sha
stringlengths 40
40
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stringlengths 0
13.4M
| id
stringlengths 2
117
| tags
list | created_at
stringlengths 25
25
| metadata
stringlengths 2
31.7M
| last_modified
stringlengths 25
25
|
|---|---|---|---|---|---|---|
285da9460510d1eb3f1d3958c037162187b435fa
|
IndianServers/diseasessymptoms
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-29T08:03:32+00:00
|
{"license": "apache-2.0"}
|
2023-03-29T08:04:18+00:00
|
|
69a03cf7f23dcf6de7924c83446d5a3c4914c6bd
|
# Dataset Card for "news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hayesyang/news
|
[
"region:us"
] |
2023-03-29T08:17:12+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "zh", "num_bytes": 342700881, "num_examples": 2771}, {"name": "en", "num_bytes": 291917240, "num_examples": 2258}, {"name": "fr", "num_bytes": 154707197, "num_examples": 1201}, {"name": "es", "num_bytes": 221805819, "num_examples": 1695}, {"name": "ru", "num_bytes": 121776777, "num_examples": 926}, {"name": "ar", "num_bytes": 118422112, "num_examples": 883}], "download_size": 528278861, "dataset_size": 1251330026}}
|
2023-03-29T08:19:45+00:00
|
9e25f537545bdef1d03919ed101711ff810e6ecf
|
# Dataset Card for "stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
librarian-bot/stats
|
[
"region:us"
] |
2023-03-29T08:21:47+00:00
|
{"dataset_info": {"features": [{"name": "createdAt", "dtype": "timestamp[us]"}, {"name": "pr_number", "dtype": "int64"}, {"name": "status", "dtype": "large_string"}, {"name": "repo_id", "dtype": "large_string"}, {"name": "type", "dtype": "large_string"}, {"name": "isPullRequest", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 235762, "num_examples": 2747}], "download_size": 95773, "dataset_size": 235762}}
|
2023-09-11T13:51:16+00:00
|
d3ebfe2a5689db5b387f5aa42258e38a9da4a70a
|
Dampish/3k-Instruction-Questions
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-03-29T08:51:32+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-03-29T08:51:32+00:00
|
|
76dad8f498bd9b12909ceda52e382d2e2e4230f2
|
BRAIN-TR/insult_external_data
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-29T09:02:09+00:00
|
{"license": "apache-2.0"}
|
2023-03-29T09:02:58+00:00
|
|
7c92791dfd2ce5d900f9ec9e1c95274dfd022606
|
AyoubChLin/20NewsGroup-AgNews-CnnNews
|
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-03-29T09:11:34+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "auto", "1": "business", "2": "entertainment", "3": "health", "4": "news", "5": "politics", "6": "sci/tech", "7": "sport", "8": "world"}}}}], "splits": [{"name": "train", "num_bytes": 227672680, "num_examples": 162076}], "download_size": 134277697, "dataset_size": 227672680}}
|
2023-04-08T10:33:23+00:00
|
|
4c46d9f038df9c64a84ebe03b051504b19a54d63
|
# Higgs
The [Higgs dataset](https://www.nature.com/articles/ncomms5308/) from "[Searching for exotic particles in high-energy physics with deep learning](https://www.nature.com/articles/ncomms5308/)".
Try to classify particles as Higgs bosons.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| higgs | Binary classification | Is the particle a Higgs boson? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/higgs")["train"]
```
# Features
|**Feature** |**Type** |
|---------------------------|-----------|
|`lepton_pT` |`[float64]`|
|`lepton_eta` |`[float64]`|
|`lepton_phi` |`[float64]`|
|`missing_energy_magnitude` |`[float64]`|
|`missing_energy_phi` |`[float64]`|
|`jet1pt` |`[float64]`|
|`jet1eta` |`[float64]`|
|`jet1phi` |`[float64]`|
|`jet1b` |`[float64]`|
|`jet2pt` |`[float64]`|
|`jet2eta` |`[float64]`|
|`jet2phi` |`[float64]`|
|`jet2b` |`[float64]`|
|`jet3pt` |`[float64]`|
|`jet3eta` |`[float64]`|
|`jet3phi` |`[float64]`|
|`jet3b` |`[float64]`|
|`jet4pt` |`[float64]`|
|`jet4eta` |`[float64]`|
|`jet4phi` |`[float64]`|
|`jet4b` |`[float64]`|
|`m_jj` |`[float64]`|
|`m_jjj` |`[float64]`|
|`m_lv` |`[float64]`|
|`m_jlv` |`[float64]`|
|`m_bb` |`[float64]`|
|`m_wbb` |`[float64]`|
|`m_wwbb` |`[float64]`|
|
mstz/higgs
|
[
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"higgs",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-03-29T09:17:37+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["tabular-classification"], "pretty_name": "Higgs", "tags": ["higgs", "tabular_classification", "binary_classification", "UCI"], "configs": ["higgs"]}
|
2023-04-16T16:31:30+00:00
|
6826e0eb7740b778e2da224a00d1a3bc6820e70c
|
Delphine is a monster trainer who was born and raised in the city of Acadie. She is a person of the Dracquin race, a race of dragon-form women. She is the daughter of a Dracquin mother and a Saurander father.
These are her main physical properties:
* At adulthood she is about 6 feet tall.
* Her color pallette is shades of purple, particularly lavender.
* Her skin is rough, with a subtropical tone and lavender freckles.
* Her hair is a rich purple, with a slight and subtle curve. She usually lets it grow out only to her shoulders.
* Her pupils are a healthy purple with no blemishes.
* Her figure is full, round and thick, like that of a European dragon. Her hips are wide and her legs are thick. She has a large round belly, but she isn't fat.
* Her ears are large, rubbery and each supported by a horn. The horn and ear skin are a shade of purple consistent with her skin and hair. (Note: In some of these images she has an extra smaller ear sticking out of her hair. These ears are artifacts of AI. They are *not* part of her actual anatomy.)
* She has a tail. It is about 6 feet long, thick, and hairless. It is the same color as her skin.
* Her nose is also like that of a dragon: large and broad, with wide, round nostrils. Despite this feature, her face is cute and charming.
* Dracquins have wings, but Delphine's wings are small and undeveloped due to a lifetime of binding. (The society in which she lives has negative attitudes about wings and horns that have resulted in some unfortunate customs and assumptions.)
Delphine dresses very modestly but she has a sense of style. She prefers clothing that fits her figure well (she has a very hard time finding any that actually do). She's not afraid of being sexy but doesn't want people to ape over her figure. She's actually very self-conscious about certain parts of her body.
Notes on the included images:
* Her horns and ears have been the hardest part to reproduce consistently with AI.
* AI doesn't produce tails very well, so many of my renders of Delphine simply don't have a tail.
* I will need several commissions of her to properly train an AI model to reproduce her accurately.
|
monmamo/delphine-fairheart
|
[
"size_categories:n<1K",
"language:en",
"license:cc",
"region:us"
] |
2023-03-29T09:34:37+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "pretty_name": "Delphine Fairheart"}
|
2023-11-09T02:14:40+00:00
|
18597c4473ae030fb35278ae5d02d3a5089b0d61
|
prompt: one woman, straight shoulder-length orange-brown hair, eyeglasses with orange frames, cinnamon freckles, large breasts, large round belly, broad nose, orange pupils, smiling, pig ears
|
monmamo/april-workerbee
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:cc",
"anthrope",
"female",
"region:us"
] |
2023-03-29T09:39:20+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "pretty_name": "April Workerbee", "tags": ["anthrope", "female"]}
|
2023-04-18T23:48:39+00:00
|
03e8ccbc84316aa748b9293dad6989255ed07bbe
|
Quake24/sumTwitter
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-29T10:29:59+00:00
|
{"license": "apache-2.0"}
|
2023-03-29T11:18:54+00:00
|
|
b3a0f29471300ef31c73133dde5c54e1a0f29dd4
|
# Dataset Card for HumanEvalPack
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigcode-project/octopack
- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see [here](https://github.com/bigcode-project/octopack/tree/main/evaluation/create/humaneval-x#modifications-muennighoff)). Refer to the [OctoPack paper](https://arxiv.org/abs/2308.07124) for more details.
>
- **Languages:** Python, JavaScript, Java, Go, C++, Rust
- **OctoPack🐙🎒:**
<table>
<tr>
<th>Data</t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t>
<td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td>
<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation</t>
<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>
## Usage
```python
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("bigcode/humanevalpack", "python")["test"]
ds[0]
```
## Dataset Structure
### Data Instances
An example looks as follows:
```json
{
"task_id": "Python/0",
"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
"declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
"canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n",
"buggy_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return False\n",
"bug_type": "missing logic",
"failure_symptoms": "incorrect output",
"entry_point": "has_close_elements",
"import": ""
"test_setup": ""
"test": "\n\n\n\n\ndef check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)",
"example_test": "def check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\ncheck(has_close_elements)\n",
"signature": "has_close_elements(numbers: List[float], threshold: float) -> bool",
"docstring": "Check if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue",
"instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following problem:\nCheck if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue"
}
```
### Data Fields
The data fields are the same among all splits:
- `task_id`: Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem
- `prompt`: the prompt for models relying on code continuation
- `declaration`: the declaration of the function (same as prompt but without the docstring)
- `canonical_solution`: the correct solution passing all unit tests for the problem
- `buggy_solution`: same as `canonical_solution` but with a subtle human-written bug causing the unit tests to fail
- `bug_type`: the type of the bug in `buggy_solution` (one of [`missing logic`, `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`])
- `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`])
- `entry_point`: the name of the function
- `import`: imports necessary for the solution (only present for Go)
- `test_setup`: imports necessary for the test execution (only present for Go)
- `test`: the unit tests for the problem
- `example_test`: additional unit tests different from `test` that could be e.g. provided to the model (these are not used in the paper)
- `signature`: the signature of the function
- `docstring`: the docstring describing the problem
- `instruction`: an instruction for HumanEvalSynthesize in the form `Write a {language_name} function {signature} to solve the following problem:\n{docstring}`
## Citation Information
```bibtex
@article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv preprint arXiv:2308.07124},
year={2023}
}
```
|
bigcode/humanevalpack
|
[
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"code",
"arxiv:2308.07124",
"region:us"
] |
2023-03-29T11:00:16+00:00
|
{"language_creators": ["expert-generated"], "language": ["code"], "license": "mit", "multilinguality": ["multilingual"], "pretty_name": "HumanEvalPack", "tags": ["code"]}
|
2024-01-19T23:09:04+00:00
|
e2f3a88d48b4a7166f65e4a21dd7b163ae91fe43
|
kallaicsaba/dataset
|
[
"license:mit",
"region:us"
] |
2023-03-29T11:19:57+00:00
|
{"license": "mit"}
|
2023-03-29T11:21:46+00:00
|
|
c2bf66e5d0a137544d7f572e9e184cf145877296
|
# Dataset Card for "MusicCaps_spectrogram_aspect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rxk/MC_aspect
|
[
"region:us"
] |
2023-03-29T11:28:50+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3440007731.837, "num_examples": 19731}], "download_size": 3421004235, "dataset_size": 3440007731.837}}
|
2023-03-29T11:57:29+00:00
|
5f355e9a64538b184edd05ef109ffe40a5be7487
|
huhlim/pdb.29k
|
[
"license:mit",
"region:us"
] |
2023-03-29T11:36:15+00:00
|
{"license": "mit"}
|
2023-03-29T11:36:15+00:00
|
|
3cd64bcb02fee4a5068e89b3c95fbe26a226a232
|
# Dataset Card for "MusicCaps_spectrogram_caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rxk/MC_caption
|
[
"region:us"
] |
2023-03-29T11:53:03+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3442217474.837, "num_examples": 19731}], "download_size": 3421342341, "dataset_size": 3442217474.837}}
|
2023-03-29T12:24:36+00:00
|
1f4cd6a427ea7599e3e23b1d8f3681e14b5da63d
|
# Distil Whisper: LibriSpeech ASR
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/librispeech_asr).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all")
# take the first sample of the validation set
sample = dataset["validation.clean"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation.clean"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc-by-4.0.
|
distil-whisper/librispeech_asr
|
[
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-03-29T11:53:48+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "task_categories": ["automatic-speech-recognition"], "-pretty_name": "LibriSpeech ASR"}
|
2023-09-25T09:30:13+00:00
|
9170aceb1d8ab749ee4f60f1ec84e60e4aeb163c
|
# Dataset Card for "pile-deduped-pythia-random-sampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
EleutherAI/pile-deduped-pythia-random-sampled
|
[
"region:us"
] |
2023-03-29T12:15:01+00:00
|
{"dataset_info": {"features": [{"name": "Index", "dtype": "int64"}, {"name": "70M", "dtype": "float64"}, {"name": "160M", "dtype": "float64"}, {"name": "410M", "dtype": "float64"}, {"name": "1B", "dtype": "float64"}, {"name": "1.4B", "dtype": "float64"}, {"name": "2.8B", "dtype": "float64"}, {"name": "6.9B", "dtype": "float64"}, {"name": "12B", "dtype": "float64"}, {"name": "Tokens", "sequence": "uint16"}], "splits": [{"name": "train", "num_bytes": 1020000000, "num_examples": 5000000}], "download_size": 915854656, "dataset_size": 1020000000}}
|
2023-08-25T06:26:47+00:00
|
904f0fe00332d63d3149b8306b2c704e89231e26
|
## Introduction
* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models.
<div align="center">
</div>
## If you find our work is useful, pleaes cite our paper:
```
@article{herethinking,
title={Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks},
author={He, Shuai and Zhang, Yongchang and Xie, Rui and Jiang, Dongxiang and Ming, Anlong},
journal={IJCAI},
year={2022},
}
```
|
Shuai1995/TAD66K_for_Image_Aesthetics_Assessment
|
[
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"license:apache-2.0",
"Image Aesthetics Assessment",
"Image Quality Assessment",
"region:us"
] |
2023-03-29T12:16:07+00:00
|
{"license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["feature-extraction"], "tags": ["Image Aesthetics Assessment", "Image Quality Assessment"]}
|
2023-03-30T00:24:17+00:00
|
8f9e0bb097697b9ba200f12db0a833889c233df9
|
# Dataset Card for "trinary_c_elegans"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ebj/trinary_c_elegans
|
[
"region:us"
] |
2023-03-29T13:32:13+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 480177378.0, "num_examples": 269}], "download_size": 51437925, "dataset_size": 480177378.0}}
|
2023-03-29T15:16:01+00:00
|
e0e57749e1d6e5c0b3390f82a8ac50092a5b9813
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880
|
[
"region:us"
] |
2023-03-29T13:37:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 673355, "num_examples": 1880}], "download_size": 226073, "dataset_size": 673355}}
|
2023-03-29T13:37:51+00:00
|
a57120c35120f2e75d904898a8479dcf4990683f
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880
|
[
"region:us"
] |
2023-03-29T13:40:31+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 546856, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1039535, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 546340, "num_examples": 1880}], "download_size": 576598, "dataset_size": 2132731}}
|
2023-03-29T14:46:11+00:00
|
7c735442e021a332bf11b4f91a0f6a7b9b241765
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880
|
[
"region:us"
] |
2023-03-29T13:43:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 224447, "num_examples": 1880}], "download_size": 19238, "dataset_size": 224447}}
|
2023-03-29T13:47:17+00:00
|
37773d35ecd73bb0828bb827ea3eebc034d8a980
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880
|
[
"region:us"
] |
2023-03-29T13:51:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 547013, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 546403, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 1038797, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 852716, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1476439, "num_examples": 1880}], "download_size": 1174480, "dataset_size": 4461368}}
|
2023-04-04T02:17:40+00:00
|
7f0c6eef81786872a00938f1fb942aee930a1508
|
# DailyDialog
- Source: https://huggingface.co/datasets/daily_dialog
- Num examples:
- 11,118 (train)
- 1,000 (validation)
- 1,000 (test)
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("vietgpt/daily_dialog_vi")
```
|
vietgpt/daily_dialog_vi
|
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:vi",
"SFT",
"region:us"
] |
2023-03-29T13:57:48+00:00
|
{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "dialog", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7803227, "num_examples": 11118}, {"name": "validation", "num_bytes": 718575, "num_examples": 1000}, {"name": "test", "num_bytes": 698896, "num_examples": 1000}], "download_size": 4841457, "dataset_size": 9220698}, "tags": ["SFT"]}
|
2023-06-21T13:11:16+00:00
|
5809adc55d8a981311b6e33d930a31e16f8679b1
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880
|
[
"region:us"
] |
2023-03-29T14:04:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 692485, "num_examples": 1880}], "download_size": 230862, "dataset_size": 692485}}
|
2023-03-29T14:04:24+00:00
|
c6c22dae24614224ae1f25a6c2fee18fd300b7ae
|
# Dataset Card for "oeis"
Work in Progress.
Data source: daily dump from March 28th, 2023.
OEIS End-User License: http://oeis.org/LICENSE
|
cakiki/oeis
|
[
"size_categories:100K<n<1M",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-03-29T14:15:43+00:00
|
{"license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "On-Line Encyclopedia of Integer Sequences Dataset", "dataset_info": {"features": [{"name": "a-number", "dtype": "string"}, {"name": "sequence", "sequence": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 155208770, "num_examples": 361596}], "download_size": 69958943, "dataset_size": 155208770}}
|
2023-03-29T14:43:56+00:00
|
154ac15e6ee74a9e04afa105a6e7595ab6b3bb15
|
YuanPJ/ami_summ
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-03-29T14:23:57+00:00
|
{"license": "cc-by-4.0"}
|
2023-03-29T23:36:27+00:00
|
|
5e9ad453cb11d18f026eed66394d2c734fe5365c
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880
|
[
"region:us"
] |
2023-03-29T14:25:04+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 692068, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 1329559, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1330297, "num_examples": 1880}], "download_size": 997973, "dataset_size": 3351924}}
|
2023-03-29T14:37:31+00:00
|
6c8fb21189edfe433d92558236e767aef5f72413
|
# PyCoder
This repository contains the dataset for the paper [Syntax-Aware On-the-Fly Code Completion](https://arxiv.org/abs/2211.04673)
The sample code to run the model can be found in directory: "`assets/notebooks/inference.ipynb`" in our GitHub: https://github.com/awsm-research/pycoder.
PyCoder is an auto code completion model which leverages a Multi-Task Training technique (MTT) to cooperatively
learn the code prediction task and the type prediction task. For the type prediction
task, we propose to leverage the standard Python token
type information (e.g., String, Number, Name, Keyword),
which is readily available and lightweight, instead of using
the AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper).
More information can be found in our paper.
If you use our code or PyCoder, please cite our paper.
<pre><code>@article{takerngsaksiri2022syntax,
title={Syntax-Aware On-the-Fly Code Completion},
author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},
journal={arXiv preprint arXiv:2211.04673},
year={2022}
}</code></pre>
|
Wannita/PyCoder-Type
|
[
"task_categories:text-generation",
"license:mit",
"code",
"arxiv:2211.04673",
"region:us"
] |
2023-03-29T14:26:00+00:00
|
{"license": "mit", "task_categories": ["text-generation"], "pretty_name": "pycoder-type", "tags": ["code"]}
|
2023-03-29T14:51:09+00:00
|
5218f33557b4e96fb203c3103a4a130bb2bca3e3
|
# Dataset Card for "balanced_augmented_dataset_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jsevisal/balanced_augmented_dataset_2
|
[
"region:us"
] |
2023-03-29T14:28:58+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "gestures", "sequence": "string"}, {"name": "label", "sequence": {"class_label": {"names": {"0": "B-BUT", "1": "I-BUT", "2": "B-CALM_DOWN", "3": "I-CALM_DOWN", "4": "B-COME_ON", "5": "I-COME_ON", "6": "B-EMPHATIC", "7": "I-EMPHATIC", "8": "B-ENTHUSIASTIC", "9": "I-ENTHUSIASTIC", "10": "B-EXPLAIN", "11": "I-EXPLAIN", "12": "B-FRONT", "13": "I-FRONT", "14": "B-GREET", "15": "I-GREET", "16": "B-ITERATE", "17": "I-ITERATE", "18": "B-NEUTRAL", "19": "I-NEUTRAL", "20": "B-NO", "21": "I-NO", "22": "B-NO_GESTURE", "23": "I-NO_GESTURE", "24": "B-OTHER_PEER", "25": "I-OTHER_PEER", "26": "B-PLEASE", "27": "I-PLEASE", "28": "B-QUESTION", "29": "I-QUESTION", "30": "B-SELF", "31": "I-SELF", "32": "B-SORRY", "33": "I-SORRY", "34": "B-THANKS", "35": "I-THANKS", "36": "B-THINKING", "37": "I-THINKING", "38": "B-THIRD_PERSON", "39": "I-THIRD_PERSON", "40": "B-YES", "41": "I-YES"}}}}], "splits": [{"name": "train", "num_bytes": 272426.0, "num_examples": 831}, {"name": "test", "num_bytes": 55785.0, "num_examples": 126}], "download_size": 58436, "dataset_size": 328211.0}}
|
2023-09-14T10:32:21+00:00
|
c6116ee5a7311713edca7e3e339510637cdc941e
|
vietgpt/alpaca_en
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"SFT",
"region:us"
] |
2023-03-29T14:52:38+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 20207911, "num_examples": 51848}], "download_size": 11466948, "dataset_size": 20207911}, "tags": ["SFT"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-03T21:23:19+00:00
|
|
7f8f16cd7c452792a1fcff6d45b9ad22888083c4
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669
|
[
"region:us"
] |
2023-03-29T15:03:03+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 984100, "num_examples": 3669}], "download_size": 217354, "dataset_size": 984100}}
|
2023-03-29T15:03:05+00:00
|
64c9d305fcec98ef04f1172e9f949151d6774b6f
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669
|
[
"region:us"
] |
2023-03-29T15:08:58+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 987114, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1553605, "num_examples": 3669}], "download_size": 235457, "dataset_size": 2540719}}
|
2023-04-03T21:10:32+00:00
|
6f5ff0d6652f5b7da93b36a1bf68ec318cbecef4
|
hasorez/finnish_lakefish_dataset
|
[
"license:mit",
"region:us"
] |
2023-03-29T15:14:31+00:00
|
{"license": "mit"}
|
2023-03-29T15:17:44+00:00
|
|
1132072e6b499e5d496dbb7207e4bdc4a903b98a
|
# NBFI
The [NBFI dataset](https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset) from the [Kaggle](https://www.kaggle.com/datasets).
Client default prediction.
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| default | Binary classification | Has the client defaulted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/nbfi")["train"]
```
# Features
|**Feature** |**Type** |
|-----------------------------------------------|---------------|
|`income` | `float32` |
|`owns_a_car` | `bool` |
|`owns_a_bike` | `bool` |
|`has_an_active_loan` | `bool` |
|`owns_a_house` | `bool` |
|`nr_children` | `int8` |
|`credit` | `float32` |
|`loan_annuity` | `float32` |
|`accompanied_by` | `string` |
|`income_type` | `string` |
|`education_level` | `float32` |
|`marital_status` | `float32` |
|`is_male` | `bool` |
|`type_of_contract` | `string` |
|`type_of_housing` | `string` |
|`residence_density` | `float32` |
|`age_in_days` | `int32` |
|`consecutive_days_of_employment` | `int16` |
|`nr_days_since_last_registration_change` | `int32` |
|`nr_days_since_last_document_change` | `int32` |
|`owned_a_house_for_nr_days` | `int32` |
|`has_provided_a_mobile_number` | `bool` |
|`has_provided_a_home_number` | `bool` |
|`was_reachable_at_work` | `bool` |
|`job` | `string` |
|`nr_family_members` | `int8` |
|`city_rating` | `int8` |
|`weekday_of_application` | `int8` |
|`hour_of_application` | `float32` |
|`same_residence_and_home` | `bool` |
|`same_work_and_home` | `bool` |
|`score_1` | `float32` |
|`score_2` | `float32` |
|`score_3` | `float32` |
|`nr_defaults_in_social_circle` | `int8` |
|`inquiries_in_last_year` | `float32` |
|
mstz/nbfi
|
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"nbfi",
"tabular_classification",
"binary_classification",
"region:us"
] |
2023-03-29T15:21:38+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "NBFI", "tags": ["nbfi", "tabular_classification", "binary_classification"], "configs": ["default"]}
|
2024-01-19T16:12:30+00:00
|
865180372fc40249b38c962c2dfc462fd66c9b4b
|
# scientific_lay_summarisation - PLOS - normalized
This dataset is a modified version of [tomasg25/scientific_lay_summarization](https://huggingface.co/datasets/tomasg25/scientific_lay_summarisation) and contains scientific lay summaries that have been preprocessed [with this code](https://gist.github.com/pszemraj/bd344637af7c0c10ecf4ab62c4d0ce91). The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.
Original dataset details:
- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)
## Data Cleaning
The text in both the "article" and "summary" columns was processed to ensure that punctuation and whitespace were consistent. The `fix_punct_whitespace` function was applied to each text sample to:
- Remove spaces before punctuation marks (except for parentheses)
- Add a space after punctuation marks (except for parentheses) if missing
- Handle spaces around parentheses
- Add a space after a closing parenthesis if followed by a word or opening parenthesis
- Handle spaces around quotation marks
- Handle spaces around single quotes
- Handle comma in numbers
## Tokenization
The length of each text sample was calculated in terms of tokens using the T5 tokenizer. The `calculate_token_length` function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.
## Data Format
The resulting processed data files are stored in Apache parquet and can be loaded using the `pandas' library or the `datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are
```python
DatasetDict({
train: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 24773
})
test: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 1376
})
validation: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 1376
})
})
```
## Usage
Load the desired parquet file(s) using `pandas` or `datasets`. Here is an example using `pandas`:
```python
# download the dataset files by clicking on 'use in datasets' and cloning
import pandas as pd
# Load train set
df = pd.read_parquet("scientific_lay_summarisation-plos-norm/train.parquet")
print(df.info())
```
And here is an example using `datasets`:
```python
from datasets import load_dataset
dataset = load_dataset("pszemraj/scientific_lay_summarisation-plos-norm")
train_set = dataset['train']
# Print the first few samples
for i in range(5):
print(train_set[i])
```
## Token Lengths
For train split:
---
|
pszemraj/scientific_lay_summarisation-plos-norm
|
[
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:tomasg25/scientific_lay_summarisation",
"language:en",
"license:mit",
"arxiv:2210.09932",
"region:us"
] |
2023-03-29T15:24:26+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "source_datasets": "tomasg25/scientific_lay_summarisation", "task_categories": ["summarization", "text2text-generation"]}
|
2023-06-20T00:06:39+00:00
|
40dd102d8f4e53439b81a71728cd3c64764dc94c
|
# scientific_lay_summarisation - elife - normalized
This is the "_elife_" split. For more words, refer to the [PLOS split README](https://huggingface.co/datasets/pszemraj/scientific_lay_summarisation-plos-norm)
## Contents
load with datasets:
```python
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("pszemraj/scientific_lay_summarisation-elife-norm")
dataset
```
Output:
```python
DatasetDict({
train: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 4346
})
test: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 241
})
validation: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 241
})
})
```
## Lengths
Train set:
|
pszemraj/scientific_lay_summarisation-elife-norm
|
[
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:tomasg25/scientific_lay_summarisation",
"language:en",
"license:mit",
"region:us"
] |
2023-03-29T15:26:37+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "source_datasets": "tomasg25/scientific_lay_summarisation", "task_categories": ["summarization", "text2text-generation"]}
|
2023-04-06T22:34:11+00:00
|
43de0f71ba24c93c64d82452e93ea7f2f293143c
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669
|
[
"region:us"
] |
2023-03-29T15:36:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 466723, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 248812, "num_examples": 3669}], "download_size": 61769, "dataset_size": 715535}}
|
2023-04-03T19:57:06+00:00
|
1ab52e117f33cfd7d78b6a3448537e61d1a1377c
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669
|
[
"region:us"
] |
2023-03-29T15:49:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1065554, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1535372, "num_examples": 3669}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2956478, "num_examples": 3669}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5797536, "num_examples": 3669}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 1526722, "num_examples": 3669}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2808638, "num_examples": 3669}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2936255, "num_examples": 3669}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5508214, "num_examples": 3669}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1469085, "num_examples": 3669}], "download_size": 3384830, "dataset_size": 25603854}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T03:49:38+00:00
|
2c7a56f6fd8654a367c916e61c0fa3ef390824ec
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669
|
[
"region:us"
] |
2023-03-29T15:54:58+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1065643, "num_examples": 3669}], "download_size": 193852, "dataset_size": 1065643}}
|
2023-03-29T15:55:01+00:00
|
7a5e5838d4620cb59e1e5f45de6c7418e6cf6e76
|
andersonbcdefg/gpt4all
|
[
"license:other",
"region:us"
] |
2023-03-29T16:06:24+00:00
|
{"license": "other"}
|
2023-03-29T18:50:03+00:00
|
|
48fedd9b3db57f7d3d0ae0cd05084ca37210248f
|
# Dataset Card for "somos-alpaca-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
monicaeme/somos-alpaca-es
|
[
"region:us"
] |
2023-03-29T16:24:49+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 1920697, "num_examples": 102}], "download_size": 0, "dataset_size": 1920697}}
|
2023-04-04T11:07:10+00:00
|
4920d6c924eda9058071deba952910f560529a62
|
# Dataset Card for "tomatoesTest2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesTest2
|
[
"region:us"
] |
2023-03-29T16:37:36+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 174243.0, "num_examples": 1}], "download_size": 23284, "dataset_size": 174243.0}}
|
2023-03-29T16:37:38+00:00
|
c357d758e79fe9e21f188f71ad6171854e504850
|
# Dataset Card for "tomatoesTest3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesTest3
|
[
"region:us"
] |
2023-03-29T16:40:13+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "test_labels", "2": "train", "3": "train_labels", "4": "val", "5": "val_labels"}}}}], "splits": [{"name": "train", "num_bytes": 41561872.854, "num_examples": 1114}, {"name": "test", "num_bytes": 4715509.0, "num_examples": 136}, {"name": "validation", "num_bytes": 12654111.0, "num_examples": 366}], "download_size": 51718608, "dataset_size": 58931492.854}}
|
2023-03-29T16:40:20+00:00
|
7b44158a39c9968eee25de59780cb0bdc3474193
|
# NASA Earth Instagram
This dataset is a moderately curated subset of the posts shown on [NASA Earth's Instagram](https://www.instagram.com/nasaearth/), with an emphasis
on finding image-text pairs where the text associated is as close as possible to being a direct caption of the image in question.
This dataset has a variety of use cases, but the one which it is originally intended for is to provide a fine-tuning dataset for image captioning models,
to be better equipped for describing the exact pheonomena in satellite imagery.
The owner of all images and text in this data is NASA.
|
nkasmanoff/nasa_earth_instagram
|
[
"task_categories:image-to-text",
"task_categories:text-to-image",
"size_categories:n<1K",
"region:us"
] |
2023-03-29T16:45:06+00:00
|
{"size_categories": ["n<1K"], "task_categories": ["image-to-text", "text-to-image"]}
|
2023-03-30T10:04:45+00:00
|
5e026a5d7859bb599f9b2b90342777358dfeab8d
|
# Dataset Card for "msp5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kiringodhwani/msp5
|
[
"region:us"
] |
2023-03-29T16:46:41+00:00
|
{"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6274340, "num_examples": 3295}], "download_size": 2765581, "dataset_size": 6274340}}
|
2023-03-29T16:46:44+00:00
|
b9b6f64bdc2dae029fb0eb46f270e3fa88f6ffcd
|
# Dataset Card for "flores200_packed2_mix_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bri25yu/flores200_packed2_mix_mt5
|
[
"region:us"
] |
2023-03-29T16:48:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 13594840169, "num_examples": 10240000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 6189830203, "dataset_size": 13606338205}}
|
2023-03-29T17:13:56+00:00
|
b0d31f1a294e6a828b72b888ecbea3453b5731a7
|
# Canny DiffusionDB
This dataset is the [DiffusionDB dataset](https://huggingface.co/datasets/poloclub/diffusiondb) that is transformed using Canny transformation.
You can see samples below 👇
**Sample:**
Original Image:
Transformed Image:
Caption:
"a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh "
Below you can find a small script used to create this dataset:
```python
def canny_convert(image):
image_array = np.array(image)
gray_image = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray_image, 100, 200)
edge_image = Image.fromarray(edges)
return edge_image
dataset = load_dataset("poloclub/diffusiondb", split = "train")
dataset_list = []
for data in dataset:
image_path = data["image"]
prompt = data["prompt"]
transformed_image_path = canny_convert(image_path)
new_data = {
"original_image": image,
"prompt": prompt,
"transformed_image": transformed_image,
}
dataset_list.append(new_data)
```
|
jax-diffusers-event/canny_diffusiondb
|
[
"region:us"
] |
2023-03-29T17:27:15+00:00
|
{"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "transformed_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 604990210.0, "num_examples": 994}], "download_size": 604849707, "dataset_size": 604990210.0}}
|
2023-03-29T18:36:28+00:00
|
3a7f5b8e0b0e42aef89fdee1257d534e252a7765
|
# Dataset Card for "markpoulierart"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ossaili/markpoulierart
|
[
"region:us"
] |
2023-03-29T18:00:20+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 273071208.65, "num_examples": 1285}], "download_size": 282892921, "dataset_size": 273071208.65}}
|
2023-09-24T19:58:25+00:00
|
2623232238645d35e5ed91e319c3643411aecd0c
|
# Dataset Card for "chatdoctor200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mrm8488/chatdoctor200k
|
[
"region:us"
] |
2023-03-29T18:57:01+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 207526604, "num_examples": 207407}], "download_size": 115879622, "dataset_size": 207526604}}
|
2023-03-29T18:57:18+00:00
|
3c1a26d43d75bd5398aca8a371fce2f2e1d04db6
|
# Dataset Card for "tomatoesTest4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesTest4
|
[
"region:us"
] |
2023-03-29T18:58:02+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "labels", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}], "splits": [{"name": "train", "num_bytes": 352830773.0, "num_examples": 557}], "download_size": 51228407, "dataset_size": 352830773.0}}
|
2023-03-29T18:58:08+00:00
|
6200574205231562746a3b60d4da3355fd60675a
|
# Dataset Card for "tomatoesTest5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesTest5
|
[
"region:us"
] |
2023-03-29T18:59:58+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}], "splits": [{"name": "train", "num_bytes": 352830773.0, "num_examples": 557}], "download_size": 51228401, "dataset_size": 352830773.0}}
|
2023-03-29T19:00:05+00:00
|
b96baf4883cd1f461d9a2f3355afd91a7a9d7570
|
# Dataset Card for "swedish-catalog-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Djarnis/swedish-catalog-data
|
[
"region:us"
] |
2023-03-29T19:02:32+00:00
|
{"dataset_info": {"features": [{"name": "layout", "dtype": "string"}, {"name": "catalog_id", "dtype": "string"}, {"name": "page", "dtype": "int64"}, {"name": "font_name", "dtype": "string"}, {"name": "size", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65421344, "num_examples": 585721}], "download_size": 5137853, "dataset_size": 65421344}}
|
2023-03-29T19:03:24+00:00
|
8715dc45262bfa7b746cb3005abf07cf47296110
|
## Dataset Multi30k: English-Ukrainian variation
Multi30K dataset is designed to develop multilingual multimodal researches.
Initially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.
We present a variation of this dataset manually translated for Ukrainian language.
Paper:
```python
@inproceedings{saichyshyna-etal-2023-extension,
title = "Extension {M}ulti30{K}: Multimodal Dataset for Integrated Vision and Language Research in {U}krainian",
author = "Saichyshyna, Nataliia and
Maksymenko, Daniil and
Turuta, Oleksii and
Yerokhin, Andriy and
Babii, Andrii and
Turuta, Olena",
booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.unlp-1.7",
pages = "54--61",
abstract = "We share the results of the project within the well-known Multi30k dataset dedicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings.The collection of multimodal datasets is essential for natural language processing tasks because it allows the development of more complex and comprehensive machine learning models that can understand and analyze different types of data. These models can learn from a variety of data types, including images, text, and audio, for more accurate and meaningful results.",
}
```
|
turuta/Multi30k-uk
|
[
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:uk",
"language:en",
"license:unknown",
"common",
"multi30k",
"ukrainian",
"region:us"
] |
2023-03-29T19:26:58+00:00
|
{"language": ["uk", "en"], "license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["translation", "text-generation"], "pretty_name": "ukr-multi30k", "tags": ["common", "multi30k", "ukrainian"]}
|
2023-05-04T18:11:45+00:00
|
18f0eac7a573ca920d7829999171302599e11788
|
# Dataset Card for "teste"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Thiagofer/teste
|
[
"region:us"
] |
2023-03-29T19:31:41+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1398770, "num_examples": 2659}, {"name": "validation", "num_bytes": 429875, "num_examples": 665}], "download_size": 995956, "dataset_size": 1828645}}
|
2023-03-29T20:08:35+00:00
|
2d2b4ee5252f88ae7ef9c256b3f89c070abc1df5
|
rgricardo/Takubgroup
|
[
"license:openrail",
"region:us"
] |
2023-03-29T19:44:05+00:00
|
{"license": "openrail"}
|
2023-03-29T19:44:05+00:00
|
|
afb8395a583dd58e7080c2492cf71ef6734bb017
|
#pragma once
#include "Board.h"
#include "GameObjects.h"
#include "Point.h"
#include <Windows.h>
#include <conio.h>
class TetrisGame {
Board boardGame;
public:
void resetGame(){
boardGame.setBoard();
}
// <<<RUN>>>
void run();
bool checkGameOver(int typeShapea);
void updateStartBoard(int typeShape);
void hideCursor();
GameObjects * createNewObject(int & type);
|
bruce69/54534654
|
[
"region:us"
] |
2023-03-29T20:00:21+00:00
|
{}
|
2023-03-29T20:03:54+00:00
|
9ae70b9dec5e9873946b104111a0a0ce101b8002
|
# Dataset Card for "segundo_harem_conll_2003_style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arubenruben/segundo_harem_conll_2003_style
|
[
"region:us"
] |
2023-03-29T21:18:41+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-MISC", "8": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 1047476, "num_examples": 93}, {"name": "validation", "num_bytes": 249755, "num_examples": 23}], "download_size": 295815, "dataset_size": 1297231}}
|
2023-04-12T07:12:31+00:00
|
79a538252342ba18cb7dd01d9f4804e767f2ae49
|
AbramPrz/faq-dataset
|
[
"license:mit",
"region:us"
] |
2023-03-29T21:47:10+00:00
|
{"license": "mit"}
|
2023-03-29T21:48:27+00:00
|
|
61f3d559eb6b9284c9b20b79a297e0fd277defd6
|
# Dataset Card for "DA_SGD_Restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Restaurants
|
[
"region:us"
] |
2023-03-29T21:48:55+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 895010.6571663469, "num_examples": 3648}, {"name": "test", "num_bytes": 233, "num_examples": 1}], "download_size": 360352, "dataset_size": 895243.6571663469}}
|
2023-03-29T21:49:00+00:00
|
bc32eeac2eadbdb590fa0f7752f994f287cb213e
|
# Dataset Card for "DA_SGD_Media"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Media
|
[
"region:us"
] |
2023-03-29T21:49:01+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 538641.80312262, "num_examples": 2625}, {"name": "test", "num_bytes": 199, "num_examples": 1}], "download_size": 207091, "dataset_size": 538840.80312262}}
|
2023-03-29T21:49:05+00:00
|
94d64c0215314f5cdb0d701aab1d8402c09d3c17
|
# Dataset Card for "DA_SGD_Events"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Events
|
[
"region:us"
] |
2023-03-29T21:49:06+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1660456.0603351956, "num_examples": 7159}, {"name": "test", "num_bytes": 327, "num_examples": 1}], "download_size": 673850, "dataset_size": 1660783.0603351956}}
|
2023-03-29T21:49:11+00:00
|
cb65aa77fd03ddbdad7447d625707745d0c0a7fc
|
# Dataset Card for "DA_SGD_Music"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Music
|
[
"region:us"
] |
2023-03-29T21:49:11+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 628520.2361015786, "num_examples": 2913}, {"name": "test", "num_bytes": 149, "num_examples": 1}], "download_size": 241847, "dataset_size": 628669.2361015786}}
|
2023-03-29T21:49:16+00:00
|
aebb9a14d9e193733900c86239f5bfc9a17f1529
|
# Dataset Card for "DA_SGD_Movies"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Movies
|
[
"region:us"
] |
2023-03-29T21:49:16+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 724582.4324491055, "num_examples": 3241}, {"name": "test", "num_bytes": 241, "num_examples": 1}], "download_size": 297231, "dataset_size": 724823.4324491055}}
|
2023-03-29T21:49:21+00:00
|
f62c0fe8aab865278fcf117238b4a176521e717a
|
# Dataset Card for "DA_SGD_Flights"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Flights
|
[
"region:us"
] |
2023-03-29T21:49:21+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2067906.6395008278, "num_examples": 7852}, {"name": "test", "num_bytes": 326, "num_examples": 1}], "download_size": 791257, "dataset_size": 2068232.6395008278}}
|
2023-03-29T21:49:27+00:00
|
3bcfa11507c2267a6fa23a3d9ef7095723355794
|
# Dataset Card for "DA_SGD_RideSharing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_RideSharing
|
[
"region:us"
] |
2023-03-29T21:49:27+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 235897.7504854369, "num_examples": 1029}, {"name": "test", "num_bytes": 196, "num_examples": 1}], "download_size": 94252, "dataset_size": 236093.7504854369}}
|
2023-03-29T21:49:32+00:00
|
92fd5bed52fa83528094422baa414ba196d1795a
|
# Dataset Card for "DA_SGD_RentalCars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_RentalCars
|
[
"region:us"
] |
2023-03-29T21:49:32+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 694211.3296910324, "num_examples": 2653}, {"name": "test", "num_bytes": 404, "num_examples": 1}], "download_size": 268297, "dataset_size": 694615.3296910324}}
|
2023-03-29T21:49:37+00:00
|
b5105da57dda1f2fd2da55173b453be6528dbdd9
|
# Dataset Card for "DA_SGD_Buses"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Buses
|
[
"region:us"
] |
2023-03-29T21:49:37+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 710108.4859689666, "num_examples": 3028}, {"name": "test", "num_bytes": 221, "num_examples": 1}], "download_size": 273885, "dataset_size": 710329.4859689666}}
|
2023-03-29T21:49:42+00:00
|
0984c1f2fc9a6b96861c92d180959ec050b03015
|
# Dataset Card for "DA_SGD_Hotels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Hotels
|
[
"region:us"
] |
2023-03-29T21:49:42+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1137138.1547469678, "num_examples": 4781}, {"name": "test", "num_bytes": 354, "num_examples": 1}], "download_size": 477647, "dataset_size": 1137492.1547469678}}
|
2023-03-29T21:49:48+00:00
|
fdc46bb9fbe5122b24b84bc997b877e34cd3b80a
|
# Dataset Card for "DA_SGD_Services"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Services
|
[
"region:us"
] |
2023-03-29T21:49:48+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1113419.0002074258, "num_examples": 4820}, {"name": "test", "num_bytes": 293, "num_examples": 1}], "download_size": 460731, "dataset_size": 1113712.0002074258}}
|
2023-03-29T21:49:53+00:00
|
842e92501b59aaf8597256845b4db360fffb0975
|
# Dataset Card for "DA_SGD_Homes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Homes
|
[
"region:us"
] |
2023-03-29T21:49:53+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 782436.7041420118, "num_examples": 3041}, {"name": "test", "num_bytes": 418, "num_examples": 1}], "download_size": 311067, "dataset_size": 782854.7041420118}}
|
2023-03-29T21:49:58+00:00
|
d4069cfe906b00132a9c5bab716d638f5b375f22
|
# Dataset Card for "DA_SGD_Banks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Banks
|
[
"region:us"
] |
2023-03-29T21:49:58+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 429700.14992503746, "num_examples": 2000}, {"name": "test", "num_bytes": 228, "num_examples": 1}], "download_size": 147054, "dataset_size": 429928.14992503746}}
|
2023-03-29T21:50:03+00:00
|
4524cdbcbcdfd26c4f2b1888d8d5e503e4bc12d2
|
# Dataset Card for "DA_SGD_Calendar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_SGD_Calendar
|
[
"region:us"
] |
2023-03-29T21:50:04+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 237228.4572815534, "num_examples": 1029}, {"name": "test", "num_bytes": 130, "num_examples": 1}], "download_size": 90115, "dataset_size": 237358.4572815534}}
|
2023-03-29T21:50:08+00:00
|
ceee66b545de4366590386127e7005a24fb8a54e
|
# Dataset Card for "tomatoesSpoof1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesSpoof1
|
[
"region:us"
] |
2023-03-29T21:53:06+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 673124095.0, "num_examples": 557}], "download_size": 34937459, "dataset_size": 673124095.0}}
|
2023-03-29T21:57:48+00:00
|
96693d13afd1e79679e5d06414782b78e102ec09
|
# Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-polaridad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alexcom/analisis-sentimientos-textos-turisitcos-mx-polaridad
|
[
"region:us"
] |
2023-03-29T22:14:41+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 71496873, "num_examples": 176192}, {"name": "test", "num_bytes": 30856228, "num_examples": 75510}], "download_size": 62497427, "dataset_size": 102353101}}
|
2023-04-01T19:12:37+00:00
|
5a64ccb93a9e27948c91dd6ba41d85f1bd64f280
|
# Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-tipo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alexcom/analisis-sentimeinto-textos-turisitcos-mx-tipo
|
[
"region:us"
] |
2023-03-29T22:14:57+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72330945, "num_examples": 176192}, {"name": "test", "num_bytes": 31152158, "num_examples": 75510}], "download_size": 62479649, "dataset_size": 103483103}}
|
2023-03-29T22:18:27+00:00
|
dfbf980e1e0dee973bb7825c6507262b4689442b
|
# Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-pais"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alexcom/analisis-sentimeinto-textos-turisitcos-mx-pais
|
[
"region:us"
] |
2023-03-29T22:16:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72164531, "num_examples": 176192}, {"name": "test", "num_bytes": 30692934, "num_examples": 75510}], "download_size": 62463153, "dataset_size": 102857465}}
|
2023-03-29T22:17:17+00:00
|
618b1378fea15e06d52d7919d328a52d4b6b8cfd
|
# Dataset Card for "flores200_baseline_all_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bri25yu/flores200_baseline_all_mt5
|
[
"region:us"
] |
2023-03-29T22:24:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 30474393243, "num_examples": 41287764}, {"name": "val", "num_bytes": 3791994, "num_examples": 5000}, {"name": "test", "num_bytes": 7604613, "num_examples": 10000}], "download_size": 15127185362, "dataset_size": 30485789850}}
|
2023-03-29T22:50:23+00:00
|
61a9690126faa8839fff46958dbe404b11891425
|
# Dataset Card for "sft_language_submix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/sft_language_submix
|
[
"region:us"
] |
2023-03-29T22:53:52+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3360236870.066389, "num_examples": 2339239}], "download_size": 1943869500, "dataset_size": 3360236870.066389}}
|
2023-03-29T22:56:41+00:00
|
e74e2a178161a385ecd26c5825f19e64a219e4ad
|
# Dataset Card for "tomatoesSpoof2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesSpoof2
|
[
"region:us"
] |
2023-03-29T23:14:39+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 673124095.0, "num_examples": 557}], "download_size": 35510907, "dataset_size": 673124095.0}}
|
2023-03-29T23:17:35+00:00
|
0ebc32dbb6f12467f5fb4cb32d3c71395840bd37
|
# Dataset Card for "procedural_gen_2operands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/procedural_gen_2operands
|
[
"region:us"
] |
2023-03-29T23:50:56+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 57720, "num_examples": 2000}, {"name": "validation", "num_bytes": 11574, "num_examples": 400}], "download_size": 26153, "dataset_size": 69294}}
|
2023-03-29T23:50:59+00:00
|
b0842720e3478f4de061b8e4e83d5c382624d0ee
|
# Dataset Card for "DA_MultiWOZ_hotel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_MultiWOZ_hotel
|
[
"region:us"
] |
2023-03-29T23:56:11+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1627308.4499394428, "num_examples": 4953}, {"name": "test", "num_bytes": 366, "num_examples": 1}], "download_size": 653642, "dataset_size": 1627674.4499394428}}
|
2023-03-29T23:56:15+00:00
|
1283a2564d3df27b7c737aeaab4f39aaf97bbef4
|
# Dataset Card for "DA_MultiWOZ_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_MultiWOZ_train
|
[
"region:us"
] |
2023-03-29T23:56:15+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 759964.6905864198, "num_examples": 2591}, {"name": "test", "num_bytes": 321, "num_examples": 1}], "download_size": 299705, "dataset_size": 760285.6905864198}}
|
2023-03-29T23:56:18+00:00
|
744b019bdfa68ac2b1272c665e2b27a8fc9fba96
|
# Dataset Card for "DA_MultiWOZ_restaurant"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_MultiWOZ_restaurant
|
[
"region:us"
] |
2023-03-29T23:56:18+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2358714.8704030397, "num_examples": 7368}, {"name": "test", "num_bytes": 292, "num_examples": 1}], "download_size": 888220, "dataset_size": 2359006.8704030397}}
|
2023-03-29T23:56:22+00:00
|
607b18a7746ff95d439cab5a7e3088795618ca54
|
# Dataset Card for "DA_MultiWOZ_taxi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_MultiWOZ_taxi
|
[
"region:us"
] |
2023-03-29T23:56:22+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 636448.1401906274, "num_examples": 2517}, {"name": "test", "num_bytes": 360, "num_examples": 1}], "download_size": 248789, "dataset_size": 636808.1401906274}}
|
2023-03-29T23:56:26+00:00
|
a1fab03930a80b38e4d69d51e5051b51eeebc9e3
|
# Dataset Card for "DA_MultiWOZ_attraction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_MultiWOZ_attraction
|
[
"region:us"
] |
2023-03-29T23:56:26+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 256994.14303638643, "num_examples": 796}, {"name": "test", "num_bytes": 390, "num_examples": 1}], "download_size": 100060, "dataset_size": 257384.14303638643}}
|
2023-03-29T23:56:29+00:00
|
2a375160e6547c26cd81eb7759b875dc0ca802b8
|
# Dataset Card for "DA_MultiWOZ_hospital"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vidhikatkoria/DA_MultiWOZ_hospital
|
[
"region:us"
] |
2023-03-29T23:56:30+00:00
|
{"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 162349.6563071298, "num_examples": 546}, {"name": "test", "num_bytes": 309, "num_examples": 1}], "download_size": 54666, "dataset_size": 162658.6563071298}}
|
2023-03-29T23:56:33+00:00
|
5d2720499ea96d109ad4c0c1f3a8e92f4fbaa630
|
# Dataset Card for "tomatoesSpoof3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesSpoof3
|
[
"region:us"
] |
2023-03-30T00:09:25+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 37099461.0, "num_examples": 557}], "download_size": 33524817, "dataset_size": 37099461.0}}
|
2023-03-30T00:12:29+00:00
|
8673d06f078e867610595b74b56a78712ae684df
|
# Dataset Card for "tomatoesSpoof4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattyhatch/tomatoesSpoof4
|
[
"region:us"
] |
2023-03-30T00:20:14+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 37049913.0, "num_examples": 557}], "download_size": 33476189, "dataset_size": 37049913.0}}
|
2023-03-30T00:21:31+00:00
|
5c004d83f3a78036a706f3d47dbce9c176248f32
|
YuanPJ/icsi_summ
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-03-30T00:27:19+00:00
|
{"license": "cc-by-4.0"}
|
2023-03-30T00:31:29+00:00
|
|
7540a209f49d8cbae9cbae03c5c0263962a78a2e
|
# Dataset Card for "cv_svamp_augmented_fold0_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/cv_svamp_augmented_fold0_ver2
|
[
"region:us"
] |
2023-03-30T00:30:14+00:00
|
{"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2713139, "num_examples": 7864}, {"name": "validation", "num_bytes": 162466, "num_examples": 412}], "download_size": 721267, "dataset_size": 2875605}}
|
2023-03-30T02:06:06+00:00
|
b3b6cb56cd8241b7f755c5e2139772046c54f10b
|
# Dataset Card for "cv_svamp_augmented_fold1_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/cv_svamp_augmented_fold1_ver2
|
[
"region:us"
] |
2023-03-30T00:30:18+00:00
|
{"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2716697, "num_examples": 7840}, {"name": "validation", "num_bytes": 158908, "num_examples": 436}], "download_size": 720144, "dataset_size": 2875605}}
|
2023-03-30T02:06:11+00:00
|
5d41b708c00594052be0ddd67e06793ee493ad69
|
# Dataset Card for "cv_svamp_augmented_fold2_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/cv_svamp_augmented_fold2_ver2
|
[
"region:us"
] |
2023-03-30T00:30:22+00:00
|
{"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2712727, "num_examples": 7822}, {"name": "validation", "num_bytes": 162878, "num_examples": 454}], "download_size": 721078, "dataset_size": 2875605}}
|
2023-03-30T02:06:16+00:00
|
ff0464569ab4447a62d434acc4be190497df8f95
|
# Dataset Card for "cv_svamp_augmented_fold3_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/cv_svamp_augmented_fold3_ver2
|
[
"region:us"
] |
2023-03-30T00:30:26+00:00
|
{"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2745859, "num_examples": 7946}, {"name": "validation", "num_bytes": 129746, "num_examples": 330}], "download_size": 720562, "dataset_size": 2875605}}
|
2023-03-30T02:06:21+00:00
|
fa441db4a571fbce9dbbad59a4828db785c580fd
|
# Dataset Card for "cv_svamp_augmented_fold4_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/cv_svamp_augmented_fold4_ver2
|
[
"region:us"
] |
2023-03-30T00:30:29+00:00
|
{"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2745369, "num_examples": 7908}, {"name": "validation", "num_bytes": 130236, "num_examples": 368}], "download_size": 720249, "dataset_size": 2875605}}
|
2023-03-30T02:06:25+00:00
|
8dc0d93d62457aed48e110dccc0938aa44c7b036
|
# Dataset Card for "yiyi_test_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
YiYiXu/yiyi_test_ds
|
[
"region:us"
] |
2023-03-30T01:07:22+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "condtioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22659003.0, "num_examples": 15}], "download_size": 22663578, "dataset_size": 22659003.0}}
|
2023-03-30T01:07:25+00:00
|
14d9a9dd2eaf56b972e1652045fbd8a12961afbc
|
nikcane/slack
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-30T01:41:15+00:00
|
{"license": "apache-2.0"}
|
2023-03-30T01:48:38+00:00
|
|
03766b09aede2926257bb90eabe63fbda3f241e7
|
# Dataset Card for "sft_code_submix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/sft_code_submix
|
[
"region:us"
] |
2023-03-30T01:43:23+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1399630171.2578096, "num_examples": 1146098}], "download_size": 501178967, "dataset_size": 1399630171.2578096}}
|
2023-03-30T01:44:13+00:00
|
d9b304cd64edadcb2958d708f91fea3fb0151938
|
# Dataset Card for "sft_language_and_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/sft_language_and_code
|
[
"region:us"
] |
2023-03-30T01:55:29+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4754005271.891349, "num_examples": 3471810}], "download_size": 2503296527, "dataset_size": 4754005271.891349}}
|
2023-03-30T01:59:27+00:00
|
70349493830a9dad469fafe2710fe1147d13be98
|
Pruned gpt4all dataset meant to reduce annoying behvaiors and nonsensical prompts
|
Nebulous/gpt4all_pruned
|
[
"license:cc",
"region:us"
] |
2023-03-30T02:16:53+00:00
|
{"license": "cc"}
|
2023-04-03T22:29:29+00:00
|
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