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8a40b423866412c952ed984391940af8c83b8393
|
zhixing-xu/train_cfn
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-05T06:00:22+00:00
|
{"license": "apache-2.0"}
|
2023-07-25T19:35:52+00:00
|
|
448175cf734c5419bb281e716c1ebebdd08bf4f3
|
dst19/UltraSharp
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-05T06:02:06+00:00
|
{"license": "apache-2.0"}
|
2023-06-05T06:02:06+00:00
|
|
d36f05bf87dd087b46efe980c4ca57783f7234c0
|
# Dataset Card for "style-compositional"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zou00080/style-compositional
|
[
"region:us"
] |
2023-06-05T06:05:03+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label_text", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33992267, "num_examples": 537972}, {"name": "test", "num_bytes": 686159, "num_examples": 7059}], "download_size": 12154876, "dataset_size": 34678426}}
|
2023-06-05T06:28:57+00:00
|
c9d352cdc119df4a4f7526d100e4acb4a72a7a5c
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https://arxiv.org/abs/2305.14045**
- **Point of Contact:[email protected]**
### Dataset Summary
The CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into language models.
While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities.
The CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\\
Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning.
We also provide a multilingual version of CoT Collection at this [link](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection).
### Supported Tasks and Leaderboards
1060 tasks chosen from the Flan Collection.
The list of categories within the CoT Collection are:
* Natural Language Inference
* Extractive Question Answering
* Closed Book Question Answering
* Science
* Toxic Classification
* Arithmetic
* Program Execution
* Dialogue
* Ethics
* Commonsense Reasoning
* Multiple Choice Question Answering
### Languages
English
## Dataset Structure
* source: The input that is given to the language model (LM).
* target: The ground truth answer to the source.
* rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source.
* task: A category that shows which dataset the source and target was extracted from.
In our paper, we trained the underlying language model to generate in the following format:
```
\{rationale\}
[RESULT]
\{target\}
```
Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```.
### Data Splits
| name | train |
|-------------------|------:|
|CoT-Collection|1837928|
### Citation Information
If you find the following model helpful, please considering citing our paper!
```
@article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
year={2023}
}
```
|
kaist-ai/CoT-Collection
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-4.0",
"arxiv:2305.14045",
"region:us"
] |
2023-06-05T06:11:17+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "text-classification"]}
|
2023-10-14T11:10:16+00:00
|
33e5a373f5fda54a28b51e696d35d9b06b9125f7
|
tasksource/QA-Feedback
|
[
"license:cc",
"region:us"
] |
2023-06-05T06:12:01+00:00
|
{"license": "cc"}
|
2023-06-05T06:12:20+00:00
|
|
6ff27b7bb83eed1eaa4b4b28aa71f7f1123bab5e
|
# Dataset Card for "ingredient_to_sugar_level"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ziq/ingredient_to_sugar_level
|
[
"region:us"
] |
2023-06-05T06:13:31+00:00
|
{"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "ingredients", "dtype": "string"}, {"name": "sugar", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 11111272.197744025, "num_examples": 22592}, {"name": "test", "num_bytes": 966432.8022559759, "num_examples": 1965}], "download_size": 5672932, "dataset_size": 12077705.0}}
|
2023-06-07T14:24:46+00:00
|
0a041f891563ce842c13127eda8eeeac1ab9b8b5
|
# 姜子牙-Visual中文评估数据集 Ziya-Visual-Eval-Chinese
### 数据介绍 Dataset Summary
数据集由[LLaVA](https://github.com/haotian-liu/LLaVA)评估集翻译而来，图片源来自coco数据集，用于评估多模态大模型的中文能力
Dataset translated from the [LLaVA](https://github.com/haotian-liu/LLaVA) evaluation set, image source from the coco dataset, used to evaluate the Chinese language capabilities of the multimodal large model.
### 语言 Languages
中文
Chinese
### 数据示例 Data Instances
```json
{"question_id": 0, "image": "000000441147.jpg", "text": "图片中两个手提箱的颜色是什么？", "category": "conv"}
```
### 数据字段 Data Fields
- id: int
- image: str
- text: str
- category: str
### 引用 Citation
```
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
|
IDEA-CCNL/Ziya-Visual-Eval-Chinese
|
[
"size_categories:n<1K",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-06-05T06:17:55+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["n<1K"], "pretty_name": "Ziya-Visual-Eval-Chinese"}
|
2023-06-05T06:45:19+00:00
|
e110d68797ab5fa144ba8b604cb971ae08fa0fe9
|
# Dataset Card for "dummy_en-asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
omar47/dummy_en-asr
|
[
"region:us"
] |
2023-06-05T06:20:11+00:00
|
{"dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 5333955, "num_examples": 40}, {"name": "validation", "num_bytes": 3749784, "num_examples": 40}, {"name": "train", "num_bytes": 13316202, "num_examples": 60}], "download_size": 21482093, "dataset_size": 22399941}}
|
2023-06-05T06:20:15+00:00
|
ef2f4ce1da6dc05594fb0c9fe8b901d929953f9b
|
personal use
|
kaienRe/SD
|
[
"region:us"
] |
2023-06-05T06:30:03+00:00
|
{}
|
2023-07-30T00:43:43+00:00
|
2b99cc18e64b1684e29ba69f966306d9cc563e8d
|
https://github.com/asaparov/prontoqa/
```
@article{saparov2022language,
title={Language models are greedy reasoners: A systematic formal analysis of chain-of-thought},
author={Saparov, Abulhair and He, He},
journal={arXiv preprint arXiv:2210.01240},
year={2022}
}
```
|
tasksource/prontoqa
|
[
"task_categories:question-answering",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-05T06:44:13+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["question-answering", "text-classification"]}
|
2023-06-05T06:46:05+00:00
|
80b272cf8e17537f27fa7ba28d4e515a8c87101d
|
# Dataset Card for "ehrsql_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nannullna/ehrsql_mimic_iii
|
[
"region:us"
] |
2023-06-05T06:48:00+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "db_id", "dtype": "string"}, {"name": "is_impossible", "dtype": "bool"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5701904, "num_examples": 9318}, {"name": "validation", "num_bytes": 489250, "num_examples": 1122}], "download_size": 1154542, "dataset_size": 6191154}}
|
2023-06-05T06:50:39+00:00
|
d4d25a2e7970203dce2824cf7742c0671bd1736f
|
polytetralhx/bcards
|
[
"license:other",
"region:us"
] |
2023-06-05T06:55:27+00:00
|
{"license": "other"}
|
2023-06-05T06:55:28+00:00
|
|
8df8523442cc51b9132480f4fdbeb8363e29d2b8
|
Athiwat/brain
|
[
"region:us"
] |
2023-06-05T06:59:10+00:00
|
{}
|
2023-06-05T07:32:29+00:00
|
|
640452e3139b6b2f4fc517811d0d342877e0e6a0
|
# Conala-mined-curated
Conala-mined-curatedd is a dataset that is based on the mined subset of the [CoNaLa dataset](https://huggingface.co/datasets/neulab/conala/viewer/mined/train).
conala is a dataset crawled from Stack Overflow. Part of it is filtered and curated to from a training set and a test set. However, the mined part is not comparably
post-processed. It is a set of 600K examples that we decided to work on.
## Dataset description
The conala datasets have 3 columns of interest. We give their description as provided by the [authors](https://conala-corpus.github.io)
- *intent* : Natural Language intent (i.e., the title of a Stack Overflow question)
- *snippet* : A code snippet that implements the intent. This is the output of systems in the challenge.
- *rewritten_intent* : Crowdsourced revised intents that try to better reflect the full meaning of the code, typically done by incorporating variable names and
- function arguments that appeared in the code into the intent. This is the input to be used by systems in the CoNaLa challenge.
For instruction fine-tuning, we would be interested to train a model to map the *rewritten_intent* to the *snippet*. However, the mined subset does not have the
column *rewritten_intent*. *intent* is to vague to be describe as an instruction so we have to find a way to build the column *rewritten_intent* for the mined subset.
That is exactly what was done in order to build this dataset.
## Method
The most valuable information that we have in order to recover the column *rewritten_intent* are the columns *intent* and *snippet*. Fortunately we also have the training set and the test set
of conala which are labeled. It means that we have a view of what a high quality triplet (*intent*, *rewritten_intent*, *snippet*) look like. We had the idea to build a Seq2Seq model whose role
would be to reconstruct the *rewritten_intent* based on the concatenation [*intent*, *snippet*].
More precisely, we fine-tuned [google UL2](https://huggingface.co/google/ul2) to solve this task.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("codeparrot/conala-mined-curated")
dataset
DatasetDict({
train: Dataset({
features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'rewritten_intent', 'id'],
num_rows: 593891
})
})
```
## Additional resources
- Official site of the [CoNala-corpus](https://conala-corpus.github.io).
- [CoNaLa's card](https://huggingface.co/datasets/neulab/conala).
- [Github repository](https://github.com/ArmelRandy/Conala) of our method.
|
codeparrot/conala-mined-curated
|
[
"doi:10.57967/hf/0755",
"region:us"
] |
2023-06-05T07:02:12+00:00
|
{"dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "parent_answer_post_id", "dtype": "int64"}, {"name": "prob", "dtype": "float64"}, {"name": "snippet", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "rewritten_intent", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 136332874, "num_examples": 593891}], "download_size": 94688053, "dataset_size": 136332874}}
|
2023-06-13T14:56:31+00:00
|
f9ea16c69c546a71608243e321d8f71bf3b48471
|
SzilvasiPeter/amazon-shoe-review
|
[
"license:mit",
"region:us"
] |
2023-06-05T07:08:09+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "labels", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16847665.2, "num_examples": 90000}, {"name": "test", "num_bytes": 1871962.8, "num_examples": 10000}], "download_size": 11141108, "dataset_size": 18719628.0}}
|
2023-06-05T07:29:54+00:00
|
|
6642862a875f14d2f8e631096437b6afe83ba7f8
|
# Dataset Card for "test_gpt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anugrah1112/test_gpt
|
[
"region:us"
] |
2023-06-05T07:20:12+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 32008.0, "num_examples": 2}, {"name": "test", "num_bytes": 16004, "num_examples": 1}], "download_size": 30762, "dataset_size": 48012.0}}
|
2023-06-05T07:20:14+00:00
|
37dbcbaecf86496e4b912236d81897a2fb162af8
|
# Dataset Card for "brain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Athiwat/brain2
|
[
"region:us"
] |
2023-06-05T07:34:03+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5590698.0, "num_examples": 5}], "download_size": 5591635, "dataset_size": 5590698.0}}
|
2023-06-05T07:34:14+00:00
|
e60f89da8343057368b4c0d202e998db3124234d
|
# Dataset Card for "HC3-ru"
This is translated version of [Hello-SimpleAI/HC3 dataset](https://huggingface.co/datasets/Hello-SimpleAI/HC3) into Russian.
## Citation
Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597)
```
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
}
```
|
d0rj/HC3-ru
|
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:Hello-SimpleAI/HC3",
"language:ru",
"license:cc-by-sa-4.0",
"ChatGPT",
"SimpleAI",
"Detection",
"OOD",
"arxiv:2301.07597",
"region:us"
] |
2023-06-05T07:37:51+00:00
|
{"language_creators": ["translated"], "language": ["ru"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["Hello-SimpleAI/HC3"], "task_categories": ["text-classification", "question-answering", "sentence-similarity", "zero-shot-classification"], "pretty_name": "HC3 (ru)", "tags": ["ChatGPT", "SimpleAI", "Detection", "OOD"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "human_answers", "sequence": "string"}, {"name": "chatgpt_answers", "sequence": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135406074.0, "num_examples": 24322}], "download_size": 62739799, "dataset_size": 135406074.0}}
|
2023-06-05T11:46:32+00:00
|
a62815516f8e0335e2eee2741cf1c7e2f673ac41
|
# Dataset Card for "brighten-300-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dqymaggie/brighten-300-dataset
|
[
"region:us"
] |
2023-06-05T07:52:34+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "ground_truth_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4739155776.0, "num_examples": 300}], "download_size": 4615985191, "dataset_size": 4739155776.0}}
|
2023-06-05T07:55:55+00:00
|
7011e51316f052810690b9caa13dd42ac4a4112e
|
# WebSRC v1.0
WebSRC v1.0 is a dataset for reading comprehension on structural web pages.
The task is to answer questions about web pages, which requires a system to
have a comprehensive understanding of the spatial structure and logical
structure. WebSRC consists of 6.4K web pages and 400K question-answer pairs
about web pages. For each web page, we manually chose one segment from it
and saved the corresponding HTML code, screenshot, and metadata like
positions and sizes. Questions in WebSRC were created for each segment.
Answers are either text spans from web pages or yes/no. Taking the HTML
code, screenshot, metadata as well as question as input, a model is to
predict the answer from the web page. Our dataset is the first one that
provides HTML documents as well as images, and is larger in the number of
domains and queries.
For more details, please refer to our paper [WebSRC: A Dataset for Web-Based Structural Reading Comprehension](https://arxiv.org/abs/2101.09465).
The Leaderboard of WebSRC v1.0 can be found [here](https://x-lance.github.io/WebSRC/).
## Data Format Description
The dataset for each website will be stored in `dataset.csv` in the directory
`{domain-name}/{website-number}`. The corresponding raw data (including HTML
files, screenshots, bounding box coordinates, and page names and urls) is
stored in the `processed_data` folder in the same directory.
In `dataset.csv`, each row corresponds to one question-answer data point
except the header. The meanings of each column are as follows:
* `question`: a string, the question of this question-answer data point.
* `id`: a unique id for this question-answer data point. Each `id` has a length 14, the first two characters are the domain indicator, the following two number is the website name. The corresponding page id can be extracted by `id[2:9]`, for example, id "sp160000100001" means this line is created from the *sport* domain, website *16*, and the corresponding page is `1600001.html`.
* `element_id`: an integer, the tag id (corresponding to the tag's `tid` attribute in the HTML files) of the deepest tag in the DOM tree which contain all the answer. For yes/no question, there is no tag associated with the answer, so the `element_id` is -1.
* `answer_start`: an integer, the char offset of the answer from the start of the content of the tag specified by `element_id`. Note that before counting this number, we first eliminate all the inner tags in the specified tag and replace all the consecutive whitespaces with one space. For yes/no questions, `answer_start` is 1 for answer "yes" and 0 for answer "no".
* `answer`: a string, the answer of this question-answer data point.
## Data Statistics
We roughly divided the questions in WebSRC v1.0 into three categories: KV,
Compare, and Table. The detailed definitions can be found in our
[paper](https://arxiv.org/abs/2101.09465). The numbers of websites, webpages,
and QAs corresponding to the three categories are as follows:
Type | # Websites | # Webpages | # QAs
---- | ---------- | ---------- | -----
KV | 34 | 3,207 | 168,606
Comparison | 15 | 1,339 | 68,578
Table | 21 | 1,901 | 163,314
The statistics of the dataset splits are as follows:
Split | # Websites | # Webpages | # QAs
----- | ---------- | ---------- | -----
Train | 50 | 4,549 | 307,315
Dev | 10 | 913 | 52,826
Test | 10 | 985 | 40,357
## Obtain Test Result
For test set evaluation, please send your prediction files to
[email protected] and [email protected] with title "WebSRC Test:
\<your model name\>+\<your institution\>". For evaluation, the prediction
files should contain two files:
```jsonc
// prediction.json
// A json format file, keys are ids and values are the predicted answers (string).
{
"sp160000100001": "predicted answer",
"sp160000100002": "...",
//...
}
// tag_prediction.json
// A json format file, keys are ids and values are the predicted tag tid (int)
{
"sp160000100001": -1,
"sp160000100002": -1,
//...
}
```
We encourage to submit results from **at least three runs with different random
seeds** to reduce the uncertainty of the experiments. Please place prediction files
for each run in different directories and submit a zipped file. The average test
result will be sent by email.
## Reference
If you use any source codes or datasets included in this repository in your work,
please cite the corresponding papers. The bibtex are listed below:
```text
@inproceedings{chen-etal-2021-websrc,
title = "{W}eb{SRC}: A Dataset for Web-Based Structural Reading Comprehension",
author = "Chen, Xingyu and
Zhao, Zihan and
Chen, Lu and
Ji, JiaBao and
Zhang, Danyang and
Luo, Ao and
Xiong, Yuxuan and
Yu, Kai",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.343",
pages = "4173--4185",
abstract = "Web search is an essential way for humans to obtain information, but it{'}s still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of web-based structural reading comprehension. Given a web page and a question about it, the task is to find an answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various strong baselines on our dataset to show the difficulty of our task. We also investigate the usefulness of structural information and visual features. Our dataset and baselines have been publicly available.",
}
```
|
X-LANCE/WebSRC_v1.0
|
[
"license:cc-by-4.0",
"arxiv:2101.09465",
"region:us"
] |
2023-06-05T07:55:30+00:00
|
{"license": "cc-by-4.0"}
|
2023-06-05T09:12:19+00:00
|
fa452c68c0460421e042fc28c0792c4c897da21e
|
# Dataset Card for "sommeli_ai"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pdjewell/sommeli_ai
|
[
"region:us"
] |
2023-06-05T08:37:11+00:00
|
{"dataset_info": {"features": [{"name": "country", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "designation", "dtype": "string"}, {"name": "points", "dtype": "int64"}, {"name": "price", "dtype": "float64"}, {"name": "province", "dtype": "string"}, {"name": "region_1", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "variety", "dtype": "string"}, {"name": "winery", "dtype": "string"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 452457367, "num_examples": 129971}], "download_size": 508142473, "dataset_size": 452457367}}
|
2023-06-05T08:39:52+00:00
|
be94a34d2ccc744feada1c24e925eaa669bfcece
|
# Dataset Card for "myyntipuheet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Amselco/myyntipuheet
|
[
"region:us"
] |
2023-06-05T08:38:30+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 81546, "num_examples": 100}], "download_size": 51340, "dataset_size": 81546}}
|
2023-06-05T08:38:31+00:00
|
76a9ca7e55adf498a2677b3bb589e9c4dda3f93d
|
# Dataset Card for "commoncrawl_tex"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anton-l/commoncrawl_tex
|
[
"region:us"
] |
2023-06-05T08:47:13+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "url_host_registered_domain", "dtype": "string"}, {"name": "crawl", "dtype": "string"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "content_mime_detected", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int64"}, {"name": "warc_record_length", "dtype": "int64"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6227483249, "num_examples": 248336}], "download_size": 2940795660, "dataset_size": 6227483249}}
|
2023-06-05T08:50:39+00:00
|
a522dc2653629376091920714de0664f66931f00
|
# Dataset Card for Janes-Tag
### Dataset Summary
Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication (CMC) consisting of mostly tweets but also blogs, forums and news comments.
### Languages
Code-switched/nonstandard Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the dataset - each word is annotated with its form (`word`), lemma, MSD tag (XPOS), and IOB2-encoded named entity tag.
```
{
'id': 'janes.news.rtvslo.279732.2',
'words': ['Jst', 'mam', 'tud', 'dons', 'rojstn', 'dan', '.'],
'lemmas': ['jaz', 'imeti', 'tudi', 'danes', 'rojsten', 'dan', '.'],
'msds': ['mte:Pp1-sn', 'mte:Vmpr1s-n', 'mte:Q', 'mte:Rgp', 'mte:Agpmsay', 'mte:Ncmsan', 'mte:Z'],
'nes': ['O', 'O', 'O', 'O', 'O', 'O', 'O']
}
```
### Data Fields
- `id`: unique identifier of the example;
- `words`: words in the example;
- `lemmas`: lemmas in the example;
- `msds`: msds in the example;
- `nes`: IOB2-encoded named entity tag (person, location, organization, misc, other)
## Additional Information
### Dataset Curators
Jakob Lenardič et al. (please see http://hdl.handle.net/11356/1732 for the full list)
### Licensing Information
CC BY-SA 4.0.
### Citation Information
```
@misc{janes_tag,
title = {{CMC} training corpus Janes-Tag 3.0},
author = {Lenardi{\v c}, Jakob and {\v C}ibej, Jaka and Arhar Holdt, {\v S}pela and Erjavec, Toma{\v z} and Fi{\v s}er, Darja and Ljube{\v s}i{\'c}, Nikola and Zupan, Katja and Dobrovoljc, Kaja},
url = {http://hdl.handle.net/11356/1732},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2022}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
cjvt/janes_tag
|
[
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:sl",
"license:cc-by-sa-4.0",
"code-mixed",
"nonstandard",
"ner",
"region:us"
] |
2023-06-05T09:35:43+00:00
|
{"language": ["sl"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "msds", "sequence": "string"}, {"name": "nes", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2653609, "num_examples": 2957}], "download_size": 2871765, "dataset_size": 2653609}, "tags": ["code-mixed", "nonstandard", "ner"]}
|
2023-06-06T09:07:53+00:00
|
6c442d76f8bc73f404110fe04b3b6fe3b4d0fb0b
|
tawounfouet/temporary-dataset
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-05T09:38:18+00:00
|
{"license": "apache-2.0"}
|
2023-06-05T09:41:11+00:00
|
|
4dbb429783fd850a15c4f572bb2a24133a829714
|
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cmani/celeb-identities
|
[
"region:us"
] |
2023-06-05T09:38:38+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Dua_Lipa", "1": "Emma_Watson", "2": "Kamal_Hassan", "3": "Kim_Kardashian", "4": "Morgan_Freeman", "5": "Rajanikanth", "6": "Robert_Downey_Jr", "7": "Salma_Hayek", "8": "Tom_Cruise"}}}}], "splits": [{"name": "train", "num_bytes": 2063036.0, "num_examples": 33}], "download_size": 2061066, "dataset_size": 2063036.0}}
|
2023-06-05T09:38:43+00:00
|
3609986bec6492f874092115e41b122aaea6e29c
|
stefano-bragaglia/dataset
|
[
"license:mit",
"region:us"
] |
2023-06-05T09:39:15+00:00
|
{"license": "mit"}
|
2023-06-05T09:45:58+00:00
|
|
b14d1ffa7a48b0284d3bf63bee4a5cb973ea2f12
|
# Dataset Card for "cl_signal_processing_attacks_assembly_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/cl-signal_processing_attacks_assembly_commonvoice
|
[
"region:us"
] |
2023-06-05T09:41:29+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0-benign", "1": "1-attacked"}}}}], "splits": [{"name": "train", "num_bytes": 153422558.0, "num_examples": 300}], "download_size": 142595903, "dataset_size": 153422558.0}}
|
2023-06-05T09:42:29+00:00
|
8d7b91944e028973f81ae93d56855f40b4ba791e
|
# Dataset Card for "cl_signal_processing_attacks_whisper_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/cl-signal_processing_attacks_whisper_commonvoice
|
[
"region:us"
] |
2023-06-05T09:47:13+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0-benign", "1": "1-attacked"}}}}], "splits": [{"name": "train", "num_bytes": 154431710.0, "num_examples": 300}], "download_size": 145378430, "dataset_size": 154431710.0}}
|
2023-06-05T09:48:43+00:00
|
6cff0981d22fdb878f0453b7121468b70c77e73b
|
# Dataset Card for "cl-signal_processing_attacks_assembly_librispeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/cl-signal_processing_attacks_assembly_librispeech
|
[
"region:us"
] |
2023-06-05T09:52:10+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0-benign", "1": "1-attacked"}}}}], "splits": [{"name": "train", "num_bytes": 162870678.0, "num_examples": 300}], "download_size": 147017888, "dataset_size": 162870678.0}}
|
2023-06-05T09:52:51+00:00
|
a3c43263d6b86d3555a15536d36922ad4e6de290
|
# Dataset Card for "contracts-extraction-instruction-llm-experiments"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
scholarly360/contracts-extraction-instruction-llm-experiments
|
[
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-05T09:52:52+00:00
|
{"language": ["en"], "license": "apache-2.0", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "instances", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "is_classification", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3522722, "num_examples": 5732}, {"name": "test", "num_bytes": 1512840, "num_examples": 2487}], "download_size": 2128894, "dataset_size": 5035562}}
|
2023-07-17T05:18:05+00:00
|
f4fd85af7140e29002fdf9c121e38cc0bb7b3a1a
|
# Dataset Card for "mini_imagenet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/mini_imagenet
|
[
"task_categories:image-classification",
"region:us"
] |
2023-06-05T09:59:59+00:00
|
{"task_categories": ["image-classification"], "pretty_name": "mini-imagenet", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2533332667, "num_examples": 38400}, {"name": "validation", "num_bytes": 623452894, "num_examples": 9600}, {"name": "test", "num_bytes": 781497663, "num_examples": 12000}], "download_size": 3938112512, "dataset_size": 3938283224}}
|
2023-12-02T22:44:53+00:00
|
fd9f4120ddbd9e5173a0f3a71ad0ba7003a9790d
|
# Dataset Card for "nyaya-ae-msmarco-distilbert-base-tas-b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sukantan/nyaya-ae-msmarco-distilbert-base-tas-b
|
[
"region:us"
] |
2023-06-05T10:15:21+00:00
|
{"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": "26", "dtype": "float32"}, {"name": "27", "dtype": "float32"}, {"name": "28", "dtype": "float32"}, {"name": "29", "dtype": "float32"}, {"name": "30", "dtype": "float32"}, {"name": "31", "dtype": "float32"}, {"name": "32", "dtype": "float32"}, {"name": "33", "dtype": "float32"}, {"name": "34", "dtype": "float32"}, {"name": "35", "dtype": "float32"}, {"name": "36", "dtype": "float32"}, {"name": "37", "dtype": "float32"}, {"name": "38", "dtype": "float32"}, {"name": "39", "dtype": "float32"}, {"name": "40", "dtype": "float32"}, {"name": "41", "dtype": "float32"}, {"name": "42", "dtype": "float32"}, {"name": "43", "dtype": "float32"}, {"name": "44", "dtype": "float32"}, {"name": "45", "dtype": "float32"}, {"name": "46", "dtype": "float32"}, {"name": "47", "dtype": "float32"}, {"name": "48", "dtype": "float32"}, {"name": "49", "dtype": "float32"}, {"name": "50", "dtype": "float32"}, {"name": "51", "dtype": "float32"}, {"name": "52", "dtype": "float32"}, {"name": "53", "dtype": "float32"}, {"name": "54", "dtype": "float32"}, {"name": "55", "dtype": "float32"}, {"name": "56", "dtype": "float32"}, {"name": "57", "dtype": "float32"}, {"name": "58", "dtype": "float32"}, {"name": "59", "dtype": "float32"}, {"name": "60", "dtype": "float32"}, {"name": "61", "dtype": "float32"}, {"name": "62", "dtype": "float32"}, {"name": "63", "dtype": "float32"}, {"name": "64", "dtype": "float32"}, {"name": "65", "dtype": "float32"}, {"name": "66", "dtype": "float32"}, {"name": "67", "dtype": "float32"}, {"name": "68", "dtype": "float32"}, {"name": "69", "dtype": "float32"}, {"name": "70", "dtype": "float32"}, {"name": "71", "dtype": "float32"}, {"name": "72", "dtype": "float32"}, {"name": "73", "dtype": "float32"}, {"name": "74", "dtype": "float32"}, {"name": "75", "dtype": "float32"}, {"name": "76", "dtype": "float32"}, {"name": "77", "dtype": "float32"}, {"name": "78", "dtype": "float32"}, {"name": "79", "dtype": "float32"}, {"name": "80", "dtype": "float32"}, {"name": "81", "dtype": "float32"}, {"name": "82", "dtype": "float32"}, {"name": "83", "dtype": "float32"}, {"name": "84", "dtype": "float32"}, {"name": "85", "dtype": "float32"}, {"name": "86", "dtype": "float32"}, {"name": "87", "dtype": "float32"}, {"name": "88", "dtype": "float32"}, {"name": "89", "dtype": "float32"}, {"name": "90", "dtype": "float32"}, {"name": "91", "dtype": "float32"}, {"name": "92", "dtype": "float32"}, {"name": "93", "dtype": "float32"}, {"name": "94", "dtype": "float32"}, {"name": "95", "dtype": "float32"}, {"name": "96", "dtype": "float32"}, {"name": "97", "dtype": "float32"}, {"name": "98", "dtype": "float32"}, {"name": "99", "dtype": "float32"}, {"name": "100", "dtype": "float32"}, {"name": "101", "dtype": "float32"}, {"name": "102", "dtype": "float32"}, {"name": "103", "dtype": "float32"}, {"name": "104", "dtype": "float32"}, {"name": "105", "dtype": "float32"}, {"name": "106", "dtype": "float32"}, {"name": "107", "dtype": "float32"}, {"name": "108", "dtype": "float32"}, {"name": "109", "dtype": "float32"}, {"name": "110", "dtype": "float32"}, {"name": "111", "dtype": "float32"}, {"name": "112", "dtype": "float32"}, {"name": "113", "dtype": "float32"}, {"name": "114", "dtype": "float32"}, {"name": "115", "dtype": "float32"}, {"name": "116", "dtype": "float32"}, {"name": "117", "dtype": "float32"}, {"name": "118", "dtype": "float32"}, {"name": "119", "dtype": "float32"}, {"name": "120", "dtype": "float32"}, {"name": "121", "dtype": "float32"}, {"name": "122", "dtype": "float32"}, {"name": "123", "dtype": "float32"}, {"name": "124", "dtype": "float32"}, {"name": "125", "dtype": "float32"}, {"name": "126", "dtype": "float32"}, {"name": "127", "dtype": "float32"}, {"name": "128", "dtype": "float32"}, {"name": "129", "dtype": "float32"}, {"name": "130", "dtype": "float32"}, {"name": "131", "dtype": "float32"}, {"name": "132", "dtype": "float32"}, {"name": "133", "dtype": "float32"}, {"name": "134", "dtype": "float32"}, {"name": "135", "dtype": "float32"}, {"name": "136", "dtype": "float32"}, {"name": "137", "dtype": "float32"}, {"name": "138", "dtype": "float32"}, {"name": "139", "dtype": "float32"}, {"name": "140", "dtype": "float32"}, {"name": "141", "dtype": "float32"}, {"name": "142", "dtype": "float32"}, {"name": "143", "dtype": "float32"}, {"name": "144", "dtype": "float32"}, {"name": "145", "dtype": "float32"}, {"name": "146", "dtype": "float32"}, {"name": "147", "dtype": "float32"}, {"name": "148", "dtype": "float32"}, {"name": "149", "dtype": "float32"}, {"name": "150", "dtype": "float32"}, {"name": "151", "dtype": "float32"}, {"name": "152", "dtype": "float32"}, {"name": "153", "dtype": "float32"}, {"name": "154", "dtype": "float32"}, {"name": "155", "dtype": "float32"}, {"name": "156", "dtype": "float32"}, {"name": "157", "dtype": "float32"}, {"name": "158", "dtype": "float32"}, {"name": "159", "dtype": "float32"}, {"name": "160", "dtype": "float32"}, {"name": "161", "dtype": "float32"}, {"name": "162", "dtype": "float32"}, {"name": "163", "dtype": 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"float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 165236736, "num_examples": 53788}], "download_size": 199550010, "dataset_size": 165236736}}
|
2023-06-26T14:04:48+00:00
|
39262ef7ccb8de16708e85b6b8d831a2505b6b66
|
# Dataset Card for "nyaya-ae-msmarco-distilbert-base-tas-b-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sukantan/nyaya-ae-msmarco-distilbert-base-tas-b-v1
|
[
"region:us"
] |
2023-06-05T10:21:15+00:00
|
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"float32"}, {"name": "677", "dtype": "float32"}, {"name": "678", "dtype": "float32"}, {"name": "679", "dtype": "float32"}, {"name": "680", "dtype": "float32"}, {"name": "681", "dtype": "float32"}, {"name": "682", "dtype": "float32"}, {"name": "683", "dtype": "float32"}, {"name": "684", "dtype": "float32"}, {"name": "685", "dtype": "float32"}, {"name": "686", "dtype": "float32"}, {"name": "687", "dtype": "float32"}, {"name": "688", "dtype": "float32"}, {"name": "689", "dtype": "float32"}, {"name": "690", "dtype": "float32"}, {"name": "691", "dtype": "float32"}, {"name": "692", "dtype": "float32"}, {"name": "693", "dtype": "float32"}, {"name": "694", "dtype": "float32"}, {"name": "695", "dtype": "float32"}, {"name": "696", "dtype": "float32"}, {"name": "697", "dtype": "float32"}, {"name": "698", "dtype": "float32"}, {"name": "699", "dtype": "float32"}, {"name": "700", "dtype": "float32"}, {"name": "701", "dtype": "float32"}, {"name": "702", "dtype": "float32"}, {"name": "703", "dtype": "float32"}, {"name": "704", "dtype": "float32"}, {"name": "705", "dtype": "float32"}, {"name": "706", "dtype": "float32"}, {"name": "707", "dtype": "float32"}, {"name": "708", "dtype": "float32"}, {"name": "709", "dtype": "float32"}, {"name": "710", "dtype": "float32"}, {"name": "711", "dtype": "float32"}, {"name": "712", "dtype": "float32"}, {"name": "713", "dtype": "float32"}, {"name": "714", "dtype": "float32"}, {"name": "715", "dtype": "float32"}, {"name": "716", "dtype": "float32"}, {"name": "717", "dtype": "float32"}, {"name": "718", "dtype": "float32"}, {"name": "719", "dtype": "float32"}, {"name": "720", "dtype": "float32"}, {"name": "721", "dtype": "float32"}, {"name": "722", "dtype": "float32"}, {"name": "723", "dtype": "float32"}, {"name": "724", "dtype": "float32"}, {"name": "725", "dtype": "float32"}, {"name": "726", "dtype": "float32"}, {"name": "727", "dtype": "float32"}, {"name": "728", "dtype": "float32"}, {"name": "729", "dtype": "float32"}, {"name": "730", "dtype": "float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 138405888, "num_examples": 45054}], "download_size": 188219292, "dataset_size": 138405888}}
|
2023-06-05T10:21:29+00:00
|
7a1116302324aa464975feac88d067ef83efedea
|
# Dataset Card for "coqa-squad-1.0-fixed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rooftopcoder/coqa-squad-1.0-fixed
|
[
"language:en",
"region:us"
] |
2023-06-05T10:21:52+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 187243501, "num_examples": 108647}, {"name": "test", "num_bytes": 13255826, "num_examples": 7983}, {"name": "validation", "num_bytes": 13255826, "num_examples": 7983}], "download_size": 17359756, "dataset_size": 213755153}}
|
2023-08-01T06:57:31+00:00
|
23362be42af39fa212cfb29514a6de770c5fe77a
|
Dataset Download: https://huggingface.co/datasets/Cainiao-AI/LaDe/tree/main
Dataset Website: https://cainiaotechai.github.io/LaDe-website/
Code Link:https://github.com/wenhaomin/LaDe
Paper Link: https://arxiv.org/abs/2306.10675
# 1 About Dataset
**LaDe** is a publicly available last-mile delivery dataset with millions of packages from industry.
It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation.
(2) Comprehensive information, it offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen.
(3) Diversity: the dataset includes data from various scenarios, such as package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations.
# 2 Download
LaDe is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario.
ii) [LaDe-P](https://huggingface.co/datasets/Cainiao-AI/LaDe-P), which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format.
LaDe can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](https://github.com/wenhaomin/LaDe). Then put the data into "./data/raw/".
The structure of "./data/raw/" should be like:
```
* ./data/raw/
* delivery
* delivery_sh.csv
* ...
* pickup
* pickup_sh.csv
* ...
```
Each sub-dataset contains 5 csv files, with each representing the data from a specific city, the detail of each city can be find in the following table.
| City | Description |
|------------|----------------------------------------------------------------------------------------------|
| Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
| Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
| Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
| Jilin | A middle-size city in China, with a small number of orders each day. |
| Yantai | A small city in China, with a small number of orders every day. |
# 3 Description
Below is the detailed field of each sub-dataset.
## 3.1 LaDe-P
| Data field | Description | Unit/format |
|----------------------------|----------------------------------------------|--------------|
| **Package information** | | |
| package_id | Unique identifier of each package | Id |
| time_window_start | Start of the required time window | Time |
| time_window_end | End of the required time window | Time |
| **Stop information** | | |
| lng/lat | Coordinates of each stop | Float |
| city | City | String |
| region_id | Id of the Region | String |
| aoi_id | Id of the AOI (Area of Interest) | Id |
| aoi_type | Type of the AOI | Categorical |
| **Courier Information** | | |
| courier_id | Id of the courier | Id |
| **Task-event Information** | | |
| accept_time | The time when the courier accepts the task | Time |
| accept_gps_time | The time of the GPS point closest to accept time | Time |
| accept_gps_lng/lat | Coordinates when the courier accepts the task | Float |
| pickup_time | The time when the courier picks up the task | Time |
| pickup_gps_time | The time of the GPS point closest to pickup_time | Time |
| pickup_gps_lng/lat | Coordinates when the courier picks up the task | Float |
| **Context information** | | |
| ds | The date of the package pickup | Date |
## 3.2 LaDe-D
| Data field | Description | Unit/format |
|-----------------------|--------------------------------------|---------------|
| **Package information** | | |
| package_id | Unique identifier of each package | Id |
| **Stop information** | | |
| lng/lat | Coordinates of each stop | Float |
| city | City | String |
| region_id | Id of the region | Id |
| aoi_id | Id of the AOI | Id |
| aoi_type | Type of the AOI | Categorical |
| **Courier Information** | | |
| courier_id | Id of the courier | Id |
| **Task-event Information**| | |
| accept_time | The time when the courier accepts the task | Time |
| accept_gps_time | The time of the GPS point whose time is the closest to accept time | Time |
| accept_gps_lng/accept_gps_lat | Coordinates when the courier accepts the task | Float |
| delivery_time | The time when the courier finishes delivering the task | Time |
| delivery_gps_time | The time of the GPS point whose time is the closest to the delivery time | Time |
| delivery_gps_lng/delivery_gps_lat | Coordinates when the courier finishes the task | Float |
| **Context information** | | |
| ds | The date of the package delivery | Date |
# 4 Leaderboard
Blow shows the performance of different methods in Shanghai.
## 4.1 Route Prediction
Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
| Method | HR@3 | KRC | LSD | ED |
|--------------|--------------|--------------|-------------|-------------|
| TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 |
| DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 |
| OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 |
| LightGBM | 73.76 | 55.71 | 3.01 | 1.84 |
| FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
| DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
| Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
## 4.2 Estimated Time of Arrival Prediction
| Method | MAE | RMSE | ACC@30 |
| ------ |--------------|--------------|-------------|
| LightGBM | 30.99 | 35.04 | 0.59 |
| SPEED | 23.75 | 27.86 | 0.73 |
| KNN | 36.00 | 31.89 | 0.58 |
| MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 ± 0.04 |
| FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 |
## 4.3 Spatio-temporal Graph Forecasting
| Method | MAE | RMSE |
|-------|-------------|-------------|
| HA | 4.63 | 9.91 |
| DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
| STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
| GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
| ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
| MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
| AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
| STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
# 5 Citation
If you find this helpful, please cite our paper:
```shell
@misc{wu2023lade,
title={LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry},
author={Lixia Wu and Haomin Wen and Haoyuan Hu and Xiaowei Mao and Yutong Xia and Ergang Shan and Jianbin Zhen and Junhong Lou and Yuxuan Liang and Liuqing Yang and Roger Zimmermann and Youfang Lin and Huaiyu Wan},
year={2023},
eprint={2306.10675},
archivePrefix={arXiv},
primaryClass={cs.DB}
}
```
|
Cainiao-AI/LaDe
|
[
"size_categories:10M<n<100M",
"license:apache-2.0",
"Logistics",
"Last-mile Delivery",
"Spatial-Temporal",
"Graph",
"arxiv:2306.10675",
"region:us"
] |
2023-06-05T10:25:20+00:00
|
{"license": "apache-2.0", "size_categories": ["10M<n<100M"], "tags": ["Logistics", "Last-mile Delivery", "Spatial-Temporal", "Graph"]}
|
2023-07-04T01:58:59+00:00
|
ac351c97007150b3c3277b2106e8d6b630520efd
|
# Dataset Card for "reddit_ah_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Deojoandco/reddit_ah_v3
|
[
"region:us"
] |
2023-06-05T10:29:55+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "over_18", "dtype": "bool"}, {"name": "created_utc", "dtype": "float64"}, {"name": "comments", "list": [{"name": "body", "dtype": "string"}, {"name": "created_utc", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "best_num_comments", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12479446, "num_examples": 2598}], "download_size": 7277543, "dataset_size": 12479446}}
|
2023-06-05T10:30:04+00:00
|
64cb8b3861a16e66fda596f54ad7a5852c39dbd3
|
# Dataset Card for "stable-bias-professions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
society-ethics/stable-bias-professions
|
[
"region:us"
] |
2023-06-05T12:05:34+00:00
|
{"dataset_info": {"features": [{"name": "profession", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "seed", "dtype": "int32"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 8338679567.447, "num_examples": 253719}], "download_size": 8917804015, "dataset_size": 8338679567.447}}
|
2023-06-05T14:19:21+00:00
|
e60a913eb6fd00bfe6a85459f8263a9590adba25
|
alujjdnd/Airbnb-Mar-2022-2023
|
[
"license:mit",
"region:us"
] |
2023-06-05T12:15:48+00:00
|
{"license": "mit"}
|
2023-06-05T13:41:47+00:00
|
|
e46938cca45bdca8481a7c3403596d1ce8b3e724
|
sherryibm/sas2py
|
[
"task_categories:conversational",
"task_categories:translation",
"license:apache-2.0",
"region:us"
] |
2023-06-05T12:17:07+00:00
|
{"license": "apache-2.0", "task_categories": ["conversational", "translation"]}
|
2023-06-06T09:56:56+00:00
|
|
1a376ba093d0a25983a08a7ed46a5f24d1d33ac4
|
# Dataset Card for "nyaya-ae-all-mpnet-base-v2-legal-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sukantan/nyaya-ae-all-mpnet-base-v2-legal-v1
|
[
"region:us"
] |
2023-06-05T12:25:25+00:00
|
{"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": "26", "dtype": "float32"}, {"name": "27", "dtype": "float32"}, {"name": "28", "dtype": "float32"}, {"name": "29", "dtype": "float32"}, {"name": "30", "dtype": "float32"}, {"name": "31", "dtype": "float32"}, {"name": "32", "dtype": "float32"}, {"name": "33", "dtype": "float32"}, {"name": "34", "dtype": "float32"}, {"name": "35", "dtype": "float32"}, {"name": "36", "dtype": "float32"}, {"name": "37", "dtype": "float32"}, {"name": "38", "dtype": "float32"}, {"name": "39", "dtype": "float32"}, {"name": "40", "dtype": "float32"}, {"name": "41", "dtype": "float32"}, {"name": "42", "dtype": "float32"}, {"name": "43", "dtype": "float32"}, {"name": "44", "dtype": "float32"}, {"name": "45", "dtype": "float32"}, {"name": "46", "dtype": "float32"}, {"name": "47", "dtype": "float32"}, {"name": "48", "dtype": "float32"}, {"name": "49", "dtype": "float32"}, {"name": "50", "dtype": "float32"}, {"name": "51", "dtype": "float32"}, {"name": "52", "dtype": "float32"}, {"name": "53", "dtype": "float32"}, {"name": "54", "dtype": "float32"}, {"name": "55", "dtype": "float32"}, {"name": "56", "dtype": "float32"}, {"name": "57", "dtype": "float32"}, {"name": "58", "dtype": "float32"}, {"name": "59", "dtype": "float32"}, {"name": "60", "dtype": "float32"}, {"name": "61", "dtype": "float32"}, {"name": "62", "dtype": "float32"}, {"name": "63", "dtype": "float32"}, {"name": "64", "dtype": "float32"}, {"name": "65", "dtype": "float32"}, {"name": "66", "dtype": "float32"}, {"name": "67", "dtype": "float32"}, {"name": "68", "dtype": "float32"}, {"name": "69", "dtype": "float32"}, {"name": "70", "dtype": "float32"}, {"name": "71", "dtype": "float32"}, {"name": "72", "dtype": "float32"}, {"name": "73", "dtype": "float32"}, {"name": "74", "dtype": "float32"}, {"name": "75", "dtype": "float32"}, {"name": "76", "dtype": "float32"}, {"name": "77", "dtype": "float32"}, {"name": "78", "dtype": "float32"}, {"name": "79", "dtype": "float32"}, {"name": "80", "dtype": "float32"}, {"name": "81", "dtype": "float32"}, {"name": "82", 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"float32"}, {"name": "137", "dtype": "float32"}, {"name": "138", "dtype": "float32"}, {"name": "139", "dtype": "float32"}, {"name": "140", "dtype": "float32"}, {"name": "141", "dtype": "float32"}, {"name": "142", "dtype": "float32"}, {"name": "143", "dtype": "float32"}, {"name": "144", "dtype": "float32"}, {"name": "145", "dtype": "float32"}, {"name": "146", "dtype": "float32"}, {"name": "147", "dtype": "float32"}, {"name": "148", "dtype": "float32"}, {"name": "149", "dtype": "float32"}, {"name": "150", "dtype": "float32"}, {"name": "151", "dtype": "float32"}, {"name": "152", "dtype": "float32"}, {"name": "153", "dtype": "float32"}, {"name": "154", "dtype": "float32"}, {"name": "155", "dtype": "float32"}, {"name": "156", "dtype": "float32"}, {"name": "157", "dtype": "float32"}, {"name": "158", "dtype": "float32"}, {"name": "159", "dtype": "float32"}, {"name": "160", "dtype": "float32"}, {"name": "161", "dtype": "float32"}, {"name": "162", "dtype": "float32"}, {"name": "163", "dtype": "float32"}, {"name": "164", "dtype": "float32"}, {"name": "165", "dtype": "float32"}, {"name": "166", "dtype": "float32"}, {"name": "167", "dtype": "float32"}, {"name": "168", "dtype": "float32"}, {"name": "169", "dtype": "float32"}, {"name": "170", "dtype": "float32"}, {"name": "171", "dtype": "float32"}, {"name": "172", "dtype": "float32"}, {"name": "173", "dtype": "float32"}, {"name": "174", "dtype": "float32"}, {"name": "175", "dtype": "float32"}, {"name": "176", "dtype": "float32"}, {"name": "177", "dtype": "float32"}, {"name": "178", "dtype": "float32"}, {"name": "179", "dtype": "float32"}, {"name": "180", "dtype": "float32"}, {"name": "181", "dtype": "float32"}, {"name": "182", "dtype": "float32"}, {"name": "183", "dtype": "float32"}, {"name": "184", "dtype": "float32"}, {"name": "185", "dtype": "float32"}, {"name": "186", "dtype": "float32"}, {"name": "187", "dtype": "float32"}, {"name": "188", "dtype": "float32"}, {"name": "189", "dtype": "float32"}, {"name": "190", "dtype": "float32"}, {"name": "191", "dtype": "float32"}, {"name": "192", "dtype": "float32"}, {"name": "193", "dtype": "float32"}, {"name": "194", "dtype": "float32"}, {"name": "195", "dtype": "float32"}, {"name": "196", "dtype": "float32"}, {"name": "197", "dtype": "float32"}, {"name": "198", "dtype": "float32"}, {"name": "199", "dtype": "float32"}, {"name": "200", "dtype": "float32"}, {"name": "201", "dtype": "float32"}, {"name": "202", "dtype": "float32"}, {"name": "203", "dtype": "float32"}, {"name": "204", "dtype": "float32"}, {"name": "205", "dtype": "float32"}, {"name": "206", "dtype": "float32"}, {"name": "207", "dtype": "float32"}, {"name": "208", "dtype": "float32"}, {"name": "209", "dtype": "float32"}, {"name": "210", "dtype": "float32"}, {"name": "211", "dtype": "float32"}, {"name": "212", "dtype": "float32"}, {"name": "213", "dtype": "float32"}, {"name": "214", "dtype": "float32"}, {"name": "215", "dtype": "float32"}, {"name": "216", "dtype": "float32"}, {"name": "217", "dtype": 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"float32"}, {"name": "326", "dtype": "float32"}, {"name": "327", "dtype": "float32"}, {"name": "328", "dtype": "float32"}, {"name": "329", "dtype": "float32"}, {"name": "330", "dtype": "float32"}, {"name": "331", "dtype": "float32"}, {"name": "332", "dtype": "float32"}, {"name": "333", "dtype": "float32"}, {"name": "334", "dtype": "float32"}, {"name": "335", "dtype": "float32"}, {"name": "336", "dtype": "float32"}, {"name": "337", "dtype": "float32"}, {"name": "338", "dtype": "float32"}, {"name": "339", "dtype": "float32"}, {"name": "340", "dtype": "float32"}, {"name": "341", "dtype": "float32"}, {"name": "342", "dtype": "float32"}, {"name": "343", "dtype": "float32"}, {"name": "344", "dtype": "float32"}, {"name": "345", "dtype": "float32"}, {"name": "346", "dtype": "float32"}, {"name": "347", "dtype": "float32"}, {"name": "348", "dtype": "float32"}, {"name": "349", "dtype": "float32"}, {"name": "350", "dtype": "float32"}, {"name": "351", "dtype": "float32"}, {"name": "352", "dtype": 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"float32"}, {"name": "434", "dtype": "float32"}, {"name": "435", "dtype": "float32"}, {"name": "436", "dtype": "float32"}, {"name": "437", "dtype": "float32"}, {"name": "438", "dtype": "float32"}, {"name": "439", "dtype": "float32"}, {"name": "440", "dtype": "float32"}, {"name": "441", "dtype": "float32"}, {"name": "442", "dtype": "float32"}, {"name": "443", "dtype": "float32"}, {"name": "444", "dtype": "float32"}, {"name": "445", "dtype": "float32"}, {"name": "446", "dtype": "float32"}, {"name": "447", "dtype": "float32"}, {"name": "448", "dtype": "float32"}, {"name": "449", "dtype": "float32"}, {"name": "450", "dtype": "float32"}, {"name": "451", "dtype": "float32"}, {"name": "452", "dtype": "float32"}, {"name": "453", "dtype": "float32"}, {"name": "454", "dtype": "float32"}, {"name": "455", "dtype": "float32"}, {"name": "456", "dtype": "float32"}, {"name": "457", "dtype": "float32"}, {"name": "458", "dtype": "float32"}, {"name": "459", "dtype": "float32"}, {"name": "460", "dtype": "float32"}, {"name": "461", "dtype": "float32"}, {"name": "462", "dtype": "float32"}, {"name": "463", "dtype": "float32"}, {"name": "464", "dtype": "float32"}, {"name": "465", "dtype": "float32"}, {"name": "466", "dtype": "float32"}, {"name": "467", "dtype": "float32"}, {"name": "468", "dtype": "float32"}, {"name": "469", "dtype": "float32"}, {"name": "470", "dtype": "float32"}, {"name": "471", "dtype": "float32"}, {"name": "472", "dtype": "float32"}, {"name": "473", "dtype": "float32"}, {"name": "474", "dtype": "float32"}, {"name": "475", "dtype": "float32"}, {"name": "476", "dtype": "float32"}, {"name": "477", "dtype": "float32"}, {"name": "478", "dtype": "float32"}, {"name": "479", "dtype": "float32"}, {"name": "480", "dtype": "float32"}, {"name": "481", "dtype": "float32"}, {"name": "482", "dtype": "float32"}, {"name": "483", "dtype": "float32"}, {"name": "484", "dtype": "float32"}, {"name": "485", "dtype": "float32"}, {"name": "486", "dtype": "float32"}, {"name": "487", "dtype": "float32"}, {"name": "488", "dtype": "float32"}, {"name": "489", "dtype": "float32"}, {"name": "490", "dtype": "float32"}, {"name": "491", "dtype": "float32"}, {"name": "492", "dtype": "float32"}, {"name": "493", "dtype": "float32"}, {"name": "494", "dtype": "float32"}, {"name": "495", "dtype": "float32"}, {"name": "496", "dtype": "float32"}, {"name": "497", "dtype": "float32"}, {"name": "498", "dtype": "float32"}, {"name": "499", "dtype": "float32"}, {"name": "500", "dtype": "float32"}, {"name": "501", "dtype": "float32"}, {"name": "502", "dtype": "float32"}, {"name": "503", "dtype": "float32"}, {"name": "504", "dtype": "float32"}, {"name": "505", "dtype": "float32"}, {"name": "506", "dtype": "float32"}, {"name": "507", "dtype": "float32"}, {"name": "508", "dtype": "float32"}, {"name": "509", "dtype": "float32"}, {"name": "510", "dtype": "float32"}, {"name": "511", "dtype": "float32"}, {"name": "512", "dtype": "float32"}, {"name": "513", "dtype": "float32"}, {"name": "514", "dtype": "float32"}, {"name": "515", "dtype": "float32"}, {"name": "516", "dtype": "float32"}, {"name": "517", "dtype": "float32"}, {"name": "518", "dtype": "float32"}, {"name": "519", "dtype": "float32"}, {"name": "520", "dtype": "float32"}, {"name": "521", "dtype": "float32"}, {"name": "522", "dtype": "float32"}, {"name": "523", "dtype": "float32"}, {"name": "524", "dtype": "float32"}, {"name": "525", "dtype": "float32"}, {"name": "526", "dtype": "float32"}, {"name": "527", "dtype": "float32"}, {"name": "528", "dtype": "float32"}, {"name": "529", "dtype": "float32"}, {"name": "530", "dtype": "float32"}, {"name": "531", "dtype": "float32"}, {"name": "532", "dtype": "float32"}, {"name": "533", "dtype": "float32"}, {"name": "534", "dtype": "float32"}, {"name": "535", "dtype": "float32"}, {"name": "536", "dtype": "float32"}, {"name": "537", "dtype": "float32"}, {"name": "538", "dtype": "float32"}, {"name": "539", "dtype": "float32"}, {"name": "540", "dtype": "float32"}, {"name": "541", "dtype": "float32"}, {"name": "542", "dtype": "float32"}, {"name": "543", "dtype": "float32"}, {"name": "544", "dtype": "float32"}, {"name": "545", "dtype": "float32"}, {"name": "546", "dtype": "float32"}, {"name": "547", "dtype": "float32"}, {"name": "548", "dtype": "float32"}, {"name": "549", "dtype": "float32"}, {"name": "550", "dtype": "float32"}, {"name": "551", "dtype": "float32"}, {"name": "552", "dtype": "float32"}, {"name": "553", "dtype": "float32"}, {"name": "554", "dtype": "float32"}, {"name": "555", "dtype": "float32"}, {"name": "556", "dtype": "float32"}, {"name": "557", "dtype": "float32"}, {"name": "558", "dtype": "float32"}, {"name": "559", "dtype": "float32"}, {"name": "560", "dtype": "float32"}, {"name": "561", "dtype": "float32"}, {"name": "562", "dtype": "float32"}, {"name": "563", "dtype": "float32"}, {"name": "564", "dtype": "float32"}, {"name": "565", "dtype": "float32"}, {"name": "566", "dtype": "float32"}, {"name": "567", "dtype": "float32"}, {"name": "568", "dtype": "float32"}, {"name": "569", "dtype": "float32"}, {"name": "570", "dtype": "float32"}, {"name": "571", "dtype": "float32"}, {"name": "572", "dtype": "float32"}, {"name": "573", "dtype": "float32"}, {"name": "574", "dtype": "float32"}, {"name": "575", "dtype": "float32"}, {"name": "576", "dtype": "float32"}, {"name": "577", "dtype": "float32"}, {"name": "578", "dtype": "float32"}, {"name": "579", "dtype": "float32"}, {"name": "580", "dtype": "float32"}, {"name": "581", "dtype": "float32"}, {"name": "582", "dtype": "float32"}, {"name": "583", "dtype": "float32"}, {"name": "584", "dtype": "float32"}, {"name": "585", "dtype": "float32"}, {"name": "586", "dtype": "float32"}, {"name": "587", "dtype": "float32"}, {"name": "588", "dtype": "float32"}, {"name": "589", "dtype": "float32"}, {"name": "590", "dtype": "float32"}, {"name": "591", "dtype": "float32"}, {"name": "592", "dtype": "float32"}, {"name": "593", "dtype": "float32"}, {"name": "594", "dtype": "float32"}, {"name": "595", "dtype": "float32"}, {"name": "596", "dtype": "float32"}, {"name": "597", "dtype": "float32"}, {"name": "598", "dtype": "float32"}, {"name": "599", "dtype": "float32"}, {"name": "600", "dtype": "float32"}, {"name": "601", "dtype": "float32"}, {"name": "602", "dtype": "float32"}, {"name": "603", "dtype": "float32"}, {"name": "604", "dtype": "float32"}, {"name": "605", "dtype": "float32"}, {"name": "606", "dtype": "float32"}, {"name": "607", "dtype": "float32"}, {"name": "608", "dtype": "float32"}, {"name": "609", "dtype": "float32"}, {"name": "610", "dtype": "float32"}, {"name": "611", "dtype": "float32"}, {"name": "612", "dtype": "float32"}, {"name": "613", "dtype": "float32"}, {"name": "614", "dtype": "float32"}, {"name": "615", "dtype": "float32"}, {"name": "616", "dtype": "float32"}, {"name": "617", "dtype": "float32"}, {"name": "618", "dtype": "float32"}, {"name": "619", "dtype": "float32"}, {"name": "620", "dtype": "float32"}, {"name": "621", "dtype": "float32"}, {"name": "622", "dtype": "float32"}, {"name": "623", "dtype": "float32"}, {"name": "624", "dtype": "float32"}, {"name": "625", "dtype": "float32"}, {"name": "626", "dtype": "float32"}, {"name": "627", "dtype": "float32"}, {"name": "628", "dtype": "float32"}, {"name": "629", "dtype": "float32"}, {"name": "630", "dtype": "float32"}, {"name": "631", "dtype": "float32"}, {"name": "632", "dtype": "float32"}, {"name": "633", "dtype": "float32"}, {"name": "634", "dtype": "float32"}, {"name": "635", "dtype": "float32"}, {"name": "636", "dtype": "float32"}, {"name": "637", "dtype": "float32"}, {"name": "638", "dtype": "float32"}, {"name": "639", "dtype": "float32"}, {"name": "640", "dtype": "float32"}, {"name": "641", "dtype": "float32"}, {"name": "642", "dtype": "float32"}, {"name": "643", "dtype": "float32"}, {"name": "644", "dtype": "float32"}, {"name": "645", "dtype": "float32"}, {"name": "646", "dtype": "float32"}, {"name": "647", "dtype": "float32"}, {"name": "648", "dtype": "float32"}, {"name": "649", "dtype": "float32"}, {"name": "650", "dtype": "float32"}, {"name": "651", "dtype": "float32"}, {"name": "652", "dtype": "float32"}, {"name": "653", "dtype": "float32"}, {"name": "654", "dtype": "float32"}, {"name": "655", "dtype": "float32"}, {"name": "656", "dtype": "float32"}, {"name": "657", "dtype": "float32"}, {"name": "658", "dtype": "float32"}, {"name": "659", "dtype": "float32"}, {"name": "660", "dtype": "float32"}, {"name": "661", "dtype": "float32"}, {"name": "662", "dtype": "float32"}, {"name": "663", "dtype": "float32"}, {"name": "664", "dtype": "float32"}, {"name": "665", "dtype": "float32"}, {"name": "666", "dtype": "float32"}, {"name": "667", "dtype": "float32"}, {"name": "668", "dtype": "float32"}, {"name": "669", "dtype": "float32"}, {"name": "670", "dtype": "float32"}, {"name": "671", "dtype": "float32"}, {"name": "672", "dtype": "float32"}, {"name": "673", "dtype": "float32"}, {"name": "674", "dtype": "float32"}, {"name": "675", "dtype": "float32"}, {"name": "676", "dtype": "float32"}, {"name": "677", "dtype": "float32"}, {"name": "678", "dtype": "float32"}, {"name": "679", "dtype": "float32"}, {"name": "680", "dtype": "float32"}, {"name": "681", "dtype": "float32"}, {"name": "682", "dtype": "float32"}, {"name": "683", "dtype": "float32"}, {"name": "684", "dtype": "float32"}, {"name": "685", "dtype": "float32"}, {"name": "686", "dtype": "float32"}, {"name": "687", "dtype": "float32"}, {"name": "688", "dtype": "float32"}, {"name": "689", "dtype": "float32"}, {"name": "690", "dtype": "float32"}, {"name": "691", "dtype": "float32"}, {"name": "692", "dtype": "float32"}, {"name": "693", "dtype": "float32"}, {"name": "694", "dtype": "float32"}, {"name": "695", "dtype": "float32"}, {"name": "696", "dtype": "float32"}, {"name": "697", "dtype": "float32"}, {"name": "698", "dtype": "float32"}, {"name": "699", "dtype": "float32"}, {"name": "700", "dtype": "float32"}, {"name": "701", "dtype": "float32"}, {"name": "702", "dtype": "float32"}, {"name": "703", "dtype": "float32"}, {"name": "704", "dtype": "float32"}, {"name": "705", "dtype": "float32"}, {"name": "706", "dtype": "float32"}, {"name": "707", "dtype": "float32"}, {"name": "708", "dtype": "float32"}, {"name": "709", "dtype": "float32"}, {"name": "710", "dtype": "float32"}, {"name": "711", "dtype": "float32"}, {"name": "712", "dtype": "float32"}, {"name": "713", "dtype": "float32"}, {"name": "714", "dtype": "float32"}, {"name": "715", "dtype": "float32"}, {"name": "716", "dtype": "float32"}, {"name": "717", "dtype": "float32"}, {"name": "718", "dtype": "float32"}, {"name": "719", "dtype": "float32"}, {"name": "720", "dtype": "float32"}, {"name": "721", "dtype": "float32"}, {"name": "722", "dtype": "float32"}, {"name": "723", "dtype": "float32"}, {"name": "724", "dtype": "float32"}, {"name": "725", "dtype": "float32"}, {"name": "726", "dtype": "float32"}, {"name": "727", "dtype": "float32"}, {"name": "728", "dtype": "float32"}, {"name": "729", "dtype": "float32"}, {"name": "730", "dtype": "float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 138405888, "num_examples": 45054}], "download_size": 189510817, "dataset_size": 138405888}}
|
2023-06-05T12:25:45+00:00
|
ad5d824ed1f9f216833ea4ae29b5f85cd2d5e86b
|
# Dataset Card for Timbre and Range Database
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/timbre_score>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
The timbre database contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range database includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format).
### Supported Tasks and Leaderboards
Audio classification
### Languages
Chinese, English
## Dataset Structure
### Data Instances
.zip(.wav, .jpg), .csv
### Data Fields
```
timbre: song1-32
range: vox1_19-22/26-29/32/33/36-38/41-47/51-55/59-64/69-71/79-81
```
### Data Splits
Train, Validation, Test
## Dataset Creation
### Curation Rationale
Promoting the development of music AI industry
### Source Data
#### Initial Data Collection and Normalization
Zijin Li, Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Composers of the songs in dataset
### Annotations
#### Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Most are Chinese songs
### Other Known Limitations
Samples are not balanced enough
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
[Yiliang, J. et al. (2019) ‘Data Augmentation based Convolutional Neural Network for Auscultation’, Journal of Fudan University(Natural Science), pp. 328–334. doi:10.15943/j.cnki.fdxb-jns.2019.03.004.](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2019&filename=FDXB201903004&uniplatform=NZKPT&v=VAszHDtjPUYMi3JYVrdSGx4fcqlEtgCeKwRGTacCj98CGEQg5CUFHxakrvuaMzm3)
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for music timbre and range
|
ccmusic-database/timbre_range
|
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-06-05T12:27:25+00:00
|
{"language": ["zh", "en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "pretty_name": "Timbre and Range Database", "tags": ["music", "art"], "viewer": false}
|
2023-12-04T16:01:58+00:00
|
6a87a18436ad00a11ff05a5169ab96849e49bbbb
|
# Dataset Card for "hh-rlhf-ru"
This is translated version of [Anthropic/hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) into Russian.
|
d0rj/hh-rlhf-ru
|
[
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:Anthropic/hh-rlhf",
"language:ru",
"license:mit",
"human-feedback",
"ChatGPT",
"reward",
"region:us"
] |
2023-06-05T12:39:37+00:00
|
{"language_creators": ["translated"], "language": ["ru"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["Anthropic/hh-rlhf"], "pretty_name": "HH for RLHF (ru)", "tags": ["human-feedback", "ChatGPT", "reward"], "dataset_info": {"features": [{"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 573845356.0, "num_examples": 160800}, {"name": "test", "num_bytes": 30792414.0, "num_examples": 8552}], "download_size": 281014419, "dataset_size": 604637770.0}}
|
2023-06-05T12:53:03+00:00
|
c93384c2b5ae2bcf4b753f207d58df839199193f
|
# Open Assistant Guanaco traduzido para o Português
- Prompts originalmente em português não foram processados
- Traduzido com a API do Chat GPT 3.5-turbo
- Prompts de baixa qualidade identificados foram removidos do dataset
### Sobre o Guanaco Original
O Guanaco (https://huggingface.co/datasets/timdettmers/openassistant-guanaco) é um conjunto de dados que faz parte do conjunto de dados Open Assistant, que pode ser encontrado aqui: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
O subconjunto de dados contém apenas os caminhos com a classificação mais alta na árvore de conversação, totalizando 9.846 amostras.
Esse conjunto de dados foi usado para treinar o Guanaco com o QLoRA.
Para obter mais informações, consulte o conjunto de dados original.
|
ocordeiro/guanaco-pt
|
[
"doi:10.57967/hf/0734",
"region:us"
] |
2023-06-05T12:49:45+00:00
|
{}
|
2023-06-05T12:55:33+00:00
|
28c96b197c8381f1a7485107f0f027364a91e929
|
# Dataset Card for "synthetic-instruct-gptj-pairwise-ru"
This is translated version of [Dahoas/synthetic-instruct-gptj-pairwise dataset](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) into Russian.
|
d0rj/synthetic-instruct-gptj-pairwise-ru
|
[
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:Dahoas/synthetic-instruct-gptj-pairwise",
"language:ru",
"license:mit",
"human-feedback",
"ChatGPT",
"reward",
"region:us"
] |
2023-06-05T12:56:21+00:00
|
{"language_creators": ["translated"], "language": ["ru"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["Dahoas/synthetic-instruct-gptj-pairwise"], "tags": ["human-feedback", "ChatGPT", "reward"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59332942.0, "num_examples": 33143}], "download_size": 28805604, "dataset_size": 59332942.0}}
|
2023-06-05T13:01:42+00:00
|
6be6032afb8390dbc3c180720aa73c4b87857744
|
smhdigital/smh_dataset_230604
|
[
"license:other",
"region:us"
] |
2023-06-05T13:04:51+00:00
|
{"license": "other"}
|
2023-06-05T13:04:51+00:00
|
|
139c41f2554ab19b7a71f920f95ef58df5463f4f
|
# Plantations Segmentation
The images consist of aerial photography of agricultural plantations with crops such as cabbage and zucchini. The dataset addresses agricultural tasks such as plant detection and counting, health assessment, and irrigation planning. The dataset consists of plantations' photographs with object and class segmentation of cabbage.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=plantations_segmentation) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **Plantations_Segmentation** - contains of original plantation images (folder **img**) and file with annotations (.xml)
- **Object_Segmentation** - includes object segmentation masks for the original images
- **Class_Segmentation** - includes class segmentation masks for the original images
# Types of segmentation
The dataset includes two types of segmentation:
- **Class Segmentation** - objects corresponding to one class are identified
- **Object Segmentation** - all objects are identified separately
# Data Format
Each image from `img` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the polygons. For each point, the x and y coordinates are provided.
# Example of XML file structure
.png?generation=1685973058340642&alt=media)
# Plantation segmentation might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=plantations_segmentation) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/plantations_segmentation
|
[
"task_categories:image-segmentation",
"language:en",
"license:cc-by-nd-4.0",
"biology",
"code",
"region:us"
] |
2023-06-05T13:11:14+00:00
|
{"language": ["en"], "license": "cc-by-nd-4.0", "task_categories": ["image-segmentation"], "tags": ["biology", "code"], "dataset_info": {"features": [{"name": "image_id", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "class_segmentation", "dtype": "image"}, {"name": "object_segmentation", "dtype": "image"}, {"name": "shapes", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48297698, "num_examples": 13}], "download_size": 48362120, "dataset_size": 48297698}}
|
2023-09-14T15:50:35+00:00
|
d93095f6a8a2602eb7abfac1bb62588389654fd2
|
# Dataset Card for "rlhf-reward-datasets-ru"
This is translated version of [yitingxie/rlhf-reward-datasets dataset](https://huggingface.co/datasets/yitingxie/rlhf-reward-datasets) into Russian.
|
d0rj/rlhf-reward-datasets-ru
|
[
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:yitingxie/rlhf-reward-datasets",
"language:ru",
"license:mit",
"human-feedback",
"ChatGPT",
"reward",
"region:us"
] |
2023-06-05T13:47:13+00:00
|
{"language_creators": ["translated"], "language": ["ru"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["yitingxie/rlhf-reward-datasets"], "pretty_name": "HH for RLHF (ru)", "tags": ["human-feedback", "ChatGPT", "reward"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 151564655.0, "num_examples": 76256}, {"name": "test", "num_bytes": 6093563.0, "num_examples": 5103}], "download_size": 78860063, "dataset_size": 157658218.0}}
|
2023-06-05T13:51:19+00:00
|
276d1a3f747cc5d1640bd7b1ce9d85e633bc54eb
|
AlexTC81/Med_ins
|
[
"license:mit",
"region:us"
] |
2023-06-05T14:05:50+00:00
|
{"license": "mit"}
|
2023-06-05T14:07:30+00:00
|
|
5858252810d091bc1735d1978b18ef5902e54bcd
|
# Dataset Card for "vggflowers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/vggflowers
|
[
"region:us"
] |
2023-06-05T14:12:19+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 452124226.125, "num_examples": 5655}, {"name": "validation", "num_bytes": 89403717.375, "num_examples": 1109}, {"name": "test", "num_bytes": 115124265.875, "num_examples": 1425}], "download_size": 656318272, "dataset_size": 656652209.375}}
|
2023-06-05T14:12:54+00:00
|
7e9c2983fb1b104dc7094218670d5e9c97a73e48
|
# Dataset Card for "LAION10K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
wtcherr/LAION10K
|
[
"region:us"
] |
2023-06-05T14:19:40+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 171092053.43, "num_examples": 8226}], "download_size": 170695611, "dataset_size": 171092053.43}}
|
2023-06-05T14:19:52+00:00
|
a7be17a7c8615188fc3c3006a7afe70f355d02e9
|
smhdigital/smh-tiny-gpt2_bert-fine-tuned
|
[
"license:other",
"region:us"
] |
2023-06-05T14:22:45+00:00
|
{"license": "other"}
|
2023-06-05T14:22:45+00:00
|
|
450df3bbb401d00923320187645be954fee52763
|
# leo_prompts_v1: a dataset collection
- collection of several prompts, nagative prompts and image urls datasets
- data is uncleaned/non-normalized, they are as tey appear in leonardo.ai
- data de-duplicated on a basic level.
## contents
```
DatasetDict({
all: Dataset({
features: ['id', 'url', 'prompt', 'negative_prompt', 'imageHeight', 'imageWidth'],
num_rows: 299934
})
})
```
## CITE
```
@misc {samact_2023,
author = { {SamAct} },
title = { leo_prompts_v1 (Revision 8c43f60) },
year = 2023,
url = { https://huggingface.co/datasets/SamAct/leo_prompts_v1 },
doi = { 10.57967/hf/0730 },
publisher = { Hugging Face }
}
```
|
SamAct/leo_prompts_v1
|
[
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"text generation",
"image generation",
"stable-diffusion",
"prompt-generator",
"doi:10.57967/hf/0730",
"region:us"
] |
2023-06-05T14:32:38+00:00
|
{"language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "pretty_name": "multi text2image prompts a dataset collection", "tags": ["text generation", "image generation", "stable-diffusion", "prompt-generator"]}
|
2023-06-05T14:53:17+00:00
|
ed3c740ff7d80e1d9dfb2c865f2c81be403ff1d1
|
mio/YGO
|
[
"license:other",
"region:us"
] |
2023-06-05T14:38:23+00:00
|
{"license": "other"}
|
2023-06-05T14:38:24+00:00
|
|
827935b0463e810bb372017a1056892638e97df2
|
Astirstroh/Sinatra
|
[
"license:unlicense",
"region:us"
] |
2023-06-05T14:44:26+00:00
|
{"license": "unlicense"}
|
2023-06-05T14:49:20+00:00
|
|
cfe471bf45880d492519f8d0158b3427e049d662
|
# Dataset Card for "ikon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
khulegu/ikon
|
[
"region:us"
] |
2023-06-05T15:33:34+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 655806729, "num_examples": 127607}], "download_size": 289188773, "dataset_size": 655806729}}
|
2023-06-05T16:22:42+00:00
|
c1c7d01c28f4d1d59a16a3724a91291d82829012
|
divi7007/ComplexWebQuestions_train
|
[
"license:other",
"region:us"
] |
2023-06-05T15:46:00+00:00
|
{"license": "other"}
|
2023-06-06T11:24:19+00:00
|
|
2312b312e015faf937e3a730548ed8cca4f82c15
|
# M2CRB
## Dataset Summary
M2CRB contains pairs of text and code data with multiple natural and programming language pairs. Namely: Spanish, Portuguese, German, and French, each paired with code snippets for: Python, Java, and JavaScript. The data is curated via an automated filtering pipeline from source files within [The Stack](https://huggingface.co/datasets/bigcode/the-stack) followed by human verification to ensure accurate language classification I.e., humans were asked to filter out data for which natural language did not correspond to a target language.
## Supported Tasks
M2CRB is a multilingual evaluation dataset for code-to-text and/or text-to-code models, both on information retrieval or conditional generation evaluations.
## Currently Supported Languages
```python
NATURAL_LANGUAGE_SET = {"es", "fr", "pt", "de"}
PROGRAMMING_LANGUAGE_SET = {"python", "java", "javascript"}
```
## How to get the data with a given language combination
```python
from datasets import load_dataset
def get_dataset(prog_lang, nat_lang):
test_data = load_dataset("blindsubmissions/M2CRB")
test_data = test_data.filter(
lambda example: example["docstring_language"] == nat_lang
and example["language"] == prog_lang
)
return test_data
```
## Dataset Structure
### Data Instances
Each data instance corresponds to function/methods occurring in licensed files that compose The Stack. That is, files with permissive licences collected from GitHub.
### Relevant Data Fields
- identifier (string): Function/method name.
- parameters (string): Function parameters.
- return_statement (string): Return statement if found during parsing.
- docstring (string): Complete docstring content.
- docstring_summary (string): Summary/processed docstring dropping args and return statements.
- function (string): Actual function/method content.
- argument_list (null): List of arguments.
- language (string): Programming language of the function.
- docstring_language (string): Natural language of the docstring.
- type (string): Return type if found during parsing.
## Summary of data curation pipeline
- Filtering out repositories that appear in [CodeSearchNet](https://huggingface.co/datasets/code_search_net).
- Filtering the files that belong to the programming languages of interest.
- Pre-filtering the files that likely contain text in the natural languages of interest.
- AST parsing with [Tree-sitter](\url{https://tree-sitter.github.io/tree-sitter/).
- Perform language identification of docstrings in the resulting set of functions/methods.
- Perform human verification/validation of the underlying language of docstrings.
## Social Impact of the dataset
M2CRB is released with the aim to increase the coverage of the NLP for code research community by providing data from scarce combinations of languages. We expect this data to help enable more accurate information retrieval systems and text-to-code or code-to-text summarization on languages other than English.
As a subset of The Stack, this dataset inherits de-risking efforts carried out when that dataset was built, though we highlight risks exist and malicious use of the data could exist such as, for instance, to aid on creation of malicious code. We highlight however that this is a risk shared by any code dataset made openly available.
Moreover, we remark that while unlikely due to human filtering, the data may contain harmful or offensive language, which could be learned by the models.
## Discussion of Biases
The data is collected from GitHub and naturally occurring text on that platform. As a consequence, certain language combinations are more or less likely to contain well documented code and, as such, resulting data will not be uniformly represented in terms of their natural and programing languages.
## Known limitations
While we cover 16 scarce combinations of programming and natural languages, our evaluation dataset can be expanded to further improve its coverage.
Moreover, we use text naturally occurring as comments or docstrings as opposed to human annotators. As such, resulting data will have high variance in terms of quality and depending on practices of sub-communities of software developers. However, we remark that the task our evaluation dataset defines is reflective of what searching on a real codebase would look like.
Finally, we note that some imbalance on data is observed due to the same reason since certain language combinations are more or less likely to contain well documented code.
## Maintenance plan:
The data will be kept up to date by following The Stack releases. We should rerun our pipeline for every new release and add non-overlapping new content to both training and testing partitions of our data.
This is so that we carry over opt-out updates and include fresh repos.
## Update plan:
- Short term:
- Cover all 6 programming languages from CodeSearchNet.
- Long-term
- Add an extra test set containing human-generated text/code pairs so the gap between in-the-wild and controlled performances can be measured.
- Include extra natural languages.
## Licensing Information
M2CRB is a subset filtered and pre-processed from [The Stack](https://huggingface.co/datasets/bigcode/the-stack), a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in M2CRB must abide by the terms of the original licenses.
|
blindsubmissions/M2CRB
|
[
"task_categories:translation",
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:pt",
"language:de",
"language:fr",
"language:es",
"license:other",
"code",
"region:us"
] |
2023-06-05T15:47:19+00:00
|
{"language": ["pt", "de", "fr", "es"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["translation", "summarization"], "pretty_name": "m", "dataset_info": {"features": [{"name": "identifier", "dtype": "string"}, {"name": "parameters", "dtype": "string"}, {"name": "return_statement", "dtype": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "docstring_summary", "dtype": "string"}, {"name": "function", "dtype": "string"}, {"name": "function_tokens", "sequence": "string"}, {"name": "start_point", "sequence": "int64"}, {"name": "end_point", "sequence": "int64"}, {"name": "argument_list", "dtype": "null"}, {"name": "language", "dtype": "string"}, {"name": "docstring_language", "dtype": "string"}, {"name": "docstring_language_predictions", "dtype": "string"}, {"name": "is_langid_reliable", "dtype": "string"}, {"name": "is_langid_extra_reliable", "dtype": "bool"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 15742687, "num_examples": 7743}], "download_size": 5530793, "dataset_size": 15742687}, "tags": ["code"]}
|
2023-08-08T14:06:30+00:00
|
4d780a6249c3a0aa5adf08303c9f63c1aca51c7b
|
# Dataset Card for "augmented_images_40_tokens"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rbeauchamp/augmented_images_40_tokens
|
[
"region:us"
] |
2023-06-05T15:48:47+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 221430807.0, "num_examples": 222}], "download_size": 220307366, "dataset_size": 221430807.0}}
|
2023-06-05T15:48:55+00:00
|
ebfb5d4b86ce93c3a351fb9f9b6cd270f04a3dc9
|
simonguest/sprites
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-05T16:13:39+00:00
|
{"license": "apache-2.0"}
|
2023-06-05T16:13:39+00:00
|
|
a43b92b48ac0bcd5e04c211d2fcc2537d6066303
|
# Dataset Card for "aircraft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/aircraft
|
[
"region:us"
] |
2023-06-05T16:24:27+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 448764494.0, "num_examples": 7000}, {"name": "validation", "num_bytes": 96057035.5, "num_examples": 1500}, {"name": "test", "num_bytes": 96864989.5, "num_examples": 1500}], "download_size": 641660472, "dataset_size": 641686519.0}}
|
2023-06-05T16:25:02+00:00
|
36502de90101943427968d7e127c8c1e508c696d
|
# Dataset Card for "cubirds200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/cubirds200
|
[
"region:us"
] |
2023-06-05T16:30:49+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 657406642.5, "num_examples": 8204}, {"name": "validation", "num_bytes": 136652007.625, "num_examples": 1771}, {"name": "test", "num_bytes": 143386937.75, "num_examples": 1770}], "download_size": 937336997, "dataset_size": 937445587.875}}
|
2023-06-05T16:31:56+00:00
|
a804c12cc85675b0a21d85a664c578f902b915ef
|
# Dataset Card for "cifarfs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/cifarfs
|
[
"region:us"
] |
2023-06-05T16:32:39+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 86489157.0, "num_examples": 38400}, {"name": "validation", "num_bytes": 21539635.0, "num_examples": 9600}, {"name": "test", "num_bytes": 26600575.0, "num_examples": 12000}], "download_size": 134961942, "dataset_size": 134629367.0}}
|
2023-06-05T16:32:51+00:00
|
d08634aefde35e291b7fa55d32b7f9ef0f3bacaa
|
joshitoppo/toppo
|
[
"license:bigscience-openrail-m",
"region:us"
] |
2023-06-05T16:34:49+00:00
|
{"license": "bigscience-openrail-m"}
|
2023-06-05T16:35:38+00:00
|
|
6e1521eed63f8bfdbe1759f99bb304af211793d2
|
# Dataset Card for "unsplash_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
wtcherr/unsplash_5k
|
[
"region:us"
] |
2023-06-05T16:39:34+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "error_message", "dtype": "null"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "original_width", "dtype": "int32"}, {"name": "original_height", "dtype": "int32"}, {"name": "exif", "dtype": "string"}, {"name": "sha256", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 656343725.0, "num_examples": 5000}], "download_size": 649576593, "dataset_size": 656343725.0}}
|
2023-06-06T02:26:05+00:00
|
992ad5b7125b0c45e6a3506091005edc20e978c8
|
# Dataset for evaluating language model using real world Bengali data
This dataset contains the prompts and questions people (Mostly from Bangladesh) asked on [Alapchari](http://chatrik.org/alapchari) from 25 February 2023 to 4 June 2023.
It provides 35218 unique prompts for those interested in the development and evaluation of Bangla language models, offering an unique opportunity for evaluating their language models with real-world data collected from Bangladesh.
Currently it's sorted from the smallest strings to the largest. Most of the good questions are in the middle.
|
fhasan85/bengali-prompts
|
[
"language:bn",
"license:openrail",
"region:us"
] |
2023-06-05T16:44:58+00:00
|
{"language": ["bn"], "license": "openrail"}
|
2023-06-05T16:51:23+00:00
|
b3c3df08f89a1a0c5e6ed230fb500ad477bc5326
|
# Dataset Card for "fc100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/fc100
|
[
"region:us"
] |
2023-06-05T16:49:09+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1119561906.0, "num_examples": 36000}, {"name": "validation", "num_bytes": 402692462.0, "num_examples": 12000}, {"name": "test", "num_bytes": 395837378.0, "num_examples": 12000}], "download_size": 1917746447, "dataset_size": 1918091746.0}}
|
2023-06-05T16:50:48+00:00
|
887fdcc06de5b9f13a5baa1f7e8b50a49c72dcc4
|
# Dataset Card for "omniglot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/omniglot
|
[
"region:us"
] |
2023-06-05T17:13:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "full", "num_bytes": 11924141.5, "num_examples": 32460}], "download_size": 10520482, "dataset_size": 11924141.5}}
|
2023-06-05T17:58:27+00:00
|
83b0f5685bcdda31b365052cf60e55528bc8a9a4
|
## Instruction Tuning: GeoSignal
Scientific domain adaptation has two main steps during instruction tuning.
- Instruction tuning with general instruction-tuning data. Here we use Alpaca-GPT4.
- Instruction tuning with restructured domain knowledge, which we call expertise instruction tuning. For K2, we use knowledge-intensive instruction data, GeoSignal.
***The following is the illustration of the training domain-specific language model recipe:***
- **Adapter Model on [Huggingface](https://huggingface.co/): [daven3/k2_it_adapter](https://huggingface.co/daven3/k2_it_adapter)**
For the design of the GeoSignal, we collect knowledge from various data sources, like:
GeoSignal is designed for knowledge-intensive instruction tuning and used for aligning with experts.
The full-version will be upload soon, or email [daven](mailto:[email protected]) for potential research cooperation.
|
daven3/geosignal
|
[
"task_categories:question-answering",
"license:apache-2.0",
"region:us"
] |
2023-06-05T17:38:16+00:00
|
{"license": "apache-2.0", "task_categories": ["question-answering"]}
|
2023-08-28T03:40:53+00:00
|
fa6f583ad38f93a7e7f5a3ea1582fe6593437747
|
# Benchmark: GeoBenchmark
In GeoBenchmark, we collect 183 multiple-choice questions in NPEE, and 1,395 in AP Test, for objective tasks.
Meanwhile, we gather all 939 subjective questions in NPEE to be the subjective tasks set and use 50 to measure the baselines with human evaluation.
|
daven3/geobench
|
[
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"license:apache-2.0",
"region:us"
] |
2023-06-05T17:41:48+00:00
|
{"license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["multiple-choice", "question-answering"]}
|
2023-08-28T03:33:59+00:00
|
27fb7f7329892227400cc7282dc03c7efb647fe7
|
<div style="background-color: #F0ECE1; padding: 10px; border-radius: 5px;">
<div style="margin-bottom: 10px; text-align: center;">
<h1 style="color: #3C3530; font-size: 36px; font-weight: bold; margin-bottom: 10px;">
A Natural Look into our AI-Driven World: An Image Classification Lookthrough
</h1>
</div>
</div>
<img src="https://images.pexels.com/photos/869258/pexels-photo-869258.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1">
TL;DR
This image classification project identifies settings and categories of images automatically from Intel's natural world dataset.
Our method utilizes Tensorflow on Python 3 to execute a classification process with a model accuracy of 74.8%. Not bad, but this suggests that there are opportunities for improvement.
The loss in the test sets that is affecting accuracy comes down to Glacier and Sea images.
AI is changing the world as we know it. Companies look to train models based on recurring patterns, and visuals such as images make for great training material to help them identify and improve upon their ability to predict patterns and accuracy.
For this project, we will be using Tensorflow. Tensorflow is an ML package from Google that enables analysts and engineers to develop everything from Simple Neural Networks to Image Classification.
We're gonna focus on the latter, so this project will concentrate on using Keras.
The dataset used: https://www.kaggle.com/datasets/puneet6060/intel-image-classification
## Installation
I highly recommend using [VS Code](https://code.visualstudio.com/) to manage your python environment.
From there, install Tensorflow by first installing Miniconda and then Tensorflow. Full instructions can be found [here](https://www.tensorflow.org/install/pip#macos_1)
I used the following for my Macbook Pro M1 Max laptop (Apple Silicon) machine:
```
#Miniconda
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-MacOSX-x86_64.sh
#Conda activation
conda create --name tf python=3.9
conda conda activate tf
#Pip Upgrade
pip install --upgrade pip
#Pip Install
pip install tensorflow
#Verify
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
```
## Access to Project
You can access the Jpegs by visiting https://drive.google.com/file/d/1Ox3W_AJdBjg5Sc6tOn9KikR2BB8zndxh/view?usp=drive_link.
To run the ipynb, download and unzip the image file and then change the file directory in the notebook to the location of the unzipped images.
|
nmccann1991/Intel-Natural-World-Study
|
[
"region:us"
] |
2023-06-05T18:11:06+00:00
|
{}
|
2023-06-05T18:15:51+00:00
|
c11e91742fe20094f0398a28513e74536a113496
|
# Dataset Card for "simplewiki2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lentan/simplewiki2023
|
[
"region:us"
] |
2023-06-05T18:13:51+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 274968375, "num_examples": 223078}, {"name": "validation", "num_bytes": 2683874, "num_examples": 2254}]}}
|
2023-06-05T18:59:45+00:00
|
5691556ca47ca0720bedc2933e0e57d6cb21e5e1
|
# Dataset Card for "areta_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gagan3012/areta_v2
|
[
"region:us"
] |
2023-06-05T18:43:17+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "raw", "sequence": "string"}, {"name": "corrected", "sequence": "string"}, {"name": "tag", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 31578211, "num_examples": 19406}, {"name": "validation", "num_bytes": 1661575, "num_examples": 1017}, {"name": "test", "num_bytes": 1580939, "num_examples": 968}], "download_size": 10794654, "dataset_size": 34820725}}
|
2023-06-05T22:20:50+00:00
|
6557adac08eaaa48e420c89c8ffb4ed074f8f826
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
Marnus123/autotrain-data-groceries_tabular
|
[
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"doi:10.57967/hf/0784",
"region:us"
] |
2023-06-05T18:45:59+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["table-question-answering"]}
|
2023-06-16T21:30:22+00:00
|
55f38cc735c0061d81336513c7de6628ec3a1876
|
# Dataset Card for "flickr_humans_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_5k
|
[
"region:us"
] |
2023-06-05T18:48:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2006264100.0, "num_examples": 5000}], "download_size": 2006360530, "dataset_size": 2006264100.0}}
|
2023-06-05T18:49:39+00:00
|
01d2bbcb612516b764bbb0192ab0887de75f4159
|
# Dataset Card for "artfaces_padded_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/artfaces_padded_32
|
[
"region:us"
] |
2023-06-05T19:03:37+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53993261.875, "num_examples": 5449}], "download_size": 53861425, "dataset_size": 53993261.875}}
|
2023-06-05T19:03:42+00:00
|
31c8a33bf042bdd8dd0645abb7853af0386f578e
|
# Dataset Card for "cosine_similarities_diffusion_db_dedup_from10k_train_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
myradeng/cosine_similarities_diffusion_db_dedup_from10k_train_v2
|
[
"region:us"
] |
2023-06-05T19:31:06+00:00
|
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"float64"}, {"name": "sentence6885", "dtype": "float64"}, {"name": "sentence6886", "dtype": "float64"}, {"name": "sentence6887", "dtype": "float64"}, {"name": "sentence6888", "dtype": "float64"}, {"name": "sentence6889", "dtype": "float64"}, {"name": "sentence6890", "dtype": "float64"}, {"name": "sentence6891", "dtype": "float64"}, {"name": "sentence6892", "dtype": "float64"}, {"name": "sentence6893", "dtype": "float64"}, {"name": "sentence6894", "dtype": "float64"}, {"name": "sentence6895", "dtype": "float64"}, {"name": "sentence6896", "dtype": "float64"}, {"name": "sentence6897", "dtype": "float64"}, {"name": "sentence6898", "dtype": "float64"}, {"name": "sentence6899", "dtype": "float64"}, {"name": "sentence6900", "dtype": "float64"}, {"name": "sentence6901", "dtype": "float64"}, {"name": "sentence6902", "dtype": "float64"}, {"name": "sentence6903", "dtype": "float64"}, {"name": "sentence6904", "dtype": "float64"}, {"name": "sentence6905", "dtype": "float64"}, {"name": "sentence6906", "dtype": "float64"}, {"name": "sentence6907", "dtype": "float64"}, {"name": "sentence6908", "dtype": "float64"}, {"name": "sentence6909", "dtype": "float64"}, {"name": "sentence6910", "dtype": "float64"}, {"name": "sentence6911", "dtype": "float64"}, {"name": "sentence6912", "dtype": "float64"}, {"name": "sentence6913", "dtype": "float64"}, {"name": "sentence6914", "dtype": "float64"}, {"name": "sentence6915", "dtype": "float64"}, {"name": "sentence6916", "dtype": "float64"}, {"name": "sentence6917", "dtype": "float64"}, {"name": "sentence6918", "dtype": "float64"}, {"name": "sentence6919", "dtype": "float64"}, {"name": "sentence6920", "dtype": "float64"}, {"name": "sentence6921", "dtype": "float64"}, {"name": "sentence6922", "dtype": "float64"}, {"name": "sentence6923", "dtype": "float64"}, {"name": "sentence6924", "dtype": "float64"}, {"name": "sentence6925", "dtype": "float64"}, {"name": "sentence6926", "dtype": "float64"}, {"name": "sentence6927", "dtype": "float64"}, {"name": "sentence6928", "dtype": "float64"}, {"name": "sentence6929", "dtype": "float64"}, {"name": "sentence6930", "dtype": "float64"}, {"name": "sentence6931", "dtype": "float64"}, {"name": "sentence6932", "dtype": "float64"}, {"name": "sentence6933", "dtype": "float64"}, {"name": "sentence6934", "dtype": "float64"}, {"name": "sentence6935", "dtype": "float64"}, {"name": "sentence6936", "dtype": "float64"}, {"name": "sentence6937", "dtype": "float64"}, {"name": "sentence6938", "dtype": "float64"}, {"name": "sentence6939", "dtype": "float64"}, {"name": "sentence6940", "dtype": "float64"}, {"name": "sentence6941", "dtype": "float64"}, {"name": "sentence6942", "dtype": "float64"}, {"name": "sentence6943", "dtype": "float64"}, {"name": "sentence6944", "dtype": "float64"}, {"name": "sentence6945", "dtype": "float64"}, {"name": "sentence6946", "dtype": "float64"}, {"name": "sentence6947", "dtype": "float64"}, {"name": "sentence6948", "dtype": "float64"}, {"name": "sentence6949", "dtype": "float64"}, {"name": "sentence6950", "dtype": "float64"}, {"name": "sentence6951", "dtype": "float64"}, {"name": "sentence6952", "dtype": "float64"}, {"name": "sentence6953", "dtype": "float64"}, {"name": "sentence6954", "dtype": "float64"}, {"name": "sentence6955", "dtype": "float64"}, {"name": "sentence6956", "dtype": "float64"}, {"name": "sentence6957", "dtype": "float64"}, {"name": "sentence6958", "dtype": "float64"}, {"name": "sentence6959", "dtype": "float64"}, {"name": "sentence6960", "dtype": "float64"}, {"name": "sentence6961", "dtype": "float64"}, {"name": "sentence6962", "dtype": "float64"}, {"name": "sentence6963", "dtype": "float64"}, {"name": "sentence6964", "dtype": "float64"}, {"name": "sentence6965", "dtype": "float64"}, {"name": "sentence6966", "dtype": "float64"}, {"name": "sentence6967", "dtype": "float64"}, {"name": "sentence6968", "dtype": "float64"}, {"name": "sentence6969", "dtype": "float64"}, {"name": "sentence6970", "dtype": "float64"}, {"name": "sentence6971", "dtype": "float64"}, {"name": "sentence6972", "dtype": "float64"}, {"name": "sentence6973", "dtype": "float64"}, {"name": "sentence6974", "dtype": "float64"}, {"name": "sentence6975", "dtype": "float64"}, {"name": "sentence6976", "dtype": "float64"}, {"name": "sentence6977", "dtype": "float64"}, {"name": "sentence6978", "dtype": "float64"}, {"name": "sentence6979", "dtype": "float64"}, {"name": "sentence6980", "dtype": "float64"}, {"name": "sentence6981", "dtype": "float64"}, {"name": "sentence6982", "dtype": "float64"}, {"name": "sentence6983", "dtype": "float64"}, {"name": "sentence6984", "dtype": "float64"}, {"name": "sentence6985", "dtype": "float64"}, {"name": "sentence6986", "dtype": "float64"}, {"name": "sentence6987", "dtype": "float64"}, {"name": "sentence6988", "dtype": "float64"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 390767853, "num_examples": 6988}], "download_size": 371043317, "dataset_size": 390767853}}
|
2023-06-05T19:31:48+00:00
|
3694caa2474fb3152ba822984573e8e5d45012bf
|
# Dataset Card for "anime_faces_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_5k
|
[
"region:us"
] |
2023-06-05T19:43:08+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 272321437.0, "num_examples": 5000}], "download_size": 266888558, "dataset_size": 272321437.0}}
|
2023-06-05T19:43:18+00:00
|
d5c0504c844f5a9a918975fc2ad32dc36c655cd2
|
# Dataset Card for Dataset Name
## Dataset Description
This dataset contains Key Figures with their properties from german tax acts. The dataset is annotated by tax experts and consists of 85 annotated paragraphs from 14 different German tax acts with 157 annotated tax key figures.
The annotation was performed based on a developed universally applicable annotation schema and a semantic model for key figures and their properties in legal texts.
More details about the schema and the semantic model can be found in our [Paper](https://ceur-ws.org/Vol-3441/paper7.pdf). There we also provide a knowledge graph populated from these annotated paragraphs.
- **Repository:** https://github.com/danielsteinigen/nlp-legal-texts
- **Paper:** https://ceur-ws.org/Vol-3441/paper7.pdf
### Supported Tasks and Leaderboards
- Entity Extraction
- Relation Extraction
### Languages
- German
## Dataset Structure
### Data Fields
- **id:** a unique ID of the data sample
- **law:** the abbreviation of the law from which this parapgraph originates
- **paragraph:** the title of the paragraph
- **text:** the actual text string of the paragraph
- **entities:** list of entity objects
- **relation:** list of relation objects
Entities:
- **id:** a unique ID of the entity
- **start:** start character offset of the entity
- **end:** end character offset of the entity
- **entity:** label/name of the entity
- **subclass:** entity subclass, if there is one, else NULL
- **text:** text string of the entity
Relations:
- **id:** a unique ID of the realation
- **head:** ID of the head entity
- **tail:** ID of the tail entity
- **relation:** label/name of the relation
## Dataset Creation
More details about the annotation process can be found in our [Paper](https://ceur-ws.org/Vol-3441/paper7.pdf).
## Additional Information
### Citation
**BibTeX:**
```
@inproceedings{steinigen2023semantic,
title={Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models},
author={Steinigen, Daniel and Namysl, Marcin and Hepperle, Markus and Krekeler, Jan and Landgraf, Susanne},
url = {https://ceur-ws.org/Vol-3441/paper7.pdf},
year={2023}
journal={Sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023)},
series = {CEUR Workshop Proceedings},
venue = {Braga, Portugal},
eventdate = {2023-06-23}
}
```
**APA:**
Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models.
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023.
CEUR-WS.org, online CEUR-WS.org/Vol-3441/paper7.pdf.
### Licensing Information
```
MIT License
Copyright (c) 2023 Daniel Steinigen
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
|
danielsteinigen/KeyFiTax
|
[
"task_categories:token-classification",
"size_categories:n<1K",
"language:de",
"license:mit",
"relation extraction",
"entity extraction",
"legal",
"tax law",
"region:us"
] |
2023-06-05T19:44:23+00:00
|
{"language": ["de"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["token-classification"], "pretty_name": "Key Figures from german Tax Acts", "tags": ["relation extraction", "entity extraction", "legal", "tax law"]}
|
2023-08-11T12:56:15+00:00
|
139981e802c62270e9be1738f6277b2e89451f1e
|
# Dataset Card for "musdb18-spec-pix2pix10-samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zachary-shah/musdb18-spec-pix2pix10-samples
|
[
"region:us"
] |
2023-06-05T19:47:44+00:00
|
{"dataset_info": {"features": [{"name": "original_prompt", "dtype": "string"}, {"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "edited_prompt", "dtype": "string"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 928237.0, "num_examples": 11}], "download_size": 930413, "dataset_size": 928237.0}}
|
2023-06-05T19:47:49+00:00
|
c7fe28b562c2068043852b037ea8cb3e777a7122
|
Uchenna/BLOOM-Tutorial
|
[
"task_categories:text-generation",
"language:en",
"license:mit",
"region:us"
] |
2023-06-05T20:13:46+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["text-generation"]}
|
2023-06-05T20:22:21+00:00
|
|
90136af9bd99eb8c839d5f1e86edc8f41602301c
|
Uchenna/Testdataset
|
[
"language:en",
"license:mit",
"region:us"
] |
2023-06-05T20:16:02+00:00
|
{"language": ["en"], "license": "mit", "dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "advert", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5766, "num_examples": 10}], "download_size": 9580, "dataset_size": 5766}}
|
2023-06-06T01:34:39+00:00
|
|
c78caa4528b197a0a21ebaf862b1d764775a07f7
|
Baktashans/Subttitles_AgeRate_Data
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-06-05T20:18:43+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]}
|
2023-06-22T10:50:00+00:00
|
|
390eaf2f1a31eed27275b49c9bafcfc8ae721733
|
# The Hateful Memes Challenge README
The Hateful Memes Challenge is a dataset and benchmark created by Facebook AI to drive and measure progress on multimodal reasoning and understanding. The task focuses on detecting hate speech in multimodal memes.
Please see the paper for further details:
[The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
D. Kiela, H. Firooz, A. Mohan, V. Goswami, A. Singh, P. Ringshia, D. Testuggine](
https://arxiv.org/abs/2005.04790)
# Dataset details
The files for this folder are arranged as follows:
img/ - the PNG images
train.jsonl - the training set
dev.jsonl - the development set
test.jsonl - the “seen” test set
An additional “unseen” test set will be released at a later date under the NeurIPS 2020 competition. Please see https://ai.facebook.com/hatefulmemes. The competition rules are provided on the competition website.
The .jsonl format contains one JSON-encoded example per line, each of which has the following fields:
‘text’ - the text occurring in the meme
‘img’ - the path to the image in the img/ directory
‘label’ - the label for the meme (0=not-hateful, 1=hateful), provided for train and dev
The metric to use is AUROC. You may also report accuracy in addition, since this is more interpretable. To compute these metrics, we recommend the roc_auc_score and accuracy_score methods in sklearn.metrics, with default settings.
# Note on Annotator Accuracy
As is to be expected with a dataset of this size and nature, some of the examples in the training set have been misclassified. We are not claiming that our dataset labels are completely accurate, or even that all annotators would agree on a particular label. Misclassifications, although possible, should be very rare in the dev and seen test set, however, and we will take extra care with the unseen test set.
As a reminder, the annotations collected for this dataset were not collected using Facebook annotators and we did not employ Facebook’s hate speech policy. As such, the dataset labels do not in any way reflect Facebook’s official stance on this matter.
# License
The dataset is licensed under the terms in the `LICENSE.txt` file.
# Image Attribution
If you wish to display example memes in your paper, please provide the following attribution:
*Image is a compilation of assets, including ©Getty Image.*
# Citations
If you wish to cite this work, please use the following BiBTeX:
```
@inproceedings{Kiela2020TheHM,
title={The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes},
author={Douwe Kiela and Hamed Firooz and Aravind Mohan and Vedanuj Goswami and Amanpreet Singh and Pratik Ringshia and Davide Testuggine},
year={2020}
}
```
# Contact
If you have any questions or comments on the dataset, please contact [email protected].
|
emily49/hateful-memes
|
[
"arxiv:2005.04790",
"region:us"
] |
2023-06-05T20:37:11+00:00
|
{}
|
2023-06-05T20:39:31+00:00
|
08855b8795fa8630464cb7128c76ba43f4e62a35
|
# Dataset Card for "chai-chatgpt-fullserved-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/chai-chatgpt-fullserved-chatml
|
[
"region:us"
] |
2023-06-05T20:37:36+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 442121603, "num_examples": 126140}], "download_size": 242078847, "dataset_size": 442121603}}
|
2023-06-12T19:55:12+00:00
|
b545ff51836d4e0bcf1308ecffba63129511f688
|
Alpaca tasks dataset translated in Greek from GPT3.5
Translation is done in chunks of 10K.
|
squaredev/translated_alpaca_tasks_gr
|
[
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:el",
"license:apache-2.0",
"region:us"
] |
2023-06-05T21:17:49+00:00
|
{"language": ["el"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "conversational", "text-generation"], "pretty_name": "Translated alpaca tasks in Greek"}
|
2023-06-18T17:44:54+00:00
|
83cf99924965a1a10a0d7cefd02a1d2ae257f7b6
|
# Dataset Card for "pg-ko-tknizer-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hac541309/pg-ko-tknizer-ko
|
[
"language:ko",
"region:us"
] |
2023-06-05T21:58:01+00:00
|
{"language": "ko", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9307769796, "num_examples": 10508255}], "download_size": 5361735760, "dataset_size": 9307769796}}
|
2023-07-13T03:45:01+00:00
|
637db2dd7acdf28f05e5bf5e28543172baabdcc4
|
# Dataset Card for "light-wild-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/light-wild-chatml
|
[
"region:us"
] |
2023-06-05T22:07:49+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 17574962, "num_examples": 11556}], "download_size": 6961878, "dataset_size": 17574962}}
|
2023-06-05T22:07:54+00:00
|
98dd5c763bdd6d16a03407c92897353d7275e5e9
|
# Dataset Card for "preprocessed_yelp_for_bert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
RamWithAPlan/preprocessed_yelp_for_bert
|
[
"region:us"
] |
2023-06-05T22:44:57+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "1 star", "1": "2 star", "2": "3 stars", "3": "4 stars", "4": "5 stars"}}}}, {"name": "text", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2488411554, "num_examples": 650000}, {"name": "test", "num_bytes": 191471188, "num_examples": 50000}], "download_size": 0, "dataset_size": 2679882742}}
|
2023-06-07T18:22:33+00:00
|
97ee89b39ebcc0661bf04844935fc700a6641de1
|
Cheetor1996/Raika_Grace
|
[
"license:cc-by-2.0",
"region:us"
] |
2023-06-05T22:51:24+00:00
|
{"license": "cc-by-2.0"}
|
2023-06-05T23:07:57+00:00
|
|
4c98b4cc282f5f8c564a7ce44ab352d90473b1d8
|
# Dataset Card for "hand-gesture-small-3000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pencaharlangit/hand-gesture-small-3000
|
[
"region:us"
] |
2023-06-05T23:14:49+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24676892.235, "num_examples": 2991}], "download_size": 23795975, "dataset_size": 24676892.235}}
|
2023-06-05T23:15:00+00:00
|
b10acdbfa9a62bad85cbaa58737a978851ab9039
|
# Dataset Card for RepoBench-R
## Dataset Description
- **Homepage:** https://github.com/Leolty/repobench
- **Paper:** https://arxiv.org/abs/2306.03091
## Dataset Summary
**RepoBench-R (Retrieval)** is a subtask of **RepoBench**([GitHub](https://github.com/Leolty/repobench), [arXiv](https://arxiv.org/abs/2306.03091)), targeting the retrieval component of a repository-level auto-completion system, focusing on retrieving the most relevant code snippet from a project repository for next-line
code prediction.
## Settings
- `cff`: short for cross_file_first, indicating the cross-file module in next line is first used in the current file.
- `cfr`: short for cross_file_random, indicating the cross-file module in next line is not first used in the current file.
## Supported Tasks
The dataset has 4 subsets:
- `python_cff`: python dataset with `cff` setting.
- `python_cfr`: python dataset with `cfr` setting.
- `java_cff`: java dataset with `cff` setting.
- `java_cfr`: java dataset with `cfr` setting.
Each subset has 4 splits:
- `train_easy`: training set with easy difficulty, where the number of code snippets in the context \\(k\\) satisfies \\( 5 \leq k < 10 \\).
- `train_hard`: training set with hard difficulty, where the number of code snippets in the context \\(k\\) satisfies \\( k \geq 10 \\).
- `test_easy`: testing set with easy difficulty.
- `test_hard`: testing set with hard difficulty.
## Loading Data
For example, if you want to load the `test` `cross_file_first` `python` dataset with `easy` difficulty, you can use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("tianyang/repobench-r", "python_cff", split="test_easy")
```
> Note: The `split` argument is optional. If not provided, the entire dataset (including, train and test data with easy and hard level) will be loaded.
## Dataset Structure
```json
{
"repo_name": "repository name of the data point",
"file_path": "path/to/file",
"context": [
"snippet 1",
"snippet 2",
// ...
"snippet k"
],
"import_statement": "all import statements in the file",
"gold_snippet_idex": 2, // the index of the gold snippet in the context list, 0~k-1
"code": "the code for next-line prediction",
"next_line": "the next line of the code"
}
```
## Licensing Information
CC BY-NC-ND 4.0
## Citation Information
```bibtex
@misc{liu2023repobench,
title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems},
author={Tianyang Liu and Canwen Xu and Julian McAuley},
year={2023},
eprint={2306.03091},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Contributions
Thanks to [@Leolty](https://github.com/Leolty) for adding this dataset.
|
tianyang/repobench-r
|
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:code",
"license:cc-by-nc-nd-4.0",
"arxiv:2306.03091",
"region:us"
] |
2023-06-05T23:52:55+00:00
|
{"language_creators": ["found"], "language": ["code"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "RepoBench-Retrieval"}
|
2023-06-17T02:06:46+00:00
|
93ae4d54ffd47b193ea8bc4714aa6a60670c5a64
|
thiagoms7/prompts
|
[
"license:unknown",
"region:us"
] |
2023-06-06T00:37:30+00:00
|
{"license": "unknown"}
|
2023-06-06T00:38:01+00:00
|
|
573cd186ad32d55b88a49c243766dad4a79130c5
|
Jairool/jairo
|
[
"license:openrail",
"region:us"
] |
2023-06-06T00:45:46+00:00
|
{"license": "openrail"}
|
2023-06-06T00:50:49+00:00
|
|
277652785075b3a6091c6c5f24f959f4330ccc66
|
# Dataset Card for "train_100_pose_excluded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vvtq/train_100_pose_excluded
|
[
"language:en",
"region:us"
] |
2023-06-06T00:49:05+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11259932.0, "num_examples": 100}], "download_size": 11259068, "dataset_size": 11259932.0}}
|
2023-07-21T09:09:25+00:00
|
7f54d80b085768f6c3db03276983db4616cd6737
|
# Dataset Card for llm-book/aio-passages
書籍『大規模言語モデル入門』で使用する、「AI王」コンペティションのパッセージデータセットです。
GitHub リポジトリ [cl-tohoku/quiz-datasets](https://github.com/cl-tohoku/quiz-datasets) で公開されているデータセットを利用しています。
## Licence
本データセットで利用している Wikipedia のコンテンツは、[クリエイティブ・コモンズ表示・継承ライセンス 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) および [GNU 自由文書ライセンス (GFDL)](https://www.gnu.org/licenses/fdl.html) の下に配布されているものです。
|
llm-book/aio-passages
|
[
"size_categories:1M<n<10M",
"language:ja",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] |
2023-06-06T01:03:33+00:00
|
{"language": ["ja"], "license": ["cc-by-sa-3.0", "gfdl"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "pageid", "dtype": "int32"}, {"name": "revid", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3054493919, "num_examples": 4288198}], "download_size": 1110830651, "dataset_size": 3054493919}}
|
2023-06-24T04:55:37+00:00
|
2895b6e29b40eaa88d5eb603d29aafc90058b740
|
# Chess
> Recent advancements in artificial intelligence (AI) underscore the progress of reasoning and planning shown by recent generalist machine learning (ML) models. The progress can be boosted by datasets that can further boost these generic capabilities when used for training foundation models of various kind. This research initiative has generated extensive synthetic datasets from complex games — chess, Rubik's Cube, and mazes — to study facilitation and the advancement of these critical generic skills in AI models.
This dataset contains 3.2 billion games, equating to approximately 608 billion individual moves.
it is generated through self-play by Stockfish engine using Fugaku and we add initial moves to expand its diversity.
Each game has three columns: 'Moves', 'Termination' and 'Result',
- 'Move': recorded chess moves of the whole game.
- 'Termination': include CHECKMATE, INSUFFICIENT_MATERIAL, ... etc.
- Please check this for detail information
https://python-chess.readthedocs.io/en/latest/core.html#chess.Outcome.termination
- 'Result': result of this game, 1-0, 1/2-1/2, 0-1.
### Call for Collaboration
We invite interested researchers and ML practitioners to explore these datasets' potential. Whether training GPT models from scratch or fine-tuning pre-existing models, we encourage the exploration of various pre-training and fine-tuning strategies using these game-based datasets standalone or as enhancement of other already composed large-scale data.
Our team is prepared to assist in securing necessary GPU resources for these explorations. We are particularly interested in collaborators eager to pre-train models of small to medium scale on our game data, subsequently transition to standard text-based training, and then perform comparative analyses against models of similar architecture trained exclusively on text data.
Conclusively, this initiative marks a significant stride toward intricate problem-solving and strategic planning in AI, extending an open invitation to the research community for collaborative advancement in this domain.
|
laion/strategic_game_chess
|
[
"license:cc-by-4.0",
"game",
"region:us"
] |
2023-06-06T01:09:13+00:00
|
{"license": "cc-by-4.0", "pretty_name": "The Chess Dataset", "tags": ["game"]}
|
2023-10-20T03:14:20+00:00
|
8e4216b1f94bd08f38c038fb349b9e1faf7ee7b4
|
1111
|
saklee/test
|
[
"task_categories:token-classification",
"size_categories:1B<n<10B",
"language:ae",
"license:creativeml-openrail-m",
"legal",
"not-for-all-audiences",
"region:us"
] |
2023-06-06T01:10:17+00:00
|
{"language": ["ae"], "license": "creativeml-openrail-m", "size_categories": ["1B<n<10B"], "task_categories": ["token-classification"], "pretty_name": "\u4fc4\u65b9\u6211\u4e30\u5bcc", "tags": ["legal", "not-for-all-audiences"]}
|
2023-06-25T05:34:09+00:00
|
3616037f3e3059234c04c1542aef312cd79a406c
|
# Dataset Card for "musdb18-spec-pix2pix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zachary-shah/musdb18-spec-pix2pix
|
[
"region:us"
] |
2023-06-06T01:54:43+00:00
|
{"dataset_info": {"features": [{"name": "original_prompt", "dtype": "string"}, {"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "edited_prompt", "dtype": "string"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2923510938.704, "num_examples": 31556}], "download_size": 2839469846, "dataset_size": 2923510938.704}}
|
2023-06-06T01:55:48+00:00
|
afabd81e459b1d84bbbeac8dc71cfaf7532dbacc
|
# Dataset Card for "cardio-chunks-tokenid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
prognosis/cardio-chunks-tokenid
|
[
"region:us"
] |
2023-06-06T02:08:17+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23794644, "num_examples": 1}], "download_size": 10557791, "dataset_size": 23794644}}
|
2023-06-06T11:30:02+00:00
|
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