=not gradient_checkpointing,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
gradient_checkpointing=gradient_checkpointing,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2Model(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gpt2_model_attention_mask_past(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPT2Model(config=config)
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = self.seq_length // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gpt2_model_past_large_inputs(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPT2Model(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
)["last_hidden_state"]
output_from_past = model(
next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
)["last_hidden_state"]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2LMHeadModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2LMHeadModel(config)
model.to(torch_device)
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def create_and_check_double_lm_head_model(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
):
model = GPT2DoubleHeadsModel(config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
inputs = {
"input_ids": multiple_choice_inputs_ids,
"mc_token_ids": mc_token_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
"labels": multiple_choice_inputs_ids,
}
result = model(**inputs)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
)
self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
def create_and_check_gpt2_for_sequence_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = GPT2ForSequenceClassification(config)
model.to(torch_device)
model.eval()
print(config.num_labels, sequence_labels.size())
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForSequenceClassification)
if is_torch_available()
else ()
)
all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
all_parallelizable_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
test_missing_keys = False
test_model_parallel = True
# special case for DoubleHeads model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "GPT2DoubleHeadsModel":
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
dtype=torch.long,
device=torch_device,
)
inputs_dict["input_ids"] = inputs_dict["labels"]
inputs_dict["token_type_ids"] = inputs_dict["labels"]
inputs_dict["mc_token_ids"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices),
dtype=torch.long,
device=torch_device,
)
inputs_dict["mc_labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = GPT2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gpt2_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
def test_gpt2_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)
def test_gpt2_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
def test_gpt2_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs)
def test_gpt2_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gpt2_double_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
def test_gpt2_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs)
def test_gpt2_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
@slow
def test_batch_generation(self):
model = GPT2LMHeadModel.from_pretrained("gpt2")
model.to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.padding_side = "left"
# Define PAD Token = EOS Token = 50256
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
# use different length sentences to test batching
sentences = [
"Hello, my dog is a little",
"Today, I",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
token_type_ids = torch.cat(
[
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
input_ids.new_full((input_ids.shape[0], 1), 500),
],
dim=-1,
)
outputs = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
outputs_tt = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
token_type_ids=token_type_ids,
)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
"Today, I'm going to be doing a lot of research on this. I",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
@slow
def test_batch_generation_2heads(self):
model = GPT2DoubleHeadsModel.from_pretrained("gpt2")
model.to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.padding_side = "left"
# This tokenizer has no pad token, so we have to set it in some way
# Define PAD Token = EOS Token = 50256
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
# use different length sentences to test batching
sentences = [
"Hello, my dog is a little",
"Today, I",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
token_type_ids = torch.cat(
[
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
input_ids.new_full((input_ids.shape[0], 1), 500),
],
dim=-1,
)
outputs = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
outputs_tt = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
token_type_ids=token_type_ids,
)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
"Today, I'm going to be doing a lot of research on this. I",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
@slow
def test_model_from_pretrained(self):
for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GPT2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class GPT2ModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_gpt2(self):
for checkpointing in [True, False]:
model = GPT2LMHeadModel.from_pretrained("gpt2", gradient_checkpointing=checkpointing)
model.to(torch_device)
input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog
expected_output_ids = [
464,
3290,
373,
1043,
287,
257,
2214,
1474,
262,
16246,
286,
2688,
290,
2688,
27262,
13,
198,
198,
464,
3290,
] # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@slow
def test_gpt2_sample(self):
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
model.to(torch_device)
torch.manual_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
input_ids = tokenized.input_ids.to(torch_device)
output_ids = model.generate(input_ids, do_sample=True)
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
token_type_ids = tokenized.token_type_ids.to(torch_device)
output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
output_seq_tt = model.generate(
input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
)
output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
EXPECTED_OUTPUT_STR = (
"Today is a nice day and if you don't know anything about the state of play during your holiday"
)
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
self.assertTrue(
all([output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))])
) # token_type_ids should change output
@slow
def test_gpt2_sample_max_time(self):
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
model.to(torch_device)
torch.manual_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
input_ids = tokenized.input_ids.to(torch_device)
MAX_TIME = 0.5
start = datetime.datetime.now()
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
AdaMix/tests/test_modeling_gpt2.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_gpt2.py",
"repo_id": "AdaMix",
"token_count": 13494
}
| 72 |
# coding=utf-8 # Copyright 2020 Huggingface
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
require_torch_multi_gpu,
slow,
torch_device,
)
from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerConfig,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerTokenizer,
)
class ReformerModelTester:
def __init__(
self,
parent,
batch_size=None,
seq_length=None,
is_training=None,
is_decoder=None,
use_input_mask=None,
use_labels=None,
vocab_size=None,
attention_head_size=None,
hidden_size=None,
num_attention_heads=None,
local_attn_chunk_length=None,
local_num_chunks_before=None,
local_num_chunks_after=None,
num_buckets=None,
num_hashes=1,
lsh_attn_chunk_length=None,
lsh_num_chunks_before=None,
lsh_num_chunks_after=None,
chunk_size_lm_head=None,
chunk_size_feed_forward=None,
feed_forward_size=None,
hidden_act=None,
hidden_dropout_prob=None,
local_attention_probs_dropout_prob=None,
lsh_attention_probs_dropout_prob=None,
max_position_embeddings=None,
initializer_range=None,
axial_norm_std=None,
layer_norm_eps=None,
axial_pos_embds=None,
axial_pos_shape=None,
axial_pos_embds_dim=None,
attn_layers=None,
pad_token_id=None,
eos_token_id=None,
scope=None,
hash_seed=None,
num_labels=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.is_decoder = is_decoder
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.attention_head_size = attention_head_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = len(attn_layers)
self.local_attn_chunk_length = local_attn_chunk_length
self.local_num_chunks_after = local_num_chunks_after
self.local_num_chunks_before = local_num_chunks_before
self.num_hashes = num_hashes
self.num_buckets = tuple(num_buckets) if isinstance(num_buckets, list) else num_buckets
self.lsh_attn_chunk_length = lsh_attn_chunk_length
self.lsh_num_chunks_after = lsh_num_chunks_after
self.lsh_num_chunks_before = lsh_num_chunks_before
self.hidden_act = hidden_act
self.feed_forward_size = feed_forward_size
self.hidden_dropout_prob = hidden_dropout_prob
self.local_attention_probs_dropout_prob = local_attention_probs_dropout_prob
self.lsh_attention_probs_dropout_prob = lsh_attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.axial_pos_embds = axial_pos_embds
self.axial_pos_shape = tuple(axial_pos_shape)
self.axial_pos_embds_dim = tuple(axial_pos_embds_dim)
self.axial_norm_std = axial_norm_std
self.chunk_size_lm_head = chunk_size_lm_head
self.chunk_size_feed_forward = chunk_size_feed_forward
self.scope = scope
self.attn_layers = attn_layers
self.pad_token_id = pad_token_id
self.hash_seed = hash_seed
attn_chunk_length = local_attn_chunk_length if local_attn_chunk_length is not None else lsh_attn_chunk_length
num_chunks_after = local_num_chunks_after if local_num_chunks_after is not None else lsh_num_chunks_after
num_chunks_before = local_num_chunks_before if local_num_chunks_before is not None else lsh_num_chunks_before
self.encoder_seq_length = seq_length // attn_chunk_length + (self.seq_length % attn_chunk_length != 0)
self.key_length = (num_chunks_before + num_chunks_after + 1) * attn_chunk_length
self.chunk_length = attn_chunk_length
self.num_labels = num_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
choice_labels = None
if self.use_labels:
choice_labels = ids_tensor([self.batch_size], 2)
config = ReformerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
feed_forward_size=self.feed_forward_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
local_attention_probs_dropout_prob=self.local_attention_probs_dropout_prob,
lsh_attention_probs_dropout_prob=self.lsh_attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
is_decoder=self.is_decoder,
axial_pos_embds=self.axial_pos_embds,
axial_pos_shape=self.axial_pos_shape,
axial_pos_embds_dim=self.axial_pos_embds_dim,
local_attn_chunk_length=self.local_attn_chunk_length,
local_num_chunks_after=self.local_num_chunks_after,
local_num_chunks_before=self.local_num_chunks_before,
num_hashes=self.num_hashes,
num_buckets=self.num_buckets,
lsh_attn_chunk_length=self.lsh_attn_chunk_length,
lsh_num_chunks_after=self.lsh_num_chunks_after,
lsh_num_chunks_before=self.lsh_num_chunks_before,
attn_layers=self.attn_layers,
pad_token_id=self.pad_token_id,
hash_seed=self.hash_seed,
)
return (
config,
input_ids,
input_mask,
choice_labels,
)
def create_and_check_reformer_model(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
# 2 * hidden_size because we use reversible resnet layers
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length, 2 * self.hidden_size)
)
def create_and_check_reformer_model_with_lm_backward(self, config, input_ids, input_mask, choice_labels):
if not self.is_training:
return
config.is_decoder = False
config.lsh_num_chunks_after = 1
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
loss = model(input_ids, attention_mask=input_mask, labels=input_ids)["loss"]
loss.backward()
def create_and_check_reformer_with_lm(self, config, input_ids, input_mask, choice_labels):
config.lsh_num_chunks_after = 0
config.is_decoder = True
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_reformer_with_mlm(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = False
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_reformer_model_with_attn_mask(
self, config, input_ids, input_mask, choice_labels, is_decoder=False
):
# no special position embeddings
config.axial_pos_embds = False
config.is_decoder = is_decoder
if self.lsh_attn_chunk_length is not None:
# need to set chunk length equal sequence length to be certain that chunking works
config.lsh_attn_chunk_length = self.seq_length
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
# set all position encodings to zero so that postions don't matter
with torch.no_grad():
embedding = model.embeddings.position_embeddings.embedding
embedding.weight = torch.nn.Parameter(torch.zeros(embedding.weight.shape).to(torch_device))
embedding.weight.requires_grad = False
half_seq_len = self.seq_length // 2
roll = self.chunk_length
half_input_ids = input_ids[:, :half_seq_len]
# normal padded
attn_mask = torch.cat(
[torch.ones_like(half_input_ids), torch.zeros_like(half_input_ids)],
dim=-1,
)
input_ids_padded = torch.cat(
[half_input_ids, ids_tensor((self.batch_size, half_seq_len), self.vocab_size)],
dim=-1,
)
# shifted padded
input_ids_roll = torch.cat(
[half_input_ids, ids_tensor((self.batch_size, half_seq_len), self.vocab_size)],
dim=-1,
)
input_ids_roll = torch.roll(input_ids_roll, roll, dims=-1)
attn_mask_roll = torch.roll(attn_mask, roll, dims=-1)
output_padded = model(input_ids_padded, attention_mask=attn_mask)[0][:, :half_seq_len]
output_padded_rolled = model(input_ids_roll, attention_mask=attn_mask_roll)[0][:, roll : half_seq_len + roll]
self.parent.assertTrue(torch.allclose(output_padded, output_padded_rolled, atol=1e-3))
def create_and_check_reformer_layer_dropout_seed(
self, config, input_ids, input_mask, choice_labels, is_decoder=False
):
config.is_decoder = is_decoder
layer = ReformerLayer(config).to(torch_device)
layer.train()
shape = (
self.batch_size,
self.seq_length,
config.hidden_size,
) # Batch x SeqLen x hiddenSize
# get random tensors
hidden_states = floats_tensor(shape)
prev_attn_output = floats_tensor(shape)
# now the random seeds for attention and feed forward is initialized
# forward tensors with dropout
layer_outputs = layer(prev_attn_output, hidden_states, attention_mask=input_mask)
next_attn_output = layer_outputs.attn_output
next_hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.attention_seed)
attn_outputs = layer.attention(hidden_states, attention_mask=input_mask)
self.parent.assertTrue(
torch.allclose(
prev_attn_output + attn_outputs.hidden_states,
next_attn_output,
atol=1e-3,
)
)
torch.manual_seed(layer.feed_forward_seed)
feed_forward_hidden_states = layer.feed_forward(next_attn_output)
self.parent.assertTrue(
torch.allclose(
next_hidden_states,
hidden_states + feed_forward_hidden_states,
atol=1e-3,
)
)
def create_and_check_reformer_feed_backward_chunking(self, config, input_ids, input_mask, choice_labels):
if not self.is_training:
return
# disable dropout
config.hidden_dropout_prob = 0
config.local_attention_probs_dropout_prob = 0
config.lsh_attention_probs_dropout_prob = 0
config.lsh_num_chunks_after = 1
config.is_decoder = False
torch.manual_seed(0)
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
model.zero_grad()
loss_no_chunk, output_no_chunk = model(input_ids, labels=input_ids, attention_mask=input_mask)[:2]
loss_no_chunk.backward()
grad_slice_word_no_chunk = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
grad_slice_position_factor_1_no_chunk = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
grad_slice_position_factor_2_no_chunk = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
config.chunk_size_lm_head = 1
config.chunk_size_feed_forward = 1
torch.manual_seed(0)
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
model.zero_grad()
loss_chunk, output_chunk = model(input_ids, labels=input_ids, attention_mask=input_mask)[:2]
loss_chunk.backward()
grad_slice_word_chunk = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
grad_slice_position_factor_1_chunk = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
grad_slice_position_factor_2_chunk = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
self.parent.assertTrue(torch.allclose(loss_chunk, loss_no_chunk, atol=1e-3))
self.parent.assertTrue(torch.allclose(grad_slice_word_no_chunk, grad_slice_word_chunk, atol=1e-3))
self.parent.assertTrue(
torch.allclose(grad_slice_position_factor_1_chunk, grad_slice_position_factor_1_no_chunk, atol=1e-3)
)
self.parent.assertTrue(
torch.allclose(grad_slice_position_factor_2_chunk, grad_slice_position_factor_2_no_chunk, atol=1e-3)
)
def create_and_check_reformer_random_seed(self, config, input_ids, input_mask, choice_labels):
layer = ReformerLayer(config).to(torch_device)
layer.train()
shape = (
self.batch_size,
self.seq_length,
config.hidden_size,
) # Batch x SeqLen x hiddenSize
hidden_states = floats_tensor(shape)
attn_output = floats_tensor(shape)
seeds = []
for _ in range(100):
layer_outputs = layer(attn_output, hidden_states, attention_mask=input_mask)
attn_output = layer_outputs.attn_output
hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.attention_seed)
seeds.append(layer.attention_seed)
self.parent.assertGreater(len(set(seeds)), 70)
seeds = []
for _ in range(100):
layer_outputs = layer(attn_output, hidden_states, attention_mask=input_mask)
attn_output = layer_outputs.attn_output
hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.feed_forward_seed)
seeds.append(layer.feed_forward_seed)
self.parent.assertGreater(len(set(seeds)), 70)
def create_and_check_reformer_model_fp16_forward(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
model.half()
model.eval()
output = model(input_ids, attention_mask=input_mask)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_model_generate(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_after = 0
config.bos_token_id = 0
config.eos_token_id = None
config.max_length = 20
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
output = model.generate()
self.parent.assertIsNotNone(output)
def create_and_check_reformer_model_fp16_generate(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_after = 0
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.half()
model.eval()
# only use last 10 inputs for generation
output = model.generate(input_ids[:, -10:], attention_mask=input_mask, do_sample=False)
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_no_chunking(self, config, input_ids, input_mask, choice_labels):
# force chunk length to be bigger than input_ids
config.lsh_attn_chunk_length = 2 * input_ids.shape[-1]
config.local_attn_chunk_length = 2 * input_ids.shape[-1]
config.lsh_num_chunks_after = 1
config.is_decoder = False
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
output_logits = model(input_ids, attention_mask=input_mask)["logits"]
self.parent.assertTrue(output_logits.shape[1] == input_ids.shape[-1])
def create_and_check_reformer_for_question_answering(self, config, input_ids, input_mask, choice_labels):
model = ReformerForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
start_positions=choice_labels,
end_positions=choice_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_past_buckets_states(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_before = 1
config.lsh_num_chunks_after = 0
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
input_ids_first = input_ids[:, :-1]
input_ids_second = input_ids[:, -1:]
# return saved cache
past_buckets_states = model(input_ids_first, use_cache=True)["past_buckets_states"]
# calculate last output with and without cache
outputs_with_cache = model(input_ids_second, past_buckets_states=past_buckets_states, use_cache=True)["logits"]
outputs_without_cache = model(input_ids)["logits"][:, -1]
# select random slice idx
random_slice_idx = torch.randint(outputs_without_cache.shape[-1], (1, 1), device=torch_device).item()
# outputs should be similar within range
self.parent.assertTrue(
torch.allclose(
outputs_with_cache[:, 0, random_slice_idx], outputs_without_cache[:, random_slice_idx], atol=1e-2
)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
def create_and_check_reformer_for_sequence_classification(
self, config, input_ids, input_mask, choice_labels, is_decoder
):
config.is_decoder = is_decoder
sequence_labels = ids_tensor([self.batch_size], config.num_labels)
model = ReformerForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
class ReformerTesterMixin:
"""
Reformer Local and Reformer LSH run essentially the same tests
"""
def test_config(self):
self.config_tester.run_common_tests()
def test_reformer_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model(*config_and_inputs)
def test_reformer_lm_model_backward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_with_lm_backward(*config_and_inputs)
def test_reformer_model_attn_masking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=True)
self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=False)
def test_reformer_with_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_with_lm(*config_and_inputs)
def test_reformer_with_mlm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_with_mlm(*config_and_inputs)
def test_reformer_layer_training_dropout(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=True)
self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=False)
def test_reformer_chunking_backward_equality(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_feed_backward_chunking(*config_and_inputs)
def test_reformer_no_chunking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_no_chunking(*config_and_inputs)
def test_reformer_qa_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_for_question_answering(*config_and_inputs)
def test_reformer_cached_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_past_buckets_states(*config_and_inputs)
def test_reformer_cached_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_generate(*config_and_inputs)
@slow
def test_dropout_random_seed_is_changing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_random_seed(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_reformer_model_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_fp16_forward(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_reformer_model_fp16_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_fp16_generate(*config_and_inputs)
@require_torch_multi_gpu
def test_multi_gpu_data_parallel_forward(self):
# Opt-out of this test.
pass
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_for_sequence_classification(*config_and_inputs, is_decoder=False)
def test_retain_grad_hidden_states_attentions(self):
# reformer cannot keep gradients in attentions or hidden states
return
def test_resize_embeddings_untied(self):
# reformer cannot resize embeddings that easily
return
@require_torch
class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
test_pruning = False
test_headmasking = False
test_torchscript = False
def prepare_kwargs(self):
return {
"batch_size": 13,
"seq_length": 32,
"is_training": True,
"is_decoder": True,
"use_input_mask": True,
"use_labels": True,
"vocab_size": 32,
"attention_head_size": 16,
"hidden_size": 32,
"num_attention_heads": 2,
"local_attn_chunk_length": 4,
"local_num_chunks_before": 1,
"local_num_chunks_after": 0,
"chunk_size_lm_head": 0,
"chunk_size_feed_forward": 0,
"feed_forward_size": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"local_attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [16, 16],
"attn_layers": ["local", "local", "local", "local"],
"pad_token_id": 0,
"eos_token_id": 2,
"scope": None,
"hash_seed": 0,
"num_labels": 2,
}
def setUp(self):
tester_kwargs = self.prepare_kwargs()
self.model_tester = ReformerModelTester(self, **tester_kwargs)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
@slow
def test_model_from_pretrained(self):
for model_name in REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ReformerModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, list) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
num_chunks = tgt_len // config.local_attn_chunk_length + (tgt_len % config.local_attn_chunk_length != 0)
tgt_chunk_len = config.local_attn_chunk_length
src_chunk_len = config.local_attn_chunk_length * (
1 + config.local_num_chunks_after + config.local_num_chunks_before
)
if use_cache:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
min_length // config.local_attn_chunk_length + 1 + idx,
)
else:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
num_chunks,
tgt_chunk_len,
src_chunk_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, list) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx
seq_len = config.local_attn_chunk_length * (
seq_len // config.local_attn_chunk_length + (seq_len % config.local_attn_chunk_length != 0)
)
if use_cache:
seq_len = 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@require_torch
class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
test_pruning = False
test_headmasking = False
test_torchscript = False
def prepare_kwargs(self):
return {
"batch_size": 13,
"seq_length": 13,
"use_input_mask": True,
"use_labels": True,
"is_training": False,
"is_decoder": True,
"vocab_size": 32,
"attention_head_size": 16,
"hidden_size": 64,
"num_attention_heads": 2,
"num_buckets": 2,
"num_hashes": 4,
"lsh_attn_chunk_length": 4,
"lsh_num_chunks_before": 1,
"lsh_num_chunks_after": 0,
"chunk_size_lm_head": 5,
"chunk_size_feed_forward": 6,
"feed_forward_size": 32,
"hidden_act": "relu",
"hidden_dropout_prob": 0.1,
"lsh_attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [16, 48],
# sanotheu
# "attn_layers": ["lsh", "lsh", "lsh", "lsh"],
"attn_layers": ["lsh"],
"pad_token_id": 0,
"eos_token_id": 2,
"scope": None,
"hash_seed": 0,
"num_labels": 2,
}
def setUp(self):
tester_kwargs = self.prepare_kwargs()
self.model_tester = ReformerModelTester(self, **tester_kwargs)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, list) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
num_chunks = tgt_len // config.lsh_attn_chunk_length + (tgt_len % config.lsh_attn_chunk_length != 0)
tgt_chunk_len = config.lsh_attn_chunk_length
src_chunk_len = config.lsh_attn_chunk_length * (
1 + config.lsh_num_chunks_after + config.lsh_num_chunks_before
)
if use_cache:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
config.num_hashes,
tgt_len,
config.num_hashes * (1 + config.lsh_num_chunks_after + config.lsh_num_chunks_before),
)
else:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
num_chunks * config.num_hashes,
tgt_chunk_len,
src_chunk_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, list) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx if not use_cache else 1
seq_len = config.lsh_attn_chunk_length * (
seq_len // config.lsh_attn_chunk_length + (seq_len % config.lsh_attn_chunk_length != 0)
)
if use_cache:
seq_len = 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@require_torch
@require_sentencepiece
@require_tokenizers
class ReformerIntegrationTests(unittest.TestCase):
"""
These integration tests test the current layer activations and gradients againts the output of the Hugging Face Reformer model at time of integration: 29/06/2020. During integration, the model was tested against the output of the official Trax ReformerLM model for various cases ("lsh" only, "lsh" only, masked / non-masked, different chunk length, ....). In order to recover the original trax integration tests, one should use patrickvonplaten's fork of trax and the code that lives on the branch `reformer_trax_tests`.
"""
def _get_basic_config_and_input(self):
config = {
"vocab_size": 320,
"attention_head_size": 8,
"hidden_size": 16,
"num_attention_heads": 2,
"num_buckets": 2,
"num_hashes": 4,
"lsh_attn_chunk_length": 4,
"local_attn_chunk_length": 4,
"lsh_num_chunks_before": 1,
"lsh_num_chunks_after": 0,
"local_num_chunks_before": 1,
"local_num_chunks_after": 0,
"chunk_size_lm_head": 0,
"chunk_size_feed_forward": 0,
"feed_forward_size": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"lsh_attention_probs_dropout_prob": 0.0,
"local_attention_probs_dropout_prob": 0.0,
"max_position_embeddings": 32,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"sinusoidal_pos_embds": False,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [8, 8],
"hash_seed": 0,
"is_decoder": True,
}
return config
def _get_hidden_states(self):
return torch.tensor(
[
[
[
1.90826353e00,
-1.45999730e00,
-6.20405462e-01,
1.52503433e00,
-3.64464232e-01,
-8.27359235e-01,
8.39670803e-01,
2.44492178e-01,
4.98332758e-01,
2.69175139e00,
-7.08081422e-03,
1.04915401e00,
-1.83476661e00,
7.67220476e-01,
2.98580543e-01,
2.84803992e-02,
],
[
-2.66374286e-02,
4.33497576e-01,
3.10386309e-01,
5.46039944e-01,
-2.47292666e-04,
-7.52305019e-01,
2.39162103e-01,
7.25216186e-01,
-7.58357372e-01,
4.20635998e-01,
-4.04739919e-02,
1.59924145e-01,
2.05135748e00,
-1.15997978e00,
5.37166397e-01,
2.62873606e-01,
],
[
1.85247482e-01,
7.07046037e-01,
-6.77089715e-01,
-2.24209655e00,
-3.75307980e-02,
-8.59380874e-01,
-2.81027884e00,
1.01276376e00,
-1.69438001e00,
4.17574660e-01,
-1.49196962e00,
-1.76483717e00,
-1.94566312e-01,
-1.71183858e00,
7.72903565e-01,
-1.11557056e00,
],
[
9.46069193e-01,
1.53417623e-01,
-9.58686996e-01,
1.18126669e-01,
1.75967724e00,
1.62194590e00,
-5.74108159e-01,
6.79920443e-01,
5.44028163e-01,
2.05466114e-01,
-3.63045868e-01,
2.41865062e-01,
3.20348382e-01,
-9.05611176e-01,
-1.92690727e-01,
-1.19917547e00,
],
]
],
dtype=torch.float32,
device=torch_device,
)
def _get_attn_mask(self):
return torch.tensor([[0, 1, 0, 0]], dtype=torch.long, device=torch_device)
def _get_input_ids_and_mask(self):
mask = torch.tensor(
[
[1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0],
],
dtype=torch.long,
device=torch_device,
)
input_ids = torch.tensor(
[
[
89,
279,
286,
84,
194,
316,
182,
28,
283,
37,
169,
7,
253,
267,
107,
250,
44,
7,
102,
62,
3,
243,
171,
265,
302,
48,
164,
264,
148,
229,
280,
150,
],
[
9,
192,
66,
112,
163,
83,
135,
70,
224,
96,
31,
80,
196,
80,
63,
22,
85,
100,
47,
283,
0,
163,
126,
143,
195,
82,
53,
82,
18,
27,
182,
52,
],
],
dtype=torch.long,
device=torch_device,
)
return input_ids, mask
def test_lsh_layer_forward(self):
config = self._get_basic_config_and_input()
config["lsh_num_chunks_before"] = 0
config["attn_layers"] = ["lsh"]
config["is_decoder"] = False
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(prev_attn_output=hidden_states.clone(), hidden_states=hidden_states)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.6879, -1.3083, -0.4708, 1.3555, -0.6292],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_lsh_layer_forward_complex(self):
config = self._get_basic_config_and_input()
config["lsh_num_chunks_before"] = 0
config["attn_layers"] = ["lsh"]
config["num_buckets"] = [2, 4]
attn_mask = self._get_attn_mask()
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(
prev_attn_output=hidden_states.clone(),
hidden_states=hidden_states,
attention_mask=attn_mask,
)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.6439, -1.2306, -0.5108, 1.3006, -0.6537],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_layer_forward(self):
config = self._get_basic_config_and_input()
config["local_num_chunks_before"] = 0
config["attn_layers"] = ["local"]
config["is_decoder"] = False
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(prev_attn_output=hidden_states, hidden_states=hidden_states)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.4212, -2.0576, -0.9688, 1.4599, -0.1344],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_layer_forward_complex(self):
config = self._get_basic_config_and_input()
config["local_num_chunks_before"] = 0
config["attn_layers"] = ["local"]
attn_mask = self._get_attn_mask()
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(
prev_attn_output=hidden_states,
hidden_states=hidden_states,
attention_mask=attn_mask,
)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.4750, -2.0235, -0.9743, 1.4463, -0.1269],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_lsh_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["lsh", "lsh", "lsh", "lsh"]
config["num_buckets"] = [2, 4]
torch.manual_seed(0)
model = ReformerModel(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[-0.9896, -0.9396, -1.0831, -0.0597, 0.2456],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "local", "local", "local"]
torch.manual_seed(0)
model = ReformerModel(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[-1.6791, 0.7171, 0.1594, 0.4063, 1.2584],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_lm_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "lsh", "local", "lsh", "local", "lsh"]
config["num_buckets"] = [2, 4]
config["is_decoder"] = False
torch.manual_seed(0)
model = ReformerForMaskedLM(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[1, -1, :5]
expected_output_slice = torch.tensor(
[0.0256, -0.0121, 0.0636, 0.0024, -0.0393],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_lm_model_grad(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "local", "local", "local"]
config["hidden_dropout_prob"] = 0.0
config["local_attention_probs_dropout_prob"] = 0.0
torch.manual_seed(0)
model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
model.train()
model.zero_grad()
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
self.assertTrue(torch.allclose(loss, torch.tensor(5.7786, dtype=torch.float, device=torch_device), atol=1e-3))
loss.backward()
# check last grads to cover all proable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
expected_grad_slice_word = torch.tensor(
[-0.0005, 0.0001, 0.0002, 0.0003, 0.0006],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_1 = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
expected_grad_slice_pos_fac_1 = torch.tensor(
[0.0037, -1.3793, -1.0231, -1.5230, -2.5306],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_2 = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
expected_grad_slice_pos_fac_2 = torch.tensor(
[-1.3165, 0.5168, 0.7785, 1.0811, -0.9830],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(grad_slice_word, expected_grad_slice_word, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, atol=1e-3))
def test_lsh_lm_model_grad(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["lsh", "lsh", "lsh", "lsh"]
config["hidden_dropout_prob"] = 0.0
config["lsh_attention_probs_dropout_prob"] = 0.0
config["num_buckets"] = [2, 4]
config["num_hashes"] = 6
torch.manual_seed(0)
model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
model.train()
model.zero_grad()
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
self.assertTrue(torch.allclose(loss, torch.tensor(5.7819, dtype=torch.float, device=torch_device), atol=1e-3))
loss.backward()
# check last grads to cover all proable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
expected_grad_slice_word = torch.tensor(
[2.6357e-05, 4.3358e-04, -8.4985e-04, 1.0094e-04, 3.8954e-04],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_1 = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
expected_grad_slice_pos_fac_1 = torch.tensor(
[-0.0984, 0.6283, 0.4282, 1.2960, 0.6897],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_2 = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
expected_grad_slice_pos_fac_2 = torch.tensor(
[0.4626, -0.0231, -0.0172, 0.1081, 0.3805],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(grad_slice_word, expected_grad_slice_word, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, atol=1e-3))
@slow
def test_pretrained_generate_crime_and_punish(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids = model.generate(
input_ids, max_length=50, num_beams=4, early_stopping=True, do_sample=False, num_hashes=8
)
output = tokenizer.decode(output_ids[0])
self.assertEqual(
output,
"A few months later state expression in his ideas, at the first entrance. He was positively for an inst",
)
@slow
def test_pretrained_generate_use_cache_equality(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids_with_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=False)
output_ids_without_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=True)
output_with_cache = tokenizer.decode(output_ids_with_cache[0])
output_without_cache = tokenizer.decode(output_ids_without_cache[0])
self.assertEqual(output_with_cache, output_without_cache)
|
AdaMix/tests/test_modeling_reformer.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_reformer.py",
"repo_id": "AdaMix",
"token_count": 26838
}
| 73 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers.models.ctrl.modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
)
class TFCTRLModelTester(object):
def __init__(
self,
parent,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_token_type_ids = True
self.use_input_mask = True
self.use_labels = True
self.use_mc_token_ids = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = CTRLConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
# intermediate_size=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings,
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFCTRLModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, None, input_mask] # None is the input for 'past'
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_ctrl_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFCTRLLMHeadModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_ctrl_for_sequence_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
config.num_labels = self.num_labels
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
inputs = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"labels": sequence_labels,
}
model = TFCTRLForSequenceClassification(config)
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFCTRLModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
all_generative_model_classes = (TFCTRLLMHeadModel,) if is_tf_available() else ()
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFCTRLModelTester(self)
self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_ctrl_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
def test_ctrl_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
def test_ctrl_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_for_sequence_classification(*config_and_inputs)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
list_lm_models = [TFCTRLLMHeadModel]
list_other_models_with_output_ebd = [TFCTRLForSequenceClassification]
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
if model_class in list_lm_models:
x = model.get_output_embeddings()
assert isinstance(x, tf.keras.layers.Layer)
name = model.get_bias()
assert isinstance(name, dict)
for k, v in name.items():
assert isinstance(v, tf.Variable)
elif model_class in list_other_models_with_output_ebd:
x = model.get_output_embeddings()
assert isinstance(x, tf.keras.layers.Layer)
name = model.get_bias()
assert name is None
else:
x = model.get_output_embeddings()
assert x is None
name = model.get_bias()
assert name is None
@slow
def test_model_from_pretrained(self):
for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFCTRLModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_tf
class TFCTRLModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_ctrl(self):
model = TFCTRLLMHeadModel.from_pretrained("ctrl")
input_ids = tf.convert_to_tensor([[11859, 0, 1611, 8]], dtype=tf.int32) # Legal the president is
expected_output_ids = [
11859,
0,
1611,
8,
5,
150,
26449,
2,
19,
348,
469,
3,
2595,
48,
20740,
246533,
246533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
|
AdaMix/tests/test_modeling_tf_ctrl.py/0
|
{
"file_path": "AdaMix/tests/test_modeling_tf_ctrl.py",
"repo_id": "AdaMix",
"token_count": 4500
}
| 74 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class FuncContiguousArgs:
def forward(self, input_ids, token_type_ids, attention_mask):
return None
class FuncNonContiguousArgs:
def forward(self, input_ids, some_other_args, token_type_ids, attention_mask):
return None
class OnnxExportTestCase(unittest.TestCase):
MODEL_TO_TEST = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def test_export_tensorflow(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(model, "tf", 12, **model_kwargs)
@require_torch
@slow
def test_export_pytorch(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(model, "pt", 12, **model_kwargs)
@require_torch
@slow
def test_export_custom_bert_model(self):
from transformers import BertModel
vocab = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t") as vocab_file:
vocab_file.write("\n".join(vocab))
vocab_file.flush()
tokenizer = BertTokenizerFast(vocab_file.name)
with TemporaryDirectory() as bert_save_dir:
model = BertModel(BertConfig(vocab_size=len(vocab)))
model.save_pretrained(bert_save_dir)
self._test_export(bert_save_dir, "pt", 12, tokenizer)
@require_tf
@slow
def test_quantize_tf(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
path = self._test_export(model, "tf", 12, **model_kwargs)
quantized_path = quantize(Path(path))
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(path).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model")
@require_torch
@slow
def test_quantize_pytorch(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
path = self._test_export(model, "pt", 12, **model_kwargs)
quantized_path = quantize(path)
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(path).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model")
def _test_export(self, model, framework, opset, tokenizer=None, **model_kwargs):
try:
# Compute path
with TemporaryDirectory() as tempdir:
path = Path(tempdir).joinpath("model.onnx")
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(framework, model, path, opset, tokenizer, **model_kwargs)
return path
except Exception as e:
self.fail(e)
@require_torch
@require_tokenizers
@slow
def test_infer_dynamic_axis_pytorch(self):
"""
Validate the dynamic axis generated for each parameters are correct
"""
from transformers import BertModel
model = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
tokenizer = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
self._test_infer_dynamic_axis(model, tokenizer, "pt")
@require_tf
@require_tokenizers
@slow
def test_infer_dynamic_axis_tf(self):
"""
Validate the dynamic axis generated for each parameters are correct
"""
from transformers import TFBertModel
model = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
tokenizer = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
self._test_infer_dynamic_axis(model, tokenizer, "tf")
def _test_infer_dynamic_axis(self, model, tokenizer, framework):
nlp = FeatureExtractionPipeline(model, tokenizer)
variable_names = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
input_vars, output_vars, shapes, tokens = infer_shapes(nlp, framework)
# Assert all variables are present
self.assertEqual(len(shapes), len(variable_names))
self.assertTrue(all([var_name in shapes for var_name in variable_names]))
self.assertSequenceEqual(variable_names[:3], input_vars)
self.assertSequenceEqual(variable_names[3:], output_vars)
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name], {0: "batch", 1: "sequence"})
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["output_0"], {0: "batch", 1: "sequence"})
self.assertDictEqual(shapes["output_1"], {0: "batch"})
def test_ensure_valid_input(self):
"""
Validate parameters are correctly exported
GPT2 has "past" parameter in the middle of input_ids, token_type_ids and attention_mask.
ONNX doesn't support export with a dictionary, only a tuple. Thus we need to ensure we remove
token_type_ids and attention_mask for now to not having a None tensor in the middle
"""
# All generated args are valid
input_names = ["input_ids", "attention_mask", "token_type_ids"]
tokens = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
ordered_input_names, inputs_args = ensure_valid_input(FuncContiguousArgs(), tokens, input_names)
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(inputs_args), 3)
# Should have exactly the same input names
self.assertEqual(set(ordered_input_names), set(input_names))
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(inputs_args, (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]))
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
ordered_input_names, inputs_args = ensure_valid_input(FuncNonContiguousArgs(), tokens, input_names)
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(inputs_args), 1)
self.assertEqual(len(ordered_input_names), 1)
# Should have only "input_ids"
self.assertEqual(inputs_args[0], tokens["input_ids"])
self.assertEqual(ordered_input_names[0], "input_ids")
def test_generate_identified_name(self):
generated = generate_identified_filename(Path("/home/something/my_fake_model.onnx"), "-test")
self.assertEqual("/home/something/my_fake_model-test.onnx", generated.as_posix())
|
AdaMix/tests/test_onnx.py/0
|
{
"file_path": "AdaMix/tests/test_onnx.py",
"repo_id": "AdaMix",
"token_count": 3207
}
| 75 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import Speech2TextFeatureExtractor, Speech2TextProcessor, Speech2TextTokenizer
from transformers.file_utils import FEATURE_EXTRACTOR_NAME
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
from .test_feature_extraction_speech_to_text import floats_list
SAMPLE_SP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
@require_torch
@require_torchaudio
@require_sentencepiece
class Speech2TextProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
vocab = ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
save_dir = Path(self.tmpdirname)
save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"])
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname)
feature_extractor_map = {
"feature_size": 24,
"num_mel_bins": 24,
"padding_value": 0.0,
"sampling_rate": 16000,
"return_attention_mask": False,
"do_normalize": True,
}
save_json(feature_extractor_map, save_dir / FEATURE_EXTRACTOR_NAME)
def get_tokenizer(self, **kwargs):
return Speech2TextTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_feature_extractor(self, **kwargs):
return Speech2TextFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = Speech2TextProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = Speech2TextProcessor(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
)
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = Speech2TextProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
with processor.as_target_processor():
encoded_processor = processor(input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
|
AdaMix/tests/test_processor_speech_to_text.py/0
|
{
"file_path": "AdaMix/tests/test_processor_speech_to_text.py",
"repo_id": "AdaMix",
"token_count": 2301
}
| 76 |
# coding=utf-8
# Copyright 2018 Salesforce and HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from .test_tokenization_common import TokenizerTesterMixin
class CTRLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = CTRLTokenizer
test_rust_tokenizer = False
test_seq2seq = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return CTRLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "adapt react readapt apt"
output_text = "adapt react readapt apt"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = CTRLTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "adapt react readapt apt"
bpe_tokens = "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
|
AdaMix/tests/test_tokenization_ctrl.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_ctrl.py",
"repo_id": "AdaMix",
"token_count": 1074
}
| 77 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from .test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = OpenAIGPTTokenizer
rust_tokenizer_class = OpenAIGPTTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
def get_input_output_texts(self, tokenizer):
return "lower newer", "lower newer"
def test_full_tokenizer(self):
tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
# tokenizer has no padding token
def test_padding_different_model_input_name(self):
pass
|
AdaMix/tests/test_tokenization_openai.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_openai.py",
"repo_id": "AdaMix",
"token_count": 2309
}
| 78 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers.file_utils import cached_property
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import require_sentencepiece, slow
from .test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
@require_sentencepiece
class XLMProphetNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = XLMProphetNetTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = XLMProphetNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_tokenizer(self):
tokenizer = XLMProphetNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"[UNK]",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"[UNK]",
".",
],
)
@cached_property
def big_tokenizer(self):
return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased")
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [35389, 6672, 49, 2]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
|
AdaMix/tests/test_tokenization_xlm_prophetnet.py/0
|
{
"file_path": "AdaMix/tests/test_tokenization_xlm_prophetnet.py",
"repo_id": "AdaMix",
"token_count": 2150
}
| 79 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script remaps classes to class strings so that it's quick to load such maps and not require
# loading all possible modeling files
#
# it can be extended to auto-generate other dicts that are needed at runtime
import os
import sys
from os.path import abspath, dirname, join
git_repo_path = abspath(join(dirname(dirname(__file__)), "src"))
sys.path.insert(1, git_repo_path)
src = "src/transformers/models/auto/modeling_auto.py"
dst = "src/transformers/utils/modeling_auto_mapping.py"
if os.path.exists(dst) and os.path.getmtime(src) < os.path.getmtime(dst):
# speed things up by only running this script if the src is newer than dst
sys.exit(0)
# only load if needed
from transformers.models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING # noqa
entries = "\n".join(
[f' ("{k.__name__}", "{v.__name__}"),' for k, v in MODEL_FOR_QUESTION_ANSWERING_MAPPING.items()]
)
content = [
"# THIS FILE HAS BEEN AUTOGENERATED. To update:",
"# 1. modify: models/auto/modeling_auto.py",
"# 2. run: python utils/class_mapping_update.py",
"from collections import OrderedDict",
"",
"",
"MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(",
" [",
entries,
" ]",
")",
"",
]
print(f"updating {dst}")
with open(dst, "w", encoding="utf-8", newline="\n") as f:
f.write("\n".join(content))
|
AdaMix/utils/class_mapping_update.py/0
|
{
"file_path": "AdaMix/utils/class_mapping_update.py",
"repo_id": "AdaMix",
"token_count": 705
}
| 80 |
ARG BASE_IMAGE=nvidia/cudagl:10.0-devel-ubuntu18.04
FROM $BASE_IMAGE
RUN apt-get update
RUN apt-get install \
git \
libglu1-mesa-dev \
pulseaudio \
python3 \
python3-pip \
sudo \
sudo \
wget \
x11-xserver-utils \
xdg-user-dirs \
unzip \
-y --no-install-recommends
RUN pip3 install setuptools wheel
RUN pip3 install airsimdroneracinglab
RUN adduser --force-badname --disabled-password --gecos '' --shell /bin/bash airsim_user && \
echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \
adduser airsim_user sudo && \
adduser airsim_user audio && \
adduser airsim_user video
USER airsim_user
ENV USER airsim_user
WORKDIR /home/airsim_user
RUN sudo chown -R airsim_user /home/airsim_user
RUN git clone https://github.com/microsoft/AirSim-Drone-Racing-Lab && \
cd AirSim-Drone-Racing-Lab && \
bash download_binaries.sh && \
mv ADRL/ ../ && \
cd ../
|
AirSim-Drone-Racing-Lab/docker/Dockerfile/0
|
{
"file_path": "AirSim-Drone-Racing-Lab/docker/Dockerfile",
"repo_id": "AirSim-Drone-Racing-Lab",
"token_count": 365
}
| 81 |
import numpy as np
from matplotlib import pyplot as plt
import cv2
import time
# open data
data_path = '/home/rb/data/il_datasets/bc_0'
vel_table = np.loadtxt(data_path + '/proc_vel.txt', delimiter=',').astype(np.float32)
with open(data_path + '/proc_images.txt') as f:
img_table = f.read().splitlines()
# sanity check
if vel_table.shape[0] != len(img_table):
raise Exception('Number of images ({}) different than number of entries in table ({}): '.format(len(img_table), vel_table.shape[0]))
idx_list = range(vel_table.shape[0])
idx_range = [5000,10000]
plt.plot(idx_list[idx_range[0]:idx_range[1]], vel_table[idx_range[0]:idx_range[1], 0], color='red')
plt.plot(idx_list[idx_range[0]:idx_range[1]], vel_table[idx_range[0]:idx_range[1], 1], color='green')
plt.plot(idx_list[idx_range[0]:idx_range[1]], vel_table[idx_range[0]:idx_range[1], 2], color='blue')
plt.plot(idx_list[idx_range[0]:idx_range[1]], vel_table[idx_range[0]:idx_range[1], 3], color='cyan')
plt.show()
time.sleep(0.5)
plt.close()
vel_scale = 10
yaw_scale = 40
for img_idx in range(10000):
img = cv2.imread(img_table[img_idx])
o_x = int(img.shape[0]/2)
o_y = int(img.shape[1]/2)
origin = (o_x, o_y)
pt_vx = (o_x, o_y - int(vel_scale * vel_table[img_idx, 0]))
pt_vy = (o_x + int(vel_scale * vel_table[img_idx, 1]), o_y)
cv2.arrowedLine(img, origin, pt_vx, (255, 0, 0), 3)
cv2.arrowedLine(img, origin, pt_vy, (0, 255, 0), 3)
cv2.imshow('image', img)
# time.sleep(0.5)
cv2.waitKey(20)
|
AirSim-Drone-Racing-VAE-Imitation/datagen/action_generator/plot_data.py/0
|
{
"file_path": "AirSim-Drone-Racing-VAE-Imitation/datagen/action_generator/plot_data.py",
"repo_id": "AirSim-Drone-Racing-VAE-Imitation",
"token_count": 701
}
| 82 |
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Flatten, Conv2D, BatchNormalization, Lambda, Concatenate, Conv2DTranspose, Reshape, ReLU
class ImgDecoder(Model):
def __init__(self):
super(ImgDecoder, self).__init__()
self.create_model()
def call(self, z):
return self.network(z)
def create_model(self):
print('[ImgDecoder] Starting create_model')
dense = Dense(units=1024, name='p_img_dense')
reshape = Reshape((1, 1, 1024))
# for 64x64 img
deconv1 = Conv2DTranspose(filters=128, kernel_size=4, strides=1, padding='valid', activation='relu')
deconv2 = Conv2DTranspose(filters=64, kernel_size=5, strides=1, padding='valid', activation='relu', dilation_rate=3)
deconv3 = Conv2DTranspose(filters=64, kernel_size=6, strides=1, padding='valid', activation='relu', dilation_rate=2)
deconv4 = Conv2DTranspose(filters=32, kernel_size=5, strides=2, padding='valid', activation='relu', dilation_rate=1)
deconv5 = Conv2DTranspose(filters=16, kernel_size=5, strides=1, padding='valid', activation='relu', dilation_rate=1)
# deconv6 = Conv2DTranspose(filters=8, kernel_size=6, strides=2, padding='valid', activation='relu')
deconv7 = Conv2DTranspose(filters=3, kernel_size=6, strides=1, padding='valid', activation='tanh')
self.network = tf.keras.Sequential([
dense,
reshape,
deconv1,
deconv2,
deconv3,
deconv4,
deconv5,
deconv7],
name='p_img')
print('[ImgDecoder] Done with create_model')
class GateDecoder(Model):
def __init__(self, gate_dim):
super(GateDecoder, self).__init__()
self.create_model(gate_dim)
def call(self, z):
return self.network(z)
def create_model(self, gate_dim):
print('[GateDecoder] Starting create_model')
dense0 = Dense(units=512, activation='relu')
dense1 = Dense(units=128, activation='relu')
dense2 = Dense(units=64, activation='relu')
dense3 = Dense(units=16, activation='relu')
dense4 = Dense(units=gate_dim, activation='linear')
self.network = tf.keras.Sequential([
# dense0,
# dense1,
# dense2,
# dense3,
dense4],
name='p_gate')
print('[GateDecoder] Done with create_model')
|
AirSim-Drone-Racing-VAE-Imitation/racing_models/decoders.py/0
|
{
"file_path": "AirSim-Drone-Racing-VAE-Imitation/racing_models/decoders.py",
"repo_id": "AirSim-Drone-Racing-VAE-Imitation",
"token_count": 1125
}
| 83 |
# !bin/bash
# **Usage**
# - for running default image, training binaries, in windowed mode:
# `$ ./run_docker_image.sh "" training`
# - for running default image, qualification binaries, in windowed mode:
# `$ ./run_docker_image.sh "" qualification`
# - for running default image, training binaries, in headless mode:
# `$ ./run_docker_image.sh "" training headless`
# - for running default image, qualification binaries, in headless mode:
# `$ ./run_docker_image.sh "" qualification headless`
# - for running a custom image in windowed mode, pass in you image name and tag:
# `# $ ./run_docker_image.sh DOCKER_IMAGE_NAME:TAG`
# - for running a custom image in headless mode, pass in you image name and tag, followed by "headless":
# # $ ./run_docker_image.sh DOCKER_IMAGE_NAME:TAG headless
# This script takes three optional arguments
# 1st argument is the name (and tag) of the dockerfile to run
# by default, it is set to "airsim_neurips:10.0-devel-ubuntu18.04"
# else user can specify a docker image as follows:
# $ ./run_docker_image.sh DOCKER_IMAGE_NAME:TAG
DOCKER_IMAGE_NAME=${1:-airsim_neurips:10.0-devel-ubuntu18.04}
# 2nd argument: can be "training" or "qualification"
TRAINING_OR_QUALIFICATION=${2:-training}
# 3rd argument: if user passes "headless", binary runs in headless mode
IS_HEADLESS=${3:-notheadless}
# this block is for running X apps in docker
XAUTH=/tmp/.docker.xauth
if [[ ! -f $XAUTH ]]
then
xauth_list=$(xauth nlist :0 | sed -e 's/^..../ffff/')
if [ ! -z "$xauth_list" ]
then
echo $xauth_list | xauth -f $XAUTH nmerge -
else
touch $XAUTH
fi
chmod a+r $XAUTH
fi
# per use the following commented out code for different options
if [[ $2 = "training" ]]; then
UNREAL_BINARY_COMMAND="bash /home/airsim_user/AirSim_Training/AirSimExe.sh -windowed -opengl"
elif [[ $2 = "qualification" ]]; then
UNREAL_BINARY_COMMAND="bash /home/airsim_user/AirSim_Qualification/AirSimExe.sh -windowed -opengl"
fi
# eleminate terminal output and run airsim process in the background
# UNREAL_BINARY_COMMAND="bash /home/airsim_user/AirSim_Training/AirSimExe.sh -windowed -opengl &>/dev/null &"
# set window resolution
# UNREAL_BINARY_COMMAND="/home/airsim_user/AirSim_Training -windowed -ResX=1080 -ResY=720"
# now, let's check if we need to run in headless mode or not
# set SDL_VIDEODRIVER_VALUE to '' if windowed mode, 'offscreen' if headless mode
SDL_VIDEODRIVER_VALUE='';
if [[ $3 = "headless" ]]; then
SDL_VIDEODRIVER_VALUE='offscreen';
fi
# now, set the environment varible SDL_VIDEODRIVER to SDL_VIDEODRIVER_VALUE
# and tell the docker container to execute UNREAL_BINARY_COMMAND
nvidia-docker run -it \
-e SDL_VIDEODRIVER=$SDL_VIDEODRIVER_VALUE \
-e SDL_HINT_CUDA_DEVICE='0' \
--net=host \
--env="DISPLAY=$DISPLAY" \
--env="QT_X11_NO_MITSHM=1" \
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
-env="XAUTHORITY=$XAUTH" \
--volume="$XAUTH:$XAUTH" \
--runtime=nvidia \
--rm \
$DOCKER_IMAGE_NAME \
/bin/bash -c "$UNREAL_BINARY_COMMAND"
|
AirSim-NeurIPS2019-Drone-Racing/docker/run_docker_image.sh/0
|
{
"file_path": "AirSim-NeurIPS2019-Drone-Racing/docker/run_docker_image.sh",
"repo_id": "AirSim-NeurIPS2019-Drone-Racing",
"token_count": 1223
}
| 84 |
[flake8]
ignore = E501, W503
|
AzureTRE/.flake8/0
|
{
"file_path": "AzureTRE/.flake8",
"repo_id": "AzureTRE",
"token_count": 13
}
| 85 |
#!/bin/bash
# 1. Remove any previously run failed flag
# 2. Run pytest, but capture the exit code so we always succeed
# 3. Output a file if the tests are not successful.
rm -f ../test-results/pytest_airlock_processor*
mkdir -p ../test-results
if ! pytest --junit-xml ../test-results/pytest_airlock_processor_unit.xml --ignore e2e_tests; then
touch ../test-results/pytest_airlock_processor_unit_failed
fi
|
AzureTRE/airlock_processor/run_tests_and_exit_succesfully.sh/0
|
{
"file_path": "AzureTRE/airlock_processor/run_tests_and_exit_succesfully.sh",
"repo_id": "AzureTRE",
"token_count": 135
}
| 86 |
from fastapi import Depends, HTTPException, Path, status
from api.helpers import get_repository
from db.errors import EntityDoesNotExist
from db.repositories.resource_templates import ResourceTemplateRepository
from models.domain.resource import ResourceType
from models.domain.resource_template import ResourceTemplate
from resources import strings
async def get_workspace_service_template_by_name_from_path(service_template_name: str = Path(...), template_repo=Depends(get_repository(ResourceTemplateRepository))) -> ResourceTemplate:
try:
return await template_repo.get_current_template(service_template_name, ResourceType.WorkspaceService)
except EntityDoesNotExist:
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=strings.WORKSPACE_SERVICE_TEMPLATE_DOES_NOT_EXIST)
|
AzureTRE/api_app/api/dependencies/workspace_service_templates.py/0
|
{
"file_path": "AzureTRE/api_app/api/dependencies/workspace_service_templates.py",
"repo_id": "AzureTRE",
"token_count": 242
}
| 87 |
from datetime import datetime
import semantic_version
from copy import deepcopy
from typing import Dict, Any, Optional
from fastapi import HTTPException, status
from db.repositories.user_resources import UserResourceRepository
from models.domain.user_resource import UserResource
from models.domain.workspace_service import WorkspaceService
from models.schemas.resource import ResourcePatch
from db.repositories.resources import ResourceRepository
from db.repositories.resources_history import ResourceHistoryRepository
from models.domain.resource_template import ResourceTemplate
from models.domain.authentication import User
from pydantic import parse_obj_as
from db.errors import DuplicateEntity, EntityDoesNotExist
from db.repositories.operations import OperationRepository
from db.repositories.resource_templates import ResourceTemplateRepository
from models.domain.resource import AvailableUpgrade, ResourceType, Resource
from models.domain.operation import Operation
from resources import strings
from service_bus.resource_request_sender import (
send_resource_request_message,
RequestAction,
)
from services.authentication import get_access_service
from services.logging import logger
async def delete_validation(resource: Resource, resource_repo: ResourceRepository):
dependency_list = await resource_repo.get_resource_dependency_list(resource)
for resource in dependency_list:
if resource["isEnabled"]:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=strings.WORKSPACE_NEEDS_TO_BE_DISABLED_BEFORE_DELETION)
return True
async def cascaded_update_resource(resource_patch: ResourcePatch, parent_resource: Resource, user: User, force_version_update: bool, resource_template_repo: ResourceTemplateRepository, resource_history_repo: ResourceHistoryRepository, resource_repo: ResourceRepository):
# Get dependecy list
dependency_list = await resource_repo.get_resource_dependency_list(parent_resource)
# Patch all resources
for child_resource in dependency_list[:-1]:
child_etag = child_resource["_etag"]
primary_parent_service_name = ""
if child_resource["resourceType"] == ResourceType.WorkspaceService:
child_resource = parse_obj_as(WorkspaceService, child_resource)
elif child_resource["resourceType"] == ResourceType.UserResource:
child_resource = parse_obj_as(UserResource, child_resource)
primary_parent_workspace_service = await resource_repo.get_resource_by_id(child_resource.parentWorkspaceServiceId)
primary_parent_service_name = primary_parent_workspace_service.templateName
child_resource_template = await resource_template_repo.get_template_by_name_and_version(child_resource.templateName, child_resource.templateVersion, child_resource.resourceType, parent_service_name=primary_parent_service_name)
await resource_repo.patch_resource(child_resource, resource_patch, child_resource_template, child_etag, resource_template_repo, resource_history_repo, user, force_version_update)
async def save_and_deploy_resource(
resource: Resource,
resource_repo: ResourceRepository,
operations_repo: OperationRepository,
resource_template_repo: ResourceTemplateRepository,
resource_history_repo: ResourceHistoryRepository,
user: User,
resource_template: ResourceTemplate,
) -> Operation:
try:
resource.user = user
resource.updatedWhen = get_timestamp()
# Making a copy to save with secrets masked
masked_resource = deepcopy(resource)
masked_resource.properties = mask_sensitive_properties(
resource.properties, resource_template
)
await resource_repo.save_item(masked_resource)
except Exception:
logger.exception(f"Failed saving resource item {resource.id}")
raise HTTPException(
status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
detail=strings.STATE_STORE_ENDPOINT_NOT_RESPONDING,
)
try:
operation = await send_resource_request_message(
resource=resource,
operations_repo=operations_repo,
resource_repo=resource_repo,
user=user,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
action=RequestAction.Install,
)
return operation
except Exception:
await resource_repo.delete_item(resource.id)
logger.exception("Failed send resource request message")
raise HTTPException(
status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
detail=strings.SERVICE_BUS_GENERAL_ERROR_MESSAGE,
)
def mask_sensitive_properties(
properties: Dict[str, Any], template: ResourceTemplate
) -> dict:
updated_resource_parameters = deepcopy(properties)
flattened_template_props = {}
# recusrse the template and flatten all properties into a list
def flatten_template_props(template_fragment: dict):
if template_fragment is None:
return
for prop_name, prop in template_fragment.items():
if prop_name == "pipeline":
continue
if (
prop_name == "properties"
and template_fragment["properties"] is not None
):
for inner_prop_name, inner_prop in template_fragment[
"properties"
].items():
flattened_template_props[inner_prop_name] = inner_prop
if isinstance(prop, list) and len(prop) > 0:
for p in prop:
if isinstance(p, dict):
flatten_template_props(p)
if isinstance(prop, dict) and prop_name != "if":
flatten_template_props(prop)
flatten_template_props(template.dict())
def recurse_input_props(prop_dict: dict):
for prop_name, prop in prop_dict.items():
if (
prop_name in flattened_template_props
and "sensitive" in flattened_template_props[prop_name]
and flattened_template_props[prop_name]["sensitive"] is True
):
prop_dict[prop_name] = strings.REDACTED_SENSITIVE_VALUE
if isinstance(prop, dict):
recurse_input_props(prop)
recurse_input_props(updated_resource_parameters)
return updated_resource_parameters
def construct_location_header(operation: Operation) -> str:
return f"/api{operation.resourcePath}/operations/{operation.id}"
def get_identity_role_assignments(user):
access_service = get_access_service()
return access_service.get_identity_role_assignments(user.id)
def get_app_user_roles_assignments_emails(app_obj_id):
access_service = get_access_service()
return access_service.get_app_user_role_assignments_emails(app_obj_id)
async def send_uninstall_message(
resource: Resource,
resource_repo: ResourceRepository,
operations_repo: OperationRepository,
resource_type: ResourceType,
resource_template_repo: ResourceTemplateRepository,
resource_history_repo: ResourceHistoryRepository,
user: User,
is_cascade: str = False
) -> Operation:
try:
operation = await send_resource_request_message(
resource=resource,
operations_repo=operations_repo,
resource_repo=resource_repo,
user=user,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
action=RequestAction.UnInstall,
is_cascade=is_cascade
)
return operation
except Exception:
logger.exception(f"Failed to send {resource_type} resource delete message")
raise HTTPException(
status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
detail=strings.SERVICE_BUS_GENERAL_ERROR_MESSAGE,
)
async def send_custom_action_message(
resource: Resource,
resource_repo: ResourceRepository,
custom_action: str,
resource_type: ResourceType,
operations_repo: OperationRepository,
resource_template_repo: ResourceTemplateRepository,
resource_history_repo: ResourceHistoryRepository,
user: User,
parent_service_name: Optional[str] = None,
) -> Operation:
# Validate that the custom_action specified is present in the resource template
resource_template = await resource_template_repo.get_template_by_name_and_version(
resource.templateName,
resource.templateVersion,
resource_type,
parent_service_name=parent_service_name,
)
if not resource_template.customActions:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=strings.CUSTOM_ACTIONS_DO_NOT_EXIST,
)
elif not any(
action.name == custom_action for action in resource_template.customActions
):
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=strings.CUSTOM_ACTION_NOT_DEFINED,
)
try:
operation = await send_resource_request_message(
resource=resource,
operations_repo=operations_repo,
resource_repo=resource_repo,
user=user,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
action=custom_action,
)
return operation
except Exception:
logger.exception(f"Failed to send {resource_type} resource custom action message")
raise HTTPException(
status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
detail=strings.SERVICE_BUS_GENERAL_ERROR_MESSAGE,
)
async def get_template(
template_name: str,
template_repo: ResourceTemplateRepository,
resource_type: ResourceType,
parent_service_template_name: str = "",
is_update: bool = False,
version: Optional[str] = None,
) -> dict:
try:
template = (
await template_repo.get_template_by_name_and_version(
template_name, version, resource_type, parent_service_template_name
)
if version
else await template_repo.get_current_template(
template_name, resource_type, parent_service_template_name
)
)
return template_repo.enrich_template(template, is_update=is_update)
except EntityDoesNotExist:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=strings.TEMPLATE_DOES_NOT_EXIST,
)
except DuplicateEntity:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=strings.NO_UNIQUE_CURRENT_FOR_TEMPLATE,
)
except Exception as e:
logger.debug(e)
raise HTTPException(
status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
detail=strings.STATE_STORE_ENDPOINT_NOT_RESPONDING,
)
def get_timestamp() -> float:
return datetime.utcnow().timestamp()
async def update_user_resource(
user_resource: UserResource,
resource_patch: ResourcePatch,
force_version_update: bool,
user: User,
etag: str,
workspace_service: WorkspaceService,
user_resource_repo: UserResourceRepository,
resource_template_repo: ResourceTemplateRepository,
operations_repo: OperationRepository,
resource_history_repo: ResourceHistoryRepository) -> Operation:
patched_user_resource, _ = await user_resource_repo.patch_user_resource(user_resource, resource_patch, etag, resource_template_repo, resource_history_repo, workspace_service.templateName, user, force_version_update)
operation = await send_resource_request_message(
resource=patched_user_resource,
operations_repo=operations_repo,
resource_repo=user_resource_repo,
user=user,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo,
action=RequestAction.Upgrade)
return operation
async def enrich_resource_with_available_upgrades(resource: Resource, resource_template_repo: ResourceTemplateRepository):
available_upgrades = []
resource_version = semantic_version.Version(resource.templateVersion)
all_versions = await resource_template_repo.get_all_template_versions(resource.templateName)
versions_higher_than_current = [version for version in all_versions if semantic_version.Version(version) > resource_version]
major_update_versions = [version for version in versions_higher_than_current if semantic_version.Version(version).major > resource_version.major]
non_major_update_versions = [version for version in versions_higher_than_current if version not in major_update_versions]
for version in sorted(non_major_update_versions, key=semantic_version.Version):
available_upgrades.append(AvailableUpgrade(version=version, forceUpdateRequired=False))
for version in sorted(major_update_versions, key=semantic_version.Version):
available_upgrades.append(AvailableUpgrade(version=version, forceUpdateRequired=True))
resource.availableUpgrades = available_upgrades
|
AzureTRE/api_app/api/routes/resource_helpers.py/0
|
{
"file_path": "AzureTRE/api_app/api/routes/resource_helpers.py",
"repo_id": "AzureTRE",
"token_count": 5123
}
| 88 |
import semantic_version
from db.repositories.workspaces import WorkspaceRepository
from services.logging import logger
class WorkspaceMigration(WorkspaceRepository):
@classmethod
async def create(cls):
cls = WorkspaceMigration()
resource_repo = await super().create()
cls._container = resource_repo._container
return cls
async def moveAuthInformationToProperties(self) -> bool:
migrated = False
for item in await self.query(query=WorkspaceRepository.workspaces_query_string()):
template_version = semantic_version.Version(item["templateVersion"])
updated = False
if (template_version < semantic_version.Version('0.2.7')):
# Rename app_id to be client_id
if "app_id" in item["properties"]:
item["properties"]["client_id"] = item["properties"]["app_id"]
del item["properties"]["app_id"]
updated = True
if "scope_id" not in item["properties"]:
item["properties"]["scope_id"] = f"api://{item['properties']['client_id']}"
updated = True
if "authInformation" in item:
logger.info(f'Upgrading authInformation in workspace {item["id"]}')
# Copy authInformation into properties
item["properties"]["sp_id"] = item["authInformation"]["sp_id"]
item["properties"]["app_role_id_workspace_researcher"] = item["authInformation"]["roles"]["WorkspaceResearcher"]
item["properties"]["app_role_id_workspace_owner"] = item["authInformation"]["roles"]["WorkspaceOwner"]
# cleanup
del item["authInformation"]
updated = True
if updated:
await self.update_item_dict(item)
logger.info(f'Upgraded authentication info for workspace id {item["id"]}')
migrated = True
return migrated
|
AzureTRE/api_app/db/migrations/workspaces.py/0
|
{
"file_path": "AzureTRE/api_app/db/migrations/workspaces.py",
"repo_id": "AzureTRE",
"token_count": 949
}
| 89 |
import asyncio
import uvicorn
from fastapi import FastAPI
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.concurrency import asynccontextmanager
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from starlette.exceptions import HTTPException
from starlette.middleware.errors import ServerErrorMiddleware
from api.routes.api import router as api_router
from api.errors.http_error import http_error_handler
from api.errors.validation_error import http422_error_handler
from api.errors.generic_error import generic_error_handler
from core import config
from db.events import bootstrap_database
from services.logging import initialize_logging, logger
from service_bus.deployment_status_updater import DeploymentStatusUpdater
from service_bus.airlock_request_status_update import AirlockStatusUpdater
@asynccontextmanager
async def lifespan(app: FastAPI):
while not await bootstrap_database():
await asyncio.sleep(5)
logger.warning("Database connection could not be established")
deploymentStatusUpdater = DeploymentStatusUpdater()
await deploymentStatusUpdater.init_repos()
airlockStatusUpdater = AirlockStatusUpdater()
await airlockStatusUpdater.init_repos()
asyncio.create_task(deploymentStatusUpdater.receive_messages())
asyncio.create_task(airlockStatusUpdater.receive_messages())
yield
def get_application() -> FastAPI:
application = FastAPI(
title=config.PROJECT_NAME,
debug=(config.LOGGING_LEVEL == "DEBUG"),
description=config.API_DESCRIPTION,
version=config.VERSION,
docs_url=None,
redoc_url=None,
openapi_url=None,
lifespan=lifespan
)
application.add_middleware(ServerErrorMiddleware, handler=generic_error_handler)
# Allow local UI debugging with local API
if config.ENABLE_LOCAL_DEBUGGING:
application.add_middleware(
CORSMiddleware,
allow_origins=["http://localhost:3000"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"])
application.add_exception_handler(HTTPException, http_error_handler)
application.add_exception_handler(RequestValidationError, http422_error_handler)
application.include_router(api_router)
return application
initialize_logging()
app = get_application()
FastAPIInstrumentor.instrument_app(app)
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000, loop="asyncio")
|
AzureTRE/api_app/main.py/0
|
{
"file_path": "AzureTRE/api_app/main.py",
"repo_id": "AzureTRE",
"token_count": 890
}
| 90 |
from pydantic import Field
from models.domain.resource import ResourceType
from models.domain.resource_template import ResourceTemplate
class UserResourceTemplate(ResourceTemplate):
parentWorkspaceService: str = Field("", title="Parent Workspace Service", description="The parent workspace service under which services with this template can be created")
resourceType = ResourceType.UserResource
|
AzureTRE/api_app/models/domain/user_resource_template.py/0
|
{
"file_path": "AzureTRE/api_app/models/domain/user_resource_template.py",
"repo_id": "AzureTRE",
"token_count": 89
}
| 91 |
from typing import List
from pydantic import BaseModel, Field
from models.domain.resource import ResourceType
from models.domain.user_resource import UserResource
def get_sample_user_resource(user_resource_id: str) -> dict:
return {
"id": user_resource_id,
"ownerId": "abc9ru33-7265-4b5f-9eae-a1a62928772e",
"workspaceId": "7289ru33-7265-4b5f-9eae-a1a62928772e",
"parentWorkspaceServiceId": "e75f1ee1-9f55-414c-83da-aff677669249",
"templateName": "vm",
"templateVersion": "0.1.0",
"properties": {
"display_name": "my user resource",
"description": "some description",
},
"azureStatus": {
"powerState": "Running",
},
"resourceType": ResourceType.UserResource
}
class UserResourceInResponse(BaseModel):
userResource: UserResource
class Config:
schema_extra = {
"example": {
"user_resource": get_sample_user_resource("933ad738-7265-4b5f-9eae-a1a62928772e")
}
}
class UserResourcesInList(BaseModel):
userResources: List[UserResource] = Field([], title="User resources")
class Config:
schema_extra = {
"example": {
"userResources": [
get_sample_user_resource("2fdc9fba-726e-4db6-a1b8-9018a2165748"),
get_sample_user_resource("abcc9fba-726e-4db6-a1b8-9018a2165748")
]
}
}
class UserResourceInCreate(BaseModel):
templateName: str = Field(title="User resource type", description="Bundle name")
properties: dict = Field({}, title="User resource parameters", description="Values for the parameters required by the user resource specification")
class Config:
schema_extra = {
"example": {
"templateName": "user-resource-type",
"properties": {
"display_name": "my user resource",
"description": "some description",
}
}
}
|
AzureTRE/api_app/models/schemas/user_resource.py/0
|
{
"file_path": "AzureTRE/api_app/models/schemas/user_resource.py",
"repo_id": "AzureTRE",
"token_count": 972
}
| 92 |
{
"$schema": "http://json-schema.org/draft-07/schema",
"$id": "https://github.com/microsoft/AzureTRE/schema/workspace.json",
"type": "object",
"title": "Workspace Default Parameters",
"description": "These parameters are required for all workspaces",
"required": [
"display_name",
"description"
],
"properties": {
"display_name": {
"type": "string",
"title": "Name for the workspace",
"description": "The name of the workspace to be displayed to users.",
"updateable": true
},
"description": {
"type": "string",
"title": "Description of the workspace",
"description": "Description of the workspace.",
"updateable": true
},
"overview": {
"type": "string",
"title": "Workspace Overview",
"description": "Long form description of the workspace, in markdown syntax.",
"updateable": true
}
}
}
|
AzureTRE/api_app/schemas/workspace.json/0
|
{
"file_path": "AzureTRE/api_app/schemas/workspace.json",
"repo_id": "AzureTRE",
"token_count": 335
}
| 93 |
from typing import Tuple
from azure.core import exceptions
from azure.servicebus.aio import ServiceBusClient
from azure.mgmt.compute.aio import ComputeManagementClient
from azure.cosmos.exceptions import CosmosHttpResponseError
from azure.cosmos.aio import ContainerProxy
from azure.servicebus.exceptions import ServiceBusConnectionError, ServiceBusAuthenticationError
from api.dependencies.database import Database
from core.config import STATE_STORE_RESOURCES_CONTAINER
from core import config
from models.schemas.status import StatusEnum
from resources import strings
from services.logging import logger
async def create_state_store_status() -> Tuple[StatusEnum, str]:
status = StatusEnum.ok
message = ""
try:
container: ContainerProxy = await Database().get_container_proxy(STATE_STORE_RESOURCES_CONTAINER)
container.query_items("SELECT TOP 1 * FROM c")
except exceptions.ServiceRequestError:
status = StatusEnum.not_ok
message = strings.STATE_STORE_ENDPOINT_NOT_RESPONDING
except CosmosHttpResponseError:
status = StatusEnum.not_ok
message = strings.STATE_STORE_ENDPOINT_NOT_ACCESSIBLE
except Exception:
logger.exception("Failed to query cosmos db status")
status = StatusEnum.not_ok
message = strings.UNSPECIFIED_ERROR
return status, message
async def create_service_bus_status(credential) -> Tuple[StatusEnum, str]:
status = StatusEnum.ok
message = ""
try:
service_bus_client = ServiceBusClient(config.SERVICE_BUS_FULLY_QUALIFIED_NAMESPACE, credential, retry_total=0)
async with service_bus_client:
receiver = service_bus_client.get_queue_receiver(queue_name=config.SERVICE_BUS_STEP_RESULT_QUEUE)
async with receiver:
pass
except ServiceBusConnectionError:
status = StatusEnum.not_ok
message = strings.SERVICE_BUS_NOT_RESPONDING
except ServiceBusAuthenticationError:
status = StatusEnum.not_ok
message = strings.SERVICE_BUS_AUTHENTICATION_ERROR
except Exception:
logger.exception("Failed to query service bus status")
status = StatusEnum.not_ok
message = strings.UNSPECIFIED_ERROR
return status, message
async def create_resource_processor_status(credential) -> Tuple[StatusEnum, str]:
status = StatusEnum.ok
message = ""
try:
vmss_name = f"vmss-rp-porter-{config.TRE_ID}"
compute_client = ComputeManagementClient(credential=credential,
subscription_id=config.SUBSCRIPTION_ID,
base_url=config.RESOURCE_MANAGER_ENDPOINT,
credential_scopes=config.CREDENTIAL_SCOPES)
async with compute_client:
vmss_list = compute_client.virtual_machine_scale_set_vms.list(config.RESOURCE_GROUP_NAME, vmss_name)
async for vm in vmss_list:
instance_view = await compute_client.virtual_machine_scale_set_vms.get_instance_view(config.RESOURCE_GROUP_NAME, vmss_name, vm.instance_id)
health_status = instance_view.vm_health.status.code
if health_status != strings.RESOURCE_PROCESSOR_HEALTHY_MESSAGE:
status = StatusEnum.not_ok
message = strings.RESOURCE_PROCESSOR_GENERAL_ERROR_MESSAGE
except Exception:
logger.exception("Failed to query resource processor status")
status = StatusEnum.not_ok
message = strings.UNSPECIFIED_ERROR
return status, message
|
AzureTRE/api_app/services/health_checker.py/0
|
{
"file_path": "AzureTRE/api_app/services/health_checker.py",
"repo_id": "AzureTRE",
"token_count": 1462
}
| 94 |
import pytest
from httpx import AsyncClient
from mock import patch
from models.schemas.status import StatusEnum
from resources import strings
pytestmark = pytest.mark.asyncio
@patch("api.routes.health.create_resource_processor_status")
@patch("api.routes.health.create_service_bus_status")
@patch("api.routes.health.create_state_store_status")
async def test_health_response_contains_cosmos_status(health_check_cosmos_mock, health_check_service_bus_mock, health_check_rp_mock, app,
client: AsyncClient) -> None:
message = ""
health_check_cosmos_mock.return_value = StatusEnum.ok, message
health_check_service_bus_mock.return_value = StatusEnum.ok, message
health_check_rp_mock.return_value = StatusEnum.ok, message
response = await client.get(app.url_path_for(strings.API_GET_HEALTH_STATUS))
assert {"message": message, "service": strings.COSMOS_DB, "status": strings.OK} in response.json()["services"]
@patch("api.routes.health.create_resource_processor_status")
@patch("api.routes.health.create_service_bus_status")
@patch("api.routes.health.create_state_store_status")
async def test_health_response_contains_service_bus_status(health_check_cosmos_mock, health_check_service_bus_mock, health_check_rp_mock, app,
client: AsyncClient) -> None:
message = ""
health_check_cosmos_mock.return_value = StatusEnum.ok, message
health_check_service_bus_mock.return_value = StatusEnum.ok, message
health_check_rp_mock.return_value = StatusEnum.ok, message
response = await client.get(app.url_path_for(strings.API_GET_HEALTH_STATUS))
assert {"message": message, "service": strings.SERVICE_BUS, "status": strings.OK} in response.json()["services"]
@patch("api.routes.health.create_resource_processor_status")
@patch("api.routes.health.create_service_bus_status")
@patch("api.routes.health.create_state_store_status")
async def test_health_response_contains_resource_processor_status(health_check_cosmos_mock, health_check_service_bus_mock, health_check_rp_mock, app,
client: AsyncClient) -> None:
message = ""
health_check_cosmos_mock.return_value = StatusEnum.ok, message
health_check_service_bus_mock.return_value = StatusEnum.ok, message
health_check_rp_mock.return_value = StatusEnum.ok, message
response = await client.get(app.url_path_for(strings.API_GET_HEALTH_STATUS))
assert {"message": message, "service": strings.RESOURCE_PROCESSOR, "status": strings.OK} in response.json()["services"]
|
AzureTRE/api_app/tests_ma/test_api/test_routes/test_health.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_api/test_routes/test_health.py",
"repo_id": "AzureTRE",
"token_count": 1046
}
| 95 |
import pytest
from mock import patch
from db.errors import UnableToAccessDatabase
from db.repositories.base import BaseRepository
pytestmark = pytest.mark.asyncio
@patch("api.dependencies.database.Database.get_container_proxy")
async def test_instantiating_a_repo_raises_unable_to_access_database_if_database_cant_be_accessed(get_container_proxy_mock):
get_container_proxy_mock.side_effect = Exception()
with pytest.raises(UnableToAccessDatabase):
await BaseRepository.create()
|
AzureTRE/api_app/tests_ma/test_db/test_repositories/test_base_repository.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_db/test_repositories/test_base_repository.py",
"repo_id": "AzureTRE",
"token_count": 167
}
| 96 |
import json
import pytest
import uuid
from azure.servicebus import ServiceBusMessage
from mock import AsyncMock, patch
from models.schemas.resource import ResourcePatch
from service_bus.helpers import (
try_update_with_retries,
update_resource_for_step,
)
from tests_ma.test_api.conftest import create_test_user
from tests_ma.test_service_bus.test_deployment_status_update import (
create_sample_operation,
)
from models.domain.workspace_service import WorkspaceService
from models.domain.resource import Resource, ResourceType
from service_bus.resource_request_sender import (
send_resource_request_message,
RequestAction,
)
from azure.cosmos.exceptions import CosmosAccessConditionFailedError
pytestmark = pytest.mark.asyncio
def create_test_resource():
return Resource(
id=str(uuid.uuid4()),
resourceType=ResourceType.Workspace,
templateName="Test resource template name",
templateVersion="2.718",
etag="",
properties={"testParameter": "testValue"},
resourcePath="test",
)
@pytest.mark.parametrize(
"request_action", [RequestAction.Install, RequestAction.UnInstall]
)
@patch("service_bus.resource_request_sender.ResourceHistoryRepository.create")
@patch("service_bus.resource_request_sender.OperationRepository.create")
@patch("service_bus.helpers.ServiceBusClient")
@patch("service_bus.resource_request_sender.ResourceRepository.create")
@patch("service_bus.resource_request_sender.ResourceTemplateRepository.create")
async def test_resource_request_message_generated_correctly(
resource_template_repo,
resource_repo,
service_bus_client_mock,
operations_repo_mock,
resource_history_repo_mock,
request_action,
multi_step_resource_template
):
service_bus_client_mock().get_queue_sender().send_messages = AsyncMock()
resource = create_test_resource()
operation = create_sample_operation(resource.id, request_action)
operations_repo_mock.create_operation_item.return_value = operation
resource_repo.get_resource_by_id.return_value = resource
resource_template_repo.get_template_by_name_and_version.return_value = multi_step_resource_template
resource_repo.patch_resource.return_value = (resource, multi_step_resource_template)
await send_resource_request_message(
resource=resource,
operations_repo=operations_repo_mock,
resource_repo=resource_repo,
user=create_test_user(),
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo_mock,
action=request_action
)
args = service_bus_client_mock().get_queue_sender().send_messages.call_args.args
assert len(args) == 1
assert isinstance(args[0], ServiceBusMessage)
sent_message = args[0]
assert sent_message.correlation_id == operation.id
sent_message_as_json = json.loads(str(sent_message))
assert sent_message_as_json["id"] == resource.id
assert sent_message_as_json["action"] == request_action
@patch("service_bus.resource_request_sender.ResourceHistoryRepository.create")
@patch("service_bus.resource_request_sender.OperationRepository.create")
@patch("service_bus.resource_request_sender.ResourceRepository.create")
@patch("service_bus.resource_request_sender.ResourceTemplateRepository.create")
async def test_multi_step_document_sends_first_step(
resource_template_repo,
resource_repo,
operations_repo_mock,
resource_history_repo_mock,
multi_step_operation,
basic_shared_service,
basic_shared_service_template,
multi_step_resource_template,
user_resource_multi,
test_user,
):
operations_repo_mock.return_value.create_operation_item.return_value = multi_step_operation
temp_workspace_service = WorkspaceService(
id="123", templateName="template-name-here", templateVersion="0.1.0", etag=""
)
# return the primary resource, a 'parent' workspace service, then the shared service to patch
resource_repo.return_value.get_resource_by_id.side_effect = [
user_resource_multi,
temp_workspace_service,
basic_shared_service,
]
resource_template_repo.get_template_by_name_and_version.side_effect = [
multi_step_resource_template,
basic_shared_service_template,
]
resource_repo.patch_resource.return_value = (basic_shared_service, basic_shared_service_template)
resource_repo.get_resource_by_id.return_value = basic_shared_service
_ = await update_resource_for_step(
operation_step=multi_step_operation.steps[0],
resource_repo=resource_repo,
resource_template_repo=resource_template_repo,
resource_history_repo=resource_history_repo_mock,
step_resource=user_resource_multi,
root_resource=None,
resource_to_update_id=basic_shared_service.id,
primary_action="install",
user=test_user,
)
expected_patch = ResourcePatch(properties={"display_name": "new name"})
# expect the patch for step 1
resource_repo.patch_resource.assert_called_once_with(
resource=basic_shared_service,
resource_patch=expected_patch,
resource_template=basic_shared_service_template,
resource_history_repo=resource_history_repo_mock,
etag=basic_shared_service.etag,
resource_template_repo=resource_template_repo,
user=test_user
)
@patch("service_bus.resource_request_sender.ResourceHistoryRepository.create")
@patch("service_bus.resource_request_sender.ResourceRepository.create")
@patch("service_bus.resource_request_sender.ResourceTemplateRepository.create")
async def test_multi_step_document_retries(
resource_template_repo,
resource_repo,
resource_history_repo,
basic_shared_service,
basic_shared_service_template,
test_user,
multi_step_resource_template,
primary_resource
):
resource_repo.get_resource_by_id.return_value = basic_shared_service
resource_template_repo.get_current_template.return_value = (
basic_shared_service_template
)
# simulate an etag mismatch
resource_repo.patch_resource.side_effect = CosmosAccessConditionFailedError
num_retries = 5
try:
await try_update_with_retries(
num_retries=num_retries,
attempt_count=0,
resource_repo=resource_repo,
resource_template_repo=resource_template_repo,
user=test_user,
resource_to_update_id="resource-id",
template_step=multi_step_resource_template.pipeline.install[0],
resource_history_repo=resource_history_repo,
primary_resource=primary_resource,
primary_parent_workspace=None,
primary_parent_workspace_svc=None
)
except CosmosAccessConditionFailedError:
pass
# check it tried to patch and re-get the item the first time + all the retries
assert len(resource_repo.patch_resource.mock_calls) == (num_retries + 1)
assert len(resource_repo.get_resource_by_id.mock_calls) == (num_retries + 1)
|
AzureTRE/api_app/tests_ma/test_service_bus/test_resource_request_sender.py/0
|
{
"file_path": "AzureTRE/api_app/tests_ma/test_service_bus/test_resource_request_sender.py",
"repo_id": "AzureTRE",
"token_count": 2631
}
| 97 |
# Collection of API HTTP request samples
This folder contains a set of .http files that can be used to test the API
- [API User Journey](./API%20User%20Journey.http): A typical scenario with registering templates, creating workspaces and other resources
- [API Template GET Endpoints](./API%20Template%20GET%20Endpoints.http)
- [API Template Modifying Endpoints](./API%20Template%20Modifying%20Endpoints.http): POST, DELETE, PATCH endpoints for templates
- [API Resource GET Endpoints](./API%20Resource%20GET%20Endpoints.http)
- [API Resource Modifying Endpoints](API%20Resource%20Modifying%20Endpoints.http): POST, DELETE, PATCH endpoints for workspaces and other resources
## Running the requests in VS Code
1. Install the [Rest Client Extension](https://marketplace.visualstudio.com/items?itemName=humao.rest-client)
1. In settings.json - add a section with environment variables that will be used for the requests
```json
"rest-client.environmentVariables": {
"$shared": {
"baseUrl": "http://localhost:8000/api",
"contentType": "application/json",
"workspaceTemplate": "my-tre-workspace",
"workspaceServiceTemplate": "my-tre-workspace-service",
"userResourceTemplate": "my-tre-user-resource",
"workspaceId": "49ab7315-49bb-48ed-b9ca-c37369f15e7a",
"workspaceServiceId": "2a3165e7-5b5c-40e5-b3b6-94f528e9fcf0",
"userResourceId": "726e00b5-9408-4d81-a913-d890b4851307",
"appId": "9d52b04f-89cf-47b4-868a-e12be7133b36",
"token": "[TOKEN FROM SWAGGER UI]"
},
},
```
> **Note:** If you prefer, you can add environment specific variables (instead of adding all to $shared, but then you have to change environment in the bottom right bar in VS code when running the HTTP requests)
1. Start the API locally - or modify the baseURL to point to an API running on Azure
1. Authenticate with the API in Swagger and make a GET request to retrieve the authentication token (Bearer) - and modify the token variable in settings
1. Run the requests in the HTTP files by clicking on **send request** above each request
## Running the requests using PyCharms Rest-client
PyCharm has a built in rest client that allows us to run all requests in a .http file.
1. Modify the variables defined in the [http-client.env.json](./http-client.env.json) file to suit your needs
1. Add a file called `http-client.private.env.json` to the API requests folder with the following contents
```json
{
"dev": {
"token": "[TOKEN FROM SWAGGER UI]"
}
}
```
1. Start the API locally or modify the **baseUrl** in `http-client.env.json` to reflect the address of the API you are testing against
1. Make a GET request in Swagger and update the token to your authentication token (Bearer)
1. Run the requests
|
AzureTRE/api_http_requests/README.md/0
|
{
"file_path": "AzureTRE/api_http_requests/README.md",
"repo_id": "AzureTRE",
"token_count": 1041
}
| 98 |
import click
import logging
from tre.api_client import ApiClient
from tre.output import output, output_option, query_option
@click.command(name="health", help="Show Health")
@output_option()
@query_option()
def health(output_format, query) -> None:
log = logging.getLogger(__name__)
client = ApiClient.get_api_client_from_config()
response = client.call_api(log, 'GET', '/api/health')
output(
response,
output_format=output_format,
query=query,
default_table_query="services")
return response.text
|
AzureTRE/cli/tre/commands/health.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/health.py",
"repo_id": "AzureTRE",
"token_count": 200
}
| 99 |
import logging
import click
from tre.commands.operation import get_operation_id_completion, operation_show
from tre.output import output_option, query_option
from .contexts import pass_workspace_operation_context, WorkspaceOperationContext
def operation_id_completion(ctx, param, incomplete):
log = logging.getLogger(__name__)
parent_ctx = ctx.parent
workspace_id = parent_ctx.params["workspace_id"]
list_url = f'/api/workspaces/{workspace_id}/operations'
return get_operation_id_completion(ctx, log, list_url, param, incomplete)
@click.group(name="operation", invoke_without_command=True, help="Perform actions on an operation")
@click.argument('operation_id', required=True, type=click.UUID, shell_complete=operation_id_completion)
@click.pass_context
def workspace_operation(ctx: click.Context, operation_id) -> None:
ctx.obj = WorkspaceOperationContext.add_operation_id_to_context_obj(ctx, operation_id)
@click.command(name="show", help="Workspace operation")
@click.option('--no-wait',
help="If an operation is in progress, do not wait for it to complete",
flag_value=True,
default=False)
@output_option()
@query_option()
@pass_workspace_operation_context
def workspace_operation_show(workspace_operation_context: WorkspaceOperationContext, no_wait, output_format, query, suppress_output: bool = False) -> None:
log = logging.getLogger(__name__)
workspace_id = workspace_operation_context.workspace_id
if workspace_id is None:
raise click.UsageError('Missing workspace ID')
operation_id = workspace_operation_context.operation_id
if operation_id is None:
raise click.UsageError('Missing operation ID')
operation_url = f'/api/workspaces/{workspace_id}/operations/{operation_id}'
operation_show(log, operation_url, no_wait, output_format, query, suppress_output)
workspace_operation.add_command(workspace_operation_show)
|
AzureTRE/cli/tre/commands/workspaces/operation.py/0
|
{
"file_path": "AzureTRE/cli/tre/commands/workspaces/operation.py",
"repo_id": "AzureTRE",
"token_count": 638
}
| 100 |
import click
from .commands.costs import costs
from .commands.health import health
from .commands.migrations import migrations
from tre.commands.get_token import get_token
from tre.commands.login import login
from tre.commands.api_call import call_api
from tre.commands.workspaces.workspace import workspace
from tre.commands.workspaces.workspaces import workspaces
from tre.commands.shared_services.shared_service import shared_service
from tre.commands.shared_services.shared_services import shared_services
from tre.commands.workspace_templates.workspace_templates import workspace_templates
from tre.commands.workspace_templates.workspace_template import workspace_template
from tre.commands.shared_service_templates.shared_service_templates import shared_service_templates
from tre.commands.shared_service_templates.shared_service_template import shared_service_template
from tre.commands.workspace_service_templates.workspace_service_templates import workspace_service_templates
from tre.commands.workspace_service_templates.workspace_service_template import workspace_service_template
@click.group()
def cli():
pass
cli.add_command(login)
cli.add_command(call_api)
cli.add_command(workspaces)
cli.add_command(workspace)
cli.add_command(shared_services)
cli.add_command(shared_service)
cli.add_command(workspace_templates)
cli.add_command(workspace_template)
cli.add_command(shared_service_templates)
cli.add_command(shared_service_template)
cli.add_command(workspace_service_templates)
cli.add_command(workspace_service_template)
cli.add_command(get_token)
cli.add_command(costs)
cli.add_command(health)
cli.add_command(migrations)
if __name__ == "__main__":
cli()
|
AzureTRE/cli/tre/main.py/0
|
{
"file_path": "AzureTRE/cli/tre/main.py",
"repo_id": "AzureTRE",
"token_count": 517
}
| 101 |
resource "azurerm_public_ip" "appgwpip" {
name = "pip-agw-${var.tre_id}"
resource_group_name = var.resource_group_name
location = var.location
allocation_method = "Static" # Static IPs are allocated immediately
sku = "Standard"
domain_name_label = var.tre_id
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags, zones] }
}
resource "azurerm_user_assigned_identity" "agw_id" {
resource_group_name = var.resource_group_name
location = var.location
name = "id-agw-${var.tre_id}"
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_application_gateway" "agw" {
name = "agw-${var.tre_id}"
resource_group_name = var.resource_group_name
location = var.location
tags = local.tre_core_tags
sku {
name = "Standard_v2"
tier = "Standard_v2"
capacity = 1
}
# User-assign managed identify id required to access certificate in KeyVault
identity {
type = "UserAssigned"
identity_ids = [azurerm_user_assigned_identity.agw_id.id]
}
# Internal subnet for gateway backend.
gateway_ip_configuration {
name = "gateway-ip-configuration"
subnet_id = var.app_gw_subnet
}
frontend_port {
name = local.insecure_frontend_port_name
port = 80
}
frontend_port {
name = local.secure_frontend_port_name
port = 443
}
# Public front-end
frontend_ip_configuration {
name = local.frontend_ip_configuration_name
public_ip_address_id = azurerm_public_ip.appgwpip.id
}
# Primary SSL cert linked to KeyVault
ssl_certificate {
name = local.certificate_name
key_vault_secret_id = azurerm_key_vault_certificate.tlscert.secret_id
}
# SSL policy
ssl_policy {
policy_type = "Predefined"
policy_name = "AppGwSslPolicy20220101"
}
# Backend pool with the static website in storage account.
backend_address_pool {
name = local.staticweb_backend_pool_name
fqdns = [azurerm_storage_account.staticweb.primary_web_host]
}
# Backend pool with the API App Service.
backend_address_pool {
name = local.api_backend_pool_name
fqdns = [var.api_fqdn]
}
# Backend settings for api.
# Using custom probe to test specific health endpoint
backend_http_settings {
name = local.api_http_setting_name
cookie_based_affinity = "Disabled"
port = 443
protocol = "Https"
request_timeout = 60
pick_host_name_from_backend_address = true
probe_name = local.api_probe_name
}
# Backend settings for static web.
# Using default probe to test root path (/)
backend_http_settings {
name = local.staticweb_http_setting_name
cookie_based_affinity = "Disabled"
port = 443
protocol = "Https"
request_timeout = 60
pick_host_name_from_backend_address = true
}
# Custom health probe for API.
probe {
name = local.api_probe_name
pick_host_name_from_backend_http_settings = true
interval = 15
protocol = "Https"
# Use the /api/ping endpoint to verify that we can connect to the API
# This still allows the richer information from /api/health to be queried
# in the event of a component being unavailable
# It also avoids incurring the Azure Management API calls to resource processor
# when not needed (which can cause throttling)
path = "/api/ping"
timeout = "30"
unhealthy_threshold = "3"
}
# Public HTTPS listener
http_listener {
name = local.secure_listener_name
frontend_ip_configuration_name = local.frontend_ip_configuration_name
frontend_port_name = local.secure_frontend_port_name
protocol = "Https"
ssl_certificate_name = local.certificate_name
}
# Public HTTP listener
http_listener {
name = local.insecure_listener_name
frontend_ip_configuration_name = local.frontend_ip_configuration_name
frontend_port_name = local.insecure_frontend_port_name
protocol = "Http"
}
request_routing_rule {
name = local.request_routing_rule_name
rule_type = "PathBasedRouting"
http_listener_name = local.secure_listener_name
url_path_map_name = local.app_path_map_name
priority = 100
}
# Routing rule to redirect non-secure traffic to HTTPS
request_routing_rule {
name = local.redirect_request_routing_rule_name
rule_type = "PathBasedRouting"
http_listener_name = local.insecure_listener_name
url_path_map_name = local.redirect_path_map_name
priority = 10
}
# Default traffic is routed to the static website. Exception is API.
url_path_map {
name = local.app_path_map_name
default_backend_address_pool_name = local.staticweb_backend_pool_name
default_backend_http_settings_name = local.staticweb_http_setting_name
path_rule {
name = "api"
paths = ["/api/*", "/openapi.json"]
backend_address_pool_name = local.api_backend_pool_name
backend_http_settings_name = local.api_http_setting_name
}
}
# Redirect any HTTP traffic to HTTPS unless its the ACME challenge path used for LetsEncrypt validation.
url_path_map {
name = local.redirect_path_map_name
default_redirect_configuration_name = local.redirect_configuration_name
path_rule {
name = "acme"
paths = ["/.well-known/acme-challenge/*"]
backend_address_pool_name = local.staticweb_backend_pool_name
backend_http_settings_name = local.staticweb_http_setting_name
}
}
# Redirect to HTTPS
redirect_configuration {
name = local.redirect_configuration_name
redirect_type = "Permanent"
target_listener_name = local.secure_listener_name
include_path = true
include_query_string = true
}
# We don't want Terraform to revert certificate cycle changes. We assume the certificate will be renewed in keyvault.
lifecycle { ignore_changes = [ssl_certificate, tags] }
}
resource "azurerm_monitor_diagnostic_setting" "agw" {
name = "diagnostics-agw-${var.tre_id}"
target_resource_id = azurerm_application_gateway.agw.id
log_analytics_workspace_id = var.log_analytics_workspace_id
dynamic "enabled_log" {
for_each = setintersection(data.azurerm_monitor_diagnostic_categories.agw.log_category_types, local.appgateway_diagnostic_categories_enabled)
content {
category = enabled_log.value
}
}
metric {
category = "AllMetrics"
enabled = true
}
lifecycle { ignore_changes = [log_analytics_destination_type] }
}
|
AzureTRE/core/terraform/appgateway/appgateway.tf/0
|
{
"file_path": "AzureTRE/core/terraform/appgateway/appgateway.tf",
"repo_id": "AzureTRE",
"token_count": 3317
}
| 102 |
resource "azurerm_cosmosdb_account" "mongo" {
name = "cosmos-mongo-${var.tre_id}"
location = azurerm_resource_group.core.location
resource_group_name = azurerm_resource_group.core.name
offer_type = "Standard"
kind = "MongoDB"
enable_automatic_failover = false
mongo_server_version = 4.2
ip_range_filter = "${local.azure_portal_cosmos_ips}${var.enable_local_debugging ? ",${local.myip}" : ""}"
capabilities {
name = "EnableServerless"
}
capabilities {
name = "EnableMongo"
}
capabilities {
name = "DisableRateLimitingResponses"
}
capabilities {
name = "mongoEnableDocLevelTTL"
}
consistency_policy {
consistency_level = "BoundedStaleness"
max_interval_in_seconds = 5
max_staleness_prefix = 100
}
geo_location {
location = var.location
failover_priority = 0
}
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_cosmosdb_mongo_database" "mongo" {
name = "porter"
resource_group_name = azurerm_resource_group.core.name
account_name = azurerm_cosmosdb_account.mongo.name
}
resource "azurerm_management_lock" "mongo" {
count = var.stateful_resources_locked ? 1 : 0
name = "mongo-lock"
scope = azurerm_cosmosdb_mongo_database.mongo.id
lock_level = "CanNotDelete"
notes = "Locked to prevent accidental deletion"
}
resource "azurerm_private_dns_zone" "mongo" {
name = module.terraform_azurerm_environment_configuration.private_links["privatelink.mongo.cosmos.azure.com"]
resource_group_name = azurerm_resource_group.core.name
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_private_dns_zone_virtual_network_link" "mongo" {
name = "cosmos_mongo_dns_link"
resource_group_name = azurerm_resource_group.core.name
private_dns_zone_name = azurerm_private_dns_zone.mongo.name
virtual_network_id = module.network.core_vnet_id
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_private_endpoint" "mongo" {
name = "pe-${azurerm_cosmosdb_account.mongo.name}"
location = azurerm_resource_group.core.location
resource_group_name = azurerm_resource_group.core.name
subnet_id = module.network.resource_processor_subnet_id
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
private_dns_zone_group {
name = "private-dns-zone-group"
private_dns_zone_ids = [azurerm_private_dns_zone.mongo.id]
}
private_service_connection {
name = "psc-${azurerm_cosmosdb_account.mongo.name}"
private_connection_resource_id = azurerm_cosmosdb_account.mongo.id
is_manual_connection = false
subresource_names = ["MongoDB"]
}
}
resource "azurerm_key_vault_secret" "cosmos_mongo_connstr" {
name = "porter-db-connection-string"
value = azurerm_cosmosdb_account.mongo.connection_strings[0]
key_vault_id = azurerm_key_vault.kv.id
tags = local.tre_core_tags
depends_on = [
azurerm_key_vault_access_policy.deployer
]
lifecycle { ignore_changes = [tags] }
}
|
AzureTRE/core/terraform/cosmos_mongo.tf/0
|
{
"file_path": "AzureTRE/core/terraform/cosmos_mongo.tf",
"repo_id": "AzureTRE",
"token_count": 1501
}
| 103 |
# Network security group for Azure Bastion subnet
# See https://docs.microsoft.com/azure/bastion/bastion-nsg
resource "azurerm_network_security_group" "bastion" {
name = "nsg-bastion-subnet"
location = var.location
resource_group_name = var.resource_group_name
tags = local.tre_core_tags
security_rule {
name = "AllowInboundInternet"
priority = 4000
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "443"
source_address_prefix = "Internet"
destination_address_prefix = "*"
}
security_rule {
name = "AllowInboundGatewayManager"
priority = 4001
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "443"
source_address_prefix = "GatewayManager"
destination_address_prefix = "*"
}
security_rule {
name = "AllowInboundAzureLoadBalancer"
priority = 4002
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "443"
source_address_prefix = "AzureLoadBalancer"
destination_address_prefix = "*"
}
security_rule {
name = "AllowInboundHostCommunication"
priority = 4003
direction = "Inbound"
access = "Allow"
protocol = "*"
source_port_range = "*"
destination_port_ranges = ["5701", "8080"]
source_address_prefix = "VirtualNetwork"
destination_address_prefix = "VirtualNetwork"
}
security_rule {
name = "AllowOutboundSshRdp"
priority = 4020
direction = "Outbound"
access = "Allow"
protocol = "*"
source_port_range = "*"
destination_port_ranges = ["22", "3389"]
source_address_prefix = "*"
destination_address_prefix = "VirtualNetwork"
}
security_rule {
name = "AllowOutboundAzureCloud"
priority = 4021
direction = "Outbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "443"
source_address_prefix = "*"
destination_address_prefix = "AzureCloud"
}
security_rule {
name = "AllowOutboundHostCommunication"
priority = 4022
direction = "Outbound"
access = "Allow"
protocol = "*"
source_port_range = "*"
destination_port_ranges = ["5701", "8080"]
source_address_prefix = "VirtualNetwork"
destination_address_prefix = "VirtualNetwork"
}
security_rule {
name = "AllowOutboundGetSessionInformation"
priority = 4023
direction = "Outbound"
access = "Allow"
protocol = "*"
source_port_range = "*"
destination_port_range = "80"
source_address_prefix = "*"
destination_address_prefix = "Internet"
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_subnet_network_security_group_association" "bastion" {
subnet_id = azurerm_subnet.bastion.id
network_security_group_id = azurerm_network_security_group.bastion.id
# depend on the last subnet we created in the vnet
depends_on = [azurerm_subnet.firewall_management]
}
# Network security group for Application Gateway
# See https://docs.microsoft.com/azure/application-gateway/configuration-infrastructure#network-security-groups
resource "azurerm_network_security_group" "app_gw" {
name = "nsg-app-gw"
location = var.location
resource_group_name = var.resource_group_name
tags = local.tre_core_tags
security_rule {
name = "AllowInboundGatewayManager"
priority = 3800
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_range = "65200-65535"
source_address_prefix = "GatewayManager"
destination_address_prefix = "*"
}
security_rule {
name = "AllowInboundInternet"
priority = 3801
direction = "Inbound"
access = "Allow"
protocol = "Tcp"
source_port_range = "*"
destination_port_ranges = ["80", "443"]
source_address_prefix = "Internet"
destination_address_prefix = "*"
}
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_subnet_network_security_group_association" "app_gw" {
subnet_id = azurerm_subnet.app_gw.id
network_security_group_id = azurerm_network_security_group.app_gw.id
depends_on = [azurerm_subnet_network_security_group_association.bastion]
}
# Network security group with only default security rules
# See https://docs.microsoft.com/azure/virtual-network/network-security-groups-overview#default-security-rules
resource "azurerm_network_security_group" "default_rules" {
name = "nsg-default-rules"
location = var.location
resource_group_name = var.resource_group_name
tags = local.tre_core_tags
lifecycle { ignore_changes = [tags] }
}
resource "azurerm_subnet_network_security_group_association" "shared" {
subnet_id = azurerm_subnet.shared.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.app_gw]
}
resource "azurerm_subnet_network_security_group_association" "web_app" {
subnet_id = azurerm_subnet.web_app.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.shared]
}
resource "azurerm_subnet_network_security_group_association" "resource_processor" {
subnet_id = azurerm_subnet.resource_processor.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.web_app]
}
resource "azurerm_subnet_network_security_group_association" "airlock_processor" {
subnet_id = azurerm_subnet.airlock_processor.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.resource_processor]
}
resource "azurerm_subnet_network_security_group_association" "airlock_storage" {
subnet_id = azurerm_subnet.airlock_storage.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.airlock_processor]
}
resource "azurerm_subnet_network_security_group_association" "airlock_events" {
subnet_id = azurerm_subnet.airlock_events.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.airlock_storage]
}
resource "azurerm_subnet_network_security_group_association" "airlock_notification" {
subnet_id = azurerm_subnet.airlock_notification.id
network_security_group_id = azurerm_network_security_group.default_rules.id
depends_on = [azurerm_subnet_network_security_group_association.airlock_events]
}
|
AzureTRE/core/terraform/network/network_security_groups.tf/0
|
{
"file_path": "AzureTRE/core/terraform/network/network_security_groups.tf",
"repo_id": "AzureTRE",
"token_count": 3935
}
| 104 |
#!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
# set -o xtrace
script_dir=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
core_rg_rid=$(terraform show -json | jq -r '.values.root_module.resources[] | select(.address=="azurerm_resource_group.core") | .values.id')
echo "Current tags:"
az tag list --resource-id "${core_rg_rid}"
version=$(cat "${script_dir}"/../version.txt)
# doesn't work with quotes
# shellcheck disable=SC2206
version_array=( ${version//=/ } ) # split by =
coded_version="${version_array[1]//\"}" # second element is what we want, remove " chars
git_origin="NA"
git_commit="NA"
is_inside_git_repo() {
git --git-dir="${1}" rev-parse --is-inside-work-tree >/dev/null 2>&1 && echo "true"
}
if command -v git &> /dev/null; then
git_dir="${PWD}/.git"
if [ "$(is_inside_git_repo "${git_dir}")" != "true" ]; then
git_dir="${OLDPWD}/.git"
if [ "$(is_inside_git_repo "${git_dir}")" != "true" ]; then
echo "Couldn't find git directory."
git_dir=""
fi
fi
if [ -n "${git_dir}" ]; then
git_origin=$(git --git-dir="${git_dir}" config --get remote.origin.url)
git_commit=$(git --git-dir="${git_dir}" show --oneline -s)
fi
fi
echo "Updated tags, new ones are:"
az tag update --operation merge --tags coded_version="${coded_version}" git_origin="${git_origin}" git_commit="${git_commit}" --resource-id "${core_rg_rid}"
|
AzureTRE/core/terraform/update_tags.sh/0
|
{
"file_path": "AzureTRE/core/terraform/update_tags.sh",
"repo_id": "AzureTRE",
"token_count": 557
}
| 105 |
#!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
# Uncomment this line to see each command for debugging (careful: this will show secrets!)
# set -o xtrace
if [ "$(yq eval ".custom.runtime_image.build" porter.yaml)" == "null" ]; then
echo "Runtime image build section isn't specified. Exiting..."
exit 0
fi
image_name=$(yq eval ".custom.runtime_image.name" porter.yaml)
version_file=$(yq eval ".custom.runtime_image.build.version_file" porter.yaml)
docker_file=$(yq eval ".custom.runtime_image.build.docker_file" porter.yaml)
docker_context=$(yq eval ".custom.runtime_image.build.docker_context" porter.yaml)
acr_domain_suffix=$(az cloud show --query suffixes.acrLoginServerEndpoint --output tsv)
version_line=$(cat "${version_file}")
# doesn't work with quotes
# shellcheck disable=SC2206
version_array=( ${version_line//=/ } ) # split by =
version="${version_array[1]//\"}" # second element is what we want, remove " chars
az acr login -n "${ACR_NAME}"
docker_cache=("--cache-from" "${FULL_IMAGE_NAME_PREFIX}/${image_name}:${version}")
if [ -n "${CI_CACHE_ACR_NAME:-}" ]; then
az acr login -n "${CI_CACHE_ACR_NAME}"
docker_cache+=("--cache-from" "${CI_CACHE_ACR_NAME}${acr_domain_suffix}/${IMAGE_NAME_PREFIX}/${image_name}:${version}")
fi
ARCHITECTURE=$(docker info --format "{{ .Architecture }}" )
if [ "${ARCHITECTURE}" == "aarch64" ]; then
DOCKER_BUILD_COMMAND="docker buildx build --platform linux/amd64"
else
DOCKER_BUILD_COMMAND="docker build"
fi
${DOCKER_BUILD_COMMAND} --build-arg BUILDKIT_INLINE_CACHE=1 \
-t "${FULL_IMAGE_NAME_PREFIX}/${image_name}:${version}" \
"${docker_cache[@]}" -f "${docker_file}" "${docker_context}"
|
AzureTRE/devops/scripts/bundle_runtime_image_build.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/bundle_runtime_image_build.sh",
"repo_id": "AzureTRE",
"token_count": 640
}
| 106 |
#!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
# set -o xtrace
#
# Usage:
# load_env.sh <.env file>
#
if [ ! -f "$1" ]; then
if [ -z "${USE_ENV_VARS_NOT_FILES:-}" ]; then
echo -e "\e[31m»»» 💥 Unable to find $1 file, please create file and try again!\e[0m"
#exit
fi
else
# Loop over the relevant lines in the file specified in $1 (passed in after the loop)
# The loop source filters the lines in the source file to those that should be treated
# as variable definitions
while read -r line
do
# split the line into name/value
IFS='=' read -r name value <<< "$line"
# Create the Terraform var name form, i.e. convert FOO=BAR to TF_VAR_foo=BAR
tf_name="TF_VAR_$(echo "$name" | tr '[:upper:]' '[:lower:]')"
# if the value is quote-delimited then strip that as we quote in the declare statement
if [[ ("${value:0:1}" == "'" && "${value: -1:1}" == "'") || (("${value:0:1}" == "\"" && "${value: -1:1}" == "\"")) ]]; then
value=${value:1:-1}
fi
# declare the variable and export to the caller's context
# shellcheck disable=SC2086
declare -g $name="$value"
export "${name?}"
# shellcheck disable=SC2086
declare -g $tf_name="$value"
export "${tf_name?}"
done < <(grep -v -e '^[[:space:]]*$' -e '^#' "$1" ) # feed in via Process Substition to avoid bash subshell (http://mywiki.wooledge.org/ProcessSubstitution)
fi
set +o nounset
|
AzureTRE/devops/scripts/load_env.sh/0
|
{
"file_path": "AzureTRE/devops/scripts/load_env.sh",
"repo_id": "AzureTRE",
"token_count": 560
}
| 107 |
provider "azurerm" {
features {}
}
# Resource group for TRE core management
resource "azurerm_resource_group" "mgmt" {
name = var.mgmt_resource_group_name
location = var.location
tags = {
project = "Azure Trusted Research Environment"
source = "https://github.com/microsoft/AzureTRE/"
}
lifecycle { ignore_changes = [tags] }
}
# Holds Terraform shared state (already exists, created by bootstrap.sh)
resource "azurerm_storage_account" "state_storage" {
name = var.mgmt_storage_account_name
resource_group_name = azurerm_resource_group.mgmt.name
location = azurerm_resource_group.mgmt.location
account_tier = "Standard"
account_kind = "StorageV2"
account_replication_type = "LRS"
allow_nested_items_to_be_public = false
lifecycle { ignore_changes = [tags] }
}
# Shared container registry
resource "azurerm_container_registry" "shared_acr" {
name = var.acr_name
resource_group_name = azurerm_resource_group.mgmt.name
location = azurerm_resource_group.mgmt.location
sku = var.acr_sku
admin_enabled = true
lifecycle { ignore_changes = [tags] }
}
# tredev is the devcontainer image name generate by our CICD
resource "azurerm_container_registry_task" "tredev_purge" {
name = "tredev_purge"
container_registry_id = azurerm_container_registry.shared_acr.id
platform {
os = "Linux"
architecture = "amd64"
}
encoded_step {
task_content = <<EOF
version: v1.1.0
steps:
- cmd: acr purge --filter 'tredev:[0-9a-fA-F]{8}' --ago 7d --untagged
disableWorkingDirectoryOverride: true
timeout: 600
EOF
}
timer_trigger {
name = "t1"
schedule = "4 1 * * *"
enabled = true
}
}
|
AzureTRE/devops/terraform/main.tf/0
|
{
"file_path": "AzureTRE/devops/terraform/main.tf",
"repo_id": "AzureTRE",
"token_count": 803
}
| 108 |
# Pre-deployment steps
## Setup Github Environment
The workflows are using Github environment to source its environment variables. Follow [this guide](https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#creating-an-environment) to define it in your github repository and provide it as an input for the workflows.
## GitHub Actions workflows (CI/CD)
Deployment is done using the `/.github/workflows/deploy_tre.yml` workflow. This method is also used to deploy the dev/test environment for the original Azure TRE repository.
## Setup instructions
Before you can run the `deploy_tre.yml` workflow there are some one-time configuration steps that we need to do, similar to the Pre-deployment steps for manual deployment.
1. Create a service principal for the subscription so that the workflow can provision Azure resources.
1. Decide on a TRE ID and the location for the Azure resources
1. Create a Teams WebHook for deployment notifications
1. Configure repository secrets
1. Deploy the TRE using the workflow
### Create a service principal for provisioning resources
1. Login to Azure
Log in to Azure using `az login` and select the Azure subscription you wish to deploy Azure TRE to:
```cmd
az login
az account list
az account set --subscription <subscription ID>
```
See [Sign in with Azure CLI](https://docs.microsoft.com/cli/azure/authenticate-azure-cli) for more details.
1. Create a service principal
A service principal needs to be created to authorize CI/CD workflows to provision resources for the TRE workspaces and workspace services.
Create a main service principal with "**Owner**" role:
```cmd
az ad sp create-for-rbac --name "sp-aztre-cicd" --role Owner --scopes /subscriptions/<subscription_id> --sdk-auth
```
!!! caution
Save the JSON output locally - as you will need it later for setting secrets in the build
1. Create a secret in your github environment named `AZURE_CREDENTIALS` and use the JSON output from the previous step as its value. Note it should look similar to this:
```json
{
"clientId": "",
"clientSecret": "",
"subscriptionId": "",
"tenantId": ""
}
```
### Configure Core Secrets
Configure the following secrets in your github environment:
| <div style="width: 230px">Secret name</div> | Description |
| ----------- | ----------- |
| `TRE_ID` | A globally unique identifier. `TRE_ID` can be found in the resource names of the Azure TRE instance; for example, a `TRE_ID` of `tre-dev-42` will result in a resource group name for Azure TRE instance of `rg-tre-dev-42`. This must be less than 12 characters. Allowed characters: lowercase alphanumerics. |
| `MGMT_RESOURCE_GROUP_NAME` | The name of the shared resource group for all Azure TRE core resources. |
| `MGMT_STORAGE_ACCOUNT_NAME` | The name of the storage account to hold the Terraform state and other deployment artifacts. E.g. `mystorageaccount`. |
| `ACR_NAME` | A globally unique name for the Azure Container Registry (ACR) that will be created to store deployment images. |
### Configure Core Variables
Configure the following **variables** in your github environment:
| <div style="width: 230px">Variable name</div> | Description |
| ----------- | ----------- |
| `LOCATION` | The Azure location (region) for all resources. E.g. `westeurope` |
| `TERRAFORM_STATE_CONTAINER_NAME` | Optional. The name of the blob container to hold the Terraform state. Default value is `tfstate`. |
| `CORE_ADDRESS_SPACE` | Optional. The address space for the Azure TRE core virtual network. Default value is `10.0.0.0/22`. |
| `TRE_ADDRESS_SPACE` | Optional. The address space for the whole TRE environment virtual network where workspaces networks will be created (can include the core network as well). Default value is `10.0.0.0/16`|
| `AZURE_ENVIRONMENT` | Optional. The name of the Azure environment. Supported values are `AzureCloud` and `AzureUSGovernment`. Default value is `AzureCloud`. |
| `CORE_APP_SERVICE_PLAN_SKU` | Optional. The SKU used for AppService plan for core infrastructure. Default value is `P1v2`. |
| `WORKSPACE_APP_SERVICE_PLAN_SKU` | Optional. The SKU used for AppService plan used in E2E tests. Default value is `P1v2`. |
| `RESOURCE_PROCESSOR_NUMBER_PROCESSES_PER_INSTANCE` | Optional. The number of processes to instantiate when the Resource Processor starts. Equates to the number of parallel deployment operations possible in your TRE. Defaults to `5`. |
| `ENABLE_SWAGGER` | Optional. Determines whether the Swagger interface for the API will be available. Default value is `false`. |
### Configure Authentication Secrets
In a previous [Setup Auth configuration](./setup-auth-entities.md) step authentication configuration was added in `config.yaml` file. Go to this file and add those env vars to your github environment:
| Secret Name | Description |
| -------- | ----------- |
| `AAD_TENANT_ID` | Tenant id against which auth is performed. |
| `APPLICATION_ADMIN_CLIENT_ID`| This client will administer Microsoft Entra ID Applications for TRE |
| `APPLICATION_ADMIN_CLIENT_SECRET`| This client will administer Microsoft Entra ID Applications for TRE |
| `TEST_ACCOUNT_CLIENT_ID`| This will be created by default, but can be disabled by editing `/devops/scripts/create_aad_assets.sh`. This is the user that will run the tests for you |
| `TEST_ACCOUNT_CLIENT_SECRET` | This will be created by default, but can be disabled by editing `/devops/scripts/create_aad_assets.sh`. This is the user that will run the tests for you |
| `API_CLIENT_ID` | API application (client) ID. |
| `API_CLIENT_SECRET` | API application client secret. |
| `SWAGGER_UI_CLIENT_ID` | Swagger (OpenAPI) UI application (client) ID. |
| `TEST_WORKSPACE_APP_ID`| Each workspace is secured behind it's own AD Application. Use the value of `WORKSPACE_API_CLIENT_ID` created in the `/config.yaml` env file |
| `TEST_WORKSPACE_APP_SECRET`| Each workspace is secured behind it's own AD Application. This is the secret for that application. Use the value of `WORKSPACE_API_CLIENT_SECRET` created in the `/config.yaml` env file|
### Create a Teams Webhook for deployment notifications
The `deploy_tre.yml` workflow sends a notification to a Microsoft Teams channel when it finishes running.
!!! note
If you don't want to notify a channel, you can also remove the **Notify dedicated teams channel** steps in the workflow
1. Follow the [Microsoft Docs](https://docs.microsoft.com/en-us/microsoftteams/platform/webhooks-and-connectors/how-to/add-incoming-webhook) to create a webhook for your channel
1. Configure the MS_TEAMS_WEBHOOK_URI repository secret
| <div style="width: 230px">Secret name</div> | Description |
| ----------- | ----------- |
| `MS_TEAMS_WEBHOOK_URI` | URI for the Teams channel webhook |
!!! info
See [Environment variables](../environment-variables.md) for full details of the deployment related variables.
### Setup Github env in workflow
In your repository you will find that the pipelines under the folder `.github/workflows` on top of each workflow there is the workflow inputs part where the used Github environment name is set, make sure to update it with yours, for example:
### Deploy the TRE using the workflow
With all the repository secrets set, you can trigger a workflow run by pushing to develop/main of your repo, or by dispatching the workflow manually.
## Next steps
* [Deploying Azure TRE in Pipelines](cicd-deployment.md)
|
AzureTRE/docs/tre-admins/setup-instructions/cicd-pre-deployment-steps.md/0
|
{
"file_path": "AzureTRE/docs/tre-admins/setup-instructions/cicd-pre-deployment-steps.md",
"repo_id": "AzureTRE",
"token_count": 2131
}
| 109 |
<!-- markdownlint-disable-file MD046 -->
# Upgrading Resources Version
Azure TRE workspaces, workspace services, workspace shared services, and user resources are [Porter](https://porter.sh/) bundles. Porter bundles are based on [Cloud Native Application Bundles (CNAB)](https://cnab.io/).
When a new bundle version becomes available, users can upgrade their resources to a newer version after building, publishing and registering the bundle template.
Upgrades (and downgrades) are based on [CNAB bundle upgrade action](https://getporter.org/bundle/manifest/#bundle-actions).
Bundle template versions follow [semantic versioning rules](../tre-workspace-authors/authoring-workspace-templates.md#versioning).
!!! Note
Only minor and patch version upgrades are automatically allowed within the Azure TRE upgrade mechanism. Major versions upgrades and any version downgrades are blocked as they are assumed to contain breaking changes or changes that require additional consideration.
For users who wish to upgrade a major version, we highly recommend to read the changelog, review what has changed and take some appropriate action before upgrading using [force version update](#force-version-update).
## How to upgrade a resource using Swagger UI
Resources can be upgrade using Swagger UI, in the following example we show how to upgrade a workspace version from 1.0.0 to 1.0.1, other resources upgrades are similar.
1. First make sure the desired template version is registered, [follow these steps if not](../tre-admins/registering-templates.md).
1. Navigate to the Swagger UI at `/api/docs`.
1. Log into the Swagger UI using `Authorize`.
1. Click `Try it out` on the `GET` `/api/workspace/{workspace_id}` operation.
1. Provide your `workspace_id` in the parameters section and click `Execute`.
1. Copy the `_etag` property from the response body.
1. Click `Try it out` on the `PATCH` `/api/workspace/{workspace_id}` operation.
1. Provide your `workspace_id` and `_etag` parameters which you've just copied.
1. Provide the following payload with the desired version in the `Request body` parameter and click `Execute`.
```json
{
"templateVersion": "1.0.1",
}
```
1. Review server response, it should include a new `operation` document with `upgrade` as an `action` and `updating` as `status` for upgrading the workspace and a message states that the Job is starting.
1. Once the upgrade is complete another operation will be created and can be viewed by executing `GET` `/api/workspace/{workspace_id}/operations`, review it and make sure its `status` is `updated`.
### Force version update
If you wish to upgrade a major version, or downgrade to any version, you can override the blocking in the upgrade mechanism by passing `force_version_update=true` query parameter to the resource `Patch` action.
For example force version patching a workspace:
|
AzureTRE/docs/tre-admins/upgrading-resources.md/0
|
{
"file_path": "AzureTRE/docs/tre-admins/upgrading-resources.md",
"repo_id": "AzureTRE",
"token_count": 784
}
| 110 |
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