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zy1111/test | zy1111 | "2024-10-15T08:34:34Z" | 20,590 | 0 | [
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] | null | "2024-09-27T07:03:45Z" | ---
license: apache-2.0
---
|
lukaemon/bbh | lukaemon | "2023-02-02T01:14:46Z" | 20,541 | 55 | [
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] | null | "2023-02-01T07:46:51Z" | ---
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---
# BIG-bench Hard dataset
homepage: https://github.com/suzgunmirac/BIG-Bench-Hard
```
@article{suzgun2022challenging,
title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them},
author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason},
journal={arXiv preprint arXiv:2210.09261},
year={2022}
}
``` |
mteb/sts12-sts | mteb | "2022-09-27T19:11:50Z" | 20,489 | 6 | [
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language:
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--- |
asahi417/seamless-align-enA-jaA.speaker-embedding.hubert-xl | asahi417 | "2024-06-14T01:51:16Z" | 20,370 | 0 | [
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---
|
japanese-asr/whisper_transcriptions.mls.wer_10.0 | japanese-asr | "2024-09-14T07:57:24Z" | 20,365 | 1 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:audio",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-09-11T09:52:44Z" | ---
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---
|
bigscience/xP3mt | bigscience | "2023-05-30T15:50:57Z" | 20,030 | 24 | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"language:gu",
"language:hi",
"language:id",
"language:ig",
"language:ki",
"language:kn",
"language:lg",
"language:ln",
"language:ml",
"language:mr",
"language:ne",
"language:nso",
"language:ny",
"language:or",
"language:pa",
"language:pt",
"language:rn",
"language:rw",
"language:sn",
"language:st",
"language:sw",
"language:ta",
"language:te",
"language:tn",
"language:ts",
"language:tum",
"language:tw",
"language:ur",
"language:vi",
"language:wo",
"language:xh",
"language:yo",
"language:zh",
"language:zu",
"license:apache-2.0",
"size_categories:10M<n<100M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2211.01786",
"region:us"
] | [
"other"
] | "2022-09-28T12:36:00Z" | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?",
"targets": "Sí"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3).
|Language|Kilobytes|%|Samples|%|Non-English prompts|
|--------|------:|-:|---:|-:|-:|
|tw|106288|0.11|265071|0.33| |
|bm|107056|0.11|265180|0.33| |
|ak|108096|0.11|265071|0.33| |
|ca|110608|0.11|271191|0.34| |
|eu|113008|0.12|281199|0.35| |
|fon|113072|0.12|265063|0.33| |
|st|114080|0.12|265063|0.33| |
|ki|115040|0.12|265180|0.33| |
|tum|116032|0.12|265063|0.33| |
|wo|122560|0.13|365063|0.46| |
|ln|126304|0.13|365060|0.46| |
|as|156256|0.16|265063|0.33| |
|or|161472|0.17|265063|0.33| |
|kn|165456|0.17|265063|0.33| |
|ml|175040|0.18|265864|0.33| |
|rn|192992|0.2|318189|0.4| |
|nso|229712|0.24|915051|1.14| |
|tn|235536|0.24|915054|1.14| |
|lg|235936|0.24|915021|1.14| |
|rw|249360|0.26|915043|1.14| |
|ts|250256|0.26|915044|1.14| |
|sn|252496|0.26|865056|1.08| |
|xh|254672|0.26|915058|1.14| |
|zu|263712|0.27|915061|1.14| |
|ny|272128|0.28|915063|1.14| |
|ig|325440|0.33|950097|1.19|✅|
|yo|339664|0.35|913021|1.14|✅|
|ne|398144|0.41|315754|0.39|✅|
|pa|529632|0.55|339210|0.42|✅|
|sw|561392|0.58|1114439|1.39|✅|
|gu|566576|0.58|347499|0.43|✅|
|mr|674000|0.69|417269|0.52|✅|
|bn|854864|0.88|428725|0.54|✅|
|ta|943440|0.97|410633|0.51|✅|
|te|1384016|1.42|573354|0.72|✅|
|ur|1944416|2.0|855756|1.07|✅|
|vi|3113184|3.2|1667306|2.08|✅|
|code|4330752|4.46|2707724|3.38| |
|hi|4469712|4.6|1543441|1.93|✅|
|id|4538768|4.67|2582272|3.22|✅|
|zh|4604112|4.74|3571636|4.46|✅|
|ar|4703968|4.84|2148970|2.68|✅|
|fr|5558912|5.72|5055942|6.31|✅|
|pt|6130016|6.31|3562772|4.45|✅|
|es|7579424|7.8|5151349|6.43|✅|
|en|39252528|40.4|32740750|40.87| |
|total|97150128|100.0|80100816|100.0|✅|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval)
- Natural Language Inference (NLI)
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. |
aklein4/OpenHermes-Llama-3.2-Instruct-Shuffled | aklein4 | "2025-01-11T19:49:18Z" | 19,970 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-11T19:46:52Z" | ---
dataset_info:
features:
- name: __key__
dtype: string
- name: __url__
dtype: string
- name: gen_mask.npy
sequence: bool
- name: input_ids.npy
sequence: uint32
- name: pad_mask.npy
sequence: bool
- name: segment_ids.npy
sequence: uint32
- name: text.txt
dtype: string
splits:
- name: train
num_bytes: 4970493095.0
num_examples: 374215
download_size: 1516295098
dataset_size: 4970493095.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ceval/ceval-exam | ceval | "2023-08-31T14:04:10Z" | 19,824 | 252 | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2305.08322",
"region:us"
] | [
"text-classification",
"multiple-choice",
"question-answering"
] | "2023-05-16T01:47:44Z" | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: C-Eval
size_categories:
- 10K<n<100K
---
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details.
Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit)
### Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"ceval/ceval-exam",name="computer_network")
print(dataset['val'][0])
# {'id': 0, 'question': '使用位填充方法,以01111110为位首flag,数据为011011111111111111110010,求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''}
```
More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data).
Please cite our paper if you use our dataset.
```
@article{huang2023ceval,
title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
journal={arXiv preprint arXiv:2305.08322},
year={2023}
}
```
|
Tuxifan/UbuntuIRC | Tuxifan | "2023-06-04T15:35:31Z" | 19,643 | 0 | [
"task_categories:text-generation",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [
"text-generation"
] | "2023-06-02T22:48:40Z" | ---
license: cc0-1.0
task_categories:
- text-generation
pretty_name: Ubuntu IRC channels
---
Completely uncurated collection of IRC logs from the Ubuntu IRC channels |
inductiva/windtunnel-20k | inductiva | "2024-10-02T11:49:47Z" | 19,639 | 5 | [
"task_categories:feature-extraction",
"task_categories:graph-ml",
"task_categories:image-to-3d",
"language:en",
"size_categories:10K<n<100K",
"modality:3d",
"modality:tabular",
"modality:text",
"region:us",
"simulation",
"openfoam",
"physics",
"windtunnel",
"inductiva",
"machine learning",
"synthetic"
] | [
"feature-extraction",
"graph-ml",
"image-to-3d"
] | "2024-09-26T09:06:47Z" | ---
pretty_name: Wind Tunnel 20K Dataset
size_categories:
- 10K<n<100K
task_categories:
- feature-extraction
- graph-ml
- image-to-3d
language:
- en
tags:
- simulation
- openfoam
- physics
- windtunnel
- inductiva
- machine learning
- synthetic
---
<p align="center">
<img src="https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/pressure_field_mesh.png", width="500px">
</p>
# Wind Tunnel Dataset
The **Wind Tunnel Dataset** contains **19,812 OpenFOAM simulations** of **1,000 unique automobile-like objects** placed in a virtual wind tunnel measuring **20 meters long, 10 meters wide, and 8 meters high.**
Each object was tested under **20 different conditions**: 4 random wind speeds ranging from **10 to 50 m/s**, and 5 rotation angles (**0°**, **180°** and **3 random angles**).
The object meshes were generated using [Instant Mesh](https://github.com/TencentARC/InstantMesh) based on images sourced from the [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset). To make sure the results are stable and reliable, each simulation runs for **300 iterations**.
The entire dataset is organized into three subsets: **70% for training, 20% for validation, and 10% for testing.**
The data generation process itself was orchestrated using the [Inductiva API](https://inductiva.ai/),
which allowed us to run hundreds of OpenFOAM simulations in parallel on the cloud.
## Motivation: Addressing the Data Gap in CFD
Recently, there’s been a lot of interest in using **machine learning (ML)** to speed up **CFD simulations**. Research has shown that for well-defined scenarios—like a virtual wind tunnel—you can train an ML model to “predict” the results of a simulation **much faster** than traditional methods, while still keeping the accuracy close to what you’d expect from classical simulations.
That said, the **ML/CFD communities** are still lacking enough **training data** for their research. We’ve identified two main reasons for this.
First, there’s a shortage of datasets with **high-quality 3D meshes** needed for running CFD simulations. Existing 3D object datasets have a lot of limitations: they’re either too small, closed-source, or have low-quality meshes. Without this input data, it’s been really hard to generate large-scale training datasets for realistic CFD scenarios, which almost always involve 3D meshes.
Second, even if you had all the 3D meshes you needed, setting up and running thousands of **CFD simulations** to generate a large, diverse dataset isn’t easy. To create a dataset like this, you’d need to define an initial simulation scenario (like the wind tunnel setup) and then run enough variations—different meshes, wind speeds, and so on—to cover a wide range of data points for training a robust **ML model**.
The problem is, running a single CFD simulation can be tricky enough with most software. Orchestrating **thousands of simulations** and handling all the resulting data? That’s a whole new level of challenge.
While both of these problems are difficult to solve in general, we decided to focus on one common CFD scenario: a **virtual wind tunnel** for **static automobiles**. Using the popular **OpenFOAM** simulation package, we produced a large dataset of CFD simulations.
Next, we’ll explain how we tackled the challenges of generating the data and orchestrating the simulations.
## Generating a Large Quantity of Automobile-like 3D Meshes
Since there aren’t many publicly available 3D meshes of automobiles, we decided to use recent image-to-mesh models to generate meshes from freely available car images.
We specifically used the open-source [InstantMesh](https://github.com/TencentARC/InstantMesh) model (Apache-2.0), which is currently state-of-the-art in **image-to-mesh generation**. We generated the automobile-like meshes by running Instant Mesh on **1,000 images** from the publicly available [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset) (Apache-2.0), which contains 16,185 images of automobiles.
Running the image-to-mesh model naturally results in some defects, like irregular surfaces, asymmetry, holes, and disconnected components. To address these issues, we implemented a custom post-processing step to improve mesh quality. We used **PCA** to align the meshes with the main axes and removed any disconnected components.
The resulting set of meshes still contains minor defects, like “spikes” or “cavities” in flat areas, unexpected holes, and asymmetry issues. However, we see these imperfections as valuable features of the dataset. From a machine learning perspective, they bring challenges that can help prevent overfitting and contribute to building more robust and generalizable models.
## Orchestrating 20k Cloud Simulations—Using Just Python
To tackle the challenge of orchestrating **20,000 OpenFOAM simulations**, we resorted to the **Inductiva API**. The Inductiva platform offers a simple Python API for running simulation workflows in the cloud and supports several popular open-source packages, including **OpenFOAM**. Here’s an [example](https://docs.inductiva.ai/en/latest/simulators/OpenFOAM.html) of how to run an OpenFOAM simulation using Inductiva.
With the Inductiva API, it’s easy to parameterize specific simulation scenarios and run variations of a base case by programmatically adjusting the input parameters and starting conditions of the simulation. More details [here](#OpenFoam-Parameters). Additionally, users can create custom Python classes that wrap these parameterized simulations, providing a simple Python interface for running simulations—no need to interact directly with the underlying simulation packages.
We used the [Inductiva API to create a Python class for the Wind Tunnel scenario](https://github.com/inductiva/wind-tunnel), which allowed us to run **20,000 simulations** across a range of input parameters.
For more on how to transform your complex simulation workflows into easy-to-use Python classes, we wrote a [blog post](https://inductiva.ai/blog/article/transform-complex-simulations) all about it.
## How Did We Generate the Dataset?
1. **Generate Input Meshes**: We first generated input meshes using the **InstantMesh model** with images from the **Stanford Cars Dataset**, followed by post-processing to improve mesh quality.
2. **Run OpenFOAM Simulations**: Using the Inductiva API, we ran OpenFOAM simulations on the input meshes under different wind speeds and angles. The result is an output mesh `openfoam_mesh.obj`that contains all the relevant simulation data.
3. **Post-process OpenFOAM Output**: We post-processed the OpenFOAM output to generate streamlines and pressure map meshes.
The code we used to generate and post-process the meshes is available on [GitHub](https://github.com/inductiva/datasets-generation/tree/main/windtunnel_dataset).
## Dataset Structure
```
data
├── train
│ ├── <SIMULATION_ID>
│ │ ├── input_mesh.obj
│ │ ├── openfoam_mesh.obj
│ │ ├── pressure_field_mesh.vtk
│ │ ├── simulation_metadata.json
│ │ └── streamlines_mesh.ply
│ └── ...
├── validation
│ └── ...
└── test
└── ...
```
## Dataset Files
Each simulation in the Wind Tunnel Dataset is accompanied by several key files that provide both the input and the output data of the simulations.
Here’s a breakdown of the files included in each simulation:
- **[input_mesh.obj](#input_meshobj)**: OBJ file with the input mesh.
- **[openfoam_mesh.obj](#openfoam_meshobj)**: OBJ file with the OpenFOAM mesh.
- **[pressure_field_mesh.vtk](#pressure_field_meshvtk)**: VTK file with the pressure field data.
- **[streamlines_mesh.ply](#streamlines_meshply)**: PLY file with the streamlines.
- **[metadata.json](#metadatajson)**: JSON with metadata about the input parameters and about some output results such as the force coefficients (obtained via simulation) and the path of the output files.
### input_mesh.obj
The input mesh we generated using the Instant Mesh model from images in the Stanford Cars Dataset, serves as the starting point for the OpenFOAM simulation.
Details on the mesh generation process can be found [here](#Generating-a-large-quantity-of-Automobile-like-3D-Meshes).
| **Input Mesh** | **# points of input meshes** |
|-------------------------------|------------------------------|
| ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/input_mesh.png) | ![Histogram](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/histogram_of_points_input.png) |
```python
import pyvista as pv
# Load the mesh
mesh_path = "input_mesh.obj"
mesh = pv.read(mesh_path)
# Get the vertices (points)
vertices = mesh.points
# Get the faces (connections)
# The faces array contains the number of vertices per face followed by the vertex indices.
# For example: [3, v1, v2, v3, 3, v4, v5, v6, ...] where 3 means a triangle.
faces = mesh.faces
```
### openfoam_mesh.obj
This mesh is the result of the OpenFOAM simulation. The number of points is reduced compared to the `input_mesh.obj` due to mesh refinement and processing steps applied by OpenFOAM during the simulation.
| **OpenFoam Mesh** | **# points of OpenFoam meshes** |
|-------------------------------|------------------------------|
| ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/openfoam_mesh.png) | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/histogram_of_points_foam.png) |
```python
import pyvista as pv
# Load the mesh
mesh_path = "openfoam_mesh.obj"
mesh = pv.read(mesh_path)
# Get the vertices (points)
vertices = mesh.points
# Get the faces (connections)
# The faces array contains the number of vertices per face followed by the vertex indices.
# For example: [3, v1, v2, v3, 3, v4, v5, v6, ...] where 3 means a triangle.
faces = mesh.faces
```
### pressure_field_mesh.vtk
Pressure values were extracted from the `openfoam_mesh.obj` and interpolated onto the `input_mesh.obj` using the closest point strategy. This approach allowed us to project the pressure values onto a higher-resolution mesh. As shown in the histogram, the the point distribution matches that of the `input_mesh.obj`.
More details can be found here [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018531ff05d0d8ef9d63d8c108800f78f/windtunnel/windtunnel_outputs.py#L111).
| **Pressure Field Mesh** | **# points of Pressure Field meshes** |
|-------------------------------|------------------------------|
| ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/pressure_field_mesh.png) | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/histogram_of_points_input.png)) |
```python
import pyvista as pv
mesh_path = "pressure_field_mesh.vtk"
mesh = pv.read(mesh_path)
# The vertices array contains the coordinates of each point in the mesh.
vertices = mesh.points
# The faces array contains the number of vertices per face followed by the vertex indices.
# For example: [3, v1, v2, v3, 3, v4, v5, v6, ...] where 3 means a triangle.
faces = mesh.faces
# Get the pressure data (scalar named "p")
# This retrieves the pressure values associated with each vertex in the mesh.
pressure_data = mesh.point_data["p"]
```
### streamlines_mesh.ply
Streamlines visually represent the flow characteristics within the simulation, illustrating how air flows around the object
More information can be found [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018531ff05d0d8ef9d63d8c108800f78f/windtunnel/windtunnel_outputs.py#L70).
| **Streamlines Mesh** | **# points of streamlines meshes** |
|-------------------------------|------------------------------|
| ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/streamlines_mesh.png) | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/histogram_of_points_streamlines.png) |
### metadata.json
This file contains metadata related to the simulation, including input parameters such as `wind_speed`, `rotate_angle`, `num_iterations`, and `resolution`. Additionally, it includes output parameters like `drag_coefficient`, `moment_coefficient`, `lift_coefficient`, `front_lift_coefficient`, and `rear_lift_coefficient`. The file also specifies the locations of the generated output meshes.
```json
{
"id": "1w63au1gpxgyn9kun5q9r7eqa",
"object_file": "object_24.obj",
"wind_speed": 35,
"rotate_angle": 332,
"num_iterations": 300,
"resolution": 5,
"drag_coefficient": 0.8322182,
"moment_coefficient": 0.3425206,
"lift_coefficient": 0.1824983,
"front_lift_coefficient": 0.4337698,
"rear_lift_coefficient": -0.2512715,
"input_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/input_mesh.obj",
"openfoam_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/openfoam_mesh.obj",
"pressure_field_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/pressure_field_mesh.vtk",
"streamlines_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/streamlines_mesh.ply"
}
```
### Dataset Statistics
The dataset includes **19,812 valid samples** out of **20,000 simulations**, with [188 submissions failing](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/failed_tasks.txt) due to numerical errors in OpenFOAM.
The full dataset requires about **300 GB** of storage, but you can also download smaller portions if needed.
## Downloading the Dataset:
To download the dataset, you’ll need to install the [Datasets package](https://huggingface.co/docs/datasets/en/index) from Hugging Face:
```python
pip install datasets
```
### 1. Using snapshot_download()
```python
import huggingface_hub
dataset_name = "inductiva/windtunnel-20k"
# Download the entire dataset
huggingface_hub.snapshot_download(repo_id=dataset_name, repo_type="dataset")
# Download to a specific local directory
huggingface_hub.snapshot_download(
repo_id=dataset_name, repo_type="dataset", local_dir="local_folder"
)
# Download only the simulation metadata across all simulations
huggingface_hub.snapshot_download(
repo_id=dataset_name,
repo_type="dataset",
local_dir="local_folder",
allow_patterns=["*/*/*/simulation_metadata.json"]
)
```
### 2. Using load_dataset()
```python
import datasets
# Load the dataset (streaming is supported)
dataset = datasets.load_dataset("inductiva/windtunnel-20k", streaming=False)
# Display dataset information
print(dataset)
# Access a sample from the training set
sample = dataset["train"][0]
print("Sample from training set:", sample)
```
## OpenFoam Parameters
We used the [Inductiva Template Manager](https://tutorials.inductiva.ai/intro_to_api/templating.html) to **parameterize** the OpenFoam configuration files.
Below are some snippets from the templates used in the wind tunnel simulations.
initialConditions.jinja
```jinja
flowVelocity ({{ wind_speed }} 0 0);
```
controlDict.jinja
```jinja
endTime {{ num_iterations }};
```
forceCoeffs.jinja
```jinja
magUInf {{ wind_speed }};
lRef {{ length }}; // Wheelbase length
Aref {{ area }}; // Estimated
```
snappyHexMeshDict.jinja
```jinja
geometry
{
object
{
type triSurfaceMesh;
file "object.obj";
}
refinementBox
{
type searchableBox;
min ({{ x_min }} {{ y_min }} {{ z_min }});
max ({{ x_max }} {{ y_max }} {{ z_max }});
}
};
features
(
{
file "object.eMesh";
level {{ resolution + 1 }};
}
);
refinementSurfaces
{
object
{
// Surface-wise min and max refinement level
level ({{ resolution }} {{ resolution + 1 }});
}
}
refinementRegions
{
refinementBox
{
mode inside;
levels ((1E15 {{ resolution - 1 }}));
}
}
locationInMesh ({{ x_min }} {{ y_min }} {{ z_min }});
```
You can find the full OpenFoam configuration on github: [https://github.com/inductiva/wind-tunnel/tree/main/windtunnel/templates](https://github.com/inductiva/wind-tunnel/tree/main/windtunnel/templates)
## What's Next?
If you encounter any issues with this dataset, feel free to reach out at [support@intuctiva.ai](support@intuctiva.ai).
If you spot any problematic meshes, let us know so we can fix them in the next version of the **Windtunnel-20k dataset**.
To learn more about how we created this dataset—or how you can generate synthetic datasets for Physics-AI models—check out our well-tested [4-step recipe for generating synthetic data](https://inductiva.ai/blog/article/supercharge-your-physics-ml-with-inductivas-cloud-based-simulation-api) or discover how to [transform your own complex simulation workflows into easy-to-use Python classes](https://inductiva.ai/blog/article/transform-complex-simulations).
You may also be interested in reading our blog post, [The 3D Mesh Resolution Threshold - 5k Points is All You Need!](https://inductiva.ai/blog/article/5k-points-is-all-you-need), where we explore just how much you can reduce the level of detail in a 3D object while still maintaining accurate aerodynamic results in a virtual wind tunnel built with OpenFOAM.
|
Forceless/Zenodo10K | Forceless | "2025-01-09T11:24:10Z" | 19,514 | 6 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.03936",
"region:us"
] | null | "2024-10-16T11:08:15Z" | ---
dataset_info:
features:
- name: filename
dtype: string
- name: size
dtype: int64
- name: url
dtype: string
- name: license
dtype: string
- name: title
dtype: string
- name: created
dtype: string
- name: updated
dtype: string
- name: doi
dtype: string
- name: checksum
dtype: string
splits:
- name: pptx
num_bytes: 3925161
num_examples: 10448
download_size: 2028492
dataset_size: 3925161
configs:
- config_name: default
data_files:
- split: pptx
path: data/pptx-*
---
# PPTAgent/Zenodo10K
This is the dataset used in [PPTAgent](https://arxiv.org/abs/2501.03936), crawled from [zenodo](http://zenodo.org).
To the best of our knowledge, it is the **largest presentation dataset** currently available, comprising over 10,000 **PowerPoint (.pptx)** files, all distributed under a clear and compliant license.
For more information, please visit our [github repo](https://github.com/icip-cas/PPTAgent).
```python
dirname = f"zenodo-pptx/pptx/{task['license']}/{task['created'][:4]}/"
basename = f"{task['checksum'][4:]}-{task['filename']}"
filepath = dirname + basename
try:
open('/tmp/'+basename,'wb').close()
except:
filepath = dirname + basename[:240] + ".pptx"
```
## Citation
If you find this project helpful, please use the following to cite it:
```bibtex
@misc{zheng2025pptagentgeneratingevaluatingpresentations,
title={PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides},
author={Hao Zheng and Xinyan Guan and Hao Kong and Jia Zheng and Hongyu Lin and Yaojie Lu and Ben He and Xianpei Han and Le Sun},
year={2025},
eprint={2501.03936},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2501.03936},
}
```
|
Matthijs/cmu-arctic-xvectors | Matthijs | "2023-02-07T14:04:48Z" | 19,478 | 44 | [
"task_categories:text-to-speech",
"task_categories:audio-to-audio",
"license:mit",
"size_categories:1K<n<10K",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [
"text-to-speech",
"audio-to-audio"
] | "2023-02-07T12:39:22Z" | ---
pretty_name: CMU ARCTIC X-Vectors
task_categories:
- text-to-speech
- audio-to-audio
license: mit
---
# Speaker embeddings extracted from CMU ARCTIC
There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors.
The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers:
- bdl (US male)
- slt (US female)
- jmk (Canadian male)
- awb (Scottish male)
- rms (US male)
- clb (US female)
- ksp (Indian male)
The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model.
Usage:
```python
from datasets import load_dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = embeddings_dataset[7306]["xvector"]
speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)
```
|
universal-dependencies/universal_dependencies | universal-dependencies | "2024-01-18T11:17:47Z" | 19,363 | 28 | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:aii",
"language:ajp",
"language:akk",
"language:am",
"language:apu",
"language:aqz",
"language:ar",
"language:be",
"language:bg",
"language:bho",
"language:bm",
"language:br",
"language:bxr",
"language:ca",
"language:ckt",
"language:cop",
"language:cs",
"language:cu",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fo",
"language:fr",
"language:fro",
"language:ga",
"language:gd",
"language:gl",
"language:got",
"language:grc",
"language:gsw",
"language:gun",
"language:gv",
"language:he",
"language:hi",
"language:hr",
"language:hsb",
"language:hu",
"language:hy",
"language:id",
"language:is",
"language:it",
"language:ja",
"language:kfm",
"language:kk",
"language:kmr",
"language:ko",
"language:koi",
"language:kpv",
"language:krl",
"language:la",
"language:lt",
"language:lv",
"language:lzh",
"language:mdf",
"language:mr",
"language:mt",
"language:myu",
"language:myv",
"language:nl",
"language:no",
"language:nyq",
"language:olo",
"language:orv",
"language:otk",
"language:pcm",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sa",
"language:sk",
"language:sl",
"language:sme",
"language:sms",
"language:soj",
"language:sq",
"language:sr",
"language:sv",
"language:swl",
"language:ta",
"language:te",
"language:th",
"language:tl",
"language:tpn",
"language:tr",
"language:ug",
"language:uk",
"language:ur",
"language:vi",
"language:wbp",
"language:wo",
"language:yo",
"language:yue",
"language:zh",
"license:unknown",
"size_categories:1K<n<10K",
"region:us",
"constituency-parsing",
"dependency-parsing"
] | [
"token-classification"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- af
- aii
- ajp
- akk
- am
- apu
- aqz
- ar
- be
- bg
- bho
- bm
- br
- bxr
- ca
- ckt
- cop
- cs
- cu
- cy
- da
- de
- el
- en
- es
- et
- eu
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- fo
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- got
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- kfm
- kk
- kmr
- ko
- koi
- kpv
- krl
- la
- lt
- lv
- lzh
- mdf
- mr
- mt
- myu
- myv
- nl
- 'no'
- nyq
- olo
- orv
- otk
- pcm
- pl
- pt
- ro
- ru
- sa
- sk
- sl
- sme
- sms
- soj
- sq
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- sv
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- te
- th
- tl
- tpn
- tr
- ug
- uk
- ur
- vi
- wbp
- wo
- yo
- yue
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
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paperswithcode_id: universal-dependencies
pretty_name: Universal Dependencies Treebank
tags:
- constituency-parsing
- dependency-parsing
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- config_name: yo_ytb
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config_names:
- af_afribooms
- aii_as
- ajp_madar
- akk_pisandub
- akk_riao
- am_att
- apu_ufpa
- aqz_tudet
- ar_nyuad
- ar_padt
- ar_pud
- be_hse
- bg_btb
- bho_bhtb
- bm_crb
- br_keb
- bxr_bdt
- ca_ancora
- ckt_hse
- cop_scriptorium
- cs_cac
- cs_cltt
- cs_fictree
- cs_pdt
- cs_pud
- cu_proiel
- cy_ccg
- da_ddt
- de_gsd
- de_hdt
- de_lit
- de_pud
- el_gdt
- en_esl
- en_ewt
- en_gum
- en_gumreddit
- en_lines
- en_partut
- en_pronouns
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- es_ancora
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- eu_bdt
- fa_perdt
- fa_seraji
- fi_ftb
- fi_ood
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- fi_tdt
- fo_farpahc
- fo_oft
- fr_fqb
- fr_ftb
- fr_gsd
- fr_partut
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- fr_sequoia
- fr_spoken
- fro_srcmf
- ga_idt
- gd_arcosg
- gl_ctg
- gl_treegal
- got_proiel
- grc_perseus
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- gsw_uzh
- gun_dooley
- gun_thomas
- gv_cadhan
- he_htb
- hi_hdtb
- hi_pud
- hr_set
- hsb_ufal
- hu_szeged
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- id_csui
- id_gsd
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- is_icepahc
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- ja_modern
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- kfm_aha
- kk_ktb
- kmr_mg
- ko_gsd
- ko_kaist
- ko_pud
- koi_uh
- kpv_ikdp
- kpv_lattice
- krl_kkpp
- la_ittb
- la_llct
- la_perseus
- la_proiel
- lt_alksnis
- lt_hse
- lv_lvtb
- lzh_kyoto
- mdf_jr
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- mt_mudt
- myu_tudet
- myv_jr
- nl_alpino
- nl_lassysmall
- no_bokmaal
- no_nynorsk
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- nyq_aha
- olo_kkpp
- orv_rnc
- orv_torot
- otk_tonqq
- pcm_nsc
- pl_lfg
- pl_pdb
- pl_pud
- pt_bosque
- pt_gsd
- pt_pud
- qhe_hiencs
- qtd_sagt
- ro_nonstandard
- ro_rrt
- ro_simonero
- ru_gsd
- ru_pud
- ru_syntagrus
- ru_taiga
- sa_ufal
- sa_vedic
- sk_snk
- sl_ssj
- sl_sst
- sme_giella
- sms_giellagas
- soj_aha
- sq_tsa
- sr_set
- sv_lines
- sv_pud
- sv_talbanken
- swl_sslc
- ta_mwtt
- ta_ttb
- te_mtg
- th_pud
- tl_trg
- tl_ugnayan
- tpn_tudet
- tr_boun
- tr_gb
- tr_imst
- tr_pud
- ug_udt
- uk_iu
- ur_udtb
- vi_vtb
- wbp_ufal
- wo_wtb
- yo_ytb
- yue_hk
- zh_cfl
- zh_gsd
- zh_gsdsimp
- zh_hk
- zh_pud
---
# Dataset Card for Universal Dependencies Treebank
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Universal Dependencies](https://universaldependencies.org/)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@jplu](https://github.com/jplu) for adding this dataset. |
wecover/OPUS_TED2020 | wecover | "2024-01-31T11:42:51Z" | 19,314 | 0 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-01-31T07:37:04Z" | ---
configs:
- config_name: default
data_files:
- split: train
path: '*/*/train.parquet'
- split: valid
path: '*/*/valid.parquet'
- split: test
path: '*/*/test.parquet'
- config_name: af
data_files:
- split: train
path: '*/*af*/train.parquet'
- split: test
path: '*/*af*/test.parquet'
- split: valid
path: '*/*af*/valid.parquet'
- config_name: am
data_files:
- split: train
path: '*/*am*/train.parquet'
- split: test
path: '*/*am*/test.parquet'
- split: valid
path: '*/*am*/valid.parquet'
- config_name: ar
data_files:
- split: train
path: '*/*ar*/train.parquet'
- split: test
path: '*/*ar*/test.parquet'
- split: valid
path: '*/*ar*/valid.parquet'
- config_name: az
data_files:
- split: train
path: '*/*az*/train.parquet'
- split: test
path: '*/*az*/test.parquet'
- split: valid
path: '*/*az*/valid.parquet'
- config_name: be
data_files:
- split: train
path: '*/*be*/train.parquet'
- split: test
path: '*/*be*/test.parquet'
- split: valid
path: '*/*be*/valid.parquet'
- config_name: bg
data_files:
- split: train
path: '*/*bg*/train.parquet'
- split: test
path: '*/*bg*/test.parquet'
- split: valid
path: '*/*bg*/valid.parquet'
- config_name: bn
data_files:
- split: train
path: '*/*bn*/train.parquet'
- split: test
path: '*/*bn*/test.parquet'
- split: valid
path: '*/*bn*/valid.parquet'
- config_name: bs
data_files:
- split: train
path: '*/*bs*/train.parquet'
- split: test
path: '*/*bs*/test.parquet'
- split: valid
path: '*/*bs*/valid.parquet'
- config_name: ca
data_files:
- split: train
path: '*/*ca*/train.parquet'
- split: test
path: '*/*ca*/test.parquet'
- split: valid
path: '*/*ca*/valid.parquet'
- config_name: cs
data_files:
- split: train
path: '*/*cs*/train.parquet'
- split: test
path: '*/*cs*/test.parquet'
- split: valid
path: '*/*cs*/valid.parquet'
- config_name: da
data_files:
- split: train
path: '*/*da*/train.parquet'
- split: test
path: '*/*da*/test.parquet'
- split: valid
path: '*/*da*/valid.parquet'
- config_name: de
data_files:
- split: train
path: '*/*de*/train.parquet'
- split: test
path: '*/*de*/test.parquet'
- split: valid
path: '*/*de*/valid.parquet'
- config_name: el
data_files:
- split: train
path: '*/*el*/train.parquet'
- split: test
path: '*/*el*/test.parquet'
- split: valid
path: '*/*el*/valid.parquet'
- config_name: en
data_files:
- split: train
path: '*/*en*/train.parquet'
- split: test
path: '*/*en*/test.parquet'
- split: valid
path: '*/*en*/valid.parquet'
- config_name: eo
data_files:
- split: train
path: '*/*eo*/train.parquet'
- split: test
path: '*/*eo*/test.parquet'
- split: valid
path: '*/*eo*/valid.parquet'
- config_name: es
data_files:
- split: train
path: '*/*es*/train.parquet'
- split: test
path: '*/*es*/test.parquet'
- split: valid
path: '*/*es*/valid.parquet'
- config_name: et
data_files:
- split: train
path: '*/*et*/train.parquet'
- split: test
path: '*/*et*/test.parquet'
- split: valid
path: '*/*et*/valid.parquet'
- config_name: eu
data_files:
- split: train
path: '*/*eu*/train.parquet'
- split: test
path: '*/*eu*/test.parquet'
- split: valid
path: '*/*eu*/valid.parquet'
- config_name: fa
data_files:
- split: train
path: '*/*fa*/train.parquet'
- split: test
path: '*/*fa*/test.parquet'
- split: valid
path: '*/*fa*/valid.parquet'
- config_name: fi
data_files:
- split: train
path: '*/*fi*/train.parquet'
- split: test
path: '*/*fi*/test.parquet'
- split: valid
path: '*/*fi*/valid.parquet'
- config_name: fr
data_files:
- split: train
path: '*/*fr*/train.parquet'
- split: test
path: '*/*fr*/test.parquet'
- split: valid
path: '*/*fr*/valid.parquet'
- config_name: ga
data_files:
- split: train
path: '*/*ga*/train.parquet'
- split: test
path: '*/*ga*/test.parquet'
- split: valid
path: '*/*ga*/valid.parquet'
- config_name: gl
data_files:
- split: train
path: '*/*gl*/train.parquet'
- split: test
path: '*/*gl*/test.parquet'
- split: valid
path: '*/*gl*/valid.parquet'
- config_name: he
data_files:
- split: train
path: '*/*he*/train.parquet'
- split: test
path: '*/*he*/test.parquet'
- split: valid
path: '*/*he*/valid.parquet'
- config_name: hi
data_files:
- split: train
path: '*/*hi*/train.parquet'
- split: test
path: '*/*hi*/test.parquet'
- split: valid
path: '*/*hi*/valid.parquet'
- config_name: hr
data_files:
- split: train
path: '*/*hr*/train.parquet'
- split: test
path: '*/*hr*/test.parquet'
- split: valid
path: '*/*hr*/valid.parquet'
- config_name: hu
data_files:
- split: train
path: '*/*hu*/train.parquet'
- split: test
path: '*/*hu*/test.parquet'
- split: valid
path: '*/*hu*/valid.parquet'
- config_name: hy
data_files:
- split: train
path: '*/*hy*/train.parquet'
- split: test
path: '*/*hy*/test.parquet'
- split: valid
path: '*/*hy*/valid.parquet'
- config_name: id
data_files:
- split: train
path: '*/*id*/train.parquet'
- split: test
path: '*/*id*/test.parquet'
- split: valid
path: '*/*id*/valid.parquet'
- config_name: is
data_files:
- split: train
path: '*/*is*/train.parquet'
- split: test
path: '*/*is*/test.parquet'
- split: valid
path: '*/*is*/valid.parquet'
- config_name: it
data_files:
- split: train
path: '*/*it*/train.parquet'
- split: test
path: '*/*it*/test.parquet'
- split: valid
path: '*/*it*/valid.parquet'
- config_name: ja
data_files:
- split: train
path: '*/*ja*/train.parquet'
- split: test
path: '*/*ja*/test.parquet'
- split: valid
path: '*/*ja*/valid.parquet'
- config_name: ka
data_files:
- split: train
path: '*/*ka*/train.parquet'
- split: test
path: '*/*ka*/test.parquet'
- split: valid
path: '*/*ka*/valid.parquet'
- config_name: kk
data_files:
- split: train
path: '*/*kk*/train.parquet'
- split: test
path: '*/*kk*/test.parquet'
- split: valid
path: '*/*kk*/valid.parquet'
- config_name: km
data_files:
- split: train
path: '*/*km*/train.parquet'
- split: test
path: '*/*km*/test.parquet'
- split: valid
path: '*/*km*/valid.parquet'
- config_name: kn
data_files:
- split: train
path: '*/*kn*/train.parquet'
- split: test
path: '*/*kn*/test.parquet'
- split: valid
path: '*/*kn*/valid.parquet'
- config_name: ko
data_files:
- split: train
path: '*/*ko*/train.parquet'
- split: test
path: '*/*ko*/test.parquet'
- split: valid
path: '*/*ko*/valid.parquet'
- config_name: ku
data_files:
- split: train
path: '*/*ku*/train.parquet'
- split: test
path: '*/*ku*/test.parquet'
- split: valid
path: '*/*ku*/valid.parquet'
- config_name: lo
data_files:
- split: train
path: '*/*lo*/train.parquet'
- split: test
path: '*/*lo*/test.parquet'
- split: valid
path: '*/*lo*/valid.parquet'
- config_name: lt
data_files:
- split: train
path: '*/*lt*/train.parquet'
- split: test
path: '*/*lt*/test.parquet'
- split: valid
path: '*/*lt*/valid.parquet'
- config_name: mg
data_files:
- split: train
path: '*/*mg*/train.parquet'
- split: test
path: '*/*mg*/test.parquet'
- split: valid
path: '*/*mg*/valid.parquet'
- config_name: mk
data_files:
- split: train
path: '*/*mk*/train.parquet'
- split: test
path: '*/*mk*/test.parquet'
- split: valid
path: '*/*mk*/valid.parquet'
- config_name: ml
data_files:
- split: train
path: '*/*ml*/train.parquet'
- split: test
path: '*/*ml*/test.parquet'
- split: valid
path: '*/*ml*/valid.parquet'
- config_name: mn
data_files:
- split: train
path: '*/*mn*/train.parquet'
- split: test
path: '*/*mn*/test.parquet'
- split: valid
path: '*/*mn*/valid.parquet'
- config_name: mr
data_files:
- split: train
path: '*/*mr*/train.parquet'
- split: test
path: '*/*mr*/test.parquet'
- split: valid
path: '*/*mr*/valid.parquet'
- config_name: ms
data_files:
- split: train
path: '*/*ms*/train.parquet'
- split: test
path: '*/*ms*/test.parquet'
- split: valid
path: '*/*ms*/valid.parquet'
- config_name: my
data_files:
- split: train
path: '*/*my*/train.parquet'
- split: test
path: '*/*my*/test.parquet'
- split: valid
path: '*/*my*/valid.parquet'
- config_name: ne
data_files:
- split: train
path: '*/*ne*/train.parquet'
- split: test
path: '*/*ne*/test.parquet'
- split: valid
path: '*/*ne*/valid.parquet'
- config_name: nl
data_files:
- split: train
path: '*/*nl*/train.parquet'
- split: test
path: '*/*nl*/test.parquet'
- split: valid
path: '*/*nl*/valid.parquet'
- config_name: pl
data_files:
- split: train
path: '*/*pl*/train.parquet'
- split: test
path: '*/*pl*/test.parquet'
- split: valid
path: '*/*pl*/valid.parquet'
- config_name: pt
data_files:
- split: train
path: '*/*pt*/train.parquet'
- split: test
path: '*/*pt*/test.parquet'
- split: valid
path: '*/*pt*/valid.parquet'
- config_name: ro
data_files:
- split: train
path: '*/*ro*/train.parquet'
- split: test
path: '*/*ro*/test.parquet'
- split: valid
path: '*/*ro*/valid.parquet'
- config_name: ru
data_files:
- split: train
path: '*/*ru*/train.parquet'
- split: test
path: '*/*ru*/test.parquet'
- split: valid
path: '*/*ru*/valid.parquet'
- config_name: sk
data_files:
- split: train
path: '*/*sk*/train.parquet'
- split: test
path: '*/*sk*/test.parquet'
- split: valid
path: '*/*sk*/valid.parquet'
- config_name: sl
data_files:
- split: train
path: '*/*sl*/train.parquet'
- split: test
path: '*/*sl*/test.parquet'
- split: valid
path: '*/*sl*/valid.parquet'
- config_name: so
data_files:
- split: train
path: '*/*so*/train.parquet'
- split: test
path: '*/*so*/test.parquet'
- split: valid
path: '*/*so*/valid.parquet'
- config_name: sq
data_files:
- split: train
path: '*/*sq*/train.parquet'
- split: test
path: '*/*sq*/test.parquet'
- split: valid
path: '*/*sq*/valid.parquet'
- config_name: sr
data_files:
- split: train
path: '*/*sr*/train.parquet'
- split: test
path: '*/*sr*/test.parquet'
- split: valid
path: '*/*sr*/valid.parquet'
- config_name: sv
data_files:
- split: train
path: '*/*sv*/train.parquet'
- split: test
path: '*/*sv*/test.parquet'
- split: valid
path: '*/*sv*/valid.parquet'
- config_name: sw
data_files:
- split: train
path: '*/*sw*/train.parquet'
- split: test
path: '*/*sw*/test.parquet'
- split: valid
path: '*/*sw*/valid.parquet'
- config_name: ta
data_files:
- split: train
path: '*/*ta*/train.parquet'
- split: test
path: '*/*ta*/test.parquet'
- split: valid
path: '*/*ta*/valid.parquet'
- config_name: te
data_files:
- split: train
path: '*/*te*/train.parquet'
- split: test
path: '*/*te*/test.parquet'
- split: valid
path: '*/*te*/valid.parquet'
- config_name: th
data_files:
- split: train
path: '*/*th*/train.parquet'
- split: test
path: '*/*th*/test.parquet'
- split: valid
path: '*/*th*/valid.parquet'
- config_name: tl
data_files:
- split: train
path: '*/*tl*/train.parquet'
- split: test
path: '*/*tl*/test.parquet'
- split: valid
path: '*/*tl*/valid.parquet'
- config_name: tr
data_files:
- split: train
path: '*/*tr*/train.parquet'
- split: test
path: '*/*tr*/test.parquet'
- split: valid
path: '*/*tr*/valid.parquet'
- config_name: ug
data_files:
- split: train
path: '*/*ug*/train.parquet'
- split: test
path: '*/*ug*/test.parquet'
- split: valid
path: '*/*ug*/valid.parquet'
- config_name: uk
data_files:
- split: train
path: '*/*uk*/train.parquet'
- split: test
path: '*/*uk*/test.parquet'
- split: valid
path: '*/*uk*/valid.parquet'
- config_name: ur
data_files:
- split: train
path: '*/*ur*/train.parquet'
- split: test
path: '*/*ur*/test.parquet'
- split: valid
path: '*/*ur*/valid.parquet'
- config_name: uz
data_files:
- split: train
path: '*/*uz*/train.parquet'
- split: test
path: '*/*uz*/test.parquet'
- split: valid
path: '*/*uz*/valid.parquet'
- config_name: vi
data_files:
- split: train
path: '*/*vi*/train.parquet'
- split: test
path: '*/*vi*/test.parquet'
- split: valid
path: '*/*vi*/valid.parquet'
- config_name: ky
data_files:
- split: train
path: '*/*ky*/train.parquet'
- split: test
path: '*/*ky*/test.parquet'
- split: valid
path: '*/*ky*/valid.parquet'
- config_name: si
data_files:
- split: train
path: '*/*si*/train.parquet'
- split: test
path: '*/*si*/test.parquet'
- split: valid
path: '*/*si*/valid.parquet'
- config_name: as
data_files:
- split: train
path: '*/*as*/train.parquet'
- split: test
path: '*/*as*/test.parquet'
- split: valid
path: '*/*as*/valid.parquet'
- config_name: ha
data_files:
- split: train
path: '*/*ha*/train.parquet'
- split: test
path: '*/*ha*/test.parquet'
- split: valid
path: '*/*ha*/valid.parquet'
- config_name: la
data_files:
- split: train
path: '*/*la*/train.parquet'
- split: test
path: '*/*la*/test.parquet'
- split: valid
path: '*/*la*/valid.parquet'
- config_name: pa
data_files:
- split: train
path: '*/*pa*/train.parquet'
- split: test
path: '*/*pa*/test.parquet'
- split: valid
path: '*/*pa*/valid.parquet'
- config_name: ps
data_files:
- split: train
path: '*/*ps*/train.parquet'
- split: test
path: '*/*ps*/test.parquet'
- split: valid
path: '*/*ps*/valid.parquet'
---
|
saiyan-world/Goku-MovieGenBench | saiyan-world | "2025-02-11T03:18:05Z" | 18,996 | 149 | [
"task_categories:text-to-video",
"size_categories:1K<n<10K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"arxiv:2502.04896",
"region:us"
] | [
"text-to-video"
] | "2025-02-06T12:47:26Z" | ---
task_categories:
- text-to-video
---
This repository contains the data associated with the paper [Goku: Flow Based Video Generative Foundation Models](https://huggingface.co/papers/2502.04896).
Project page: https://saiyan-world.github.io/goku/ |
eriktks/conll2003 | eriktks | "2024-01-18T09:34:17Z" | 18,867 | 133 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|other-reuters-corpus",
"language:en",
"license:other",
"size_categories:10K<n<100K",
"region:us"
] | [
"token-classification"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2003
pretty_name: CoNLL-2003
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
- name: chunk_tags
sequence:
class_label:
names:
'0': O
'1': B-ADJP
'2': I-ADJP
'3': B-ADVP
'4': I-ADVP
'5': B-CONJP
'6': I-CONJP
'7': B-INTJ
'8': I-INTJ
'9': B-LST
'10': I-LST
'11': B-NP
'12': I-NP
'13': B-PP
'14': I-PP
'15': B-PRT
'16': I-PRT
'17': B-SBAR
'18': I-SBAR
'19': B-UCP
'20': I-UCP
'21': B-VP
'22': I-VP
- 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
config_name: conll2003
splits:
- name: train
num_bytes: 6931345
num_examples: 14041
- name: validation
num_bytes: 1739223
num_examples: 3250
- name: test
num_bytes: 1582054
num_examples: 3453
download_size: 982975
dataset_size: 10252622
train-eval-index:
- config: conll2003
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for "conll2003"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.aclweb.org/anthology/W03-0419/](https://www.aclweb.org/anthology/W03-0419/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
### Dataset Summary
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.
For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### conll2003
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
An example of 'train' looks as follows.
```
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
```
The original data files have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
### Data Fields
The data fields are the same among all splits.
#### conll2003
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'"': 0, "''": 1, '#': 2, '$': 3, '(': 4, ')': 5, ',': 6, '.': 7, ':': 8, '``': 9, 'CC': 10, 'CD': 11, 'DT': 12,
'EX': 13, 'FW': 14, 'IN': 15, 'JJ': 16, 'JJR': 17, 'JJS': 18, 'LS': 19, 'MD': 20, 'NN': 21, 'NNP': 22, 'NNPS': 23,
'NNS': 24, 'NN|SYM': 25, 'PDT': 26, 'POS': 27, 'PRP': 28, 'PRP$': 29, 'RB': 30, 'RBR': 31, 'RBS': 32, 'RP': 33,
'SYM': 34, 'TO': 35, 'UH': 36, 'VB': 37, 'VBD': 38, 'VBG': 39, 'VBN': 40, 'VBP': 41, 'VBZ': 42, 'WDT': 43,
'WP': 44, 'WP$': 45, 'WRB': 46}
```
- `chunk_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-ADJP': 1, 'I-ADJP': 2, 'B-ADVP': 3, 'I-ADVP': 4, 'B-CONJP': 5, 'I-CONJP': 6, 'B-INTJ': 7, 'I-INTJ': 8,
'B-LST': 9, 'I-LST': 10, 'B-NP': 11, 'I-NP': 12, 'B-PP': 13, 'I-PP': 14, 'B-PRT': 15, 'I-PRT': 16, 'B-SBAR': 17,
'I-SBAR': 18, 'B-UCP': 19, 'I-UCP': 20, 'B-VP': 21, 'I-VP': 22}
```
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|conll2003|14041| 3250|3453|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
From the [CoNLL2003 shared task](https://www.clips.uantwerpen.be/conll2003/ner/) page:
> The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST.
The copyrights are defined below, from the [Reuters Corpus page](https://trec.nist.gov/data/reuters/reuters.html):
> The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:
>
> [Organizational agreement](https://trec.nist.gov/data/reuters/org_appl_reuters_v4.html)
>
> This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.
>
> [Individual agreement](https://trec.nist.gov/data/reuters/ind_appl_reuters_v4.html)
>
> This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization.
### Citation Information
```
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and
De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://www.aclweb.org/anthology/W03-0419",
pages = "142--147",
}
```
### Contributions
Thanks to [@jplu](https://github.com/jplu), [@vblagoje](https://github.com/vblagoje), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
orionweller/reddit_mds_incremental | orionweller | "2024-07-23T17:17:42Z" | 18,803 | 0 | [
"region:us"
] | null | "2024-06-24T14:44:04Z" | ---
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|
nyu-mll/blimp | nyu-mll | "2024-01-23T09:58:08Z" | 18,760 | 37 | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
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"arxiv:1912.00582",
"region:us"
] | [
"text-classification"
] | "2022-03-02T23:29:22Z" | ---
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- config_name: determiner_noun_agreement_with_adj_irregular_2
data_files:
- split: train
path: determiner_noun_agreement_with_adj_irregular_2/train-*
- config_name: determiner_noun_agreement_with_adjective_1
data_files:
- split: train
path: determiner_noun_agreement_with_adjective_1/train-*
- config_name: distractor_agreement_relational_noun
data_files:
- split: train
path: distractor_agreement_relational_noun/train-*
- config_name: distractor_agreement_relative_clause
data_files:
- split: train
path: distractor_agreement_relative_clause/train-*
- config_name: drop_argument
data_files:
- split: train
path: drop_argument/train-*
- config_name: ellipsis_n_bar_1
data_files:
- split: train
path: ellipsis_n_bar_1/train-*
- config_name: ellipsis_n_bar_2
data_files:
- split: train
path: ellipsis_n_bar_2/train-*
- config_name: existential_there_object_raising
data_files:
- split: train
path: existential_there_object_raising/train-*
- config_name: existential_there_quantifiers_1
data_files:
- split: train
path: existential_there_quantifiers_1/train-*
- config_name: existential_there_quantifiers_2
data_files:
- split: train
path: existential_there_quantifiers_2/train-*
- config_name: existential_there_subject_raising
data_files:
- split: train
path: existential_there_subject_raising/train-*
- config_name: expletive_it_object_raising
data_files:
- split: train
path: expletive_it_object_raising/train-*
- config_name: inchoative
data_files:
- split: train
path: inchoative/train-*
- config_name: intransitive
data_files:
- split: train
path: intransitive/train-*
- config_name: irregular_past_participle_adjectives
data_files:
- split: train
path: irregular_past_participle_adjectives/train-*
- config_name: irregular_past_participle_verbs
data_files:
- split: train
path: irregular_past_participle_verbs/train-*
- config_name: irregular_plural_subject_verb_agreement_1
data_files:
- split: train
path: irregular_plural_subject_verb_agreement_1/train-*
- config_name: irregular_plural_subject_verb_agreement_2
data_files:
- split: train
path: irregular_plural_subject_verb_agreement_2/train-*
- config_name: left_branch_island_echo_question
data_files:
- split: train
path: left_branch_island_echo_question/train-*
- config_name: left_branch_island_simple_question
data_files:
- split: train
path: left_branch_island_simple_question/train-*
- config_name: matrix_question_npi_licensor_present
data_files:
- split: train
path: matrix_question_npi_licensor_present/train-*
- config_name: npi_present_1
data_files:
- split: train
path: npi_present_1/train-*
- config_name: npi_present_2
data_files:
- split: train
path: npi_present_2/train-*
- config_name: only_npi_licensor_present
data_files:
- split: train
path: only_npi_licensor_present/train-*
- config_name: only_npi_scope
data_files:
- split: train
path: only_npi_scope/train-*
- config_name: passive_1
data_files:
- split: train
path: passive_1/train-*
- config_name: passive_2
data_files:
- split: train
path: passive_2/train-*
- config_name: principle_A_c_command
data_files:
- split: train
path: principle_A_c_command/train-*
- config_name: principle_A_case_1
data_files:
- split: train
path: principle_A_case_1/train-*
- config_name: principle_A_case_2
data_files:
- split: train
path: principle_A_case_2/train-*
- config_name: principle_A_domain_1
data_files:
- split: train
path: principle_A_domain_1/train-*
- config_name: principle_A_domain_2
data_files:
- split: train
path: principle_A_domain_2/train-*
- config_name: principle_A_domain_3
data_files:
- split: train
path: principle_A_domain_3/train-*
- config_name: principle_A_reconstruction
data_files:
- split: train
path: principle_A_reconstruction/train-*
- config_name: regular_plural_subject_verb_agreement_1
data_files:
- split: train
path: regular_plural_subject_verb_agreement_1/train-*
- config_name: regular_plural_subject_verb_agreement_2
data_files:
- split: train
path: regular_plural_subject_verb_agreement_2/train-*
- config_name: sentential_negation_npi_licensor_present
data_files:
- split: train
path: sentential_negation_npi_licensor_present/train-*
- config_name: sentential_negation_npi_scope
data_files:
- split: train
path: sentential_negation_npi_scope/train-*
- config_name: sentential_subject_island
data_files:
- split: train
path: sentential_subject_island/train-*
- config_name: superlative_quantifiers_1
data_files:
- split: train
path: superlative_quantifiers_1/train-*
- config_name: superlative_quantifiers_2
data_files:
- split: train
path: superlative_quantifiers_2/train-*
- config_name: tough_vs_raising_1
data_files:
- split: train
path: tough_vs_raising_1/train-*
- config_name: tough_vs_raising_2
data_files:
- split: train
path: tough_vs_raising_2/train-*
- config_name: transitive
data_files:
- split: train
path: transitive/train-*
- config_name: wh_island
data_files:
- split: train
path: wh_island/train-*
- config_name: wh_questions_object_gap
data_files:
- split: train
path: wh_questions_object_gap/train-*
- config_name: wh_questions_subject_gap
data_files:
- split: train
path: wh_questions_subject_gap/train-*
- config_name: wh_questions_subject_gap_long_distance
data_files:
- split: train
path: wh_questions_subject_gap_long_distance/train-*
- config_name: wh_vs_that_no_gap
data_files:
- split: train
path: wh_vs_that_no_gap/train-*
- config_name: wh_vs_that_no_gap_long_distance
data_files:
- split: train
path: wh_vs_that_no_gap_long_distance/train-*
- config_name: wh_vs_that_with_gap
data_files:
- split: train
path: wh_vs_that_with_gap/train-*
- config_name: wh_vs_that_with_gap_long_distance
data_files:
- split: train
path: wh_vs_that_with_gap_long_distance/train-*
---
# Dataset Card for "blimp"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/alexwarstadt/blimp
- **Paper:** [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://doi.org/10.1162/tacl_a_00321)
- **Paper:** https://arxiv.org/abs/1912.00582
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 29.58 MB
- **Size of the generated dataset:** 11.45 MB
- **Total amount of disk used:** 41.03 MB
### Dataset Summary
BLiMP is a challenge set for evaluating what language models (LMs) know about
major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
containing 1000 minimal pairs isolating specific contrasts in syntax,
morphology, or semantics. The data is automatically generated according to
expert-crafted grammars.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### adjunct_island
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.17 MB
- **Total amount of disk used:** 0.52 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### anaphor_gender_agreement
- **Size of downloaded dataset files:** 0.44 MB
- **Size of the generated dataset:** 0.14 MB
- **Total amount of disk used:** 0.57 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### anaphor_number_agreement
- **Size of downloaded dataset files:** 0.45 MB
- **Size of the generated dataset:** 0.14 MB
- **Total amount of disk used:** 0.59 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### animate_subject_passive
- **Size of downloaded dataset files:** 0.46 MB
- **Size of the generated dataset:** 0.15 MB
- **Total amount of disk used:** 0.61 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### animate_subject_trans
- **Size of downloaded dataset files:** 0.43 MB
- **Size of the generated dataset:** 0.13 MB
- **Total amount of disk used:** 0.57 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
### Data Fields
The data fields are the same among all splits.
#### adjunct_island
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### anaphor_gender_agreement
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### anaphor_number_agreement
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### animate_subject_passive
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### animate_subject_trans
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
### Data Splits
| name |train|
|------------------------|----:|
|adjunct_island | 1000|
|anaphor_gender_agreement| 1000|
|anaphor_number_agreement| 1000|
|animate_subject_passive | 1000|
|animate_subject_trans | 1000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
BLiMP is distributed under a [CC-BY](https://creativecommons.org/licenses/by/4.0/) license. Source: https://github.com/alexwarstadt/blimp#license
### Citation Information
```
@article{warstadt2020blimp,
author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
journal = {Transactions of the Association for Computational Linguistics},
volume = {8},
number = {},
pages = {377-392},
year = {2020},
doi = {10.1162/tacl\_a\_00321},
URL = {https://doi.org/10.1162/tacl_a_00321},
eprint = {https://doi.org/10.1162/tacl_a_00321},
abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
}
```
#### Errata
Some results were misreported in the published TACL version. Please refer to the corrected version on arXiv: https://arxiv.org/abs/1912.00582
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
Cohere/wikipedia-2023-11-embed-multilingual-v3 | Cohere | "2024-03-19T10:09:12Z" | 18,506 | 232 | [
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-01-11T16:28:41Z" | ---
configs:
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data_files:
- split: train
path: ee/*
- config_name: "el"
data_files:
- split: train
path: el/*
- config_name: "eml"
data_files:
- split: train
path: eml/*
- config_name: "en"
data_files:
- split: train
path: en/*
- config_name: "eo"
data_files:
- split: train
path: eo/*
- config_name: "es"
data_files:
- split: train
path: es/*
- config_name: "et"
data_files:
- split: train
path: et/*
- config_name: "eu"
data_files:
- split: train
path: eu/*
- config_name: "ext"
data_files:
- split: train
path: ext/*
- config_name: "fa"
data_files:
- split: train
path: fa/*
- config_name: "fat"
data_files:
- split: train
path: fat/*
- config_name: "ff"
data_files:
- split: train
path: ff/*
- config_name: "fi"
data_files:
- split: train
path: fi/*
- config_name: "fiu-vro"
data_files:
- split: train
path: fiu-vro/*
- config_name: "fj"
data_files:
- split: train
path: fj/*
- config_name: "fo"
data_files:
- split: train
path: fo/*
- config_name: "fon"
data_files:
- split: train
path: fon/*
- config_name: "fr"
data_files:
- split: train
path: fr/*
- config_name: "frp"
data_files:
- split: train
path: frp/*
- config_name: "frr"
data_files:
- split: train
path: frr/*
- config_name: "fur"
data_files:
- split: train
path: fur/*
- config_name: "fy"
data_files:
- split: train
path: fy/*
- config_name: "ga"
data_files:
- split: train
path: ga/*
- config_name: "gag"
data_files:
- split: train
path: gag/*
- config_name: "gan"
data_files:
- split: train
path: gan/*
- config_name: "gcr"
data_files:
- split: train
path: gcr/*
- config_name: "gd"
data_files:
- split: train
path: gd/*
- config_name: "gl"
data_files:
- split: train
path: gl/*
- config_name: "glk"
data_files:
- split: train
path: glk/*
- config_name: "gn"
data_files:
- split: train
path: gn/*
- config_name: "gom"
data_files:
- split: train
path: gom/*
- config_name: "gor"
data_files:
- split: train
path: gor/*
- config_name: "got"
data_files:
- split: train
path: got/*
- config_name: "gpe"
data_files:
- split: train
path: gpe/*
- config_name: "gu"
data_files:
- split: train
path: gu/*
- config_name: "guc"
data_files:
- split: train
path: guc/*
- config_name: "gur"
data_files:
- split: train
path: gur/*
- config_name: "guw"
data_files:
- split: train
path: guw/*
- config_name: "gv"
data_files:
- split: train
path: gv/*
- config_name: "ha"
data_files:
- split: train
path: ha/*
- config_name: "hak"
data_files:
- split: train
path: hak/*
- config_name: "haw"
data_files:
- split: train
path: haw/*
- config_name: "he"
data_files:
- split: train
path: he/*
- config_name: "hi"
data_files:
- split: train
path: hi/*
- config_name: "hif"
data_files:
- split: train
path: hif/*
- config_name: "hr"
data_files:
- split: train
path: hr/*
- config_name: "hsb"
data_files:
- split: train
path: hsb/*
- config_name: "ht"
data_files:
- split: train
path: ht/*
- config_name: "hu"
data_files:
- split: train
path: hu/*
- config_name: "hy"
data_files:
- split: train
path: hy/*
- config_name: "hyw"
data_files:
- split: train
path: hyw/*
- config_name: "ia"
data_files:
- split: train
path: ia/*
- config_name: "id"
data_files:
- split: train
path: id/*
- config_name: "ie"
data_files:
- split: train
path: ie/*
- config_name: "ig"
data_files:
- split: train
path: ig/*
- config_name: "ik"
data_files:
- split: train
path: ik/*
- config_name: "ilo"
data_files:
- split: train
path: ilo/*
- config_name: "inh"
data_files:
- split: train
path: inh/*
- config_name: "io"
data_files:
- split: train
path: io/*
- config_name: "is"
data_files:
- split: train
path: is/*
- config_name: "it"
data_files:
- split: train
path: it/*
- config_name: "iu"
data_files:
- split: train
path: iu/*
- config_name: "ja"
data_files:
- split: train
path: ja/*
- config_name: "jam"
data_files:
- split: train
path: jam/*
- config_name: "jbo"
data_files:
- split: train
path: jbo/*
- config_name: "jv"
data_files:
- split: train
path: jv/*
- config_name: "ka"
data_files:
- split: train
path: ka/*
- config_name: "kaa"
data_files:
- split: train
path: kaa/*
- config_name: "kab"
data_files:
- split: train
path: kab/*
- config_name: "kbd"
data_files:
- split: train
path: kbd/*
- config_name: "kbp"
data_files:
- split: train
path: kbp/*
- config_name: "kcg"
data_files:
- split: train
path: kcg/*
- config_name: "kg"
data_files:
- split: train
path: kg/*
- config_name: "ki"
data_files:
- split: train
path: ki/*
- config_name: "kk"
data_files:
- split: train
path: kk/*
- config_name: "kl"
data_files:
- split: train
path: kl/*
- config_name: "km"
data_files:
- split: train
path: km/*
- config_name: "kn"
data_files:
- split: train
path: kn/*
- config_name: "ko"
data_files:
- split: train
path: ko/*
- config_name: "koi"
data_files:
- split: train
path: koi/*
- config_name: "krc"
data_files:
- split: train
path: krc/*
- config_name: "ks"
data_files:
- split: train
path: ks/*
- config_name: "ksh"
data_files:
- split: train
path: ksh/*
- config_name: "ku"
data_files:
- split: train
path: ku/*
- config_name: "kv"
data_files:
- split: train
path: kv/*
- config_name: "kw"
data_files:
- split: train
path: kw/*
- config_name: "ky"
data_files:
- split: train
path: ky/*
- config_name: "la"
data_files:
- split: train
path: la/*
- config_name: "lad"
data_files:
- split: train
path: lad/*
- config_name: "lb"
data_files:
- split: train
path: lb/*
- config_name: "lbe"
data_files:
- split: train
path: lbe/*
- config_name: "lez"
data_files:
- split: train
path: lez/*
- config_name: "lfn"
data_files:
- split: train
path: lfn/*
- config_name: "lg"
data_files:
- split: train
path: lg/*
- config_name: "li"
data_files:
- split: train
path: li/*
- config_name: "lij"
data_files:
- split: train
path: lij/*
- config_name: "lld"
data_files:
- split: train
path: lld/*
- config_name: "lmo"
data_files:
- split: train
path: lmo/*
- config_name: "ln"
data_files:
- split: train
path: ln/*
- config_name: "lo"
data_files:
- split: train
path: lo/*
- config_name: "lt"
data_files:
- split: train
path: lt/*
- config_name: "ltg"
data_files:
- split: train
path: ltg/*
- config_name: "lv"
data_files:
- split: train
path: lv/*
- config_name: "mad"
data_files:
- split: train
path: mad/*
- config_name: "mai"
data_files:
- split: train
path: mai/*
- config_name: "map-bms"
data_files:
- split: train
path: map-bms/*
- config_name: "mdf"
data_files:
- split: train
path: mdf/*
- config_name: "mg"
data_files:
- split: train
path: mg/*
- config_name: "mhr"
data_files:
- split: train
path: mhr/*
- config_name: "mi"
data_files:
- split: train
path: mi/*
- config_name: "min"
data_files:
- split: train
path: min/*
- config_name: "mk"
data_files:
- split: train
path: mk/*
- config_name: "ml"
data_files:
- split: train
path: ml/*
- config_name: "mn"
data_files:
- split: train
path: mn/*
- config_name: "mni"
data_files:
- split: train
path: mni/*
- config_name: "mnw"
data_files:
- split: train
path: mnw/*
- config_name: "mr"
data_files:
- split: train
path: mr/*
- config_name: "mrj"
data_files:
- split: train
path: mrj/*
- config_name: "ms"
data_files:
- split: train
path: ms/*
- config_name: "mt"
data_files:
- split: train
path: mt/*
- config_name: "mwl"
data_files:
- split: train
path: mwl/*
- config_name: "my"
data_files:
- split: train
path: my/*
- config_name: "myv"
data_files:
- split: train
path: myv/*
- config_name: "mzn"
data_files:
- split: train
path: mzn/*
- config_name: "nah"
data_files:
- split: train
path: nah/*
- config_name: "nap"
data_files:
- split: train
path: nap/*
- config_name: "nds"
data_files:
- split: train
path: nds/*
- config_name: "nds-nl"
data_files:
- split: train
path: nds-nl/*
- config_name: "ne"
data_files:
- split: train
path: ne/*
- config_name: "new"
data_files:
- split: train
path: new/*
- config_name: "nia"
data_files:
- split: train
path: nia/*
- config_name: "nl"
data_files:
- split: train
path: nl/*
- config_name: "nn"
data_files:
- split: train
path: nn/*
- config_name: "no"
data_files:
- split: train
path: no/*
- config_name: "nov"
data_files:
- split: train
path: nov/*
- config_name: "nqo"
data_files:
- split: train
path: nqo/*
- config_name: "nrm"
data_files:
- split: train
path: nrm/*
- config_name: "nso"
data_files:
- split: train
path: nso/*
- config_name: "nv"
data_files:
- split: train
path: nv/*
- config_name: "ny"
data_files:
- split: train
path: ny/*
- config_name: "oc"
data_files:
- split: train
path: oc/*
- config_name: "olo"
data_files:
- split: train
path: olo/*
- config_name: "om"
data_files:
- split: train
path: om/*
- config_name: "or"
data_files:
- split: train
path: or/*
- config_name: "os"
data_files:
- split: train
path: os/*
- config_name: "pa"
data_files:
- split: train
path: pa/*
- config_name: "pag"
data_files:
- split: train
path: pag/*
- config_name: "pam"
data_files:
- split: train
path: pam/*
- config_name: "pap"
data_files:
- split: train
path: pap/*
- config_name: "pcd"
data_files:
- split: train
path: pcd/*
- config_name: "pcm"
data_files:
- split: train
path: pcm/*
- config_name: "pdc"
data_files:
- split: train
path: pdc/*
- config_name: "pfl"
data_files:
- split: train
path: pfl/*
- config_name: "pi"
data_files:
- split: train
path: pi/*
- config_name: "pih"
data_files:
- split: train
path: pih/*
- config_name: "pl"
data_files:
- split: train
path: pl/*
- config_name: "pms"
data_files:
- split: train
path: pms/*
- config_name: "pnb"
data_files:
- split: train
path: pnb/*
- config_name: "pnt"
data_files:
- split: train
path: pnt/*
- config_name: "ps"
data_files:
- split: train
path: ps/*
- config_name: "pt"
data_files:
- split: train
path: pt/*
- config_name: "pwn"
data_files:
- split: train
path: pwn/*
- config_name: "qu"
data_files:
- split: train
path: qu/*
- config_name: "rm"
data_files:
- split: train
path: rm/*
- config_name: "rmy"
data_files:
- split: train
path: rmy/*
- config_name: "rn"
data_files:
- split: train
path: rn/*
- config_name: "ro"
data_files:
- split: train
path: ro/*
- config_name: "roa-rup"
data_files:
- split: train
path: roa-rup/*
- config_name: "roa-tara"
data_files:
- split: train
path: roa-tara/*
- config_name: "ru"
data_files:
- split: train
path: ru/*
- config_name: "rue"
data_files:
- split: train
path: rue/*
- config_name: "rw"
data_files:
- split: train
path: rw/*
- config_name: "sa"
data_files:
- split: train
path: sa/*
- config_name: "sah"
data_files:
- split: train
path: sah/*
- config_name: "sat"
data_files:
- split: train
path: sat/*
- config_name: "sc"
data_files:
- split: train
path: sc/*
- config_name: "scn"
data_files:
- split: train
path: scn/*
- config_name: "sco"
data_files:
- split: train
path: sco/*
- config_name: "sd"
data_files:
- split: train
path: sd/*
- config_name: "se"
data_files:
- split: train
path: se/*
- config_name: "sg"
data_files:
- split: train
path: sg/*
- config_name: "sh"
data_files:
- split: train
path: sh/*
- config_name: "shi"
data_files:
- split: train
path: shi/*
- config_name: "shn"
data_files:
- split: train
path: shn/*
- config_name: "si"
data_files:
- split: train
path: si/*
- config_name: "simple"
data_files:
- split: train
path: simple/*
- config_name: "sk"
data_files:
- split: train
path: sk/*
- config_name: "skr"
data_files:
- split: train
path: skr/*
- config_name: "sl"
data_files:
- split: train
path: sl/*
- config_name: "sm"
data_files:
- split: train
path: sm/*
- config_name: "smn"
data_files:
- split: train
path: smn/*
- config_name: "sn"
data_files:
- split: train
path: sn/*
- config_name: "so"
data_files:
- split: train
path: so/*
- config_name: "sq"
data_files:
- split: train
path: sq/*
- config_name: "sr"
data_files:
- split: train
path: sr/*
- config_name: "srn"
data_files:
- split: train
path: srn/*
- config_name: "ss"
data_files:
- split: train
path: ss/*
- config_name: "st"
data_files:
- split: train
path: st/*
- config_name: "stq"
data_files:
- split: train
path: stq/*
- config_name: "su"
data_files:
- split: train
path: su/*
- config_name: "sv"
data_files:
- split: train
path: sv/*
- config_name: "sw"
data_files:
- split: train
path: sw/*
- config_name: "szl"
data_files:
- split: train
path: szl/*
- config_name: "szy"
data_files:
- split: train
path: szy/*
- config_name: "ta"
data_files:
- split: train
path: ta/*
- config_name: "tay"
data_files:
- split: train
path: tay/*
- config_name: "tcy"
data_files:
- split: train
path: tcy/*
- config_name: "te"
data_files:
- split: train
path: te/*
- config_name: "tet"
data_files:
- split: train
path: tet/*
- config_name: "tg"
data_files:
- split: train
path: tg/*
- config_name: "th"
data_files:
- split: train
path: th/*
- config_name: "ti"
data_files:
- split: train
path: ti/*
- config_name: "tk"
data_files:
- split: train
path: tk/*
- config_name: "tl"
data_files:
- split: train
path: tl/*
- config_name: "tly"
data_files:
- split: train
path: tly/*
- config_name: "tn"
data_files:
- split: train
path: tn/*
- config_name: "to"
data_files:
- split: train
path: to/*
- config_name: "tpi"
data_files:
- split: train
path: tpi/*
- config_name: "tr"
data_files:
- split: train
path: tr/*
- config_name: "trv"
data_files:
- split: train
path: trv/*
- config_name: "ts"
data_files:
- split: train
path: ts/*
- config_name: "tt"
data_files:
- split: train
path: tt/*
- config_name: "tum"
data_files:
- split: train
path: tum/*
- config_name: "tw"
data_files:
- split: train
path: tw/*
- config_name: "ty"
data_files:
- split: train
path: ty/*
- config_name: "tyv"
data_files:
- split: train
path: tyv/*
- config_name: "udm"
data_files:
- split: train
path: udm/*
- config_name: "ug"
data_files:
- split: train
path: ug/*
- config_name: "uk"
data_files:
- split: train
path: uk/*
- config_name: "ur"
data_files:
- split: train
path: ur/*
- config_name: "uz"
data_files:
- split: train
path: uz/*
- config_name: "ve"
data_files:
- split: train
path: ve/*
- config_name: "vec"
data_files:
- split: train
path: vec/*
- config_name: "vep"
data_files:
- split: train
path: vep/*
- config_name: "vi"
data_files:
- split: train
path: vi/*
- config_name: "vls"
data_files:
- split: train
path: vls/*
- config_name: "vo"
data_files:
- split: train
path: vo/*
- config_name: "wa"
data_files:
- split: train
path: wa/*
- config_name: "war"
data_files:
- split: train
path: war/*
- config_name: "wo"
data_files:
- split: train
path: wo/*
- config_name: "wuu"
data_files:
- split: train
path: wuu/*
- config_name: "xal"
data_files:
- split: train
path: xal/*
- config_name: "xh"
data_files:
- split: train
path: xh/*
- config_name: "xmf"
data_files:
- split: train
path: xmf/*
- config_name: "yi"
data_files:
- split: train
path: yi/*
- config_name: "yo"
data_files:
- split: train
path: yo/*
- config_name: "za"
data_files:
- split: train
path: za/*
- config_name: "zea"
data_files:
- split: train
path: zea/*
- config_name: "zh"
data_files:
- split: train
path: zh/*
- config_name: "zh-classical"
data_files:
- split: train
path: zh-classical/*
- config_name: "zh-min-nan"
data_files:
- split: train
path: zh-min-nan/*
- config_name: "zh-yue"
data_files:
- split: train
path: zh-yue/*
- config_name: "zu"
data_files:
- split: train
path: zu/*
---
# Multilingual Embeddings for Wikipedia in 300+ Languages
This dataset contains the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset dump from 2023-11-01 from Wikipedia in all 300+ languages.
The individual articles have been chunked and embedded with the state-of-the-art multilingual [Cohere Embed V3 embedding model](https://txt.cohere.com/introducing-embed-v3/). This enables an easy way to semantically search across all of Wikipedia or to use it as a knowledge source for your RAG application. In total is it close to 250M paragraphs / embeddings.
You can also use the model to perform cross-lingual search: Enter your search query in any language and get the most relevant results back.
## Loading the dataset
### Loading the document embeddings
You can either load the dataset like this:
```python
from datasets import load_dataset
lang = "simple" #Use the Simple English Wikipedia subset
docs = load_dataset("Cohere/wikipedia-2023-11-embed-multilingual-v3", lang, split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
lang = "simple" #Use the Simple English Wikipedia subset
docs = load_dataset("Cohere/wikipedia-2023-11-embed-multilingual-v3", lang, split="train", streaming=True)
for doc in docs:
doc_id = doc['_id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
Note, depending on the language, the download can be quite large.
## Search
A full search example (on the first 1,000 paragraphs):
```python
#Run: pip install cohere datasets numpy
from datasets import load_dataset
import numpy as np
import cohere
co = cohere.Client("YOUR_COHERE_API_KEY") # Add your cohere API key from www.cohere.com
lang = "simple"
top_k = 3
#Load at max 1000 chunks + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-2023-11-embed-multilingual-v3", lang, split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = np.asarray(doc_embeddings)
query = 'Who was Alan Turing'
response = co.embed(texts=[query], model='embed-multilingual-v3.0', input_type="search_query")
query_embedding = response.embeddings
query_embedding = np.asarray(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = np.matmul(query_embedding, doc_embeddings.transpose())[0]
top_k_hits = np.argpartition(dot_scores, -top_k)[-top_k:].tolist()
# Sort top_k_hits by dot score
top_k_hits.sort(key=lambda x: dot_scores[x], reverse=True)
# Print results
print("Query:", query)
for doc_id in top_k_hits:
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
print(docs[doc_id]['url'], "\n")
```
## Overview
The following table contains all language codes together with the total numbers of passages.
| Language | #Docs |
|---|:---:|
| en | 41,488,110 |
| de | 20,772,081 |
| fr | 17,813,768 |
| ru | 13,734,543 |
| es | 12,905,284 |
| it | 10,462,162 |
| ceb | 9,818,657 |
| uk | 6,901,192 |
| ja | 6,626,537 |
| nl | 6,101,353 |
| pl | 5,973,650 |
| pt | 5,637,930 |
| sv | 4,911,480 |
| ca | 4,156,889 |
| ar | 3,691,451 |
| cs | 3,118,524 |
| he | 2,948,882 |
| hu | 2,924,609 |
| vi | 2,835,049 |
| zh | 2,775,260 |
| fi | 2,427,097 |
| id | 2,358,335 |
| no | 2,211,270 |
| sr | 2,154,631 |
| fa | 2,073,154 |
| tr | 1,775,036 |
| ro | 1,770,527 |
| el | 1,599,770 |
| ko | 1,513,291 |
| bg | 1,455,765 |
| hy | 1,386,140 |
| eu | 1,327,579 |
| da | 1,224,982 |
| eo | 1,216,706 |
| war | 1,185,097 |
| sh | 1,139,309 |
| tt | 1,119,546 |
| arz | 1,089,164 |
| gl | 1,056,990 |
| et | 1,054,770 |
| ce | 1,013,217 |
| ast | 1,010,445 |
| sl | 984,855 |
| hr | 910,923 |
| sk | 874,014 |
| ms | 869,579 |
| be | 857,033 |
| th | 839,712 |
| az | 817,631 |
| uz | 811,028 |
| mk | 784,576 |
| lt | 770,069 |
| bn | 767,965 |
| cy | 762,338 |
| ta | 685,975 |
| simple | 646,424 |
| te | 634,778 |
| kk | 627,085 |
| ka | 595,401 |
| hi | 541,822 |
| nn | 530,590 |
| lv | 484,957 |
| af | 461,674 |
| ba | 434,939 |
| ur | 434,269 |
| bs | 396,692 |
| sq | 388,788 |
| ml | 384,795 |
| min | 373,156 |
| la | 340,521 |
| pnb | 335,958 |
| be-x-old | 314,600 |
| kn | 309,085 |
| azb | 294,248 |
| oc | 283,297 |
| zh-min-nan | 278,547 |
| fy | 248,075 |
| my | 241,025 |
| lb | 216,558 |
| ky | 216,344 |
| als | 206,387 |
| mr | 203,479 |
| br | 200,486 |
| pa | 188,091 |
| is | 177,272 |
| mg | 171,947 |
| sw | 171,650 |
| ha | 167,807 |
| tl | 166,907 |
| nds | 166,019 |
| an | 143,163 |
| jv | 142,104 |
| ps | 138,240 |
| ig | 132,250 |
| new | 128,696 |
| tg | 128,237 |
| ga | 125,456 |
| lld | 125,094 |
| su | 124,390 |
| cv | 122,671 |
| ckb | 120,886 |
| si | 119,223 |
| mn | 114,878 |
| lmo | 103,836 |
| io | 101,713 |
| gu | 99,450 |
| vec | 95,072 |
| zh-yue | 89,145 |
| bar | 88,238 |
| sco | 83,906 |
| ne | 83,598 |
| ku | 82,935 |
| hyw | 82,343 |
| pms | 77,834 |
| as | 76,093 |
| km | 74,177 |
| sah | 71,599 |
| li | 69,267 |
| or | 65,510 |
| mt | 64,038 |
| szl | 56,836 |
| yi | 55,375 |
| ht | 55,079 |
| dag | 53,343 |
| sa | 51,735 |
| nv | 49,355 |
| bpy | 47,757 |
| vo | 47,375 |
| ug | 44,764 |
| sat | 43,500 |
| ia | 42,012 |
| bo | 41,438 |
| mwl | 41,273 |
| sd | 40,395 |
| bcl | 39,967 |
| mnw | 39,578 |
| hsb | 39,560 |
| avk | 39,001 |
| scn | 38,359 |
| rm | 37,436 |
| diq | 34,743 |
| vep | 33,654 |
| xmf | 33,238 |
| ban | 32,320 |
| wa | 32,132 |
| ilo | 31,046 |
| nds-nl | 30,918 |
| qu | 30,529 |
| so | 29,936 |
| mhr | 29,619 |
| vls | 29,227 |
| sc | 28,977 |
| fo | 28,809 |
| gd | 28,149 |
| rw | 28,037 |
| gom | 27,792 |
| yo | 27,789 |
| tum | 26,743 |
| wuu | 26,532 |
| frr | 26,010 |
| sn | 25,941 |
| tk | 24,269 |
| blk | 24,194 |
| mzn | 23,837 |
| co | 23,065 |
| szy | 22,854 |
| am | 22,467 |
| shn | 22,432 |
| skr | 21,081 |
| lfn | 20,781 |
| tyv | 20,762 |
| lij | 20,553 |
| ie | 19,994 |
| rue | 19,916 |
| crh | 19,016 |
| gor | 18,146 |
| ary | 17,463 |
| dv | 16,941 |
| lg | 16,751 |
| roa-tara | 16,572 |
| bjn | 16,429 |
| tw | 16,304 |
| bh | 15,938 |
| pam | 15,134 |
| os | 15,096 |
| myv | 15,062 |
| gn | 14,983 |
| lez | 14,152 |
| mai | 13,806 |
| kv | 13,534 |
| pcd | 13,057 |
| zh-classical | 12,791 |
| zea | 12,528 |
| lo | 12,525 |
| gv | 12,074 |
| stq | 11,890 |
| zu | 11,680 |
| smn | 11,672 |
| kw | 11,539 |
| bat-smg | 11,240 |
| hif | 11,215 |
| ext | 10,967 |
| ace | 10,821 |
| trv | 10,546 |
| ami | 10,538 |
| tcy | 10,531 |
| lad | 10,386 |
| alt | 10,256 |
| pap | 10,187 |
| kab | 10,179 |
| fur | 10,148 |
| nap | 10,079 |
| mrj | 9,771 |
| kaa | 9,548 |
| nqo | 9,153 |
| glk | 9,120 |
| pfl | 8,790 |
| fiu-vro | 8,757 |
| nso | 8,635 |
| jbo | 8,577 |
| bxr | 8,549 |
| wo | 8,549 |
| olo | 8,530 |
| map-bms | 8,393 |
| ksh | 8,226 |
| csb | 8,085 |
| av | 7,873 |
| mni | 7,740 |
| udm | 7,730 |
| mi | 7,643 |
| kbp | 7,616 |
| dsb | 7,536 |
| frp | 7,294 |
| om | 7,045 |
| ang | 7,023 |
| hak | 6,866 |
| gur | 6,761 |
| se | 6,733 |
| anp | 6,704 |
| tay | 6,434 |
| mdf | 6,351 |
| gcr | 6,347 |
| koi | 6,300 |
| krc | 6,293 |
| ay | 5,985 |
| cdo | 5,917 |
| nrm | 5,786 |
| xh | 5,756 |
| tn | 5,712 |
| tly | 5,598 |
| shi | 5,179 |
| pcm | 5,076 |
| fat | 4,968 |
| nia | 4,795 |
| dty | 4,728 |
| kbd | 4,667 |
| gpe | 4,289 |
| cbk-zam | 4,224 |
| ff | 4,166 |
| dz | 4,117 |
| guw | 3,982 |
| eml | 3,979 |
| ln | 3,774 |
| inh | 3,768 |
| nah | 3,720 |
| ab | 3,465 |
| ks | 3,255 |
| mad | 3,236 |
| haw | 3,227 |
| gag | 3,076 |
| tet | 3,030 |
| ny | 2,933 |
| pag | 2,727 |
| guc | 2,454 |
| roa-rup | 2,409 |
| jam | 2,387 |
| awa | 2,242 |
| pdc | 2,239 |
| to | 2,165 |
| za | 2,132 |
| st | 2,051 |
| ltg | 2,005 |
| atj | 1,967 |
| nov | 1,916 |
| ss | 1,904 |
| pwn | 1,881 |
| ee | 1,819 |
| sm | 1,659 |
| ts | 1,645 |
| gan | 1,626 |
| xal | 1,619 |
| kcg | 1,555 |
| cu | 1,477 |
| srn | 1,395 |
| got | 1,280 |
| fon | 1,247 |
| din | 1,214 |
| arc | 1,167 |
| fj | 1,164 |
| rmy | 1,113 |
| ady | 1,040 |
| rn | 1,033 |
| bm | 1,017 |
| tpi | 957 |
| ve | 919 |
| ki | 798 |
| pnt | 796 |
| chr | 788 |
| kl | 770 |
| lbe | 766 |
| bi | 718 |
| ti | 706 |
| kg | 609 |
| pih | 606 |
| ch | 513 |
| bug | 429 |
| ty | 297 |
| ik | 275 |
| iu | 263 |
| pi | 260 |
| sg | 204 |
| chy | 57 |
| cr | 41 |
| Total | 247,154,006 |
|
Open-Orca/FLAN | Open-Orca | "2023-08-02T15:08:01Z" | 18,459 | 171 | [
"language:en",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2301.13688",
"arxiv:2109.01652",
"arxiv:2110.08207",
"arxiv:2204.07705",
"region:us"
] | null | "2023-07-21T13:45:12Z" | ---
license: cc-by-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- Open-Orca/OpenOrca
size_categories:
- 1B<n<10B
---
<p><h1>🍮 The WHOLE FLAN Collection! 🍮</h1></p>
![OO-FLAN Logo](https://huggingface.co/datasets/Open-Orca/FLAN/resolve/main/OOFlanLogo.png "OO-FLAN Logo")
# Overview
This repository includes the full dataset from the [FLAN Collection](https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html), totalling ~300GB as parquets.
Generated using the official seqio templating from the [Google FLAN Collection GitHub repo](https://github.com/google-research/FLAN/tree/main/flan/v2).
The data is subject to all the same licensing of the component datasets.
To keep up with our continued work on OpenOrca and other exciting research, find our Discord here:
https://AlignmentLab.ai
# Motivation
This work was done as part of the requirements for the OpenOrca project.
There was not a large enough subset of FLAN Collection generated publicly to subsample from to complete the work.
So, we opted to process the entire collection ourselves.
Generating this requires an understanding of seqio and a Linux server with 512GB of CPU ram, as well as fast drives and custom limits for many parameters beyond what is default on Linux server distributions (e.g., requiring up to 45,000 threads running at once).
It takes downloading over 400GB of datasets, working around tfds bugs, and then processing the datasets over the course of several days.
We provide this repo as a resource to other ML researchers, as it saves these time consuming and laborious steps to getting the data into a more accessible format for further consumption.
# Data
## Organization
* JSON files at top level are used for subsampling in OpenOrca
* Parquets in subdirectories contain the entire FLAN collection in Dask-sharded folders by submix fractions
## Zero-Shot vs Few-Shot and Options vs No-Options
The core sub-collections of FLAN are `CoT`, `Dialog`, `NIv2`, `T0`, and `flan2021`.
Within those sub-collections are four "remixes" of the data that are templated differently:
* `Zero-Shot` and `Few-Shot`
* `Zero-Shot` provides a prompt, question, or challenge without any exemplaries prior
* `Few-Shot` provides exemplaries first
* `Options` and `No-Options`
* `Options` provides a question or challenge with multiple-choice (e.g. A/B/C/D) answer options provided to select from
* `No-Options` requires a free-form answer
For every sub-collection, only some of the "remixes" may officially be provided. All available have been generated in full without any redaction or sub-sampling.
An example: `t0_fsopt_data` folder contains the sub-collection `T0`'s Few-Shot (FS), Options (OPT) remix set.
Notably, this is the largest "remix" and the one that necessitates 512GB CPU ram to generate. The raw json output is nearly 200GB.
## Parquet Sizes
Each sub-collection's individual remixes are provided as [Parquet](https://huggingface.co/docs/datasets/loading#parquet) files which have been sharded by [Dask](https://huggingface.co/docs/datasets/main/en/filesystems#dask) into ~160MB chunks (starting from 256MB blocks of the source jsonl files).
The folder structure along with size sums is provided below.
```
$ du -h --max-depth=1 ./
9.1G ./niv2_fsopt_data
2.4G ./niv2_zsopt_data
59G ./flan_fsopt_data
984M ./dialog_zsopt_data
11G ./flan_zsopt_data
8.6G ./dialog_fsopt_data
16G ./t0_zsnoopt_data
149M ./cot_fsopt_data
20M ./cot_zsopt_data
17G ./t0_zsopt_data
11G ./flan_zsnoopt_data
101G ./t0_fsopt_data
25G ./flan_fsnoopt_data
39G ./t0_fsnoopt_data
296G ./
```
# Citations
```bibtex
@misc{goodson2023huggyflan
title={Fine FLAN: Seqio to Parquet So You Don't Have To},
author={Bleys Goodson},
year={2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/datasets/Open-Orca/FLAN},
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{wei2022finetuned,
title={Finetuned Language Models Are Zero-Shot Learners},
author={Jason Wei and Maarten Bosma and Vincent Y. Zhao and Kelvin Guu and Adams Wei Yu and Brian Lester and Nan Du and Andrew M. Dai and Quoc V. Le},
year={2022},
eprint={2109.01652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{sanh2022multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Tali Bers and Stella Biderman and Leo Gao and Thomas Wolf and Alexander M. Rush},
year={2022},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
```bibtex
@misc{wang2022supernaturalinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
asahi417/seamless-align-enA-jaA.speaker-embedding.xlsr-2b | asahi417 | "2024-06-24T08:26:07Z" | 18,325 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-12T06:54:19Z" | ---
dataset_info:
- config_name: subset_1
features:
- name: line_no
dtype: int64
- name: enA.id
dtype: string
- name: enA.laser_score
dtype: float64
- name: jaA.id
dtype: string
- name: jaA.laser_score
dtype: float64
- name: jaA.audio.speaker_embedding
sequence: float32
- name: jaA.audio.speaker_embedding.full
sequence:
sequence: float32
- name: enA.audio.speaker_embedding
sequence: float32
- name: enA.audio.speaker_embedding.full
sequence:
sequence: float32
splits:
- name: train
num_bytes: 16310450373
num_examples: 2073
download_size: 16362791193
dataset_size: 16310450373
- config_name: subset_10
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dtype: int64
- name: enA.id
dtype: string
- name: enA.laser_score
dtype: float64
- name: jaA.id
dtype: string
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dtype: float64
- name: jaA.audio.speaker_embedding
sequence: float32
- name: jaA.audio.speaker_embedding.full
sequence:
sequence: float32
- name: enA.audio.speaker_embedding
sequence: float32
- name: enA.audio.speaker_embedding.full
sequence:
sequence: float32
splits:
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num_bytes: 14507997777
num_examples: 1961
download_size: 14554505952
dataset_size: 14507997777
- config_name: subset_100
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dtype: int64
- name: enA.id
dtype: string
- name: enA.laser_score
dtype: float64
- name: jaA.id
dtype: string
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dtype: float64
- name: jaA.audio.speaker_embedding
sequence: float32
- name: jaA.audio.speaker_embedding.full
sequence:
sequence: float32
- name: enA.audio.speaker_embedding
sequence: float32
- name: enA.audio.speaker_embedding.full
sequence:
sequence: float32
splits:
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num_bytes: 14129720831
num_examples: 1757
download_size: 14173975107
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- config_name: subset_101
features:
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dtype: string
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dtype: float64
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sequence: float32
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sequence:
sequence: float32
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sequence: float32
splits:
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dtype: float64
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sequence:
sequence: float32
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splits:
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sequence: float32
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sequence: float32
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dtype: string
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dtype: float64
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dtype: string
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dtype: float64
- name: jaA.audio.speaker_embedding
sequence: float32
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sequence:
sequence: float32
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sequence: float32
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sequence:
sequence: float32
splits:
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dtype: string
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dtype: string
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dtype: float64
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sequence: float32
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---
|
CohereForAI/aya_collection | CohereForAI | "2024-06-28T08:04:56Z" | 18,032 | 218 | [
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:translation",
"language:ace",
"language:afr",
"language:amh",
"language:ara",
"language:aze",
"language:ban",
"language:bbc",
"language:bel",
"language:bem",
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"language:vie",
"language:wol",
"language:xho",
"language:yid",
"language:yor",
"language:zho",
"language:zul",
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2402.06619",
"region:us"
] | [
"text-classification",
"summarization",
"translation"
] | "2024-01-31T21:40:43Z" | ---
language:
- ace
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- amh
- ara
- aze
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license: apache-2.0
size_categories:
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task_categories:
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- summarization
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pretty_name: Aya Collection
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data_files:
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- config_name: templated_urdu_news_gen
data_files:
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- config_name: templated_urdu_news_headline
data_files:
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- config_name: templated_xlel_wd
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- config_name: templated_xwikis
data_files:
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path: templated_xwikis/validation-*
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data_files:
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path: translated_adversarial_qa/test-*
- split: train
path: translated_adversarial_qa/train-*
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- config_name: translated_cnn_dailymail
data_files:
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path: translated_cnn_dailymail/train-*
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- config_name: translated_dolly
data_files:
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path: translated_dolly/train-*
- config_name: translated_flan_coqa
data_files:
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path: translated_flan_coqa/train-*
- config_name: translated_flan_cot
data_files:
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- config_name: translated_flan_gem_wiki
data_files:
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path: translated_flan_gem_wiki/train-*
- config_name: translated_flan_lambada
data_files:
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- config_name: translated_flan_qa
data_files:
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- config_name: translated_hotpotqa
data_files:
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path: translated_hotpotqa/train-*
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- config_name: translated_joke_explaination
data_files:
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- config_name: translated_mintaka
data_files:
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- config_name: translated_mlqa
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path: translated_mlqa/test-*
- split: validation
path: translated_mlqa/validation-*
- config_name: translated_nqopen
data_files:
- split: train
path: translated_nqopen/train-*
- split: validation
path: translated_nqopen/validation-*
- config_name: translated_paws
data_files:
- split: test
path: translated_paws/test-*
- split: train
path: translated_paws/train-*
- split: validation
path: translated_paws/validation-*
- config_name: translated_piqa
data_files:
- split: train
path: translated_piqa/train-*
- split: validation
path: translated_piqa/validation-*
- config_name: translated_soda
data_files:
- split: test
path: translated_soda/test-*
- split: validation
path: translated_soda/validation-*
- split: train
path: translated_soda/train-*
- config_name: translated_wiki_split
data_files:
- split: test
path: translated_wiki_split/test-*
- split: train
path: translated_wiki_split/train-*
- split: validation
path: translated_wiki_split/validation-*
- config_name: translated_wikiqa
data_files:
- split: test
path: translated_wikiqa/test-*
- split: train
path: translated_wikiqa/train-*
- split: validation
path: translated_wikiqa/validation-*
- config_name: translated_xlel_wd
data_files:
- split: test
path: translated_xlel_wd/test-*
- split: validation
path: translated_xlel_wd/validation-*
- split: train
path: translated_xlel_wd/train-*
---
![Aya Header](https://huggingface.co/datasets/CohereForAI/aya_collection/resolve/main/aya_header.png)
****This dataset is uploaded in two places: here and additionally [here](https://huggingface.co/datasets/CohereForAI/aya_collection_language_split) as 'Aya Collection Language Split.' These datasets are identical in content but differ in structure of upload. This dataset is structured by folders split according to dataset name. The version [here](https://huggingface.co/datasets/CohereForAI/aya_collection_language_split) instead divides the Aya collection into folders split by language. We recommend you use the language split version if you are only interested in downloading data for a single or smaller set of languages, and this version if you want to download dataset according to data source or the entire collection.****
# Dataset Summary
The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks.
This collection incorporates instruction-style templates from fluent speakers and applies them to a curated list of datasets, as well as translations of instruction-style datasets into 101 languages. Aya Dataset, a human-curated multilingual instruction and response dataset, is also part of this collection. See our paper for more details regarding the collection.
- **Curated by:** Contributors of [Aya Open Science Intiative](https://cohere.com/research/aya)
- **Language(s):** 115 languages
- **License:** [Apache 2.0](https://opensource.org/license/apache-2-0)
- **Aya Datasets Family:**
| Name | Explanation |
|------|--------------|
| [aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) | Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages. |
| [aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection) | Created by applying instruction-style templates from fluent speakers to 44 datasets, including translations of 19 instruction-style datasets into 101 languages. This collection structured based on dataset level subsets. An alternative version of the collection structured by language subsets is also available.|
| [aya_collection_language_split](https://huggingface.co/datasets/CohereForAI/aya_collection_language_split) | Aya Collection structured based on language level subsets. |
| [aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite) | A diverse evaluation set for multilingual open-ended generation, featuring 250 culturally grounded prompts in 7 languages, 200 translated prompts in 24 languages, and human-edited versions selected for cross-cultural relevance from English Dolly in 6 languages.|
| [aya_redteaming](https://huggingface.co/datasets/CohereForAI/aya_redteaming)| A red-teaming dataset consisting of harmful prompts in 8 languages across 9 different categories of harm with explicit labels for "global" and "local" harm.|
# Dataset
The `Aya Collection` is a comprehensive, large corpus of datasets that can be used by researchers around the world to train multilingual models. Our goal is only to include datasets with permissive licensing for manipulation and redistribution.
The `Aya Collection` consists of three different sources of data:
1. Templated data: We collaborated with fluent speakers to create templates that allowed for the automatic expansion of existing datasets into various languages.
2. Translated data: We translated a hand-selected subset of 19 datasets into 101 languages (114 dialects) using the NLLB 3.3B parameter machine translation model.
3. Aya Dataset: We release the [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) as a subset of the overall collection. This is the only dataset in the collection that is human-annotated in its entirety.
## Load with Datasets
To load this dataset with Datasets, you'll need to install Datasets as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("CohereForAI/aya_collection", "templated_mintaka")
```
In the above code snippet, "templated_mintaka" refers to a subset of the aya_collection. You can load other subsets by specifying its name at the time of loading the dataset.
## Data Instances
An example of a `train` instance looks as follows:
```json
{'id': 246001,
'inputs': 'The following query in English is taken from the geography category. What could be the answer to the question?\nWhat is the seventh tallest mountain in North America?',
'targets': 'The answer is Mount Lucania.',
'dataset_name': 'Mintaka-inst',
'sub_dataset_name': '-',
'task_type': 'question-answering',
'template_id': 3,
'language': 'eng',
'split': 'train',
'script': 'Latn'
}
```
## Data Fields
The data fields are the same among all splits:
- `id:` Unique id of the data point
- `inputs:` Prompt or input to the language model.
- `targets:` Completion or output of the language model.
- `dataset_name:` The name of the source dataset that the data point was taken from
- `sub_dataset_name:` If the source is a collection, this field indicates which part of that collection the data point was taken from. If it is not a collection, this field is left blank.
- `task_type:` The task type that this conversation belongs to.
- `template_id`: The id of the template applied to this data point.
- `language:` The ISO code of the dialect of the conversation.
- `script:` The script of the language.
- `split:` Indicates whether the data point is part of the `train` or the `test` split.
### Statistics
The total number of data points, including the Aya Dataset` is 513,758,189. To view the breakdown of dialect codes and the respective templated and translated data point counts in the Aya Collection , refer to the toggled table below.
<details>
<summary> <b> Breakdown of Aya Collection data point counts grouped by dialects </b> </summary>
|dialect code|language|translated data point count|templated data point count|total count |
|------------|--------|---------------------------|--------------------------|---------------|
|ace |Achinese|8240684 |2000 |8242684 |
|acm |Arabic |4120342 |0 |4120342 |
|acq |Arabic |4120342 |0 |4120342 |
|aeb |Arabic |4120342 |0 |4120342 |
|afr |Afrikaans|4120342 |6108 |4126450 |
|ajp |Arabic |4120342 |0 |4120342 |
|als |Albanian|4120342 |0 |4120342 |
|amh |Amharic |4120342 |25327 |4145669 |
|apc |Arabic |4120342 |0 |4120342 |
|arb |Arabic |6424999 |216430 |6641429 |
|ars |Arabic |4120342 |0 |4120342 |
|ary |Arabic |4120342 |18076 |4138418 |
|arz |Arabic |4120342 |0 |4120342 |
|azb |Azerbaijani|4120342 |0 |4120342 |
|azj |Azerbaijani|4120342 |0 |4120342 |
|bel |Belarusian|4120342 |21273 |4141615 |
|ben |Bengali |4120342 |30661 |4151003 |
|bjn |Banjar |8240684 |2000 |8242684 |
|bul |Bulgarian|4120342 |37722 |4158064 |
|cat |Catalan |4120342 |66900 |4187242 |
|ceb |Cebuano |4120342 |0 |4120342 |
|ces |Czech |4120342 |179604 |4299946 |
|ckb |Kurdish |4120342 |0 |4120342 |
|cym |Welsh |4120342 |0 |4120342 |
|dan |Danish |4120342 |36310 |4156652 |
|deu |German |4120342 |1326722 |5447064 |
|ell |Greek |4120342 |40291 |4160633 |
|eng |English |9771427 |8066678 |17838105 |
|epo |Esperanto|4120342 |0 |4120342 |
|est |Estonian|4120342 |0 |4120342 |
|eus |Basque |4120342 |0 |4120342 |
|fin |Finnish |4120342 |457895 |4578237 |
|fra |French |4120342 |835520 |4955862 |
|gla |Scottish Gaelic|4120342 |0 |4120342 |
|gle |Irish |4120342 |0 |4120342 |
|glg |Galician|4120342 |0 |4120342 |
|guj |Gujarati|4120342 |2157 |4122499 |
|hat |Haitian Creole|4120342 |0 |4120342 |
|hau |Hausa |4120342 |51396 |4171738 |
|heb |Hebrew |4120342 |103466 |4223808 |
|hin |Hindi |4120342 |260387 |4380729 |
|hun |Hungarian|4120342 |82039 |4202381 |
|hye |Armenian|4120342 |7080 |4127422 |
|ibo |Igbo |4120342 |36312 |4156654 |
|ind |Indonesian|4120342 |45709 |4166051 |
|isl |Icelandic|4120342 |0 |4120342 |
|ita |Italian |4120342 |405682 |4526024 |
|jav |Javanese|4120342 |829 |4121171 |
|jpn |Japanese|4120342 |2693177 |6813519 |
|kan |Kannada |4120342 |1156 |4121498 |
|kas |Kashmiri|4120342 |0 |4120342 |
|kat |Georgian|4120342 |0 |4120342 |
|kaz |Kazakh |4120342 |0 |4120342 |
|khk |Mongolian|4120342 |0 |4120342 |
|khm |Khmer |4120342 |0 |4120342 |
|kir |Kyrgyz |4120342 |0 |4120342 |
|kmr |Kurdish |4120342 |0 |4120342 |
|knc |Kanuri |8240684 |0 |8240684 |
|kor |Korean |4120342 |41011 |4161353 |
|lao |Lao |4120342 |0 |4120342 |
|lit |Lithuanian|4120342 |0 |4120342 |
|ltz |Luxembourgish|4120342 |0 |4120342 |
|lvs |Latvian |4120342 |0 |4120342 |
|mal |Malayalam|4120342 |4347 |4124689 |
|mar |Marathi |4120342 |3678 |4124020 |
|min |Minangkabau|6753788 |2000 |6755788 |
|mkd |Macedonian|4120342 |0 |4120342 |
|mlt |Maltese |4120342 |0 |4120342 |
|mni |Manipuri|4120342 |0 |4120342 |
|mri |Maori |4120342 |0 |4120342 |
|mya |Burmese |4120342 |0 |4120342 |
|nld |Dutch |4120342 |220181 |4340523 |
|nno |Norwegian|4120342 |0 |4120342 |
|nob |Norwegian|4120342 |0 |4120342 |
|npi |Nepali |4120342 |0 |4120342 |
|nso |Northern Sotho|4120342 |0 |4120342 |
|pbt |Pashto |4120342 |0 |4120342 |
|pes |Persian |4120342 |245520 |4365862 |
|plt |Malagasy|4120342 |0 |4120342 |
|pol |Polish |4120342 |332503 |4452845 |
|por |Portuguese|4120342 |287432 |4407774 |
|ron |Romanian|4120342 |36359 |4156701 |
|rus |Russian |4120342 |545920 |4666262 |
|sin |Sinhala |4120342 |195 |4120537 |
|slk |Slovak |4120342 |27845 |4148187 |
|slv |Slovenian|4120342 |25731 |4146073 |
|smo |Samoan |4120342 |0 |4120342 |
|sna |Shona |4120342 |3684 |4124026 |
|snd |Sindhi |4120342 |0 |4120342 |
|som |Somali |4120342 |2926 |4123268 |
|sot |Southern Sotho|4120342 |0 |4120342 |
|spa |Spanish |4120342 |379194 |4499536 |
|srp |Serbian |4120342 |77124 |4197466 |
|sun |Sundanese|4120342 |2208 |4122550 |
|swe |Swedish |4120342 |76486 |4196828 |
|swh |Swahili |4120342 |12726 |4133068 |
|tam |Tamil |4120342 |11462 |4131804 |
|taq |Tamasheq|4120342 |0 |4120342 |
|tel |Telugu |4120342 |477821 |4598163 |
|tgk |Tajik |4120342 |0 |4120342 |
|tha |Thai |4120342 |2125180 |6245522 |
|tur |Turkish |4120342 |59932 |4180274 |
|ukr |Ukrainian|4120342 |189384 |4309726 |
|urd |Urdu |4120342 |337739 |4458081 |
|uzn |Uzbek |4120342 |0 |4120342 |
|vie |Vietnamese|4120342 |42232 |4162574 |
|xho |Xhosa |4120342 |2952 |4123294 |
|ydd |Yiddish |4120342 |0 |4120342 |
|yor |Yoruba |4120342 |4907 |4125249 |
|yue |Chinese |4120342 |0 |4120342 |
|zho-Hans |Chinese |4120342 |54528 |4174870 |
|zho-Hant |Chinese |4120342 |0 |4120342 |
|zsm |Malay |4120342 |13950 |4134292 |
|zul |Zulu |4120342 |786 |4121128 |
|arq |Arabic |0 |6046 |6046 |
|ban |Balinese|0 |2000 |2000 |
|bbc |Toba Batak|0 |2000 |2000 |
|bem |Bemba |0 |776 |776 |
|fil |Filipino|0 |220 |220 |
|fon |Fon |0 |845 |845 |
|hrv |Croatian|0 |9007 |9007 |
|kin |Kinyarwanda|0 |11165 |11165 |
|lij |Ligurian|0 |6409 |6409 |
|mad |Madurese|0 |2000 |2000 |
|nij |Ngaju |0 |2000 |2000 |
|nor |Norwegian|0 |72352 |72352 |
|pan |Punjabi |0 |2156 |2156 |
|twi |Twi |0 |10840 |10840 |
|wol |Wolof |0 |785 |785 |
|zho |Chinese |0 |74972 |74972 |
PS: Templated data also includes Mozambican Portuguese, which doesn't have its own ISO language code.
</details>
<br>
# Motivations & Intentions
- **Curation Rationale:** Automatic augmentation of existing datasets serves to enhance the available linguistic resources for multiple languages. The list of languages was initially established from mT5 and aligned with the annotators’ language list and NLLB translation model. The datasets were translated directly from English for all languages.
# Additional Information
## Provenance
- **Methods Used:** A combination of crowd-sourced templating and automatic translation was employed to source this dataset.
- **Methodology Details:**
- *Source:* Existing NLP datasets
- *Dates of Collection:* May 2023 - Dec 2023
## Dataset Version and Maintenance
- **Maintenance Status:** Actively Maintained
- **Version Details:**
- *Current version:* 1.0
- *Last Update:* 02/2024
- *First Release:* 02/2024
## Authorship
- **Publishing Organization:** [Cohere For AI](https://cohere.com/research)
- **Industry Type:** Not-for-profit - Tech
- **Contact Details:** https://cohere.com/research/aya
## Licensing Information
This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License.
## Citation Information
```bibtex
@misc{singh2024aya,
title={Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning},
author={Shivalika Singh and Freddie Vargus and Daniel Dsouza and Börje F. Karlsson and Abinaya Mahendiran and Wei-Yin Ko and Herumb Shandilya and Jay Patel and Deividas Mataciunas and Laura OMahony and Mike Zhang and Ramith Hettiarachchi and Joseph Wilson and Marina Machado and Luisa Souza Moura and Dominik Krzemiński and Hakimeh Fadaei and Irem Ergün and Ifeoma Okoh and Aisha Alaagib and Oshan Mudannayake and Zaid Alyafeai and Vu Minh Chien and Sebastian Ruder and Surya Guthikonda and Emad A. Alghamdi and Sebastian Gehrmann and Niklas Muennighoff and Max Bartolo and Julia Kreutzer and Ahmet Üstün and Marzieh Fadaee and Sara Hooker},
year={2024},
eprint={2402.06619},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bigscience/xP3all | bigscience | "2023-05-30T15:51:40Z" | 17,822 | 28 | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"language:gu",
"language:hi",
"language:id",
"language:ig",
"language:ki",
"language:kn",
"language:lg",
"language:ln",
"language:ml",
"language:mr",
"language:ne",
"language:nso",
"language:ny",
"language:or",
"language:pa",
"language:pt",
"language:rn",
"language:rw",
"language:sn",
"language:st",
"language:sw",
"language:ta",
"language:te",
"language:tn",
"language:ts",
"language:tum",
"language:tw",
"language:ur",
"language:vi",
"language:wo",
"language:xh",
"language:yo",
"language:zh",
"language:zu",
"license:apache-2.0",
"size_categories:10M<n<100M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2211.01786",
"region:us"
] | [
"other"
] | "2022-07-30T21:05:02Z" | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?",
"targets": "Yes"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage.
|Language|Kilobytes|%|Samples|%|
|--------|------:|-:|---:|-:|
|tw|106288|0.11|265071|0.33|
|bm|107056|0.11|265180|0.33|
|ak|108096|0.11|265071|0.33|
|ca|110608|0.11|271191|0.33|
|eu|113008|0.11|281199|0.35|
|fon|113072|0.11|265063|0.33|
|st|114080|0.11|265063|0.33|
|ki|115040|0.12|265180|0.33|
|tum|116032|0.12|265063|0.33|
|wo|122560|0.12|365063|0.45|
|ln|126304|0.13|365060|0.45|
|as|156256|0.16|265063|0.33|
|or|161472|0.16|265063|0.33|
|kn|165456|0.17|265063|0.33|
|ml|175040|0.18|265864|0.33|
|rn|192992|0.19|318189|0.39|
|nso|229712|0.23|915051|1.13|
|tn|235536|0.24|915054|1.13|
|lg|235936|0.24|915021|1.13|
|rw|249360|0.25|915043|1.13|
|ts|250256|0.25|915044|1.13|
|sn|252496|0.25|865056|1.07|
|xh|254672|0.26|915058|1.13|
|zu|263712|0.26|915061|1.13|
|ny|272128|0.27|915063|1.13|
|ig|325232|0.33|950097|1.17|
|yo|352784|0.35|918416|1.13|
|ne|393680|0.39|315754|0.39|
|pa|523248|0.52|339210|0.42|
|gu|560688|0.56|347499|0.43|
|sw|566656|0.57|1130481|1.4|
|mr|666240|0.67|417269|0.52|
|bn|832720|0.83|428843|0.53|
|ta|926912|0.93|415433|0.51|
|te|1343232|1.35|584590|0.72|
|ur|1918272|1.92|855756|1.06|
|vi|3102512|3.11|1672106|2.07|
|code|4330752|4.34|2707724|3.34|
|hi|4403568|4.41|1554667|1.92|
|zh|4599440|4.61|3589234|4.43|
|id|4612256|4.62|2643418|3.27|
|ar|4683456|4.69|2160181|2.67|
|fr|6591120|6.6|5316403|6.57|
|pt|6886800|6.9|3752156|4.63|
|es|8587920|8.6|5413205|6.69|
|en|39252528|39.33|32740750|40.44|
|total|99807184|100.0|80956089|100.0|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for HumanEval)
- Natural Language Inference
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
#### Additional [xP3all](https://huggingface.co/datasets/bigscience/xP3all) datasets
- Coreference Resolution
- [WSC (Fixed)](https://huggingface.co/datasets/super_glue)
- Sentence Completion
- [HellaSwag](https://huggingface.co/datasets/hellaswag)
- Translation
- [MultiEurlex](https://huggingface.co/datasets/multi_eurlex)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. |
cardiffnlp/databench | cardiffnlp | "2025-01-24T14:37:22Z" | 17,783 | 8 | [
"task_categories:table-question-answering",
"task_categories:question-answering",
"language:en",
"language:es",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"table-question-answering",
"table",
"qa"
] | [
"table-question-answering",
"question-answering"
] | "2023-12-21T08:08:56Z" | ---
language:
- en
- es
pretty_name: " 💾🏋️💾 DataBench 💾🏋️💾"
tags:
- table-question-answering
- table
- qa
license: mit
task_categories:
- table-question-answering
- question-answering
default: qa
configs:
- config_name: qa
data_files:
- data/001_Forbes/qa.parquet
- data/002_Titanic/qa.parquet
- data/003_Love/qa.parquet
- data/004_Taxi/qa.parquet
- data/005_NYC/qa.parquet
- data/006_London/qa.parquet
- data/007_Fifa/qa.parquet
- data/008_Tornados/qa.parquet
- data/009_Central/qa.parquet
- data/010_ECommerce/qa.parquet
- data/011_SF/qa.parquet
- data/012_Heart/qa.parquet
- data/013_Roller/qa.parquet
- data/014_Airbnb/qa.parquet
- data/015_Food/qa.parquet
- data/016_Holiday/qa.parquet
- data/017_Hacker/qa.parquet
- data/018_Staff/qa.parquet
- data/019_Aircraft/qa.parquet
- data/020_Real/qa.parquet
- data/021_Telco/qa.parquet
- data/022_Airbnbs/qa.parquet
- data/023_Climate/qa.parquet
- data/024_Salary/qa.parquet
- data/025_Data/qa.parquet
- data/026_Predicting/qa.parquet
- data/027_Supermarket/qa.parquet
- data/028_Predict/qa.parquet
- data/029_NYTimes/qa.parquet
- data/030_Professionals/qa.parquet
- data/031_Trustpilot/qa.parquet
- data/032_Delicatessen/qa.parquet
- data/033_Employee/qa.parquet
- data/034_World/qa.parquet
- data/035_Billboard/qa.parquet
- data/036_US/qa.parquet
- data/037_Ted/qa.parquet
- data/038_Stroke/qa.parquet
- data/039_Happy/qa.parquet
- data/040_Speed/qa.parquet
- data/041_Airline/qa.parquet
- data/042_Predict/qa.parquet
- data/043_Predict/qa.parquet
- data/044_IMDb/qa.parquet
- data/045_Predict/qa.parquet
- data/046_120/qa.parquet
- data/047_Bank/qa.parquet
- data/048_Data/qa.parquet
- data/049_Boris/qa.parquet
- data/050_ING/qa.parquet
- data/051_Pokemon/qa.parquet
- data/052_Professional/qa.parquet
- data/053_Patents/qa.parquet
- data/054_Joe/qa.parquet
- data/055_German/qa.parquet
- data/056_Emoji/qa.parquet
- data/057_Spain/qa.parquet
- data/058_US/qa.parquet
- data/059_Second/qa.parquet
- data/060_Bakery/qa.parquet
- data/061_Disneyland/qa.parquet
- data/062_Trump/qa.parquet
- data/063_Influencers/qa.parquet
- data/064_Clustering/qa.parquet
- data/065_RFM/qa.parquet
# - split: 001_Forbes
# path: data/001_Forbes/qa.parquet
# - split: 002_Titanic
# path: data/002_Titanic/qa.parquet
# - split: 003_Love
# path: data/003_Love/qa.parquet
# - split: 004_Taxi
# path: data/004_Taxi/qa.parquet
# - split: 005_NYC
# path: data/005_NYC/qa.parquet
# - split: 006_London
# path: data/006_London/qa.parquet
# - split: 007_Fifa
# path: data/007_Fifa/qa.parquet
# - split: 008_Tornados
# path: data/008_Tornados/qa.parquet
# - split: 009_Central
# path: data/009_Central/qa.parquet
# - split: 010_ECommerce
# path: data/010_ECommerce/qa.parquet
# - split: 011_SF
# path: data/011_SF/qa.parquet
# - split: 012_Heart
# path: data/012_Heart/qa.parquet
# - split: 013_Roller
# path: data/013_Roller/qa.parquet
# - split: 014_Airbnb
# path: data/014_Airbnb/qa.parquet
# - split: 015_Food
# path: data/015_Food/qa.parquet
# - split: 016_Holiday
# path: data/016_Holiday/qa.parquet
# - split: 017_Hacker
# path: data/017_Hacker/qa.parquet
# - split: 018_Staff
# path: data/018_Staff/qa.parquet
# - split: 019_Aircraft
# path: data/019_Aircraft/qa.parquet
# - split: 020_Real
# path: data/020_Real/qa.parquet
# - split: 021_Telco
# path: data/021_Telco/qa.parquet
# - split: 022_Airbnbs
# path: data/022_Airbnbs/qa.parquet
# - split: 023_Climate
# path: data/023_Climate/qa.parquet
# - split: 024_Salary
# path: data/024_Salary/qa.parquet
# - split: 025_Data
# path: data/025_Data/qa.parquet
# - split: 026_Predicting
# path: data/026_Predicting/qa.parquet
# - split: 027_Supermarket
# path: data/027_Supermarket/qa.parquet
# - split: 028_Predict
# path: data/028_Predict/qa.parquet
# - split: 029_NYTimes
# path: data/029_NYTimes/qa.parquet
# - split: 030_Professionals
# path: data/030_Professionals/qa.parquet
# - split: 031_Trustpilot
# path: data/031_Trustpilot/qa.parquet
# - split: 032_Delicatessen
# path: data/032_Delicatessen/qa.parquet
# - split: 033_Employee
# path: data/033_Employee/qa.parquet
# - split: 034_World
# path: data/034_World/qa.parquet
# - split: 035_Billboard
# path: data/035_Billboard/qa.parquet
# - split: 036_US
# path: data/036_US/qa.parquet
# - split: 037_Ted
# path: data/037_Ted/qa.parquet
# - split: 038_Stroke
# path: data/038_Stroke/qa.parquet
# - split: 039_Happy
# path: data/039_Happy/qa.parquet
# - split: 040_Speed
# path: data/040_Speed/qa.parquet
# - split: 041_Airline
# path: data/041_Airline/qa.parquet
# - split: 042_Predict
# path: data/042_Predict/qa.parquet
# - split: 043_Predict
# path: data/043_Predict/qa.parquet
# - split: 044_IMDb
# path: data/044_IMDb/qa.parquet
# - split: 045_Predict
# path: data/045_Predict/qa.parquet
# - split: "046_120"
# path: data/046_120/qa.parquet
# - split: 047_Bank
# path: data/047_Bank/qa.parquet
# - split: 048_Data
# path: data/048_Data/qa.parquet
# - split: 049_Boris
# path: data/049_Boris/qa.parquet
# - split: 050_ING
# path: data/050_ING/qa.parquet
# - split: 051_Pokemon
# path: data/051_Pokemon/qa.parquet
# - split: 052_Professional
# path: data/052_Professional/qa.parquet
# - split: 053_Patents
# path: data/053_Patents/qa.parquet
# - split: 054_Joe
# path: data/054_Joe/qa.parquet
# - split: 055_German
# path: data/055_German/qa.parquet
# - split: 056_Emoji
# path: data/056_Emoji/qa.parquet
# - split: 057_Spain
# path: data/057_Spain/qa.parquet
# - split: 058_US
# path: data/058_US/qa.parquet
# - split: 059_Second
# path: data/059_Second/qa.parquet
# - split: 060_Bakery
# path: data/060_Bakery/qa.parquet
# - split: 061_Disneyland
# path: data/061_Disneyland/qa.parquet
# - split: 062_Trump
# path: data/062_Trump/qa.parquet
# - split: 063_Influencers
# path: data/063_Influencers/qa.parquet
# - split: 064_Clustering
# path: data/064_Clustering/qa.parquet
# - split: 065_RFM
# path: data/065_RFM/qa.parquet
# - config_name: 001_Forbes
# data_files:
# - split: full
# path: data/001_Forbes/all.parquet
# - split: lite
# path: data/001_Forbes/sample.parquet
# - config_name: 002_Titanic
# data_files:
# - split: full
# path: data/002_Titanic/all.parquet
# - split: lite
# path: data/002_Titanic/sample.parquet
# - config_name: 003_Love
# data_files:
# - split: full
# path: data/003_Love/all.parquet
# - split: lite
# path: data/003_Love/sample.parquet
# - config_name: 004_Taxi
# data_files:
# - split: full
# path: data/004_Taxi/all.parquet
# - split: lite
# path: data/004_Taxi/sample.parquet
# - config_name: 005_NYC
# data_files:
# - split: full
# path: data/005_NYC/all.parquet
# - split: lite
# path: data/005_NYC/sample.parquet
# - config_name: 006_London
# data_files:
# - split: full
# path: data/006_London/all.parquet
# - split: lite
# path: data/006_London/sample.parquet
# - config_name: 007_Fifa
# data_files:
# - split: full
# path: data/007_Fifa/all.parquet
# - split: lite
# path: data/007_Fifa/sample.parquet
# - config_name: 008_Tornados
# data_files:
# - split: full
# path: data/008_Tornados/all.parquet
# - split: lite
# path: data/008_Tornados/sample.parquet
# - config_name: 009_Central
# data_files:
# - split: full
# path: data/009_Central/all.parquet
# - split: lite
# path: data/009_Central/sample.parquet
# - config_name: 010_ECommerce
# data_files:
# - split: full
# path: data/010_ECommerce/all.parquet
# - split: lite
# path: data/010_ECommerce/sample.parquet
# - config_name: 011_SF
# data_files:
# - split: full
# path: data/011_SF/all.parquet
# - split: lite
# path: data/011_SF/sample.parquet
# - config_name: 012_Heart
# data_files:
# - split: full
# path: data/012_Heart/all.parquet
# - split: lite
# path: data/012_Heart/sample.parquet
# - config_name: 013_Roller
# data_files:
# - split: full
# path: data/013_Roller/all.parquet
# - split: lite
# path: data/013_Roller/sample.parquet
# - config_name: 014_Airbnb
# data_files:
# - split: full
# path: data/014_Airbnb/all.parquet
# - split: lite
# path: data/014_Airbnb/sample.parquet
# - config_name: 015_Food
# data_files:
# - split: full
# path: data/015_Food/all.parquet
# - split: lite
# path: data/015_Food/sample.parquet
# - config_name: 016_Holiday
# data_files:
# - split: full
# path: data/016_Holiday/all.parquet
# - split: lite
# path: data/016_Holiday/sample.parquet
# - config_name: 017_Hacker
# data_files:
# - split: full
# path: data/017_Hacker/all.parquet
# - split: lite
# path: data/017_Hacker/sample.parquet
# - config_name: 018_Staff
# data_files:
# - split: full
# path: data/018_Staff/all.parquet
# - split: lite
# path: data/018_Staff/sample.parquet
# - config_name: 019_Aircraft
# data_files:
# - split: full
# path: data/019_Aircraft/all.parquet
# - split: lite
# path: data/019_Aircraft/sample.parquet
# - config_name: 020_Real
# data_files:
# - split: full
# path: data/020_Real/all.parquet
# - split: lite
# path: data/020_Real/sample.parquet
# - config_name: 021_Telco
# data_files:
# - split: full
# path: data/021_Telco/all.parquet
# - split: lite
# path: data/021_Telco/sample.parquet
# - config_name: 022_Airbnbs
# data_files:
# - split: full
# path: data/022_Airbnbs/all.parquet
# - split: lite
# path: data/022_Airbnbs/sample.parquet
# - config_name: 023_Climate
# data_files:
# - split: full
# path: data/023_Climate/all.parquet
# - split: lite
# path: data/023_Climate/sample.parquet
# - config_name: 024_Salary
# data_files:
# - split: full
# path: data/024_Salary/all.parquet
# - split: lite
# path: data/024_Salary/sample.parquet
# - config_name: 025_Data
# data_files:
# - split: full
# path: data/025_Data/all.parquet
# - split: lite
# path: data/025_Data/sample.parquet
# - config_name: 026_Predicting
# data_files:
# - split: full
# path: data/026_Predicting/all.parquet
# - split: lite
# path: data/026_Predicting/sample.parquet
# - config_name: 027_Supermarket
# data_files:
# - split: full
# path: data/027_Supermarket/all.parquet
# - split: lite
# path: data/027_Supermarket/sample.parquet
# - config_name: 028_Predict
# data_files:
# - split: full
# path: data/028_Predict/all.parquet
# - split: lite
# path: data/028_Predict/sample.parquet
# - config_name: 029_NYTimes
# data_files:
# - split: full
# path: data/029_NYTimes/all.parquet
# - split: lite
# path: data/029_NYTimes/sample.parquet
# - config_name: 030_Professionals
# data_files:
# - split: full
# path: data/030_Professionals/all.parquet
# - split: lite
# path: data/030_Professionals/sample.parquet
# - config_name: 031_Trustpilot
# data_files:
# - split: full
# path: data/031_Trustpilot/all.parquet
# - split: lite
# path: data/031_Trustpilot/sample.parquet
# - config_name: 032_Delicatessen
# data_files:
# - split: full
# path: data/032_Delicatessen/all.parquet
# - split: lite
# path: data/032_Delicatessen/sample.parquet
# - config_name: 033_Employee
# data_files:
# - split: full
# path: data/033_Employee/all.parquet
# - split: lite
# path: data/033_Employee/sample.parquet
# - config_name: 034_World
# data_files:
# - split: full
# path: data/034_World/all.parquet
# - split: lite
# path: data/034_World/sample.parquet
# - config_name: 035_Billboard
# data_files:
# - split: full
# path: data/035_Billboard/all.parquet
# - split: lite
# path: data/035_Billboard/sample.parquet
# - config_name: 036_US
# data_files:
# - split: full
# path: data/036_US/all.parquet
# - split: lite
# path: data/036_US/sample.parquet
# - config_name: 037_Ted
# data_files:
# - split: full
# path: data/037_Ted/all.parquet
# - split: lite
# path: data/037_Ted/sample.parquet
# - config_name: 038_Stroke
# data_files:
# - split: full
# path: data/038_Stroke/all.parquet
# - split: lite
# path: data/038_Stroke/sample.parquet
# - config_name: 039_Happy
# data_files:
# - split: full
# path: data/039_Happy/all.parquet
# - split: lite
# path: data/039_Happy/sample.parquet
# - config_name: 040_Speed
# data_files:
# - split: full
# path: data/040_Speed/all.parquet
# - split: lite
# path: data/040_Speed/sample.parquet
# - config_name: 041_Airline
# data_files:
# - split: full
# path: data/041_Airline/all.parquet
# - split: lite
# path: data/041_Airline/sample.parquet
# - config_name: 042_Predict
# data_files:
# - split: full
# path: data/042_Predict/all.parquet
# - split: lite
# path: data/042_Predict/sample.parquet
# - config_name: 043_Predict
# data_files:
# - split: full
# path: data/043_Predict/all.parquet
# - split: lite
# path: data/043_Predict/sample.parquet
# - config_name: 044_IMDb
# data_files:
# - split: full
# path: data/044_IMDb/all.parquet
# - split: lite
# path: data/044_IMDb/sample.parquet
# - config_name: 045_Predict
# data_files:
# - split: full
# path: data/045_Predict/all.parquet
# - split: lite
# path: data/045_Predict/sample.parquet
# - config_name: "046_120"
# data_files:
# - split: full
# path: data/046_120/all.parquet
# - split: lite
# path: data/046_120/sample.parquet
# - config_name: 047_Bank
# data_files:
# - split: full
# path: data/047_Bank/all.parquet
# - split: lite
# path: data/047_Bank/sample.parquet
# - config_name: 048_Data
# data_files:
# - split: full
# path: data/048_Data/all.parquet
# - split: lite
# path: data/048_Data/sample.parquet
# - config_name: 049_Boris
# data_files:
# - split: full
# path: data/049_Boris/all.parquet
# - split: lite
# path: data/049_Boris/sample.parquet
# - config_name: 050_ING
# data_files:
# - split: full
# path: data/050_ING/all.parquet
# - split: lite
# path: data/050_ING/sample.parquet
# - config_name: 051_Pokemon
# data_files:
# - split: full
# path: data/051_Pokemon/all.parquet
# - split: lite
# path: data/051_Pokemon/sample.parquet
# - config_name: 052_Professional
# data_files:
# - split: full
# path: data/052_Professional/all.parquet
# - split: lite
# path: data/052_Professional/sample.parquet
# - config_name: 053_Patents
# data_files:
# - split: full
# path: data/053_Patents/all.parquet
# - split: lite
# path: data/053_Patents/sample.parquet
# - config_name: 054_Joe
# data_files:
# - split: full
# path: data/054_Joe/all.parquet
# - split: lite
# path: data/054_Joe/sample.parquet
# - config_name: 055_German
# data_files:
# - split: full
# path: data/055_German/all.parquet
# - split: lite
# path: data/055_German/sample.parquet
# - config_name: 056_Emoji
# data_files:
# - split: full
# path: data/056_Emoji/all.parquet
# - split: lite
# path: data/056_Emoji/sample.parquet
# - config_name: 057_Spain
# data_files:
# - split: full
# path: data/057_Spain/all.parquet
# - split: lite
# path: data/057_Spain/sample.parquet
# - config_name: 058_US
# data_files:
# - split: full
# path: data/058_US/all.parquet
# - split: lite
# path: data/058_US/sample.parquet
# - config_name: 059_Second
# data_files:
# - split: full
# path: data/059_Second/all.parquet
# - split: lite
# path: data/059_Second/sample.parquet
# - config_name: 060_Bakery
# data_files:
# - split: full
# path: data/060_Bakery/all.parquet
# - split: lite
# path: data/060_Bakery/sample.parquet
# - config_name: 061_Disneyland
# data_files:
# - split: full
# path: data/061_Disneyland/all.parquet
# - split: lite
# path: data/061_Disneyland/sample.parquet
# - config_name: 062_Trump
# data_files:
# - split: full
# path: data/062_Trump/all.parquet
# - split: lite
# path: data/062_Trump/sample.parquet
# - config_name: 063_Influencers
# data_files:
# - split: full
# path: data/063_Influencers/all.parquet
# - split: lite
# path: data/063_Influencers/sample.parquet
# - config_name: 064_Clustering
# data_files:
# - split: full
# path: data/064_Clustering/all.parquet
# - split: lite
# path: data/064_Clustering/sample.parquet
# - config_name: 065_RFM
# data_files:
# - split: full
# path: data/065_RFM/all.parquet
# - split: lite
# path: data/065_RFM/sample.parquet
- config_name: semeval
data_files:
- split: train
path:
- data/001_Forbes/qa.parquet
- data/002_Titanic/qa.parquet
- data/003_Love/qa.parquet
- data/004_Taxi/qa.parquet
- data/005_NYC/qa.parquet
- data/006_London/qa.parquet
- data/007_Fifa/qa.parquet
- data/008_Tornados/qa.parquet
- data/009_Central/qa.parquet
- data/010_ECommerce/qa.parquet
- data/011_SF/qa.parquet
- data/012_Heart/qa.parquet
- data/013_Roller/qa.parquet
- data/014_Airbnb/qa.parquet
- data/015_Food/qa.parquet
- data/016_Holiday/qa.parquet
- data/017_Hacker/qa.parquet
- data/018_Staff/qa.parquet
- data/019_Aircraft/qa.parquet
- data/020_Real/qa.parquet
- data/021_Telco/qa.parquet
- data/022_Airbnbs/qa.parquet
- data/023_Climate/qa.parquet
- data/024_Salary/qa.parquet
- data/025_Data/qa.parquet
- data/026_Predicting/qa.parquet
- data/027_Supermarket/qa.parquet
- data/028_Predict/qa.parquet
- data/029_NYTimes/qa.parquet
- data/030_Professionals/qa.parquet
- data/031_Trustpilot/qa.parquet
- data/032_Delicatessen/qa.parquet
- data/033_Employee/qa.parquet
- data/034_World/qa.parquet
- data/035_Billboard/qa.parquet
- data/036_US/qa.parquet
- data/037_Ted/qa.parquet
- data/038_Stroke/qa.parquet
- data/039_Happy/qa.parquet
- data/040_Speed/qa.parquet
- data/041_Airline/qa.parquet
- data/042_Predict/qa.parquet
- data/043_Predict/qa.parquet
- data/044_IMDb/qa.parquet
- data/045_Predict/qa.parquet
- data/046_120/qa.parquet
- data/047_Bank/qa.parquet
- data/048_Data/qa.parquet
- data/049_Boris/qa.parquet
- split: dev
path:
- data/050_ING/qa.parquet
- data/051_Pokemon/qa.parquet
- data/052_Professional/qa.parquet
- data/053_Patents/qa.parquet
- data/054_Joe/qa.parquet
- data/055_German/qa.parquet
- data/056_Emoji/qa.parquet
- data/057_Spain/qa.parquet
- data/058_US/qa.parquet
- data/059_Second/qa.parquet
- data/060_Bakery/qa.parquet
- data/061_Disneyland/qa.parquet
- data/062_Trump/qa.parquet
- data/063_Influencers/qa.parquet
- data/064_Clustering/qa.parquet
- data/065_RFM/qa.parquet
---
# 💾🏋️💾 DataBench 💾🏋️💾
This repository contains the original 65 datasets used for the paper [Question Answering over Tabular Data with DataBench:
A Large-Scale Empirical Evaluation of LLMs](https://huggingface.co/datasets/cardiffnlp/databench/resolve/main/Databench-LREC-Coling-2024.pdf) which appeared in LREC-COLING 2024.
Large Language Models (LLMs) are showing emerging abilities, and one of the latest recognized ones is tabular
reasoning in question answering on tabular data. Although there are some available datasets to assess question
answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs.
To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.
## Usage
```python
from datasets import load_dataset
# Load all QA pairs
all_qa = load_dataset("cardiffnlp/databench", name="qa", split="train")
# Load SemEval 2025 task 8 Question-Answer splits
semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")
```
You can use any of the individual [integrated libraries](https://huggingface.co/docs/hub/datasets-libraries#libraries) to load the actual data where the answer is to be retrieved.
For example, using pandas in Python:
```python
import pandas as pd
# "001_Forbes", the id of the dataset
ds_id = all_qa['dataset'][0]
# full dataset
df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/all.parquet")
# sample dataset
df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/sample.parquet")
```
## 📚 Datasets
By clicking on each name in the table below, you will be able to explore each dataset.
| | Name | Rows | Cols | Domain | Source (Reference) |
|---:|:-------------------------------|-------:|-------:|:---------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
| 1 | [Forbes](https://public.graphext.com/0b211530c7e213d3/index.html?section=data) | 2668 | 17 | Business | [Forbes](https://www.forbes.com/billionaires/)|
| 2 | [Titanic](https://public.graphext.com/8577225c5ffd88fd/index.html) | 887 | 8 | Travel and Locations | [Kaggle](https://www.kaggle.com/competitions/titanic/data)|
| 3 | [Love](https://public.graphext.com/be7a566b0c485916/index.html) | 373 | 35 | Social Networks and Surveys | [Graphext](https://public.graphext.com/1de78f6820cfd5ba/index.html) |
| 4 | [Taxi](https://public.graphext.com/bcee13c23070f333/index.html) | 100000 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/competitions/nyc-taxi-trip-duration/overview) |
| 5 | [NYC Calls](https://public.graphext.com/1ce2f5fae408621e/index.html) | 100000 | 46 | Business | [City of New York](https://data.cityofnewyork.us/Social-Services/NYC-311-Data/jrb2-thup) |
| 6 | [London Airbnbs](https://public.graphext.com/6bbf4bbd3ff279c0/index.html) | 75241 | 74 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/labdmitriy/airbnb) |
| 7 | [Fifa](https://public.graphext.com/37bca51494c10a79/index.html) | 14620 | 59 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/stefanoleone992/fifa-21-complete-player-dataset) |
| 8 | [Tornados](https://public.graphext.com/4be9872e031199c3/index.html) | 67558 | 14 | Health | [Kaggle](https://www.kaggle.com/datasets/danbraswell/us-tornado-dataset-1950-2021) |
| 9 | [Central Park](https://public.graphext.com/7b3d3a4d7bf1e9b5/index.html) | 56245 | 6 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/danbraswell/new-york-city-weather-18692022) |
| 10 | [ECommerce Reviews](https://public.graphext.com/a5b8911b215958ad/index.html) | 23486 | 10 | Business | [Kaggle](https://www.kaggle.com/datasets/nicapotato/womens-ecommerce-clothing-reviews) |
| 11 | [SF Police](https://public.graphext.com/ab815ab14f88115c/index.html) | 713107 | 35 | Social Networks and Surveys | [US Gov](https://catalog.data.gov/dataset/police-department-incident-reports-2018-to-present) |
| 12 | [Heart Failure](https://public.graphext.com/245cec64075f5542/index.html) | 918 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) |
| 13 | [Roller Coasters](https://public.graphext.com/1e550e6c24fc1930/index.html) | 1087 | 56 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/robikscube/rollercoaster-database) |
| 14 | [Madrid Airbnbs](https://public.graphext.com/77265ea3a63e650f/index.html) | 20776 | 75 | Travel and Locations | [Inside Airbnb](http://data.insideairbnb.com/spain/comunidad-de-madrid/madrid/2023-09-07/data/listings.parquet.gz) |
| 15 | [Food Names](https://public.graphext.com/5aad4c5d6ef140b3/index.html) | 906 | 4 | Business | [Data World](https://data.world/alexandra/generic-food-database) |
| 16 | [Holiday Package Sales](https://public.graphext.com/fbc34d3f24282e46/index.html) | 4888 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/susant4learning/holiday-package-purchase-prediction) |
| 17 | [Hacker News](https://public.graphext.com/f20501a9d616b5a5/index.html) | 9429 | 20 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/hacker-news/hacker-news) |
| 18 | [Staff Satisfaction](https://public.graphext.com/6822ac1ce6307fec/index.html) | 14999 | 11 | Business | [Kaggle](https://www.kaggle.com/datasets/mohamedharris/employee-satisfaction-index-dataset) |
| 19 | [Aircraft Accidents](https://public.graphext.com/1802117b1b14f5c5/index.html) | 23519 | 23 | Health | [Kaggle](https://www.kaggle.com/datasets/ramjasmaurya/aviation-accidents-history1919-april-2022) |
| 20 | [Real Estate Madrid](https://public.graphext.com/5f83ec219a7ea84f/index.html) | 26026 | 59 | Business | [Idealista](https://public.graphext.com/5f83ec219a7ea84f/index.html) |
| 21 | [Telco Customer Churn](https://public.graphext.com/362cd8e3e96f70d4/index.html) | 7043 | 21 | Business | [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn) |
| 22 | [Airbnbs Listings NY](https://public.graphext.com/77265ea3a63e650f/index.html) | 37012 | 33 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data) |
| 23 | [Climate in Madrid](https://public.graphext.com/83a75b4f1cea8df4/index.html?section=data) | 36858 | 26 | Travel and Locations | [AEMET](https://public.graphext.com/83a75b4f1cea8df4/index.html?section=data) |
| 24 | [Salary Survey Spain 2018](https://public.graphext.com/24d1e717ba01aa3d/index.html) | 216726 | 29 | Business | [INE](ine.es) |
| 25 | [Data Driven SEO ](https://public.graphext.com/4e5b1cac9ebdfa44/index.html) | 62 | 5 | Business | [Graphext](https://www.graphext.com/post/data-driven-seo-a-keyword-optimization-guide-using-web-scraping-co-occurrence-analysis-graphext-deepnote-adwords) |
| 26 | [Predicting Wine Quality](https://public.graphext.com/de04acf5d18a9aea/index.html) | 1599 | 12 | Business | [Kaggle](https://www.kaggle.com/datasets/yasserh/wine-quality-dataset) |
| 27 | [Supermarket Sales](https://public.graphext.com/9a6742da6a8d8f7f/index.html) | 1000 | 17 | Business | [Kaggle](https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales) |
| 28 | [Predict Diabetes](https://public.graphext.com/def4bada27af324c/index.html) | 768 | 9 | Health | [Kaggle](https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset) |
| 29 | [NYTimes World In 2021](https://public.graphext.com/af4c8eef1757973c/index.html?section=data) | 52588 | 5 | Travel and Locations | [New York Times](https://public.graphext.com/af4c8eef1757973c/index.html) |
| 30 | [Professionals Kaggle Survey](https://public.graphext.com/3a2e87f90363a85d/index.html) | 19169 | 64 | Business | [Kaggle](https://www.kaggle.com/c/kaggle-survey-2021/data) |
| 31 | [Trustpilot Reviews](https://public.graphext.com/367e29432331fbfd/index.html?section=data) | 8020 | 6 | Business | [TrustPilot](https://public.graphext.com/367e29432331fbfd/index.html?section=data) |
| 32 | [Delicatessen Customers](https://public.graphext.com/a1687589fbde07bc/index.html) | 2240 | 29 | Business | [Kaggle](https://www.kaggle.com/datasets/rodsaldanha/arketing-campaign) |
| 33 | [Employee Attrition](https://public.graphext.com/07a91a15ecf2b8f6/index.html) | 14999 | 11 | Business | [Kaggle(modified)](https://www.kaggle.com/datasets/pavan9065/predicting-employee-attrition) |
| 34 | [World Happiness Report 2020](https://public.graphext.com/754c83ff0a7ba087/index.html) | 153 | 20 | Social Networks and Surveys | [World Happiness](https://worldhappiness.report/data/) |
| 35 | [Billboard Lyrics](https://public.graphext.com/7e0b009e8d0af719/index.html) | 5100 | 6 | Sports and Entertainment | [Brown University](https://cs.brown.edu/courses/cs100/students/project11/) |
| 36 | [US Migrations 2012-2016](https://public.graphext.com/dbdadf87a5c21695/index.html) | 288300 | 9 | Social Networks and Surveys | [US Census](https://www.census.gov/topics/population/migration/guidance/county-to-county-migration-flows.html) |
| 37 | [Ted Talks](https://public.graphext.com/07e48466fb670904/index.html) | 4005 | 19 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ashishjangra27/ted-talks) |
| 38 | [Stroke Likelihood](https://public.graphext.com/20ccfee9e84948e3/index.html) | 5110 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease) |
| 39 | [Happy Moments](https://public.graphext.com/9b86efff48989701/index.html) | 100535 | 11 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ritresearch/happydb) |
| 40 | [Speed Dating](https://public.graphext.com/f1912daad7870be0/index.html) | 8378 | 123 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ulrikthygepedersen/speed-dating) |
| 41 | [Airline Mentions X (former Twitter)](https://public.graphext.com/29cb7f73f6e17a38/index.html) | 14640 | 15 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/7e6999327d1f83fd/index.html) |
| 42 | [Predict Student Performance](https://public.graphext.com/def4bada27af324c/index.html) | 395 | 33 | Business | [Kaggle](https://www.kaggle.com/datasets/impapan/student-performance-data-set) |
| 43 | [Loan Defaults](https://public.graphext.com/0c7fb68ab8071a1f/index.html) | 83656 | 20 | Business | [SBA](https://www.kaggle.com/datasets/mirbektoktogaraev/should-this-loan-be-approved-or-denied) |
| 44 | [IMDb Movies](https://public.graphext.com/e23e33774872c496/index.html) | 85855 | 22 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows) |
| 45 | [Spotify Song Popularity](https://public.graphext.com/def4bada27af324c/index.html) | 21000 | 19 | Sports and Entertainment | [Spotify](https://www.kaggle.com/datasets/tomigelo/spotify-audio-features) |
| 46 | [120 Years Olympics](https://public.graphext.com/e57d5e2f172c9a99/index.html) | 271116 | 15 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results) |
| 47 | [Bank Customer Churn](https://public.graphext.com/e8f7aeacd209f74a/index.html) | 7088 | 15 | Business | [Kaggle](https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers) |
| 48 | [Data Science Salary Data](https://public.graphext.com/4e5b1cac9ebdfa44/index.html) | 742 | 28 | Business | [Kaggle](https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries) |
| 49 | [Boris Johnson UK PM Tweets](https://public.graphext.com/f6623a1ca0f41c8e/index.html) | 3220 | 34 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/f6623a1ca0f41c8e/index.html) |
| 50 | [ING 2019 X Mentions](https://public.graphext.com/075030310aa702c6/index.html) | 7244 | 22 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/075030310aa702c6/index.html) |
| 51 | [Pokemon Features](https://public.graphext.com/f30d4d863a2e6b01/index.html) | 1072 | 13 | Business | [Kaggle](https://www.kaggle.com/datasets/rounakbanik/pokemon) |
| 52 | [Professional Map](https://public.graphext.com/70af2240cb751968/index.html) | 1227 | 12 | Business | [Kern et al, PNAS'20](https://github.com/behavioral-ds/VocationMap) |
| 53 | [Google Patents](https://public.graphext.com/a262300e31874716/index.html) | 9999 | 20 | Business | [BigQuery](https://www.kaggle.com/datasets/bigquery/patents/data) |
| 54 | [Joe Biden Tweets](https://public.graphext.com/33fa2efa41541ab1/index.html) | 491 | 34 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/339cee259f0a9b32/index.html?section=data) |
55 | [German Loans](https://public.graphext.com/d3f5e425e9d4b0a1/index.html) | 1000 | 18 | Business | [Kaggle](https://www.kaggle.com/datasets/uciml/german-credit/data) |
| 56 | [Emoji Diet](https://public.graphext.com/e721cc7d790c06d4/index.html) | 58 | 35 | Health | [Kaggle](https://www.kaggle.com/datasets/ofrancisco/emoji-diet-nutritional-data-sr28) |
| 57 | [Spain Survey 2015](https://public.graphext.com/90ca7539b160fdfa/index.html?section=data) | 20000 | 45 | Social Networks and Surveys | [CIS](https://public.graphext.com/90ca7539b160fdfa/index.html?section=data) |
| 58 | [US Polls 2020](https://public.graphext.com/dbdadf87a5c21695/index.html) | 3523 | 52 | Social Networks and Surveys | [Brandwatch](https://www.brandwatch.com/p/us-election-raw-polling-data/) |
| 59 | [Second Hand Cars](https://public.graphext.com/543d0c49d7120ca0/index.html) | 50000 | 21 | Business | [DataMarket](https://www.kaggle.com/datasets/datamarket/venta-de-coches) |
| 60 | [Bakery Purchases](https://public.graphext.com/6f2102e80f47a192/index.html) | 20507 | 5 | Business | [Kaggle](https://www.kaggle.com/code/xvivancos/market-basket-analysis/report) |
| 61 | [Disneyland Customer Reviews](https://public.graphext.com/b1037bb566b7b316/index.html) | 42656 | 6 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/arushchillar/disneyland-reviews) |
| 62 | [Trump Tweets](https://public.graphext.com/7aff94c3b7f159fc/index.html) | 15039 | 20 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/be903c098a90e46f/index.html?section=data) |
| 63 | [Influencers](https://public.graphext.com/e097f1ea03d761a9/index.html) | 1039 | 14 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/e097f1ea03d761a9/index.html) |
| 64 | [Clustering Zoo Animals](https://public.graphext.com/d1b66902e46a712a/index.html) | 101 | 18 | Health | [Kaggle](https://www.kaggle.com/datasets/jirkadaberger/zoo-animals) |
| 65 | [RFM Analysis](https://public.graphext.com/4db2e54e29006a21/index.html) | 541909 | 8 | Business | [UCI ML](https://www.kaggle.com/datasets/carrie1/ecommerce-data) |
## 🏗️ Folder structure
Each folder represents one dataset. You will find the following files within:
* all.parquet: the processed data, with each column tagged with our typing system, in [parquet](https://arrow.apache.org/docs/python/parquet.html).
* qa.parquet: contains the human-made set of questions, tagged by type and columns used, for the dataset (sample_answer indicates the answers for DataBench lite)
* sample.parquet: sample containing 20 rows of the original dataset (DataBench lite)
* info.yml: additional information about the dataset
## 🗂️ Column typing system
In an effort to map the stage for later analysis, we have categorized the columns by type. This information allows us to segment different kinds of data so that we can subsequently analyze the model's behavior on each column type separately. All parquet files have been casted to their smallest viable data type using the open source [Lector](https://github.com/graphext/lector) reader.
What this means is that in the data types we have more granular information that allows us to know if the column contains NaNs or not (following panda’s convention of Int vs int), as well as whether small numerical values contain negatives (Uint vs int) and their range. We also have dates with potential timezone information (although for now they’re all UTC), as well as information about categories’ cardinality coming from the arrow types.
In the table below you can see all the data types assigned to each column, as well as the number of columns for each type. The most common data types are numbers and categories with 1336 columns of the total of 1615 included in DataBench. These are followed by some other more rare types as urls, booleans, dates or lists of elements.
| Type | Columns | Example |
| -------------- | ------- | ----------------------- |
| number | 788 | 55 |
| category | 548 | apple |
| date | 50 | 1970-01-01 |
| text | 46 | A red fox ran... |
| url | 31 | google.com |
| boolean | 18 | True |
| list[number] | 14 | [1,2,3] |
| list[category] | 112 | [apple, orange, banana] |
| list[url] | 8 | [google.com, apple.com] |
## 🔗 Reference
You can download the paper [here](https://huggingface.co/datasets/cardiffnlp/databench/resolve/main/Databench-LREC-Coling-2024.pdf).
If you use this resource, please use the following reference:
```
@inproceedings{oses-etal-2024-databench,
title = "Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs",
author = "Jorge Osés Grijalba and Luis Alfonso Ureña-López and
Eugenio Martínez Cámara and Jose Camacho-Collados",
booktitle = "Proceedings of LREC-COLING 2024",
year = "2024",
address = "Turin, Italy"
}
``` |
fixie-ai/librispeech_asr | fixie-ai | "2024-08-05T18:38:33Z" | 17,737 | 2 | [
"language:en",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-19T02:34:30Z" | ---
language:
- en
dataset_info:
- config_name: clean
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
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num_examples: 2939
download_size: 57019309170
dataset_size: 58539138084.417
configs:
- config_name: clean
data_files:
- split: test
path: clean/test-*
- split: validation
path: clean/validation-*
- split: train.360
path: clean/train.360-*
- split: train.100
path: clean/train.100-*
- config_name: other
data_files:
- split: train.500
path: other/train.500-*
- split: validation
path: other/validation-*
- split: test
path: other/test-*
---
|
Upabjojr/elevation-data-ASTER-compressed-retiled | Upabjojr | "2024-07-22T13:04:07Z" | 17,442 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2024-07-20T10:05:04Z" | ---
license: apache-2.0
pretty_name: Elevation data from ASTER GDEM compressed and retiled
---
# World elevation dataset
High resolution dataset containing the world elevation above the sea level in meters.
See python example to get the estimated elevation from a coordinate.
## Info
This dataset comprises global elevation data sourced from [ASTER GDEM](https://asterweb.jpl.nasa.gov/GDEM.asp), which has been compressed and retiled for efficiency. The retiled data adheres to the common web map tile convention used by platforms such as OpenStreetMap, Google Maps, and Bing Maps, providing compatibility with zoom level 8 tiles. More details on this tiling system can be found on the [OpenStreetMap wiki](https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames).
To minimize data size, a unique compression technique was utilized, encoding the elevation data into a combination of JPG and PNG images. This innovative method reduced the dataset size significantly, from approximately 560 gigabytes to just 22 gigabytes, with minimal loss of information.
## Usage
Install by cloning the project from github:
```shell
git clone https://github.com/Upabjojr/peaknav-tools
cd peaknav-tools
pip install -e .
```
Example usage, get the estimated elevation of Mount Mitchell, North Carolina, in meters:
```python
from peaknav_tools import get_elevation_from_coordinates
get_elevation_from_coordinates(35.7649563, -82.2651155)
```
Currently, this returns an elevation of 2024 meters for this coordinate (the actual elevation of Mount Mitchell is 2038 meters).
The elevation error typically ranges between 10-20 meters.
## References
This dataset has been generously donated by the [PeakNav](https://peaknav.com) app.
Citation of the source data:
```
NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global
Digital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/ASTER/ASTGTM.003
``` |
fixie-ai/peoples_speech | fixie-ai | "2024-08-11T17:26:01Z" | 17,431 | 2 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-08-05T18:35:01Z" | ---
dataset_info:
- config_name: clean
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dtype: int32
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data_files:
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path: clean/validation-*
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path: clean/test-*
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path: data/train-*
- config_name: dirty_sa
data_files:
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path: dirty_sa/train-*
- split: validation
path: dirty_sa/validation-*
- split: test
path: dirty_sa/test-*
---
|
MU-NLPC/Calc-svamp | MU-NLPC | "2023-10-30T15:05:26Z" | 17,398 | 0 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2305.15017",
"region:us",
"math world problems",
"math",
"arithmetics"
] | [
"text-generation"
] | "2023-09-08T14:56:46Z" | ---
language:
- en
license: mit
size_categories:
- n<1K
task_categories:
- text-generation
tags:
- math world problems
- math
- arithmetics
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: question
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
- name: problem_type
dtype: string
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configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: original-splits
data_files:
- split: test
path: original-splits/test-*
---
# Dataset Card for Calc-SVAMP
## Summary
The dataset is a collection of simple math word problems focused on arithmetics. It is derived from <https://github.com/arkilpatel/SVAMP/>.
The main addition in this dataset variant is the `chain` column. It was created by converting the solution to a simple html-like language that can be easily
parsed (e.g. by BeautifulSoup). The data contains 3 types of tags:
- gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
- output: An output of the external tool
- result: The final answer to the mathematical problem (a number)
## Supported Tasks
This variant of the dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses.
This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.
## Construction process
We created the dataset by converting the **equation** attribute in the original dataset to a sequence (chain) of calculations, with final one being the result to the math problem.
We also perform in-dataset and cross-dataset data-leak detection within the [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
However, for SVAMP specifically, we detected no data leaks and filtered no data.
## Content and data splits
The dataset contains the same data instances as the original dataset except for a correction of inconsistency between `equation` and `answer` in one data instance.
To the best of our knowledge, the original dataset does not contain an official train-test split. We treat the whole dataset as a testing benchmark.
## Attributes:
- **id**: problem id from the original dataset
- **question**: the question intended to answer
- **chain**: series of simple operations (derived from `equation`) that leads to the solution
- **result**: the result (number) as a string
- **result_float**: result converted to a floating point
- **equation**: a nested expression that evaluates to the correct result
- **problem_type**: a category of the problem
Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
## Related work
This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers.
- [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers
- [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF
- [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017)
- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
Here are links to the original dataset:
- [**original SVAMP dataset and repo**](https://github.com/arkilpatel/SVAMP/)
- [**original SVAMP paper**](https://www.semanticscholar.org/paper/Are-NLP-Models-really-able-to-Solve-Simple-Math-Patel-Bhattamishra/13c4e5a6122f3fa2663f63e49537091da6532f35)
## Licence
MIT, consistent with the original source dataset linked above.
## Cite
If you use this version of dataset in research, please cite the original [SVAMP paper](https://www.semanticscholar.org/paper/Are-NLP-Models-really-able-to-Solve-Simple-Math-Patel-Bhattamishra/13c4e5a6122f3fa2663f63e49537091da6532f35), and [Calc-X collection](https://arxiv.org/abs/2305.15017) as follows:
```bibtex
@inproceedings{kadlcik-etal-2023-soft,
title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
month = dec,
year = "2023",
address = "Singapore, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2305.15017",
}
``` |
Helsinki-NLP/euconst | Helsinki-NLP | "2024-02-27T09:42:27Z" | 17,252 | 8 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"translation"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- found
language_creators:
- found
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- cs
- da
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- el
- en
- es
- et
- fi
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- ga
- hu
- it
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- lv
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- nl
- pl
- pt
- sk
- sl
- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: OPUS EUconst
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---
# Dataset Card for OPUS EUconst
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://opus.nlpl.eu/EUconst/corpus/version/EUconst
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
A parallel corpus collected from the European Constitution.
EUconst's Numbers:
- Languages: 21
- Bitexts: 210
- Number of files: 986
- Number of tokens: 3.01M
- Sentence fragments: 0.22M
### Supported Tasks and Leaderboards
The underlying task is machine translation.
### Languages
The languages in the dataset are:
- Czech (`cs`)
- Danish (`da`)
- German (`de`)
- Greek (`el`)
- English (`en`)
- Spanish (`es`)
- Estonian (`et`)
- Finnish (`fi`)
- French (`fr`)
- Irish (`ga`)
- Hungarian (`hu`)
- Italian (`it`)
- Lithuanian (`lt`)
- Latvian (`lv`)
- Maltese (`mt`)
- Dutch (`nl`)
- Polish (`pl`)
- Portuguese (`pt`)
- Slovak (`sk`)
- Slovenian (`sl`)
- Swedish (`sv`)
## Dataset Structure
### Data Instances
```
{
"translation": {
"cs": "Celex Test ",
"da": "Celex Test "
}
}
```
### Data Fields
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single "train" split for each language pair.
## 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
If you use any part of the corpus in your own work, please cite the following article:
```
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.",
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
|
boettiger-lab/ca-30x30 | boettiger-lab | "2025-02-04T21:30:35Z" | 17,238 | 0 | [
"license:bsd-2-clause",
"region:us"
] | null | "2024-10-03T17:43:10Z" | ---
license: bsd-2-clause
---
|
huggingface/release-assets | huggingface | "2024-09-26T12:48:50Z" | 17,104 | 1 | [
"license:mit",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us"
] | null | "2024-09-25T10:32:15Z" | ---
license: mit
---
|
tiange/Cap3D | tiange | "2025-02-12T01:36:44Z" | 17,024 | 101 | [
"task_categories:text-to-3d",
"task_categories:image-to-3d",
"license:odc-by",
"arxiv:2306.07279",
"arxiv:2404.07984",
"arxiv:2212.08051",
"arxiv:2307.05663",
"arxiv:2110.06199",
"arxiv:1512.03012",
"region:us"
] | [
"text-to-3d",
"image-to-3d"
] | "2023-05-28T18:31:58Z" | ---
license: odc-by
viewer: false
task_categories:
- text-to-3d
- image-to-3d
---
## Dataset Description
- **Paper:** [Scalable 3D Captioning with Pretrained Models](https://arxiv.org/abs/2306.07279)
- **Paper:** [View Selection for 3D Captioning via Diffusion Ranking](https://arxiv.org/abs/2404.07984)
- **Repository**: [Github_Cap3D](https://github.com/crockwell/Cap3D)
- **Repository**: [Github_DiffuRank](https://github.com/tiangeluo/DiffuRank)
- **Project**: [Project](https://cap3d-um.github.io/)
This repository hosts data for [Scalable 3D Captioning with Pretrained Models](https://cap3d-um.github.io/) and [View Selection for 3D Captioning via Diffusion Ranking](http://arxiv.org/abs/2404.07984), including descriptive **captions** for 3D objects in [Objaverse](https://arxiv.org/abs/2212.08051), [Objaverse-XL](https://arxiv.org/pdf/2307.05663.pdf), [ABO](https://arxiv.org/abs/2110.06199), and [ShapeNet](https://arxiv.org/abs/1512.03012). This repo also includes **point clouds** and **rendered images with camera, depth, and MatAlpha information** of Objaverse objects, as well as their Shap-E latent codes. All the captions and data provided by our papers are released under ODC-By 1.0 license.
## Usage
Please download and unzip files from [**Page**](https://huggingface.co/datasets/tiange/Cap3D/tree/main) according to your usage. Below is a table listing fiels descriptions, followed by example Python scripts for data loading.
| Filename | Description |
| -------------------------------------- | ------------------------------------------------------------ |
| **Cap3D_automated_Objaverse_full.csv** | By integrating text descriptions initially generated by [Cap3D](https://arxiv.org/abs/2306.07279) and subsequently refined by [DiffuRank](https://arxiv.org/abs/2404.07984), we have produced a total of **1,153,779** 3D-caption pairs. Out of the total, **785,150** pairs have been contributed to the whole [Objaverse](https://arxiv.org/abs/2212.08051) dataset, with the balance for the [Objaverse-XL](https://arxiv.org/pdf/2307.05663.pdf) dataset (specifically the highquality subset described in Section 4.1 Alignment Finetuning of [Objaverse-XL](https://proceedings.neurips.cc/paper_files/paper/2023/file/70364304877b5e767de4e9a2a511be0c-Paper-Datasets_and_Benchmarks.pdf)). For the object identifier in the left column, strings with a length of 32 characters are **UIDs** from Objaverse 1.0 (retrieved using `import objaverse; uids = objaverse.load_uids()`). Strings with a length of 64 characters are **SHA256** hashes provided by Objaverse-XL. |
| Cap3D_automated_**ABO**.csv | Our captions generated by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984) for the [ABO dataset](https://arxiv.org/abs/2110.06199), including both general and compositional descriptions. |
| Cap3D_automated_**ShapeNet**.csv | Our captions generated by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984) for the [ShapeNet dataset](https://arxiv.org/abs/1512.03012). |
| **PointCloud_zips** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **1,006,782** PointClouds (16,384 colorful points) extracted from Objaverse objects. Saved as `.ply` file. |
| PointCloud_zips_**ABO** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **7,953** PointClouds (16,384 colorful points) extracted from ABO objects. Saved as `.ply` file. |
| PointCloud_zips_**ShapeNet** | Provided by [Cap3D](https://arxiv.org/abs/2306.07279) and [DiffuRank](https://arxiv.org/abs/2404.07984), **52,472** PointClouds (16,384 colorful points) extracted from ShapeNet objects. Saved as `.ply` file. |
| **RenderedImage_perobj_zips** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **1,006,782** Objaverse objects. Once unzip `compressed_imgs_perobj_xx.zip` will have multiple zip files which consists of **20** rendered images along with camera details (intrinsic & extrinsic), depth data, and masks ([one example](https://huggingface.co/datasets/tiange/Cap3D/tree/main/RenderedImage_perobj_zips/example_zipfile)). Please specify the unzip path, such as `unzip ed51a51909ee46c780db3a85e821feb2.zip -d ed51a51909ee46c780db3a85e821feb2`. More information are in [here](https://huggingface.co/datasets/tiange/Cap3D/blob/main/RenderedImage_perobj_zips/README.md). |
| RenderedImage_perobj_zips_**ABO** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **7,953** ABO objects. Details similar to the above. |
| RenderedImage_perobj_zips_**ShapeNet** | Provided by [DiffuRank](https://arxiv.org/abs/2404.07984), Rendered images for **52,472** ShapeNet objects. Similar to the above but with 8 rendered images. |
| misc | Including miscellaneous files such as human-authored captions, finetuned models, objaverse pointclouds stored as .pt, shapE latent codes, and etc. Please refer to this [README](https://huggingface.co/datasets/tiange/Cap3D/blob/main/misc/README.md) |
``` python
# load our captions
import pandas as pd
captions = pd.read_csv('Cap3D_automated_Objaverse_full.csv', header=None)
## captions:
## 0 1
## 0 ed51a51909ee46c780db3a85e821feb2 Matte green rifle with a long barrel, stock, a...
## 1 9110b606f6c547b2980fcb3c8c4b6a1c Rustic single-story building with a weathered ...
## 2 80d9caaa1fa04502af666135196456e1 a pair of purple and black swords with white h...
## 3 28d43a218cd8466a8c1f82b29b71e314 3D model of a cluttered outdoor scene with veg...
## 4 75582285fab442a2ba31733f9c8fae66 Floating terrain piece with grassy landscape a...
## ... ... ...
## 1002417 3623e74f34c1c3c523af6b2bb8ffcbe2d2dce897ef61b9... Abstract 3D composition with human figures and...
## 1002418 64e9f7b7a1fc4c4ec56ed8b5917dfd610930043ac5e15f... 3D object with a rough, irregular pink surface...
## 1002419 fcd089d6a237fee21dfd5f0d6d9b74b2fd1150cdc61c7f... Bright pink abstract 3D model of a building wi...
## 1002420 f812dc980050f2d5f4b37df2a8620372f810dd6456a5f2... Monochromatic gray 3D model of a stylized huma...
## 1002421 77c09500b4d8e4b881e1ce6929d56c23658b87173c0996... Modular futuristic spacecraft with red and ora...
## if u want to obtain the caption for specific UID
caption = captions[captions[0] == '80d9caaa1fa04502af666135196456e1'][1].values[0]
# load point clouds (unzip https://huggingface.co/datasets/tiange/Cap3D/tree/main/PointCloud_pt_zips)
import torch
pts = torch.load('Cap3D_pcs_pt/80d9caaa1fa04502af666135196456e1.pt')
## pts.shape == torch.Size([6, 16384])
```
If you have any questions, please contact [Tiange](mailto:tiange.cs@gmail.com) or [Chris](mailto:cnris@umich.edu).
## Citation Information
If you find our data or code useful, please consider citing:
```bibtex
@article{luo2023scalable,
title={Scalable 3D Captioning with Pretrained Models},
author={Luo, Tiange and Rockwell, Chris and Lee, Honglak and Johnson, Justin},
journal={arXiv preprint arXiv:2306.07279},
year={2023}
}
@article{luo2024view,
title={View Selection for 3D Captioning via Diffusion Ranking},
author={Luo, Tiange and Johnson, Justin and Lee, Honglak},
journal={arXiv preprint arXiv:2404.07984},
year={2024}
}
```
Please cite ***Objaverse*** and ***ABO*** paper accordingly, if you use related data.
```
@inproceedings{deitke2023objaverse,
title={Objaverse: A universe of annotated 3d objects},
author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13142--13153},
year={2023}
}
@article{deitke2024objaverse,
title={Objaverse-xl: A universe of 10m+ 3d objects},
author={Deitke, Matt and Liu, Ruoshi and Wallingford, Matthew and Ngo, Huong and Michel, Oscar and Kusupati, Aditya and Fan, Alan and Laforte, Christian and Voleti, Vikram and Gadre, Samir Yitzhak and others},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}
@inproceedings{collins2022abo,
title={Abo: Dataset and benchmarks for real-world 3d object understanding},
author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Vicente, Tomas F Yago and Dideriksen, Thomas and Arora, Himanshu and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={21126--21136},
year={2022}
}
```
|
roneneldan/TinyStories | roneneldan | "2024-08-12T13:27:26Z" | 16,822 | 616 | [
"task_categories:text-generation",
"language:en",
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.07759",
"region:us"
] | [
"text-generation"
] | "2023-05-12T19:04:09Z" | ---
license: cdla-sharing-1.0
task_categories:
- text-generation
language:
- en
---
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story.
TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper) |
andstor/the_pile_github | andstor | "2023-03-20T23:39:53Z" | 16,765 | 8 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:10M<n<100M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | [
"text-generation",
"fill-mask",
"text-classification"
] | "2023-03-07T15:53:05Z" | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: The Pile GitHub
size_categories: []
source_datasets:
- original
tags: []
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids: []
---
# Dataset Card for The Pile GitHub
## Table of Contents
- [Dataset Card for Smart Contracts](#dataset-card-for-the-pile-github)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ElutherAI](https://pile.eleuther.ai)
- **Repository:** [GitHub](https://github.com/andstor/the-pile-github)
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This is the GitHub subset of EleutherAi/The Pile dataset and contains GitHub repositories. The programming languages are identified using the [guesslang library](https://github.com/yoeo/guesslang). A total of 54 programming languages are included in the dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The following languages are covered by the dataset:
```
'Assembly', 'Batchfile', 'C', 'C#', 'C++', 'CMake', 'COBOL', 'CSS', 'CSV', 'Clojure', 'CoffeeScript', 'DM', 'Dart', 'Dockerfile', 'Elixir', 'Erlang', 'Fortran', 'Go', 'Groovy', 'HTML', 'Haskell', 'INI', 'JSON', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'Makefile', 'Markdown', 'Matlab', 'None', 'OCaml', 'Objective-C', 'PHP', 'Pascal', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Shell', 'Swift', 'TOML', 'TeX', 'TypeScript', 'Verilog', 'Visual Basic', 'XML', 'YAML'
```
The [guesslang library](https://github.com/yoeo/guesslang) is used to identify the programming languages. It has a guessing accuracy of above 90%. Hence, there will be some misclassifications in the language identification.
## Dataset Structure
### Data Instances
[More Information Needed]
```
{
'text': ...,
'meta': {'language': ...}
}
```
### Data Fields
- `text` (`string`): the source code.
- `meta` (`dict`): the metadata of the source code.
- `language` (`string`): the programming language of the source code.
### Data Splits
[More Information Needed]
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The data is purely a subset of the [EleutherAI/The Pile dataset](https://huggingface.co/datasets/the_pile). See the original [dataset](https://arxiv.org/abs/2201.07311) for more details.
## Additional Information
### Licensing Information
The Pile dataset was released on January 1st, 2021. It is licensed under the MIT License. See the [dataset](https://arxiv.org/abs/2201.07311) for more details.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@andstor](https://github.com/andstor) for adding this dataset. |
OpenGVLab/OmniCorpus-CC | OpenGVLab | "2024-11-17T07:08:46Z" | 16,734 | 12 | [
"task_categories:image-to-text",
"task_categories:visual-question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.08418",
"region:us"
] | [
"image-to-text",
"visual-question-answering"
] | "2024-08-30T06:16:02Z" | ---
language:
- en
license: cc-by-4.0
size_categories:
- 100M<n<1B
task_categories:
- image-to-text
- visual-question-answering
dataset_info:
- config_name: CC-MAIN-2013-20
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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sequence: string
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sequence: string
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list:
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dtype: float64
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---
⭐️ **NOTE:** Several parquet files were marked unsafe (viruses) by official scaning of hf, while they are reported safe by ClamAV and Virustotal.
We found [many false positive cases](https://discuss.huggingface.co/u/mcpotato/summary) of the hf automatic scanning in hf discussions and raise [one discussion](https://discuss.huggingface.co/t/one-parquet-file-of-my-dataset-was-marked-unsafe/113745) to ask for a re-scanning.
# OmniCorpus-CC
This is the repository of OmniCorpus-CC, which contains 988 million image-text interleaved documents collected from [Common Crawl](https://commoncrawl.org/).
- Repository: https://github.com/OpenGVLab/OmniCorpus
- Paper: https://arxiv.org/abs/2406.08418
OmniCorpus dataset is a large-scale image-text interleaved dataset, which pushes the boundaries of scale and diversity by encompassing **8.6 billion images** interleaved with **1,696 text tokens** from diverse sources, significantly surpassing previous datasets.
This dataset demonstrates several advantages over its counterparts:
1. **Larger data scale:** Our dataset is 1.7 times larger in images and 12.5 times larger in texts compared to the previously largest multimodal dataset, LAION-5B, while maintaining excellent data quality.
2. **Richer data diversity:** Drawing from a broader range of data sources, our dataset is more diverse than other image-text interleaved datasets. It includes bilingual multimodal data in both Chinese and English, and encompasses text-centric and vision-centric documents extracted from common websites and video platforms.
3. **More flexible format:** The streaming data format of our dataset offers exceptional flexibility, allowing adaptation to various data structures, including pure text corpora, image-text pairs, and interleaved data formats.
<img width="578" alt="image" src="https://github.com/OpenGVLab/OmniCorpus/assets/47669167/641a6427-ba50-41e6-8634-8810113fd803">
The OmniCorpus contains three sections:
- **OmniCorpus-CC**: processed from dumps in Common Crawl from 2013 to Nov./Dec. 2023.
- **OmniCorpus-CW**: sourced from Chinese internet resources, will be availiable in [OpenDataLab](https://opendatalab.com/) platform.
- **OmniCorpus-YT**: samples Youtube video frames as images and collects subtitles as texts.
Code for pre-training, evaluating, main body extracting, and filtering have been released in the official [repository](https://github.com/OpenGVLab/OmniCorpus). A pre-trained model is availiable [here](https://huggingface.co/Qingyun/OmniCorpus-InternVL).
# Data Pipeline
Our data pipeline consists of five key stages: main body extraction, preliminary text filtering, document deduplication, image downloading \& filtering, and detailed text filtering. Each stage efficiently reduces the dataset to retain only high-quality data.
Please refer to our paper for more details about the data pipeline.
<img width="723" alt="image" src="https://github.com/OpenGVLab/OmniCorpus/assets/47669167/a6de8928-58fb-4ff4-8ef9-4bd90e9ada5f">
# Usages
The image-text interleaved documents are recommanded for the following usages:
- Pre-training multimodal large language model (MLLM): Recent MLLMs (such as Flamingo series, EMU series, IDEFICS series, MM1, Cambrian-1, and xGen-MM) have shown that image-text interleaved data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning.
- Long text-image retrieval: We provide image-text similarities calculated with CLIP, which can convert the documents to image-text retrieval dataset with longer text. A retrieval model pre-trained on such data can retrieval images based on longer text, which can be used for multimodal RAG, converting pure text to multimodal sample, etc.
- Source for futher dataset research: Our data is large-scale, which can serve as the source for researches for data curation strategies. We provide many useful attributes as metadata for each document, which can enrich the filtering strategy and reduce the cost.
- ......
# Data Format
Following common practices, the data is organized into Parquet file format.
You might encounter errors when using `pandas.read_parquet` (because the data structure contains nested elements). We recommend using fastparquet to load the parquet files.
```Python
import fastparquet
df = fastparquet.ParquetFile(parquet_file_path).to_pandas()
# You can also use iter_batches
parquet_file = pq.ParquetFile(filepath)
for batch in parquet_file.iter_batches():
df = batch.to_pandas()
```
You can convert the i-th document and convert it into a dictionary.
```Python
doc_dict = df.iloc[i].to_dict()
```
The document format is as follow:
```json
{
'images': [
<str: image_1_url>,
None,
<str: image_2_url>,
None,
],
'texts': [
None,
<str: text_paragraph_1_content>
None,
<str: text_paragraph_2_content>,
]
'metadata': [
<dict: image_1_metadata>,
None,
<dict: image_2_metadata>,
None
],
'general_metadata': {
"url": <str: document url>,
"id": <str: document id>,
"domain": <list[str]: domains extracted from document url>,
"fluency_prob": <float: the probability of fluency>,
"non_advertisement_prob": <float: the probability of non-advertisement>,
"porn_prob": <float: the probability of porn content>,
"politics_prob": <float: the probability of politics content>,
"toxic_prob": <float: the probability of toxic content>,
}
}
```
Each image metadata is as follow:
```json
{
"img_url_sha": <str: sha code of image url>,
"width": <int: image width>,
"height": <int: image height>,
"bytes": <int: byte number of the image file>,
"d_hash": <str: d_hash code of the image, used for image deduplication>,
"p_hash": <str: p_hash code of the image, used for image deduplication>,
"d_hash_dup_count": <int: duplicated times detected by d_hash code>,
"p_hash_dup_count": <int: duplicated times detected by p_hash code>,
"aesthetic prob": <float: aesthetic probility>,
"unsafe prob": <float: NSFW probility>,
}
```
# License
OmniCorpus is released under a [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/deed.en) license, with the primary intent of supporting research activities.
# Citation
```
@article{li2024omnicorpus,
title={OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text},
author={Li, Qingyun and Chen, Zhe and Wang, Weiyun and Wang, Wenhai and Ye, Shenglong and Jin, Zhenjiang and others},
journal={arXiv preprint arXiv:2406.08418},
year={2024}
}
```
|
open-llm-leaderboard/contents | open-llm-leaderboard | "2025-02-15T17:10:27Z" | 16,721 | 14 | [
"region:us"
] | null | "2024-06-26T08:33:17Z" | ---
dataset_info:
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dtype: string
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dtype: float64
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dtype: float64
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dtype: float64
- name: Merged
dtype: bool
- name: Official Providers
dtype: bool
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dtype: string
- name: Generation
dtype: int64
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dtype: string
splits:
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num_examples: 3834
download_size: 926793
dataset_size: 3352963
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AI-MO/NuminaMath-TIR | AI-MO | "2024-11-25T05:32:53Z" | 16,602 | 96 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"math",
"aimo"
] | [
"text-generation"
] | "2024-07-16T07:44:24Z" | ---
language:
- en
license: apache-2.0
task_categories:
- text-generation
pretty_name: NuminaMath TIR
dataset_info:
features:
- name: problem
dtype: string
- name: solution
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
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num_bytes: 327147067
num_examples: 72441
- name: test
num_bytes: 461331
num_examples: 99
download_size: 147557990
dataset_size: 327608398
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- math
- aimo
---
# Dataset Card for NuminaMath CoT
## Dataset Description
- **Homepage:** https://projectnumina.ai
- **Repository:** https://github.com/project-numina/aimo-progress-prize
- **Paper:** https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf
- **Leaderboard:**
- **Point of Contact:** [Jia Li](jia@projectnumina.ai)
### Dataset Summary
Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming. To address this, we selected approximately 70k problems from the NuminaMath-CoT dataset, focusing on those with numerical outputs, most of which are integers. We then utilized a pipeline leveraging GPT-4 to generate TORA-like reasoning paths, executing the code and producing results until the solution was complete. We filtered out solutions where the final answer did not match the reference and repeated this process three times to ensure accuracy and consistency. This iterative approach allowed us to generate high-quality TORA data efficiently.
### Licensing Information
The dataset is available under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@misc{numina_math_datasets,
author = {Jia LI, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu},
title = {NuminaMath TIR},
year = {2024},
publisher = {Numina},
journal = {Hugging Face repository},
howpublished = {\url{[https://huggingface.co/AI-MO/NuminaMath-TIR](https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf)}}
}
``` |
mteb/sts14-sts | mteb | "2022-09-27T19:11:37Z" | 16,601 | 1 | [
"language:en",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2022-04-20T10:47:52Z" | ---
language:
- en
--- |
mteb/sts13-sts | mteb | "2022-09-27T19:12:02Z" | 16,534 | 1 | [
"language:en",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2022-04-20T10:47:41Z" | ---
language:
- en
--- |
Avelina/smollm-corpus | Avelina | "2025-01-11T16:41:28Z" | 16,521 | 5 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"region:us"
] | [
"text-generation"
] | "2025-01-11T01:39:39Z" | ---
license: odc-by
dataset_info:
- config_name: default
features:
- name: text
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data*/train-*
task_categories:
- text-generation
language:
- en
size_categories:
- 100M<n<1B
---
# SmolLM-Corpus: Now shuffled and sharded!
This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 `jsonl.zst` files for easy streaming!
The dataset is comprised of the `cosmopedia-v2` and `fineweb-edu-dedup` subsets from the original [SmolLM-Corpus repo](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus), with the `python-edu` subset being pulled from my [python-edu repo](https://huggingface.co/datasets/Avelina/python-edu).
## Dataset Structure
The dataset is split into 24 subdirectories, with the first 23 containing 1000 shards and the 24th containing the final 698. The repository is structured as follows:
```
data00/
├── train-00000-of-23698.jsonl.zst
├── ...
└── train-00999-of-23698.jsonl.zst
data01/
├── train-01000-of-23698.jsonl.zst
├── ...
└── train-01999-of-23698.jsonl.zst
...
data22/
├── train-22000-of-23698.jsonl.zst
├── ...
└── train-22999-of-23698.jsonl.zst
data23/
├── train-23000-of-23698.jsonl.zst
├── ...
└── train-23697-of-23698.jsonl.zst
```
In general, you can obtain the exact download URL for all shards using the following python function:
```py
def get_url_from_shard( index: int ) -> str:
if index >= 23_698:
raise ValueError( f'Shard index must be less than 23,698 but received {index}' )
group = index // 1000
return f'https://huggingface.co/datasets/Avelina/smollm-corpus/resolve/main/data{group:02d}/train-{index:05d}-of-23698.jsonl.zst'
```
## Generation Code
Here is the code which was used to generate the shuffled shards. Note the use of non-contiguous interleaving in attempt to uniformly pull documents from across entire subsets to loosely decouple shard index from original document position.
Please make sure you `pip install zstandard`!!!
```py
import tqdm
import datasets
from datasets import load_dataset
# Output directory and file format. Note that the file extension enforces zst compression is used.
OUTPUT_FMT = '/YOUR/FILE/PATH/HERE/data/train-{index:05d}-of-{num_shards:05d}.jsonl.zst'
# Total number of shards giving approximately 10,000 documents per shard
OUTPUT_NUM_SHARDS = 23698
# Grab the three datasets
ds_python = load_dataset( 'Avelina/python-edu' )
ds_cosmo = load_dataset( 'HuggingFaceTB/smollm-corpus', 'cosmopedia-v2' )
ds_edu = load_dataset( 'HuggingFaceTB/smollm-corpus', 'fineweb-edu-dedup' )
# Retain only the text columns and the train splits
ds_python = ds_python.select_columns( 'text' )[ 'train' ]
ds_cosmo = ds_cosmo.select_columns( 'text' )[ 'train' ]
ds_edu = ds_edu.select_columns( 'text' )[ 'train' ]
# Iterate over all shards with a nice progbar
for index in tqdm.tqdm( range( OUTPUT_NUM_SHARDS ) ):
# Get non-contiguous in-memory sub-shards for the three datasets
curr_python = ds_python.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True )
curr_cosmo = ds_cosmo.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True )
curr_edu = ds_edu.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True )
# Concatenate the sub-shards
curr_shard = datasets.concatenate_datasets( [ curr_python, curr_cosmo, curr_edu ] )
# Deterministically shuffle using the current shard index for reproducibility
curr_shard = curr_shard.shuffle( seed=index, keep_in_memory=True )
# Dump the shards to .jsonl.zst
curr_shard.to_json( OUTPUT_FMT.format( index=index, num_shards=OUTPUT_NUM_SHARDS ) )
```
## In-Memory Decompression
Zstandard was chosen as it enables trivial in-memory decompression to minimise the storage impact of the dataset. Here is some example code which creates a python generator that yields each json line from a compressed shard stored at `file_name`, and a second function which creates a python generator that parses and yields the compressed shard.
```py
import json
from json import JSONDecodeError
import zstandard
def read_lines_zst( file_name ):
# Open the file for reading in binary mode
with open( file_name, 'rb' ) as file_handle:
# Initialise an empty buffer
buffer = ''
# Create a reader for the opened file
reader = zstandard.ZstdDecompressor( max_window_size=2**31 ).stream_reader( file_handle )
while True:
# Read a chunk of up to 128MB
chunk = reader.read( 2**27 ).decode()
# If chunk is empty we've reached the end of the file and can break out
if not chunk:
break
# Combine any prior buffer with the current chunk and split by newline
lines = ( buffer + chunk ).split( '\n' )
# Yield the full lines so far
for line in lines[ : -1 ]:
yield line
# The last 'line' is incomplete, so place in buffer for next chunk
buffer = lines[ -1 ]
# Always remember to close your reader!
reader.close()
def parse_jsonl_zst( file_name ):
# Iterate over the yielded lines of the compressed shard
for i, line in enumerate( read_lines_zst( file_name ) ):
try:
# Convert the line into a python dict and yield the text field
yield json.loads( line )[ 'text' ]
except ( KeyError, JSONDecodeError ):
# Catch KeyError for 'text' not present in dict
# Catch JSONDecodeError for malformed line
print( f'JSON error @ shard={file_name}, line={i}' )
```
Of course you *could* use HuggingFace's in-built streaming mechanics to handle things for you, but in my experience that approach is less reliable, doesn't handle `JSONDecodeError`s if there are malformed lines, can cause memory leaks, and has forced sharding behaviour when used inside a multi-worker PyTorch `DataLoader` which I've not yet found a way to disable! |
OpenGVLab/MVBench | OpenGVLab | "2024-10-18T02:25:19Z" | 16,494 | 29 | [
"task_categories:visual-question-answering",
"task_categories:video-classification",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2311.17005",
"region:us"
] | [
"visual-question-answering",
"video-classification"
] | "2023-11-28T12:03:30Z" | ---
license: mit
extra_gated_prompt: >-
You agree to not use the dataset to conduct experiments that cause harm to
human subjects. Please note that the data in this dataset may be subject to
other agreements. Before using the data, be sure to read the relevant
agreements carefully to ensure compliant use. Video copyrights belong to the
original video creators or platforms and are for academic research use only.
task_categories:
- visual-question-answering
- video-classification
extra_gated_fields:
Name: text
Company/Organization: text
Country: text
E-Mail: text
modalities:
- Video
- Text
configs:
- config_name: action_sequence
data_files: json/action_sequence.json
- config_name: moving_count
data_files: json/moving_count.json
- config_name: action_prediction
data_files: json/action_prediction.json
- config_name: episodic_reasoning
data_files: json/episodic_reasoning.json
- config_name: action_antonym
data_files: json/action_antonym.json
- config_name: action_count
data_files: json/action_count.json
- config_name: scene_transition
data_files: json/scene_transition.json
- config_name: object_shuffle
data_files: json/object_shuffle.json
- config_name: object_existence
data_files: json/object_existence.json
- config_name: fine_grained_pose
data_files: json/fine_grained_pose.json
- config_name: unexpected_action
data_files: json/unexpected_action.json
- config_name: moving_direction
data_files: json/moving_direction.json
- config_name: state_change
data_files: json/state_change.json
- config_name: object_interaction
data_files: json/object_interaction.json
- config_name: character_order
data_files: json/character_order.json
- config_name: action_localization
data_files: json/action_localization.json
- config_name: counterfactual_inference
data_files: json/counterfactual_inference.json
- config_name: fine_grained_action
data_files: json/fine_grained_action.json
- config_name: moving_attribute
data_files: json/moving_attribute.json
- config_name: egocentric_navigation
data_files: json/egocentric_navigation.json
language:
- en
size_categories:
- 1K<n<10K
---
# MVBench
## Dataset Description
- **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb)
- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005)
- **Point of Contact:** mailto:[kunchang li](likunchang@pjlab.org.cn)
## <span style="color: red;">Important Update</span>
[18/10/2024] Due to NTU RGB+D License, 320 videos from NTU RGB+D need to be downloaded manually. Please visit [ROSE Lab](https://rose1.ntu.edu.sg/dataset/actionRecognition/) to access the data. We also provide a [list of the 320 videos](https://huggingface.co/datasets/OpenGVLab/MVBench/blob/main/video/MVBench_videos_ntu.txt) used in MVBench for your reference.
![images](./assert/generation.png)
We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then **automatically transform public video annotations into multiple-choice QA** for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The **20** temporal task examples are as follows.
![images](./assert/task_example.png)
## Evaluation
An evaluation example is provided in [mvbench.ipynb](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb). Please follow the pipeline to prepare the evaluation code for various MLLMs.
- **Preprocess**: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.
- **Prompt**: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.
## Leadrboard
While an [Online leaderboard]() is under construction, the current standings are as follows:
![images](./assert/leaderboard.png) |
cardiffnlp/tweet_eval | cardiffnlp | "2024-01-04T16:40:33Z" | 16,443 | 118 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|other-tweet-datasets",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2010.12421",
"region:us"
] | [
"text-classification"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- extended|other-tweet-datasets
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
- sentiment-classification
paperswithcode_id: tweeteval
pretty_name: TweetEval
config_names:
- emoji
- emotion
- hate
- irony
- offensive
- sentiment
- stance_abortion
- stance_atheism
- stance_climate
- stance_feminist
- stance_hillary
dataset_info:
- config_name: emoji
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ❤
'1': 😍
'2': 😂
'3': 💕
'4': 🔥
'5': 😊
'6': 😎
'7': ✨
'8': 💙
'9': 😘
'10': 📷
'11': 🇺🇸
'12': ☀
'13': 💜
'14': 😉
'15': 💯
'16': 😁
'17': 🎄
'18': 📸
'19': 😜
splits:
- name: train
num_bytes: 3803167
num_examples: 45000
- name: test
num_bytes: 4255901
num_examples: 50000
- name: validation
num_bytes: 396079
num_examples: 5000
download_size: 5939308
dataset_size: 8455147
- config_name: emotion
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': anger
'1': joy
'2': optimism
'3': sadness
splits:
- name: train
num_bytes: 338871
num_examples: 3257
- name: test
num_bytes: 146645
num_examples: 1421
- name: validation
num_bytes: 38273
num_examples: 374
download_size: 367016
dataset_size: 523789
- config_name: hate
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-hate
'1': hate
splits:
- name: train
num_bytes: 1223650
num_examples: 9000
- name: test
num_bytes: 428934
num_examples: 2970
- name: validation
num_bytes: 154144
num_examples: 1000
download_size: 1196346
dataset_size: 1806728
- config_name: irony
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non_irony
'1': irony
splits:
- name: train
num_bytes: 259187
num_examples: 2862
- name: test
num_bytes: 75897
num_examples: 784
- name: validation
num_bytes: 86017
num_examples: 955
download_size: 297647
dataset_size: 421101
- config_name: offensive
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-offensive
'1': offensive
splits:
- name: train
num_bytes: 1648061
num_examples: 11916
- name: test
num_bytes: 135473
num_examples: 860
- name: validation
num_bytes: 192417
num_examples: 1324
download_size: 1234528
dataset_size: 1975951
- config_name: sentiment
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
splits:
- name: train
num_bytes: 5425122
num_examples: 45615
- name: test
num_bytes: 1279540
num_examples: 12284
- name: validation
num_bytes: 239084
num_examples: 2000
download_size: 4849675
dataset_size: 6943746
- config_name: stance_abortion
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 68694
num_examples: 587
- name: test
num_bytes: 33171
num_examples: 280
- name: validation
num_bytes: 7657
num_examples: 66
download_size: 73517
dataset_size: 109522
- config_name: stance_atheism
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 54775
num_examples: 461
- name: test
num_bytes: 25716
num_examples: 220
- name: validation
num_bytes: 6320
num_examples: 52
download_size: 62265
dataset_size: 86811
- config_name: stance_climate
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 40249
num_examples: 355
- name: test
num_bytes: 19925
num_examples: 169
- name: validation
num_bytes: 4801
num_examples: 40
download_size: 48493
dataset_size: 64975
- config_name: stance_feminist
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 70509
num_examples: 597
- name: test
num_bytes: 33305
num_examples: 285
- name: validation
num_bytes: 8035
num_examples: 67
download_size: 76345
dataset_size: 111849
- config_name: stance_hillary
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 69596
num_examples: 620
- name: test
num_bytes: 34487
num_examples: 295
- name: validation
num_bytes: 7532
num_examples: 69
download_size: 74057
dataset_size: 111615
configs:
- config_name: emoji
data_files:
- split: train
path: emoji/train-*
- split: test
path: emoji/test-*
- split: validation
path: emoji/validation-*
- config_name: emotion
data_files:
- split: train
path: emotion/train-*
- split: test
path: emotion/test-*
- split: validation
path: emotion/validation-*
- config_name: hate
data_files:
- split: train
path: hate/train-*
- split: test
path: hate/test-*
- split: validation
path: hate/validation-*
- config_name: irony
data_files:
- split: train
path: irony/train-*
- split: test
path: irony/test-*
- split: validation
path: irony/validation-*
- config_name: offensive
data_files:
- split: train
path: offensive/train-*
- split: test
path: offensive/test-*
- split: validation
path: offensive/validation-*
- config_name: sentiment
data_files:
- split: train
path: sentiment/train-*
- split: test
path: sentiment/test-*
- split: validation
path: sentiment/validation-*
- config_name: stance_abortion
data_files:
- split: train
path: stance_abortion/train-*
- split: test
path: stance_abortion/test-*
- split: validation
path: stance_abortion/validation-*
- config_name: stance_atheism
data_files:
- split: train
path: stance_atheism/train-*
- split: test
path: stance_atheism/test-*
- split: validation
path: stance_atheism/validation-*
- config_name: stance_climate
data_files:
- split: train
path: stance_climate/train-*
- split: test
path: stance_climate/test-*
- split: validation
path: stance_climate/validation-*
- config_name: stance_feminist
data_files:
- split: train
path: stance_feminist/train-*
- split: test
path: stance_feminist/test-*
- split: validation
path: stance_feminist/validation-*
- config_name: stance_hillary
data_files:
- split: train
path: stance_hillary/train-*
- split: test
path: stance_hillary/test-*
- split: validation
path: stance_hillary/validation-*
train-eval-index:
- config: emotion
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: hate
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: irony
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: offensive
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
- config: sentiment
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for tweet_eval
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval)
- **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf)
- **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval)
- **Point of Contact:** [Needs More Information]
### Dataset Summary
TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.
### Supported Tasks and Leaderboards
- `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.
### Languages
The text in the dataset is in English, as spoken by Twitter users.
## Dataset Structure
### Data Instances
An instance from `emoji` config:
```
{'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'}
```
An instance from `emotion` config:
```
{'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry"}
```
An instance from `hate` config:
```
{'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'}
```
An instance from `irony` config:
```
{'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'}
```
An instance from `offensive` config:
```
{'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'}
```
An instance from `sentiment` config:
```
{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}
```
An instance from `stance_abortion` config:
```
{'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'}
```
An instance from `stance_atheism` config:
```
{'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'}
```
An instance from `stance_climate` config:
```
{'label': 0, 'text': 'Why Is The Pope Upset? via @user #UnzippedTruth #PopeFrancis #SemST'}
```
An instance from `stance_feminist` config:
```
{'label': 1, 'text': "@user @user is the UK's answer to @user and @user #GamerGate #SemST"}
```
An instance from `stance_hillary` config:
```
{'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"}
```
### Data Fields
For `emoji` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: ❤
`1`: 😍
`2`: 😂
`3`: 💕
`4`: 🔥
`5`: 😊
`6`: 😎
`7`: ✨
`8`: 💙
`9`: 😘
`10`: 📷
`11`: 🇺🇸
`12`: ☀
`13`: 💜
`14`: 😉
`15`: 💯
`16`: 😁
`17`: 🎄
`18`: 📸
`19`: 😜
For `emotion` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: anger
`1`: joy
`2`: optimism
`3`: sadness
For `hate` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: non-hate
`1`: hate
For `irony` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: non_irony
`1`: irony
For `offensive` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: non-offensive
`1`: offensive
For `sentiment` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: negative
`1`: neutral
`2`: positive
For `stance_abortion` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_atheism` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_climate` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_feminist` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
For `stance_hillary` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: none
`1`: against
`2`: favor
### Data Splits
| name | train | validation | test |
| --------------- | ----- | ---------- | ----- |
| emoji | 45000 | 5000 | 50000 |
| emotion | 3257 | 374 | 1421 |
| hate | 9000 | 1000 | 2970 |
| irony | 2862 | 955 | 784 |
| offensive | 11916 | 1324 | 860 |
| sentiment | 45615 | 2000 | 12284 |
| stance_abortion | 587 | 66 | 280 |
| stance_atheism | 461 | 52 | 220 |
| stance_climate | 355 | 40 | 169 |
| stance_feminist | 597 | 67 | 285 |
| stance_hillary | 620 | 69 | 295 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.
### Licensing Information
This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions).
All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy)
Additionally the license are:
- emoji: Undefined
- emotion(EmoInt): Undefined
- hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html)
- irony: Undefined
- Offensive: Undefined
- Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ)
- Stance: Undefined
### Citation Information
```
@inproceedings{barbieri2020tweeteval,
title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
booktitle={Proceedings of Findings of EMNLP},
year={2020}
}
```
If you use any of the TweetEval datasets, please cite their original publications:
#### Emotion Recognition:
```
@inproceedings{mohammad2018semeval,
title={Semeval-2018 task 1: Affect in tweets},
author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
booktitle={Proceedings of the 12th international workshop on semantic evaluation},
pages={1--17},
year={2018}
}
```
#### Emoji Prediction:
```
@inproceedings{barbieri2018semeval,
title={Semeval 2018 task 2: Multilingual emoji prediction},
author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and
Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio},
booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
pages={24--33},
year={2018}
}
```
#### Irony Detection:
```
@inproceedings{van2018semeval,
title={Semeval-2018 task 3: Irony detection in english tweets},
author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique},
booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
pages={39--50},
year={2018}
}
```
#### Hate Speech Detection:
```
@inproceedings{basile-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and
Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/S19-2007",
doi = "10.18653/v1/S19-2007",
pages = "54--63"
}
```
#### Offensive Language Identification:
```
@inproceedings{zampieri2019semeval,
title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
pages={75--86},
year={2019}
}
```
#### Sentiment Analysis:
```
@inproceedings{rosenthal2017semeval,
title={SemEval-2017 task 4: Sentiment analysis in Twitter},
author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav},
booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)},
pages={502--518},
year={2017}
}
```
#### Stance Detection:
```
@inproceedings{mohammad2016semeval,
title={Semeval-2016 task 6: Detecting stance in tweets},
author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin},
booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
pages={31--41},
year={2016}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
HuggingFaceTB/finemath | HuggingFaceTB | "2025-02-06T10:31:11Z" | 16,433 | 282 | [
"license:odc-by",
"size_categories:10M<n<100M",
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"modality:tabular",
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"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2502.02737",
"doi:10.57967/hf/3847",
"region:us"
] | null | "2024-11-25T15:23:13Z" | ---
license: odc-by
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configs:
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data_files:
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path: finemath-3plus/train-*
- config_name: finemath-4plus
data_files:
- split: train
path: finemath-4plus/train-*
- config_name: infiwebmath-3plus
data_files:
- split: train
path: infiwebmath-3plus/train-*
- config_name: infiwebmath-4plus
data_files:
- split: train
path: infiwebmath-4plus/train-*
---
# 📐 FineMath
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/0GAdY8wZx6bGtUzqX4Lvi.png)
## What is it?
📐 FineMath consists of **34B tokens** (FineMath-3+) and **54B tokens** (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content [classifier](https://huggingface.co/HuggingFaceTB/finemath-classifier) using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than advanced academic papers.
The [Dataset Curation](#dataset-curation) section details the process for creating the dataset. More details in our paper: https://arxiv.org/abs/2502.02737v1.
<img src="assets/train_curves.png" width="800"/>
## What is being released?
The dataset is released in two versions:
- **FineMath-3+**: 34B tokens, 21.4M documents containing mathematical reasoning and problem solving, formatted with Markdown and LaTeX.
- **FineMath-4+** (a subset of FineMath-3+): 9.6B tokens, 6.7M documents of higher quality with detailed explanations. Models trained on this dataset perform better on GSM8k and MATH.
<!-- (the image looks kinda meh) <img src="assets/stats.png" width="512"/> -->
We also release a filtered English text-only portion of the **[InfiMM-WebMath-40B](https://huggingface.co/datasets/Infi-MM/InfiMM-WebMath-40B)** dataset, classified using the same approach as FineMath:
- **InfiMM-WebMath-3+**: 20.5B tokens, 13.9M documents.
- **InfiMM-WebMath-4+** (a subset of InfiMM-WebMath-3+): 8.5B tokens, 6.3M documents.
## How to load the dataset
Use one of the available configs: `finemath-3plus`, `finemath-4plus`, `infiwebmath-3plus`, or `infiwebmath-4plus`.
```python
from datasets import load_dataset
# Load the high-quality subset
data = load_dataset("HuggingFaceTB/finemath", "finemath-4plus", split="train", num_proc=8)
# Or load the larger subset
data = load_dataset("HuggingFaceTB/finemath", "finemath-3plus", split="train", num_proc=8)
```
## Dataset curation
Recent language models like DeepSeekMath and MathStral have demonstrated strong mathematical capabilities, trained on specialized datasets that aren't publicly available. We developed a pipeline to identify and extract high-quality mathematical content from CommonCrawl, with several iterations of refinement to improve quality.
### Phase 1: Initial content extraction and classification
We began by re-extracting pages from CommonCrawl WARCs using URLs from the FineWeb dataset, collecting both the latest and largest versions of each page to capture the evolution of pages across the years.
Unlike FineWeb which uses Trafilatura, we employed Resiliparse for text extraction as it better preserves forum discussions and QA answers that often contain crucial reasoning steps and solutions.
For initial quality assessment, we used [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) to generate annotations on a 3-point scale:
1. Contains general mathematical content
2. Shows logical reasoning in mathematical context
3. Contains clear step-by-step solutions at appropriate level
A `multilingual-e5-small`-based classifier finetuned on these annotations was used to score the initial corpus.
However, this first version performed below the OpenWebMath baseline, leading to several important refinements.
### Phase 2: Recalling more candidate pages
Analysis revealed that FineWeb's C4 filter removes pages containing '{' characters, inadvertently filtering out content with LaTeX notation. To address this and expand coverage, we:
1. Identified promising website domains by selecting those where at least 10% of pages received a classifier score ≥ 2
2. Added URLs from OpenWebMath and InfiMM-WebMath datasets
3. Recovered URLs of pages filtered by FineWeb's '{' rule from its rejection logs
4. Re-extracted all content from scratch using the [OpenWebMath pipeline](https://github.com/keirp/OpenWebMath), which properly handles mathematical notation across various HTML markup formats and standardizes them to LaTeX
### Phase 3: Refined quality assessment
The expanded corpus underwent a more fine-grained quality evaluation:
Once again, we used LLama-3.1-70B-Instruct to score a sample of newly extracted pages on a 5-point scale (full prompt available in [here](assets/prompt.txt)):
We finetuned a new [classifier](https://huggingface.co/HuggingFaceTB/finemath-classifier) on these annotations and scored the entire corpus.
After leaving only pages with a score of 3 or higher, and deduplicating the samples using simple single-band MinHash-LSH, we obtained FineMath-3+ with 34B tokens.
The same classifier was applied to InfiMM-WebMath's text content, focusing more on reasoning rather than advanced mathematics.
Both datasets were additionally filtered using FineWeb's language classification pipeline to remove non-English content.
### Decontamination
Following Qwen2.5-Math's approach, we removed samples with 13-gram overlaps against test sets from GSM8k, MATH, MMLU and ARC. Decontamination logs are available at [HuggingFaceTB/finemath_contamination_report](https://huggingface.co/datasets/HuggingFaceTB/finemath_contamination_report).
## Results and Performance
<img src="assets/eval_bar.png" width="600"/>
Our evaluations show several key findings:
1. FineMath-3+ outperforms the base InfiWebMath on GSM8k and MATH benchmarks
2. FineMath-4+ demonstrates superior performance compared to both FineMath-3+ and InfiWebMath-4+ on GSM8k and MATH
3. Combining the datasets (50% FineMath-3+ with 50% InfiWebMath-3+) yields approximately 50B tokens while matching the performance of FineMath-3+
4. Deduplicating the pages repeated between FineMath and InfiWebMath reduces performance compared to a non-deduplicated combination
## Dataset Schema
```python
{
'url': string, # Source page URL
'fetch_time': int64, # Crawler timestamp
'content_mime_type': string, # MIME type
'warc_filename': string, # Common Crawl WARC source file
'warc_record_offset': int32, # WARC record offset, in bytes
'warc_record_length': int32, # WARC record size, in bytes
'text': string, # Page content
'token_count': int32, # Number of Llama tokens
'char_count': int32, # Character count
'metadata': string, # Additional OpenWebMath metadata
'score': float64, # Raw quality score
'int_score': int64, # Integer quality score
'crawl': string, # Common Crawl crawl identifier
'snapshot_type': string, # Whether the page is the latest or the largest for this URL
'language': string, # Document language
'language_score': float64 # LangID probability
}
```
## Considerations for Using the Data
### Social Impact of Dataset
With the release of this dataset, we aim to make high-quality mathematical educational content more accessible to the machine learning community. While multiple language models have demonstrated strong mathematical capabilities, the datasets used to train these capabilities are often not publicly available. By releasing FineMath, we hope to:
- Make the dataset creation process more transparent
- Reduce the barrier to entry for training models with strong mathematical capabilities
- Provide a benchmark for mathematical content quality filtering
### Discussion of Biases
The dataset may have certain inherent biases:
- Focus on English language content
- Emphasis on popular educational approaches to mathematics
- Bias towards certain types of mathematical notation and formatting
### Other Known Limitations
- The dataset is limited to English language content
- The filtering criteria may not capture advanced mathematical content (e.g. advanced research subjects)
- Some mathematical notation (e.g. image-based) may not be preserved
- Long-form content may have varying quality even within high-scoring documents
## Licensing Information
The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use).
## Future work
There are several avenues for future work:
- Expand language coverage beyond English
- Improve mathematical notation extraction and preservation
- Develop more sophisticated quality metrics
- Create specialized subsets for different educational levels
### Citation Information
```
@misc{allal2025smollm2smolgoesbig,
title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Guilherme Penedo and Lewis Tunstall and Andrés Marafioti and Hynek Kydlíček and Agustín Piqueres Lajarín and Vaibhav Srivastav and Joshua Lochner and Caleb Fahlgren and Xuan-Son Nguyen and Clémentine Fourrier and Ben Burtenshaw and Hugo Larcher and Haojun Zhao and Cyril Zakka and Mathieu Morlon and Colin Raffel and Leandro von Werra and Thomas Wolf},
year={2025},
eprint={2502.02737},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.02737},
}
``` |
bigscience/P3 | bigscience | "2024-03-04T18:08:03Z" | 16,320 | 212 | [
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"arxiv:2110.08207",
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] | [
"other"
] | "2022-03-02T23:29:22Z" | ---
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path: adversarial_qa_dbert_answer_the_following_q/validation-*
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path: adversarial_qa_dbert_based_on/validation-*
- config_name: adversarial_qa_dbert_generate_question
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path: adversarial_qa_dbert_generate_question/validation-*
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path: adversarial_qa_dbert_generate_question/test-*
- config_name: adversarial_qa_dbert_question_context_answer
data_files:
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path: adversarial_qa_dbert_question_context_answer/train-*
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path: adversarial_qa_dbert_question_context_answer/validation-*
- config_name: adversarial_qa_dbert_tell_what_it_is
data_files:
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path: adversarial_qa_dbert_tell_what_it_is/validation-*
- config_name: adversarial_qa_dbidaf_answer_the_following_q
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path: adversarial_qa_dbidaf_generate_question/test-*
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data_files:
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- config_name: adversarial_qa_dbidaf_tell_what_it_is
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- config_name: adversarial_qa_droberta_answer_the_following_q
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data_files:
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data_files:
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- config_name: ag_news_classify
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path: ag_news_classify/train-*
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path: ag_news_classify/test-*
- config_name: ag_news_classify_question_first
data_files:
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path: ag_news_classify_question_first/train-*
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path: ag_news_classify_question_first/test-*
- config_name: ag_news_classify_with_choices
data_files:
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path: ag_news_classify_with_choices/train-*
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path: ag_news_classify_with_choices/test-*
- config_name: ag_news_classify_with_choices_question_first
data_files:
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- config_name: ag_news_recommend
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path: ag_news_recommend/test-*
- config_name: ag_news_which_section
data_files:
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path: ag_news_which_section/train-*
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path: ag_news_which_section/test-*
- config_name: ag_news_which_section_choices
data_files:
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path: ag_news_which_section_choices/test-*
- config_name: ai2_arc_ARC_Challenge_heres_a_problem
data_files:
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path: ai2_arc_ARC_Challenge_heres_a_problem/validation-*
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path: ai2_arc_ARC_Challenge_heres_a_problem/test-*
- config_name: ai2_arc_ARC_Challenge_i_am_hesitating
data_files:
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- config_name: ai2_arc_ARC_Challenge_multiple_choice
data_files:
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- config_name: ai2_arc_ARC_Challenge_pick_false_options
data_files:
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- config_name: ai2_arc_ARC_Challenge_pick_the_most_correct_option
data_files:
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- config_name: ai2_arc_ARC_Challenge_qa_options
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- config_name: ai2_arc_ARC_Easy_heres_a_problem
data_files:
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path: ai2_arc_ARC_Easy_heres_a_problem/train-*
- split: validation
path: ai2_arc_ARC_Easy_heres_a_problem/validation-*
- split: test
path: ai2_arc_ARC_Easy_heres_a_problem/test-*
- config_name: ai2_arc_ARC_Easy_i_am_hesitating
data_files:
- split: train
path: ai2_arc_ARC_Easy_i_am_hesitating/train-*
- split: validation
path: ai2_arc_ARC_Easy_i_am_hesitating/validation-*
- split: test
path: ai2_arc_ARC_Easy_i_am_hesitating/test-*
- config_name: ai2_arc_ARC_Easy_multiple_choice
data_files:
- split: train
path: ai2_arc_ARC_Easy_multiple_choice/train-*
- split: validation
path: ai2_arc_ARC_Easy_multiple_choice/validation-*
- split: test
path: ai2_arc_ARC_Easy_multiple_choice/test-*
- config_name: ai2_arc_ARC_Easy_pick_false_options
data_files:
- split: train
path: ai2_arc_ARC_Easy_pick_false_options/train-*
- split: validation
path: ai2_arc_ARC_Easy_pick_false_options/validation-*
- split: test
path: ai2_arc_ARC_Easy_pick_false_options/test-*
- config_name: ai2_arc_ARC_Easy_pick_the_most_correct_option
data_files:
- split: train
path: ai2_arc_ARC_Easy_pick_the_most_correct_option/train-*
- split: validation
path: ai2_arc_ARC_Easy_pick_the_most_correct_option/validation-*
- split: test
path: ai2_arc_ARC_Easy_pick_the_most_correct_option/test-*
- config_name: ai2_arc_ARC_Easy_qa_options
data_files:
- split: train
path: ai2_arc_ARC_Easy_qa_options/train-*
- split: validation
path: ai2_arc_ARC_Easy_qa_options/validation-*
- split: test
path: ai2_arc_ARC_Easy_qa_options/test-*
- config_name: amazon_polarity_Is_this_product_review_positive
data_files:
- split: train
path: amazon_polarity_Is_this_product_review_positive/train-*
- split: test
path: amazon_polarity_Is_this_product_review_positive/test-*
- config_name: amazon_polarity_Is_this_review
data_files:
- split: train
path: amazon_polarity_Is_this_review/train-*
- split: test
path: amazon_polarity_Is_this_review/test-*
- config_name: amazon_polarity_Is_this_review_negative
data_files:
- split: train
path: amazon_polarity_Is_this_review_negative/train-*
- split: test
path: amazon_polarity_Is_this_review_negative/test-*
- config_name: amazon_polarity_User_recommend_this_product
data_files:
- split: train
path: amazon_polarity_User_recommend_this_product/train-*
- split: test
path: amazon_polarity_User_recommend_this_product/test-*
- config_name: amazon_polarity_convey_negative_or_positive_sentiment
data_files:
- split: train
path: amazon_polarity_convey_negative_or_positive_sentiment/train-*
- split: test
path: amazon_polarity_convey_negative_or_positive_sentiment/test-*
- config_name: amazon_polarity_flattering_or_not
data_files:
- split: train
path: amazon_polarity_flattering_or_not/train-*
- split: test
path: amazon_polarity_flattering_or_not/test-*
- config_name: amazon_polarity_negative_or_positive_tone
data_files:
- split: train
path: amazon_polarity_negative_or_positive_tone/train-*
- split: test
path: amazon_polarity_negative_or_positive_tone/test-*
- config_name: amazon_polarity_user_satisfied
data_files:
- split: train
path: amazon_polarity_user_satisfied/train-*
- split: test
path: amazon_polarity_user_satisfied/test-*
- config_name: amazon_polarity_would_you_buy
data_files:
- split: train
path: amazon_polarity_would_you_buy/train-*
- split: test
path: amazon_polarity_would_you_buy/test-*
- config_name: anli_GPT_3_style_r1
data_files:
- split: train
path: anli_GPT_3_style_r1/train-*
- split: validation
path: anli_GPT_3_style_r1/validation-*
- split: test
path: anli_GPT_3_style_r1/test-*
- config_name: anli_GPT_3_style_r1_score_eval
data_files:
- split: train
path: anli_GPT_3_style_r1_score_eval/train-*
- split: validation
path: anli_GPT_3_style_r1_score_eval/validation-*
- split: test
path: anli_GPT_3_style_r1_score_eval/test-*
- config_name: anli_GPT_3_style_r2
data_files:
- split: train
path: anli_GPT_3_style_r2/train-*
- split: validation
path: anli_GPT_3_style_r2/validation-*
- split: test
path: anli_GPT_3_style_r2/test-*
- config_name: anli_GPT_3_style_r2_score_eval
data_files:
- split: train
path: anli_GPT_3_style_r2_score_eval/train-*
- split: validation
path: anli_GPT_3_style_r2_score_eval/validation-*
- split: test
path: anli_GPT_3_style_r2_score_eval/test-*
- config_name: anli_GPT_3_style_r3
data_files:
- split: train
path: anli_GPT_3_style_r3/train-*
- split: validation
path: anli_GPT_3_style_r3/validation-*
- split: test
path: anli_GPT_3_style_r3/test-*
- config_name: anli_GPT_3_style_r3_score_eval
data_files:
- split: train
path: anli_GPT_3_style_r3_score_eval/train-*
- split: validation
path: anli_GPT_3_style_r3_score_eval/validation-*
- split: test
path: anli_GPT_3_style_r3_score_eval/test-*
- config_name: anli_MNLI_crowdsource_r1
data_files:
- split: train
path: anli_MNLI_crowdsource_r1/train-*
- split: validation
path: anli_MNLI_crowdsource_r1/validation-*
- split: test
path: anli_MNLI_crowdsource_r1/test-*
- config_name: anli_MNLI_crowdsource_r1_score_eval
data_files:
- split: train
path: anli_MNLI_crowdsource_r1_score_eval/train-*
- split: validation
path: anli_MNLI_crowdsource_r1_score_eval/validation-*
- split: test
path: anli_MNLI_crowdsource_r1_score_eval/test-*
- config_name: anli_MNLI_crowdsource_r2
data_files:
- split: train
path: anli_MNLI_crowdsource_r2/train-*
- split: validation
path: anli_MNLI_crowdsource_r2/validation-*
- split: test
path: anli_MNLI_crowdsource_r2/test-*
- config_name: anli_MNLI_crowdsource_r2_score_eval
data_files:
- split: train
path: anli_MNLI_crowdsource_r2_score_eval/train-*
- split: validation
path: anli_MNLI_crowdsource_r2_score_eval/validation-*
- split: test
path: anli_MNLI_crowdsource_r2_score_eval/test-*
- config_name: anli_MNLI_crowdsource_r3
data_files:
- split: train
path: anli_MNLI_crowdsource_r3/train-*
- split: validation
path: anli_MNLI_crowdsource_r3/validation-*
- split: test
path: anli_MNLI_crowdsource_r3/test-*
- config_name: anli_MNLI_crowdsource_r3_score_eval
data_files:
- split: train
path: anli_MNLI_crowdsource_r3_score_eval/train-*
- split: validation
path: anli_MNLI_crowdsource_r3_score_eval/validation-*
- split: test
path: anli_MNLI_crowdsource_r3_score_eval/test-*
- config_name: anli_always_sometimes_never_r1
data_files:
- split: train
path: anli_always_sometimes_never_r1/train-*
- split: validation
path: anli_always_sometimes_never_r1/validation-*
- split: test
path: anli_always_sometimes_never_r1/test-*
- config_name: anli_always_sometimes_never_r1_score_eval
data_files:
- split: train
path: anli_always_sometimes_never_r1_score_eval/train-*
- split: validation
path: anli_always_sometimes_never_r1_score_eval/validation-*
- split: test
path: anli_always_sometimes_never_r1_score_eval/test-*
- config_name: anli_always_sometimes_never_r2
data_files:
- split: train
path: anli_always_sometimes_never_r2/train-*
- split: validation
path: anli_always_sometimes_never_r2/validation-*
- split: test
path: anli_always_sometimes_never_r2/test-*
- config_name: anli_always_sometimes_never_r2_score_eval
data_files:
- split: train
path: anli_always_sometimes_never_r2_score_eval/train-*
- split: validation
path: anli_always_sometimes_never_r2_score_eval/validation-*
- split: test
path: anli_always_sometimes_never_r2_score_eval/test-*
- config_name: anli_always_sometimes_never_r3
data_files:
- split: train
path: anli_always_sometimes_never_r3/train-*
- split: validation
path: anli_always_sometimes_never_r3/validation-*
- split: test
path: anli_always_sometimes_never_r3/test-*
- config_name: anli_always_sometimes_never_r3_score_eval
data_files:
- split: train
path: anli_always_sometimes_never_r3_score_eval/train-*
- split: validation
path: anli_always_sometimes_never_r3_score_eval/validation-*
- split: test
path: anli_always_sometimes_never_r3_score_eval/test-*
- config_name: anli_based_on_the_previous_passage_r1
data_files:
- split: train
path: anli_based_on_the_previous_passage_r1/train-*
- split: validation
path: anli_based_on_the_previous_passage_r1/validation-*
- split: test
path: anli_based_on_the_previous_passage_r1/test-*
- config_name: anli_based_on_the_previous_passage_r1_score_eval
data_files:
- split: train
path: anli_based_on_the_previous_passage_r1_score_eval/train-*
- split: validation
path: anli_based_on_the_previous_passage_r1_score_eval/validation-*
- split: test
path: anli_based_on_the_previous_passage_r1_score_eval/test-*
- config_name: anli_based_on_the_previous_passage_r2
data_files:
- split: train
path: anli_based_on_the_previous_passage_r2/train-*
- split: validation
path: anli_based_on_the_previous_passage_r2/validation-*
- split: test
path: anli_based_on_the_previous_passage_r2/test-*
- config_name: anli_based_on_the_previous_passage_r2_score_eval
data_files:
- split: train
path: anli_based_on_the_previous_passage_r2_score_eval/train-*
- split: validation
path: anli_based_on_the_previous_passage_r2_score_eval/validation-*
- split: test
path: anli_based_on_the_previous_passage_r2_score_eval/test-*
- config_name: anli_based_on_the_previous_passage_r3
data_files:
- split: train
path: anli_based_on_the_previous_passage_r3/train-*
- split: validation
path: anli_based_on_the_previous_passage_r3/validation-*
- split: test
path: anli_based_on_the_previous_passage_r3/test-*
- config_name: anli_based_on_the_previous_passage_r3_score_eval
data_files:
- split: train
path: anli_based_on_the_previous_passage_r3_score_eval/train-*
- split: validation
path: anli_based_on_the_previous_passage_r3_score_eval/validation-*
- split: test
path: anli_based_on_the_previous_passage_r3_score_eval/test-*
- config_name: anli_can_we_infer_r1
data_files:
- split: train
path: anli_can_we_infer_r1/train-*
- split: validation
path: anli_can_we_infer_r1/validation-*
- split: test
path: anli_can_we_infer_r1/test-*
- config_name: anli_can_we_infer_r1_score_eval
data_files:
- split: train
path: anli_can_we_infer_r1_score_eval/train-*
- split: validation
path: anli_can_we_infer_r1_score_eval/validation-*
- split: test
path: anli_can_we_infer_r1_score_eval/test-*
- config_name: anli_can_we_infer_r2
data_files:
- split: train
path: anli_can_we_infer_r2/train-*
- split: validation
path: anli_can_we_infer_r2/validation-*
- split: test
path: anli_can_we_infer_r2/test-*
- config_name: anli_can_we_infer_r2_score_eval
data_files:
- split: train
path: anli_can_we_infer_r2_score_eval/train-*
- split: validation
path: anli_can_we_infer_r2_score_eval/validation-*
- split: test
path: anli_can_we_infer_r2_score_eval/test-*
- config_name: anli_can_we_infer_r3
data_files:
- split: train
path: anli_can_we_infer_r3/train-*
- split: validation
path: anli_can_we_infer_r3/validation-*
- split: test
path: anli_can_we_infer_r3/test-*
- config_name: anli_can_we_infer_r3_score_eval
data_files:
- split: train
path: anli_can_we_infer_r3_score_eval/train-*
- split: validation
path: anli_can_we_infer_r3_score_eval/validation-*
- split: test
path: anli_can_we_infer_r3_score_eval/test-*
- config_name: anli_claim_true_false_inconclusive_r1
data_files:
- split: train
path: anli_claim_true_false_inconclusive_r1/train-*
- split: validation
path: anli_claim_true_false_inconclusive_r1/validation-*
- split: test
path: anli_claim_true_false_inconclusive_r1/test-*
- config_name: anli_claim_true_false_inconclusive_r1_score_eval
data_files:
- split: train
path: anli_claim_true_false_inconclusive_r1_score_eval/train-*
- split: validation
path: anli_claim_true_false_inconclusive_r1_score_eval/validation-*
- split: test
path: anli_claim_true_false_inconclusive_r1_score_eval/test-*
- config_name: anli_claim_true_false_inconclusive_r2
data_files:
- split: train
path: anli_claim_true_false_inconclusive_r2/train-*
- split: validation
path: anli_claim_true_false_inconclusive_r2/validation-*
- split: test
path: anli_claim_true_false_inconclusive_r2/test-*
- config_name: anli_claim_true_false_inconclusive_r2_score_eval
data_files:
- split: train
path: anli_claim_true_false_inconclusive_r2_score_eval/train-*
- split: validation
path: anli_claim_true_false_inconclusive_r2_score_eval/validation-*
- split: test
path: anli_claim_true_false_inconclusive_r2_score_eval/test-*
- config_name: anli_claim_true_false_inconclusive_r3
data_files:
- split: train
path: anli_claim_true_false_inconclusive_r3/train-*
- split: validation
path: anli_claim_true_false_inconclusive_r3/validation-*
- split: test
path: anli_claim_true_false_inconclusive_r3/test-*
- config_name: anli_claim_true_false_inconclusive_r3_score_eval
data_files:
- split: train
path: anli_claim_true_false_inconclusive_r3_score_eval/train-*
- split: validation
path: anli_claim_true_false_inconclusive_r3_score_eval/validation-*
- split: test
path: anli_claim_true_false_inconclusive_r3_score_eval/test-*
- config_name: anli_consider_always_sometimes_never_r1
data_files:
- split: train
path: anli_consider_always_sometimes_never_r1/train-*
- split: validation
path: anli_consider_always_sometimes_never_r1/validation-*
- split: test
path: anli_consider_always_sometimes_never_r1/test-*
- config_name: anli_consider_always_sometimes_never_r1_score_eval
data_files:
- split: train
path: anli_consider_always_sometimes_never_r1_score_eval/train-*
- split: validation
path: anli_consider_always_sometimes_never_r1_score_eval/validation-*
- split: test
path: anli_consider_always_sometimes_never_r1_score_eval/test-*
- config_name: anli_consider_always_sometimes_never_r2
data_files:
- split: train
path: anli_consider_always_sometimes_never_r2/train-*
- split: validation
path: anli_consider_always_sometimes_never_r2/validation-*
- split: test
path: anli_consider_always_sometimes_never_r2/test-*
- config_name: anli_consider_always_sometimes_never_r2_score_eval
data_files:
- split: train
path: anli_consider_always_sometimes_never_r2_score_eval/train-*
- split: validation
path: anli_consider_always_sometimes_never_r2_score_eval/validation-*
- split: test
path: anli_consider_always_sometimes_never_r2_score_eval/test-*
- config_name: anli_consider_always_sometimes_never_r3
data_files:
- split: train
path: anli_consider_always_sometimes_never_r3/train-*
- split: validation
path: anli_consider_always_sometimes_never_r3/validation-*
- split: test
path: anli_consider_always_sometimes_never_r3/test-*
- config_name: anli_consider_always_sometimes_never_r3_score_eval
data_files:
- split: train
path: anli_consider_always_sometimes_never_r3_score_eval/train-*
- split: validation
path: anli_consider_always_sometimes_never_r3_score_eval/validation-*
- split: test
path: anli_consider_always_sometimes_never_r3_score_eval/test-*
- config_name: anli_does_it_follow_that_r1
data_files:
- split: train
path: anli_does_it_follow_that_r1/train-*
- split: validation
path: anli_does_it_follow_that_r1/validation-*
- split: test
path: anli_does_it_follow_that_r1/test-*
- config_name: anli_does_it_follow_that_r1_score_eval
data_files:
- split: train
path: anli_does_it_follow_that_r1_score_eval/train-*
- split: validation
path: anli_does_it_follow_that_r1_score_eval/validation-*
- split: test
path: anli_does_it_follow_that_r1_score_eval/test-*
- config_name: anli_does_it_follow_that_r2
data_files:
- split: train
path: anli_does_it_follow_that_r2/train-*
- split: validation
path: anli_does_it_follow_that_r2/validation-*
- split: test
path: anli_does_it_follow_that_r2/test-*
- config_name: anli_does_it_follow_that_r2_score_eval
data_files:
- split: train
path: anli_does_it_follow_that_r2_score_eval/train-*
- split: validation
path: anli_does_it_follow_that_r2_score_eval/validation-*
- split: test
path: anli_does_it_follow_that_r2_score_eval/test-*
- config_name: anli_does_it_follow_that_r3
data_files:
- split: train
path: anli_does_it_follow_that_r3/train-*
- split: validation
path: anli_does_it_follow_that_r3/validation-*
- split: test
path: anli_does_it_follow_that_r3/test-*
- config_name: anli_does_it_follow_that_r3_score_eval
data_files:
- split: train
path: anli_does_it_follow_that_r3_score_eval/train-*
- split: validation
path: anli_does_it_follow_that_r3_score_eval/validation-*
- split: test
path: anli_does_it_follow_that_r3_score_eval/test-*
- config_name: anli_does_this_imply_r1
data_files:
- split: train
path: anli_does_this_imply_r1/train-*
- split: validation
path: anli_does_this_imply_r1/validation-*
- split: test
path: anli_does_this_imply_r1/test-*
- config_name: anli_does_this_imply_r1_score_eval
data_files:
- split: train
path: anli_does_this_imply_r1_score_eval/train-*
- split: validation
path: anli_does_this_imply_r1_score_eval/validation-*
- split: test
path: anli_does_this_imply_r1_score_eval/test-*
- config_name: anli_does_this_imply_r2
data_files:
- split: train
path: anli_does_this_imply_r2/train-*
- split: validation
path: anli_does_this_imply_r2/validation-*
- split: test
path: anli_does_this_imply_r2/test-*
- config_name: anli_does_this_imply_r2_score_eval
data_files:
- split: train
path: anli_does_this_imply_r2_score_eval/train-*
- split: validation
path: anli_does_this_imply_r2_score_eval/validation-*
- split: test
path: anli_does_this_imply_r2_score_eval/test-*
- config_name: anli_does_this_imply_r3
data_files:
- split: train
path: anli_does_this_imply_r3/train-*
- split: validation
path: anli_does_this_imply_r3/validation-*
- split: test
path: anli_does_this_imply_r3/test-*
- config_name: anli_does_this_imply_r3_score_eval
data_files:
- split: train
path: anli_does_this_imply_r3_score_eval/train-*
- split: validation
path: anli_does_this_imply_r3_score_eval/validation-*
- split: test
path: anli_does_this_imply_r3_score_eval/test-*
- config_name: anli_guaranteed_possible_impossible_r1
data_files:
- split: train
path: anli_guaranteed_possible_impossible_r1/train-*
- split: validation
path: anli_guaranteed_possible_impossible_r1/validation-*
- split: test
path: anli_guaranteed_possible_impossible_r1/test-*
- config_name: anli_guaranteed_possible_impossible_r1_score_eval
data_files:
- split: train
path: anli_guaranteed_possible_impossible_r1_score_eval/train-*
- split: validation
path: anli_guaranteed_possible_impossible_r1_score_eval/validation-*
- split: test
path: anli_guaranteed_possible_impossible_r1_score_eval/test-*
- config_name: anli_guaranteed_possible_impossible_r2
data_files:
- split: train
path: anli_guaranteed_possible_impossible_r2/train-*
- split: validation
path: anli_guaranteed_possible_impossible_r2/validation-*
- split: test
path: anli_guaranteed_possible_impossible_r2/test-*
- config_name: anli_guaranteed_possible_impossible_r2_score_eval
data_files:
- split: train
path: anli_guaranteed_possible_impossible_r2_score_eval/train-*
- split: validation
path: anli_guaranteed_possible_impossible_r2_score_eval/validation-*
- split: test
path: anli_guaranteed_possible_impossible_r2_score_eval/test-*
- config_name: anli_guaranteed_possible_impossible_r3
data_files:
- split: train
path: anli_guaranteed_possible_impossible_r3/train-*
- split: validation
path: anli_guaranteed_possible_impossible_r3/validation-*
- split: test
path: anli_guaranteed_possible_impossible_r3/test-*
- config_name: anli_guaranteed_possible_impossible_r3_score_eval
data_files:
- split: train
path: anli_guaranteed_possible_impossible_r3_score_eval/train-*
- split: validation
path: anli_guaranteed_possible_impossible_r3_score_eval/validation-*
- split: test
path: anli_guaranteed_possible_impossible_r3_score_eval/test-*
- config_name: anli_guaranteed_true_r1
data_files:
- split: train
path: anli_guaranteed_true_r1/train-*
- split: validation
path: anli_guaranteed_true_r1/validation-*
- split: test
path: anli_guaranteed_true_r1/test-*
- config_name: anli_guaranteed_true_r1_score_eval
data_files:
- split: train
path: anli_guaranteed_true_r1_score_eval/train-*
- split: validation
path: anli_guaranteed_true_r1_score_eval/validation-*
- split: test
path: anli_guaranteed_true_r1_score_eval/test-*
- config_name: anli_guaranteed_true_r2
data_files:
- split: train
path: anli_guaranteed_true_r2/train-*
- split: validation
path: anli_guaranteed_true_r2/validation-*
- split: test
path: anli_guaranteed_true_r2/test-*
- config_name: anli_guaranteed_true_r2_score_eval
data_files:
- split: train
path: anli_guaranteed_true_r2_score_eval/train-*
- split: validation
path: anli_guaranteed_true_r2_score_eval/validation-*
- split: test
path: anli_guaranteed_true_r2_score_eval/test-*
- config_name: anli_guaranteed_true_r3
data_files:
- split: train
path: anli_guaranteed_true_r3/train-*
- split: validation
path: anli_guaranteed_true_r3/validation-*
- split: test
path: anli_guaranteed_true_r3/test-*
- config_name: anli_guaranteed_true_r3_score_eval
data_files:
- split: train
path: anli_guaranteed_true_r3_score_eval/train-*
- split: validation
path: anli_guaranteed_true_r3_score_eval/validation-*
- split: test
path: anli_guaranteed_true_r3_score_eval/test-*
- config_name: anli_justified_in_saying_r1
data_files:
- split: train
path: anli_justified_in_saying_r1/train-*
- split: validation
path: anli_justified_in_saying_r1/validation-*
- split: test
path: anli_justified_in_saying_r1/test-*
- config_name: anli_justified_in_saying_r1_score_eval
data_files:
- split: train
path: anli_justified_in_saying_r1_score_eval/train-*
- split: validation
path: anli_justified_in_saying_r1_score_eval/validation-*
- split: test
path: anli_justified_in_saying_r1_score_eval/test-*
- config_name: anli_justified_in_saying_r2
data_files:
- split: train
path: anli_justified_in_saying_r2/train-*
- split: validation
path: anli_justified_in_saying_r2/validation-*
- split: test
path: anli_justified_in_saying_r2/test-*
- config_name: anli_justified_in_saying_r2_score_eval
data_files:
- split: train
path: anli_justified_in_saying_r2_score_eval/train-*
- split: validation
path: anli_justified_in_saying_r2_score_eval/validation-*
- split: test
path: anli_justified_in_saying_r2_score_eval/test-*
- config_name: anli_justified_in_saying_r3
data_files:
- split: train
path: anli_justified_in_saying_r3/train-*
- split: validation
path: anli_justified_in_saying_r3/validation-*
- split: test
path: anli_justified_in_saying_r3/test-*
- config_name: anli_justified_in_saying_r3_score_eval
data_files:
- split: train
path: anli_justified_in_saying_r3_score_eval/train-*
- split: validation
path: anli_justified_in_saying_r3_score_eval/validation-*
- split: test
path: anli_justified_in_saying_r3_score_eval/test-*
- config_name: anli_must_be_true_r1
data_files:
- split: train
path: anli_must_be_true_r1/train-*
- split: validation
path: anli_must_be_true_r1/validation-*
- split: test
path: anli_must_be_true_r1/test-*
- config_name: anli_must_be_true_r1_score_eval
data_files:
- split: train
path: anli_must_be_true_r1_score_eval/train-*
- split: validation
path: anli_must_be_true_r1_score_eval/validation-*
- split: test
path: anli_must_be_true_r1_score_eval/test-*
- config_name: anli_must_be_true_r2
data_files:
- split: train
path: anli_must_be_true_r2/train-*
- split: validation
path: anli_must_be_true_r2/validation-*
- split: test
path: anli_must_be_true_r2/test-*
- config_name: anli_must_be_true_r2_score_eval
data_files:
- split: train
path: anli_must_be_true_r2_score_eval/train-*
- split: validation
path: anli_must_be_true_r2_score_eval/validation-*
- split: test
path: anli_must_be_true_r2_score_eval/test-*
- config_name: anli_must_be_true_r3
data_files:
- split: train
path: anli_must_be_true_r3/train-*
- split: validation
path: anli_must_be_true_r3/validation-*
- split: test
path: anli_must_be_true_r3/test-*
- config_name: anli_must_be_true_r3_score_eval
data_files:
- split: train
path: anli_must_be_true_r3_score_eval/train-*
- split: validation
path: anli_must_be_true_r3_score_eval/validation-*
- split: test
path: anli_must_be_true_r3_score_eval/test-*
- config_name: anli_should_assume_r1
data_files:
- split: train
path: anli_should_assume_r1/train-*
- split: validation
path: anli_should_assume_r1/validation-*
- split: test
path: anli_should_assume_r1/test-*
- config_name: anli_should_assume_r1_score_eval
data_files:
- split: train
path: anli_should_assume_r1_score_eval/train-*
- split: validation
path: anli_should_assume_r1_score_eval/validation-*
- split: test
path: anli_should_assume_r1_score_eval/test-*
- config_name: anli_should_assume_r2
data_files:
- split: train
path: anli_should_assume_r2/train-*
- split: validation
path: anli_should_assume_r2/validation-*
- split: test
path: anli_should_assume_r2/test-*
- config_name: anli_should_assume_r2_score_eval
data_files:
- split: train
path: anli_should_assume_r2_score_eval/train-*
- split: validation
path: anli_should_assume_r2_score_eval/validation-*
- split: test
path: anli_should_assume_r2_score_eval/test-*
- config_name: anli_should_assume_r3
data_files:
- split: train
path: anli_should_assume_r3/train-*
- split: validation
path: anli_should_assume_r3/validation-*
- split: test
path: anli_should_assume_r3/test-*
- config_name: anli_should_assume_r3_score_eval
data_files:
- split: train
path: anli_should_assume_r3_score_eval/train-*
- split: validation
path: anli_should_assume_r3_score_eval/validation-*
- split: test
path: anli_should_assume_r3_score_eval/test-*
- config_name: anli_take_the_following_as_truth_r1
data_files:
- split: train
path: anli_take_the_following_as_truth_r1/train-*
- split: validation
path: anli_take_the_following_as_truth_r1/validation-*
- split: test
path: anli_take_the_following_as_truth_r1/test-*
- config_name: anli_take_the_following_as_truth_r1_score_eval
data_files:
- split: train
path: anli_take_the_following_as_truth_r1_score_eval/train-*
- split: validation
path: anli_take_the_following_as_truth_r1_score_eval/validation-*
- split: test
path: anli_take_the_following_as_truth_r1_score_eval/test-*
- config_name: anli_take_the_following_as_truth_r2
data_files:
- split: train
path: anli_take_the_following_as_truth_r2/train-*
- split: validation
path: anli_take_the_following_as_truth_r2/validation-*
- split: test
path: anli_take_the_following_as_truth_r2/test-*
- config_name: anli_take_the_following_as_truth_r2_score_eval
data_files:
- split: train
path: anli_take_the_following_as_truth_r2_score_eval/train-*
- split: validation
path: anli_take_the_following_as_truth_r2_score_eval/validation-*
- split: test
path: anli_take_the_following_as_truth_r2_score_eval/test-*
- config_name: anli_take_the_following_as_truth_r3
data_files:
- split: train
path: anli_take_the_following_as_truth_r3/train-*
- split: validation
path: anli_take_the_following_as_truth_r3/validation-*
- split: test
path: anli_take_the_following_as_truth_r3/test-*
- config_name: anli_take_the_following_as_truth_r3_score_eval
data_files:
- split: train
path: anli_take_the_following_as_truth_r3_score_eval/train-*
- split: validation
path: anli_take_the_following_as_truth_r3_score_eval/validation-*
- split: test
path: anli_take_the_following_as_truth_r3_score_eval/test-*
- config_name: app_reviews_categorize_rating_using_review
data_files:
- split: train
path: app_reviews_categorize_rating_using_review/train-*
- config_name: app_reviews_convert_to_rating
data_files:
- split: train
path: app_reviews_convert_to_rating/train-*
- config_name: app_reviews_convert_to_star_rating
data_files:
- split: train
path: app_reviews_convert_to_star_rating/train-*
- config_name: app_reviews_generate_review
data_files:
- split: train
path: app_reviews_generate_review/train-*
- config_name: cnn_dailymail_3.0.0_2_or_3_sentences
data_files:
- split: train
path: cnn_dailymail_3.0.0_2_or_3_sentences/train-*
- split: validation
path: cnn_dailymail_3.0.0_2_or_3_sentences/validation-*
- split: test
path: cnn_dailymail_3.0.0_2_or_3_sentences/test-*
- config_name: cnn_dailymail_3.0.0_generate_story
data_files:
- split: train
path: cnn_dailymail_3.0.0_generate_story/train-*
- split: validation
path: cnn_dailymail_3.0.0_generate_story/validation-*
- split: test
path: cnn_dailymail_3.0.0_generate_story/test-*
- config_name: cnn_dailymail_3.0.0_news_card_view
data_files:
- split: train
path: cnn_dailymail_3.0.0_news_card_view/train-*
- split: validation
path: cnn_dailymail_3.0.0_news_card_view/validation-*
- split: test
path: cnn_dailymail_3.0.0_news_card_view/test-*
- config_name: cnn_dailymail_3.0.0_news_stock
data_files:
- split: train
path: cnn_dailymail_3.0.0_news_stock/train-*
- split: validation
path: cnn_dailymail_3.0.0_news_stock/validation-*
- split: test
path: cnn_dailymail_3.0.0_news_stock/test-*
- config_name: cnn_dailymail_3.0.0_news_summary
data_files:
- split: train
path: cnn_dailymail_3.0.0_news_summary/train-*
- split: validation
path: cnn_dailymail_3.0.0_news_summary/validation-*
- split: test
path: cnn_dailymail_3.0.0_news_summary/test-*
- config_name: cnn_dailymail_3.0.0_spice_up_story
data_files:
- split: train
path: cnn_dailymail_3.0.0_spice_up_story/train-*
- split: validation
path: cnn_dailymail_3.0.0_spice_up_story/validation-*
- split: test
path: cnn_dailymail_3.0.0_spice_up_story/test-*
- config_name: cnn_dailymail_3.0.0_sum_in_brief
data_files:
- split: train
path: cnn_dailymail_3.0.0_sum_in_brief/train-*
- split: validation
path: cnn_dailymail_3.0.0_sum_in_brief/validation-*
- split: test
path: cnn_dailymail_3.0.0_sum_in_brief/test-*
- config_name: cnn_dailymail_3.0.0_tldr_summary
data_files:
- split: train
path: cnn_dailymail_3.0.0_tldr_summary/train-*
- split: validation
path: cnn_dailymail_3.0.0_tldr_summary/validation-*
- split: test
path: cnn_dailymail_3.0.0_tldr_summary/test-*
- config_name: cnn_dailymail_3.0.0_write_an_outline
data_files:
- split: train
path: cnn_dailymail_3.0.0_write_an_outline/train-*
- split: validation
path: cnn_dailymail_3.0.0_write_an_outline/validation-*
- split: test
path: cnn_dailymail_3.0.0_write_an_outline/test-*
- config_name: common_gen_Example_prompt
data_files:
- split: train
path: common_gen_Example_prompt/train-*
- split: validation
path: common_gen_Example_prompt/validation-*
- split: test
path: common_gen_Example_prompt/test-*
- config_name: common_gen_Given_concepts_type_1
data_files:
- split: train
path: common_gen_Given_concepts_type_1/train-*
- split: validation
path: common_gen_Given_concepts_type_1/validation-*
- split: test
path: common_gen_Given_concepts_type_1/test-*
- config_name: common_gen_Given_concepts_type_2
data_files:
- split: train
path: common_gen_Given_concepts_type_2/train-*
- split: validation
path: common_gen_Given_concepts_type_2/validation-*
- split: test
path: common_gen_Given_concepts_type_2/test-*
- config_name: common_gen_Put_together
data_files:
- split: train
path: common_gen_Put_together/train-*
- split: validation
path: common_gen_Put_together/validation-*
- split: test
path: common_gen_Put_together/test-*
- config_name: common_gen_choice_in_concept_centric_sentence_generation
data_files:
- split: train
path: common_gen_choice_in_concept_centric_sentence_generation/train-*
- split: validation
path: common_gen_choice_in_concept_centric_sentence_generation/validation-*
- split: test
path: common_gen_choice_in_concept_centric_sentence_generation/test-*
- config_name: common_gen_random_task_template_prompt
data_files:
- split: train
path: common_gen_random_task_template_prompt/train-*
- split: validation
path: common_gen_random_task_template_prompt/validation-*
- split: test
path: common_gen_random_task_template_prompt/test-*
- config_name: common_gen_sentence_to_concepts
data_files:
- split: train
path: common_gen_sentence_to_concepts/train-*
- split: validation
path: common_gen_sentence_to_concepts/validation-*
- split: test
path: common_gen_sentence_to_concepts/test-*
- config_name: common_gen_topic_to_sentence
data_files:
- split: train
path: common_gen_topic_to_sentence/train-*
- split: validation
path: common_gen_topic_to_sentence/validation-*
- split: test
path: common_gen_topic_to_sentence/test-*
- config_name: common_gen_topics_from_the_sentence
data_files:
- split: train
path: common_gen_topics_from_the_sentence/train-*
- split: validation
path: common_gen_topics_from_the_sentence/validation-*
- split: test
path: common_gen_topics_from_the_sentence/test-*
- config_name: cos_e_v1.11_aligned_with_common_sense
data_files:
- split: train
path: cos_e_v1.11_aligned_with_common_sense/train-*
- split: validation
path: cos_e_v1.11_aligned_with_common_sense/validation-*
- config_name: cos_e_v1.11_description_question_option_id
data_files:
- split: train
path: cos_e_v1.11_description_question_option_id/train-*
- split: validation
path: cos_e_v1.11_description_question_option_id/validation-*
- config_name: cos_e_v1.11_description_question_option_text
data_files:
- split: train
path: cos_e_v1.11_description_question_option_text/train-*
- split: validation
path: cos_e_v1.11_description_question_option_text/validation-*
- config_name: cos_e_v1.11_explain_why_human
data_files:
- split: train
path: cos_e_v1.11_explain_why_human/train-*
- split: validation
path: cos_e_v1.11_explain_why_human/validation-*
- config_name: cos_e_v1.11_generate_explanation_given_text
data_files:
- split: train
path: cos_e_v1.11_generate_explanation_given_text/train-*
- split: validation
path: cos_e_v1.11_generate_explanation_given_text/validation-*
- config_name: cos_e_v1.11_i_think
data_files:
- split: train
path: cos_e_v1.11_i_think/train-*
- split: validation
path: cos_e_v1.11_i_think/validation-*
- config_name: cos_e_v1.11_question_description_option_id
data_files:
- split: train
path: cos_e_v1.11_question_description_option_id/train-*
- split: validation
path: cos_e_v1.11_question_description_option_id/validation-*
- config_name: cos_e_v1.11_question_description_option_text
data_files:
- split: train
path: cos_e_v1.11_question_description_option_text/train-*
- split: validation
path: cos_e_v1.11_question_description_option_text/validation-*
- config_name: cos_e_v1.11_question_option_description_id
data_files:
- split: train
path: cos_e_v1.11_question_option_description_id/train-*
- split: validation
path: cos_e_v1.11_question_option_description_id/validation-*
- config_name: cos_e_v1.11_question_option_description_text
data_files:
- split: train
path: cos_e_v1.11_question_option_description_text/train-*
- split: validation
path: cos_e_v1.11_question_option_description_text/validation-*
- config_name: cos_e_v1.11_rationale
data_files:
- split: train
path: cos_e_v1.11_rationale/train-*
- split: validation
path: cos_e_v1.11_rationale/validation-*
- config_name: cosmos_qa_context_answer_to_question
data_files:
- split: train
path: cosmos_qa_context_answer_to_question/train-*
- split: validation
path: cosmos_qa_context_answer_to_question/validation-*
- split: test
path: cosmos_qa_context_answer_to_question/test-*
- config_name: cosmos_qa_context_description_question_answer_id
data_files:
- split: train
path: cosmos_qa_context_description_question_answer_id/train-*
- split: validation
path: cosmos_qa_context_description_question_answer_id/validation-*
- split: test
path: cosmos_qa_context_description_question_answer_id/test-*
- config_name: cosmos_qa_context_description_question_answer_text
data_files:
- split: train
path: cosmos_qa_context_description_question_answer_text/train-*
- split: validation
path: cosmos_qa_context_description_question_answer_text/validation-*
- split: test
path: cosmos_qa_context_description_question_answer_text/test-*
- config_name: cosmos_qa_context_description_question_text
data_files:
- split: train
path: cosmos_qa_context_description_question_text/train-*
- split: validation
path: cosmos_qa_context_description_question_text/validation-*
- split: test
path: cosmos_qa_context_description_question_text/test-*
- config_name: cosmos_qa_context_question_description_answer_id
data_files:
- split: train
path: cosmos_qa_context_question_description_answer_id/train-*
- split: validation
path: cosmos_qa_context_question_description_answer_id/validation-*
- split: test
path: cosmos_qa_context_question_description_answer_id/test-*
- config_name: cosmos_qa_context_question_description_answer_text
data_files:
- split: train
path: cosmos_qa_context_question_description_answer_text/train-*
- split: validation
path: cosmos_qa_context_question_description_answer_text/validation-*
- split: test
path: cosmos_qa_context_question_description_answer_text/test-*
- config_name: cosmos_qa_context_question_description_text
data_files:
- split: train
path: cosmos_qa_context_question_description_text/train-*
- split: validation
path: cosmos_qa_context_question_description_text/validation-*
- split: test
path: cosmos_qa_context_question_description_text/test-*
- config_name: cosmos_qa_description_context_question_answer_id
data_files:
- split: train
path: cosmos_qa_description_context_question_answer_id/train-*
- split: validation
path: cosmos_qa_description_context_question_answer_id/validation-*
- split: test
path: cosmos_qa_description_context_question_answer_id/test-*
- config_name: cosmos_qa_description_context_question_answer_text
data_files:
- split: train
path: cosmos_qa_description_context_question_answer_text/train-*
- split: validation
path: cosmos_qa_description_context_question_answer_text/validation-*
- split: test
path: cosmos_qa_description_context_question_answer_text/test-*
- config_name: cosmos_qa_description_context_question_text
data_files:
- split: train
path: cosmos_qa_description_context_question_text/train-*
- split: validation
path: cosmos_qa_description_context_question_text/validation-*
- split: test
path: cosmos_qa_description_context_question_text/test-*
- config_name: cosmos_qa_no_prompt_id
data_files:
- split: train
path: cosmos_qa_no_prompt_id/train-*
- split: validation
path: cosmos_qa_no_prompt_id/validation-*
- split: test
path: cosmos_qa_no_prompt_id/test-*
- config_name: cosmos_qa_no_prompt_text
data_files:
- split: train
path: cosmos_qa_no_prompt_text/train-*
- split: validation
path: cosmos_qa_no_prompt_text/validation-*
- split: test
path: cosmos_qa_no_prompt_text/test-*
- config_name: cosmos_qa_only_question_answer
data_files:
- split: train
path: cosmos_qa_only_question_answer/train-*
- split: validation
path: cosmos_qa_only_question_answer/validation-*
- split: test
path: cosmos_qa_only_question_answer/test-*
- config_name: dbpedia_14_given_a_choice_of_categories_
data_files:
- split: train
path: dbpedia_14_given_a_choice_of_categories_/train-*
- split: test
path: dbpedia_14_given_a_choice_of_categories_/test-*
- config_name: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to
data_files:
- split: train
path: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to/train-*
- split: test
path: dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to/test-*
- config_name: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to
data_files:
- split: train
path: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to/train-*
- split: test
path: dbpedia_14_given_list_what_category_does_the_paragraph_belong_to/test-*
- config_name: dbpedia_14_pick_one_category_for_the_following_text
data_files:
- split: train
path: dbpedia_14_pick_one_category_for_the_following_text/train-*
- split: test
path: dbpedia_14_pick_one_category_for_the_following_text/test-*
- config_name: dream_answer_to_dialogue
data_files:
- split: train
path: dream_answer_to_dialogue/train-*
- split: validation
path: dream_answer_to_dialogue/validation-*
- split: test
path: dream_answer_to_dialogue/test-*
- config_name: dream_baseline
data_files:
- split: train
path: dream_baseline/train-*
- split: validation
path: dream_baseline/validation-*
- split: test
path: dream_baseline/test-*
- config_name: dream_generate_first_utterance
data_files:
- split: train
path: dream_generate_first_utterance/train-*
- split: validation
path: dream_generate_first_utterance/validation-*
- split: test
path: dream_generate_first_utterance/test-*
- config_name: dream_generate_last_utterance
data_files:
- split: train
path: dream_generate_last_utterance/train-*
- split: validation
path: dream_generate_last_utterance/validation-*
- split: test
path: dream_generate_last_utterance/test-*
- config_name: dream_read_the_following_conversation_and_answer_the_question
data_files:
- split: train
path: dream_read_the_following_conversation_and_answer_the_question/train-*
- split: validation
path: dream_read_the_following_conversation_and_answer_the_question/validation-*
- split: test
path: dream_read_the_following_conversation_and_answer_the_question/test-*
- config_name: duorc_ParaphraseRC_answer_question
data_files:
- split: train
path: duorc_ParaphraseRC_answer_question/train-*
- split: validation
path: duorc_ParaphraseRC_answer_question/validation-*
- split: test
path: duorc_ParaphraseRC_answer_question/test-*
- config_name: duorc_ParaphraseRC_build_story_around_qa
data_files:
- split: train
path: duorc_ParaphraseRC_build_story_around_qa/train-*
- split: validation
path: duorc_ParaphraseRC_build_story_around_qa/validation-*
- split: test
path: duorc_ParaphraseRC_build_story_around_qa/test-*
- config_name: duorc_ParaphraseRC_decide_worth_it
data_files:
- split: train
path: duorc_ParaphraseRC_decide_worth_it/train-*
- split: validation
path: duorc_ParaphraseRC_decide_worth_it/validation-*
- split: test
path: duorc_ParaphraseRC_decide_worth_it/test-*
- config_name: duorc_ParaphraseRC_extract_answer
data_files:
- split: train
path: duorc_ParaphraseRC_extract_answer/train-*
- split: validation
path: duorc_ParaphraseRC_extract_answer/validation-*
- split: test
path: duorc_ParaphraseRC_extract_answer/test-*
- config_name: duorc_ParaphraseRC_generate_question
data_files:
- split: train
path: duorc_ParaphraseRC_generate_question/train-*
- split: validation
path: duorc_ParaphraseRC_generate_question/validation-*
- split: test
path: duorc_ParaphraseRC_generate_question/test-*
- config_name: duorc_ParaphraseRC_generate_question_by_answer
data_files:
- split: train
path: duorc_ParaphraseRC_generate_question_by_answer/train-*
- split: validation
path: duorc_ParaphraseRC_generate_question_by_answer/validation-*
- split: test
path: duorc_ParaphraseRC_generate_question_by_answer/test-*
- config_name: duorc_ParaphraseRC_movie_director
data_files:
- split: train
path: duorc_ParaphraseRC_movie_director/train-*
- split: validation
path: duorc_ParaphraseRC_movie_director/validation-*
- split: test
path: duorc_ParaphraseRC_movie_director/test-*
- config_name: duorc_ParaphraseRC_question_answering
data_files:
- split: train
path: duorc_ParaphraseRC_question_answering/train-*
- split: validation
path: duorc_ParaphraseRC_question_answering/validation-*
- split: test
path: duorc_ParaphraseRC_question_answering/test-*
- config_name: duorc_ParaphraseRC_title_generation
data_files:
- split: train
path: duorc_ParaphraseRC_title_generation/train-*
- split: validation
path: duorc_ParaphraseRC_title_generation/validation-*
- split: test
path: duorc_ParaphraseRC_title_generation/test-*
- config_name: duorc_SelfRC_answer_question
data_files:
- split: train
path: duorc_SelfRC_answer_question/train-*
- split: validation
path: duorc_SelfRC_answer_question/validation-*
- split: test
path: duorc_SelfRC_answer_question/test-*
- config_name: duorc_SelfRC_build_story_around_qa
data_files:
- split: train
path: duorc_SelfRC_build_story_around_qa/train-*
- split: validation
path: duorc_SelfRC_build_story_around_qa/validation-*
- split: test
path: duorc_SelfRC_build_story_around_qa/test-*
- config_name: duorc_SelfRC_decide_worth_it
data_files:
- split: train
path: duorc_SelfRC_decide_worth_it/train-*
- split: validation
path: duorc_SelfRC_decide_worth_it/validation-*
- split: test
path: duorc_SelfRC_decide_worth_it/test-*
- config_name: duorc_SelfRC_extract_answer
data_files:
- split: train
path: duorc_SelfRC_extract_answer/train-*
- split: validation
path: duorc_SelfRC_extract_answer/validation-*
- split: test
path: duorc_SelfRC_extract_answer/test-*
- config_name: duorc_SelfRC_generate_question
data_files:
- split: train
path: duorc_SelfRC_generate_question/train-*
- split: validation
path: duorc_SelfRC_generate_question/validation-*
- split: test
path: duorc_SelfRC_generate_question/test-*
- config_name: duorc_SelfRC_generate_question_by_answer
data_files:
- split: train
path: duorc_SelfRC_generate_question_by_answer/train-*
- split: validation
path: duorc_SelfRC_generate_question_by_answer/validation-*
- split: test
path: duorc_SelfRC_generate_question_by_answer/test-*
- config_name: duorc_SelfRC_movie_director
data_files:
- split: train
path: duorc_SelfRC_movie_director/train-*
- split: validation
path: duorc_SelfRC_movie_director/validation-*
- split: test
path: duorc_SelfRC_movie_director/test-*
- config_name: duorc_SelfRC_question_answering
data_files:
- split: train
path: duorc_SelfRC_question_answering/train-*
- split: validation
path: duorc_SelfRC_question_answering/validation-*
- split: test
path: duorc_SelfRC_question_answering/test-*
- config_name: duorc_SelfRC_title_generation
data_files:
- split: train
path: duorc_SelfRC_title_generation/train-*
- split: validation
path: duorc_SelfRC_title_generation/validation-*
- split: test
path: duorc_SelfRC_title_generation/test-*
- config_name: gigaword_TLDR
data_files:
- split: train
path: gigaword_TLDR/train-*
- split: validation
path: gigaword_TLDR/validation-*
- split: test
path: gigaword_TLDR/test-*
- config_name: gigaword_first_sentence_title
data_files:
- split: train
path: gigaword_first_sentence_title/train-*
- split: validation
path: gigaword_first_sentence_title/validation-*
- split: test
path: gigaword_first_sentence_title/test-*
- config_name: gigaword_generate_summary_for_this
data_files:
- split: train
path: gigaword_generate_summary_for_this/train-*
- split: validation
path: gigaword_generate_summary_for_this/validation-*
- split: test
path: gigaword_generate_summary_for_this/test-*
- config_name: gigaword_in_a_nutshell
data_files:
- split: train
path: gigaword_in_a_nutshell/train-*
- split: validation
path: gigaword_in_a_nutshell/validation-*
- split: test
path: gigaword_in_a_nutshell/test-*
- config_name: gigaword_make_a_title
data_files:
- split: train
path: gigaword_make_a_title/train-*
- split: validation
path: gigaword_make_a_title/validation-*
- split: test
path: gigaword_make_a_title/test-*
- config_name: gigaword_reverse_writing
data_files:
- split: train
path: gigaword_reverse_writing/train-*
- split: validation
path: gigaword_reverse_writing/validation-*
- split: test
path: gigaword_reverse_writing/test-*
- config_name: gigaword_write_a_title_for_this_sentence
data_files:
- split: train
path: gigaword_write_a_title_for_this_sentence/train-*
- split: validation
path: gigaword_write_a_title_for_this_sentence/validation-*
- split: test
path: gigaword_write_a_title_for_this_sentence/test-*
- config_name: gigaword_write_an_article
data_files:
- split: train
path: gigaword_write_an_article/train-*
- split: validation
path: gigaword_write_an_article/validation-*
- split: test
path: gigaword_write_an_article/test-*
- config_name: gigaword_write_its_sentence
data_files:
- split: train
path: gigaword_write_its_sentence/train-*
- split: validation
path: gigaword_write_its_sentence/validation-*
- split: test
path: gigaword_write_its_sentence/test-*
- config_name: glue_mrpc_equivalent
data_files:
- split: train
path: glue_mrpc_equivalent/train-*
- split: validation
path: glue_mrpc_equivalent/validation-*
- split: test
path: glue_mrpc_equivalent/test-*
- config_name: glue_mrpc_generate_paraphrase
data_files:
- split: train
path: glue_mrpc_generate_paraphrase/train-*
- split: validation
path: glue_mrpc_generate_paraphrase/validation-*
- split: test
path: glue_mrpc_generate_paraphrase/test-*
- config_name: glue_mrpc_generate_sentence
data_files:
- split: train
path: glue_mrpc_generate_sentence/train-*
- split: validation
path: glue_mrpc_generate_sentence/validation-*
- split: test
path: glue_mrpc_generate_sentence/test-*
- config_name: glue_mrpc_paraphrase
data_files:
- split: train
path: glue_mrpc_paraphrase/train-*
- split: validation
path: glue_mrpc_paraphrase/validation-*
- split: test
path: glue_mrpc_paraphrase/test-*
- config_name: glue_mrpc_replace
data_files:
- split: train
path: glue_mrpc_replace/train-*
- split: validation
path: glue_mrpc_replace/validation-*
- split: test
path: glue_mrpc_replace/test-*
- config_name: glue_mrpc_same_thing
data_files:
- split: train
path: glue_mrpc_same_thing/train-*
- split: validation
path: glue_mrpc_same_thing/validation-*
- split: test
path: glue_mrpc_same_thing/test-*
- config_name: glue_mrpc_want_to_know
data_files:
- split: train
path: glue_mrpc_want_to_know/train-*
- split: validation
path: glue_mrpc_want_to_know/validation-*
- split: test
path: glue_mrpc_want_to_know/test-*
- config_name: glue_qqp_answer
data_files:
- split: train
path: glue_qqp_answer/train-*
- split: validation
path: glue_qqp_answer/validation-*
- split: test
path: glue_qqp_answer/test-*
- config_name: glue_qqp_duplicate
data_files:
- split: train
path: glue_qqp_duplicate/train-*
- split: validation
path: glue_qqp_duplicate/validation-*
- split: test
path: glue_qqp_duplicate/test-*
- config_name: glue_qqp_duplicate_or_not
data_files:
- split: train
path: glue_qqp_duplicate_or_not/train-*
- split: validation
path: glue_qqp_duplicate_or_not/validation-*
- split: test
path: glue_qqp_duplicate_or_not/test-*
- config_name: glue_qqp_meaning
data_files:
- split: train
path: glue_qqp_meaning/train-*
- split: validation
path: glue_qqp_meaning/validation-*
- split: test
path: glue_qqp_meaning/test-*
- config_name: glue_qqp_quora
data_files:
- split: train
path: glue_qqp_quora/train-*
- split: validation
path: glue_qqp_quora/validation-*
- split: test
path: glue_qqp_quora/test-*
- config_name: glue_qqp_same_thing
data_files:
- split: train
path: glue_qqp_same_thing/train-*
- split: validation
path: glue_qqp_same_thing/validation-*
- split: test
path: glue_qqp_same_thing/test-*
- config_name: hellaswag_Appropriate_continuation_Yes_or_No
data_files:
- split: train
path: hellaswag_Appropriate_continuation_Yes_or_No/train-*
- split: validation
path: hellaswag_Appropriate_continuation_Yes_or_No/validation-*
- split: test
path: hellaswag_Appropriate_continuation_Yes_or_No/test-*
- config_name: hellaswag_Open_ended_completion
data_files:
- split: train
path: hellaswag_Open_ended_completion/train-*
- split: validation
path: hellaswag_Open_ended_completion/validation-*
- split: test
path: hellaswag_Open_ended_completion/test-*
- config_name: hellaswag_Open_ended_start
data_files:
- split: train
path: hellaswag_Open_ended_start/train-*
- split: validation
path: hellaswag_Open_ended_start/validation-*
- split: test
path: hellaswag_Open_ended_start/test-*
- config_name: hellaswag_Predict_ending_with_hint
data_files:
- split: train
path: hellaswag_Predict_ending_with_hint/train-*
- split: validation
path: hellaswag_Predict_ending_with_hint/validation-*
- split: test
path: hellaswag_Predict_ending_with_hint/test-*
- config_name: hellaswag_Predict_ending_with_hint_score_eval
data_files:
- split: train
path: hellaswag_Predict_ending_with_hint_score_eval/train-*
- split: validation
path: hellaswag_Predict_ending_with_hint_score_eval/validation-*
- split: test
path: hellaswag_Predict_ending_with_hint_score_eval/test-*
- config_name: hellaswag_Randomized_prompts_template
data_files:
- split: train
path: hellaswag_Randomized_prompts_template/train-*
- split: validation
path: hellaswag_Randomized_prompts_template/validation-*
- split: test
path: hellaswag_Randomized_prompts_template/test-*
- config_name: hellaswag_Randomized_prompts_template_score_eval
data_files:
- split: train
path: hellaswag_Randomized_prompts_template_score_eval/train-*
- split: validation
path: hellaswag_Randomized_prompts_template_score_eval/validation-*
- split: test
path: hellaswag_Randomized_prompts_template_score_eval/test-*
- config_name: hellaswag_Reversed_appropriate_continuation_Yes_or_No
data_files:
- split: train
path: hellaswag_Reversed_appropriate_continuation_Yes_or_No/train-*
- split: validation
path: hellaswag_Reversed_appropriate_continuation_Yes_or_No/validation-*
- split: test
path: hellaswag_Reversed_appropriate_continuation_Yes_or_No/test-*
- config_name: hellaswag_Topic_of_the_context
data_files:
- split: train
path: hellaswag_Topic_of_the_context/train-*
- split: validation
path: hellaswag_Topic_of_the_context/validation-*
- split: test
path: hellaswag_Topic_of_the_context/test-*
- config_name: hellaswag_Topic_without_the_ending_answer
data_files:
- split: train
path: hellaswag_Topic_without_the_ending_answer/train-*
- split: validation
path: hellaswag_Topic_without_the_ending_answer/validation-*
- split: test
path: hellaswag_Topic_without_the_ending_answer/test-*
- config_name: hellaswag_complete_first_then
data_files:
- split: train
path: hellaswag_complete_first_then/train-*
- split: validation
path: hellaswag_complete_first_then/validation-*
- split: test
path: hellaswag_complete_first_then/test-*
- config_name: hellaswag_complete_first_then_score_eval
data_files:
- split: train
path: hellaswag_complete_first_then_score_eval/train-*
- split: validation
path: hellaswag_complete_first_then_score_eval/validation-*
- split: test
path: hellaswag_complete_first_then_score_eval/test-*
- config_name: hellaswag_how_ends
data_files:
- split: train
path: hellaswag_how_ends/train-*
- split: validation
path: hellaswag_how_ends/validation-*
- split: test
path: hellaswag_how_ends/test-*
- config_name: hellaswag_if_begins_how_continues
data_files:
- split: train
path: hellaswag_if_begins_how_continues/train-*
- split: validation
path: hellaswag_if_begins_how_continues/validation-*
- split: test
path: hellaswag_if_begins_how_continues/test-*
- config_name: hellaswag_if_begins_how_continues_score_eval
data_files:
- split: train
path: hellaswag_if_begins_how_continues_score_eval/train-*
- split: validation
path: hellaswag_if_begins_how_continues_score_eval/validation-*
- split: test
path: hellaswag_if_begins_how_continues_score_eval/test-*
- config_name: imdb_Movie_Expressed_Sentiment
data_files:
- split: train
path: imdb_Movie_Expressed_Sentiment/train-*
- split: test
path: imdb_Movie_Expressed_Sentiment/test-*
- split: unsupervised
path: imdb_Movie_Expressed_Sentiment/unsupervised-*
- config_name: imdb_Movie_Expressed_Sentiment_2
data_files:
- split: train
path: imdb_Movie_Expressed_Sentiment_2/train-*
- split: test
path: imdb_Movie_Expressed_Sentiment_2/test-*
- split: unsupervised
path: imdb_Movie_Expressed_Sentiment_2/unsupervised-*
- config_name: imdb_Negation_template_for_positive_and_negative
data_files:
- split: train
path: imdb_Negation_template_for_positive_and_negative/train-*
- split: test
path: imdb_Negation_template_for_positive_and_negative/test-*
- split: unsupervised
path: imdb_Negation_template_for_positive_and_negative/unsupervised-*
- config_name: imdb_Reviewer_Enjoyment
data_files:
- split: train
path: imdb_Reviewer_Enjoyment/train-*
- split: test
path: imdb_Reviewer_Enjoyment/test-*
- split: unsupervised
path: imdb_Reviewer_Enjoyment/unsupervised-*
- config_name: imdb_Reviewer_Enjoyment_Yes_No
data_files:
- split: train
path: imdb_Reviewer_Enjoyment_Yes_No/train-*
- split: test
path: imdb_Reviewer_Enjoyment_Yes_No/test-*
- split: unsupervised
path: imdb_Reviewer_Enjoyment_Yes_No/unsupervised-*
- config_name: imdb_Reviewer_Expressed_Sentiment
data_files:
- split: train
path: imdb_Reviewer_Expressed_Sentiment/train-*
- split: test
path: imdb_Reviewer_Expressed_Sentiment/test-*
- split: unsupervised
path: imdb_Reviewer_Expressed_Sentiment/unsupervised-*
- config_name: imdb_Reviewer_Opinion_bad_good_choices
data_files:
- split: train
path: imdb_Reviewer_Opinion_bad_good_choices/train-*
- split: test
path: imdb_Reviewer_Opinion_bad_good_choices/test-*
- split: unsupervised
path: imdb_Reviewer_Opinion_bad_good_choices/unsupervised-*
- config_name: imdb_Reviewer_Sentiment_Feeling
data_files:
- split: train
path: imdb_Reviewer_Sentiment_Feeling/train-*
- split: test
path: imdb_Reviewer_Sentiment_Feeling/test-*
- split: unsupervised
path: imdb_Reviewer_Sentiment_Feeling/unsupervised-*
- config_name: imdb_Sentiment_with_choices_
data_files:
- split: train
path: imdb_Sentiment_with_choices_/train-*
- split: test
path: imdb_Sentiment_with_choices_/test-*
- split: unsupervised
path: imdb_Sentiment_with_choices_/unsupervised-*
- config_name: imdb_Text_Expressed_Sentiment
data_files:
- split: train
path: imdb_Text_Expressed_Sentiment/train-*
- split: test
path: imdb_Text_Expressed_Sentiment/test-*
- split: unsupervised
path: imdb_Text_Expressed_Sentiment/unsupervised-*
- config_name: imdb_Writer_Expressed_Sentiment
data_files:
- split: train
path: imdb_Writer_Expressed_Sentiment/train-*
- split: test
path: imdb_Writer_Expressed_Sentiment/test-*
- split: unsupervised
path: imdb_Writer_Expressed_Sentiment/unsupervised-*
- config_name: kilt_tasks_hotpotqa_combining_facts
data_files:
- split: train
path: kilt_tasks_hotpotqa_combining_facts/train-*
- split: validation
path: kilt_tasks_hotpotqa_combining_facts/validation-*
- config_name: kilt_tasks_hotpotqa_complex_question
data_files:
- split: train
path: kilt_tasks_hotpotqa_complex_question/train-*
- split: validation
path: kilt_tasks_hotpotqa_complex_question/validation-*
- config_name: kilt_tasks_hotpotqa_final_exam
data_files:
- split: train
path: kilt_tasks_hotpotqa_final_exam/train-*
- split: validation
path: kilt_tasks_hotpotqa_final_exam/validation-*
- config_name: kilt_tasks_hotpotqa_formulate
data_files:
- split: train
path: kilt_tasks_hotpotqa_formulate/train-*
- split: validation
path: kilt_tasks_hotpotqa_formulate/validation-*
- config_name: kilt_tasks_hotpotqa_straighforward_qa
data_files:
- split: train
path: kilt_tasks_hotpotqa_straighforward_qa/train-*
- split: validation
path: kilt_tasks_hotpotqa_straighforward_qa/validation-*
- config_name: multi_news_distill
data_files:
- split: train
path: multi_news_distill/train-*
- split: validation
path: multi_news_distill/validation-*
- split: test
path: multi_news_distill/test-*
- config_name: multi_news_expand_reverse_task_
data_files:
- split: train
path: multi_news_expand_reverse_task_/train-*
- split: validation
path: multi_news_expand_reverse_task_/validation-*
- split: test
path: multi_news_expand_reverse_task_/test-*
- config_name: multi_news_summarize
data_files:
- split: train
path: multi_news_summarize/train-*
- split: validation
path: multi_news_summarize/validation-*
- split: test
path: multi_news_summarize/test-*
- config_name: multi_news_summary_scenario
data_files:
- split: train
path: multi_news_summary_scenario/train-*
- split: validation
path: multi_news_summary_scenario/validation-*
- split: test
path: multi_news_summary_scenario/test-*
- config_name: multi_news_synthesize
data_files:
- split: train
path: multi_news_synthesize/train-*
- split: validation
path: multi_news_synthesize/validation-*
- split: test
path: multi_news_synthesize/test-*
- config_name: multi_news_what_are_the_key_points
data_files:
- split: train
path: multi_news_what_are_the_key_points/train-*
- split: validation
path: multi_news_what_are_the_key_points/validation-*
- split: test
path: multi_news_what_are_the_key_points/test-*
- config_name: openbookqa_main_choices
data_files:
- split: train
path: openbookqa_main_choices/train-*
- split: validation
path: openbookqa_main_choices/validation-*
- split: test
path: openbookqa_main_choices/test-*
- config_name: openbookqa_main_choose_an_answer_with_options
data_files:
- split: train
path: openbookqa_main_choose_an_answer_with_options/train-*
- split: validation
path: openbookqa_main_choose_an_answer_with_options/validation-*
- split: test
path: openbookqa_main_choose_an_answer_with_options/test-*
- config_name: openbookqa_main_only_options
data_files:
- split: train
path: openbookqa_main_only_options/train-*
- split: validation
path: openbookqa_main_only_options/validation-*
- split: test
path: openbookqa_main_only_options/test-*
- config_name: openbookqa_main_pick_answer_with_options
data_files:
- split: train
path: openbookqa_main_pick_answer_with_options/train-*
- split: validation
path: openbookqa_main_pick_answer_with_options/validation-*
- split: test
path: openbookqa_main_pick_answer_with_options/test-*
- config_name: openbookqa_main_pick_using_id
data_files:
- split: train
path: openbookqa_main_pick_using_id/train-*
- split: validation
path: openbookqa_main_pick_using_id/validation-*
- split: test
path: openbookqa_main_pick_using_id/test-*
- config_name: openbookqa_main_which_correct
data_files:
- split: train
path: openbookqa_main_which_correct/train-*
- split: validation
path: openbookqa_main_which_correct/validation-*
- split: test
path: openbookqa_main_which_correct/test-*
- config_name: openbookqa_main_which_correct_inverse
data_files:
- split: train
path: openbookqa_main_which_correct_inverse/train-*
- split: validation
path: openbookqa_main_which_correct_inverse/validation-*
- split: test
path: openbookqa_main_which_correct_inverse/test-*
- config_name: paws_labeled_final_Concatenation
data_files:
- split: train
path: paws_labeled_final_Concatenation/train-*
- split: validation
path: paws_labeled_final_Concatenation/validation-*
- split: test
path: paws_labeled_final_Concatenation/test-*
- config_name: paws_labeled_final_Concatenation_no_label
data_files:
- split: train
path: paws_labeled_final_Concatenation_no_label/train-*
- split: validation
path: paws_labeled_final_Concatenation_no_label/validation-*
- split: test
path: paws_labeled_final_Concatenation_no_label/test-*
- config_name: paws_labeled_final_Meaning
data_files:
- split: train
path: paws_labeled_final_Meaning/train-*
- split: validation
path: paws_labeled_final_Meaning/validation-*
- split: test
path: paws_labeled_final_Meaning/test-*
- config_name: paws_labeled_final_Meaning_no_label
data_files:
- split: train
path: paws_labeled_final_Meaning_no_label/train-*
- split: validation
path: paws_labeled_final_Meaning_no_label/validation-*
- split: test
path: paws_labeled_final_Meaning_no_label/test-*
- config_name: paws_labeled_final_PAWS_ANLI_GPT3
data_files:
- split: train
path: paws_labeled_final_PAWS_ANLI_GPT3/train-*
- split: validation
path: paws_labeled_final_PAWS_ANLI_GPT3/validation-*
- split: test
path: paws_labeled_final_PAWS_ANLI_GPT3/test-*
- config_name: paws_labeled_final_PAWS_ANLI_GPT3_no_label
data_files:
- split: train
path: paws_labeled_final_PAWS_ANLI_GPT3_no_label/train-*
- split: validation
path: paws_labeled_final_PAWS_ANLI_GPT3_no_label/validation-*
- split: test
path: paws_labeled_final_PAWS_ANLI_GPT3_no_label/test-*
- config_name: paws_labeled_final_Rewrite
data_files:
- split: train
path: paws_labeled_final_Rewrite/train-*
- split: validation
path: paws_labeled_final_Rewrite/validation-*
- split: test
path: paws_labeled_final_Rewrite/test-*
- config_name: paws_labeled_final_Rewrite_no_label
data_files:
- split: train
path: paws_labeled_final_Rewrite_no_label/train-*
- split: validation
path: paws_labeled_final_Rewrite_no_label/validation-*
- split: test
path: paws_labeled_final_Rewrite_no_label/test-*
- config_name: paws_labeled_final_context_question
data_files:
- split: train
path: paws_labeled_final_context_question/train-*
- split: validation
path: paws_labeled_final_context_question/validation-*
- split: test
path: paws_labeled_final_context_question/test-*
- config_name: paws_labeled_final_context_question_no_label
data_files:
- split: train
path: paws_labeled_final_context_question_no_label/train-*
- split: validation
path: paws_labeled_final_context_question_no_label/validation-*
- split: test
path: paws_labeled_final_context_question_no_label/test-*
- config_name: paws_labeled_final_paraphrase_task
data_files:
- split: train
path: paws_labeled_final_paraphrase_task/train-*
- split: validation
path: paws_labeled_final_paraphrase_task/validation-*
- split: test
path: paws_labeled_final_paraphrase_task/test-*
- config_name: paws_labeled_final_task_description_no_label
data_files:
- split: train
path: paws_labeled_final_task_description_no_label/train-*
- split: validation
path: paws_labeled_final_task_description_no_label/validation-*
- split: test
path: paws_labeled_final_task_description_no_label/test-*
- config_name: piqa_Correct_the_solution
data_files:
- split: train
path: piqa_Correct_the_solution/train-*
- split: validation
path: piqa_Correct_the_solution/validation-*
- split: test
path: piqa_Correct_the_solution/test-*
- config_name: piqa_Correct_the_solution_if_false_from_sol_1
data_files:
- split: train
path: piqa_Correct_the_solution_if_false_from_sol_1/train-*
- split: validation
path: piqa_Correct_the_solution_if_false_from_sol_1/validation-*
- split: test
path: piqa_Correct_the_solution_if_false_from_sol_1/test-*
- config_name: piqa_Correct_the_solution_if_false_from_sol_2
data_files:
- split: train
path: piqa_Correct_the_solution_if_false_from_sol_2/train-*
- split: validation
path: piqa_Correct_the_solution_if_false_from_sol_2/validation-*
- split: test
path: piqa_Correct_the_solution_if_false_from_sol_2/test-*
- config_name: piqa_Does_this_solution_make_sense_sol1
data_files:
- split: train
path: piqa_Does_this_solution_make_sense_sol1/train-*
- split: validation
path: piqa_Does_this_solution_make_sense_sol1/validation-*
- split: test
path: piqa_Does_this_solution_make_sense_sol1/test-*
- config_name: piqa_Does_this_solution_make_sense_sol2
data_files:
- split: train
path: piqa_Does_this_solution_make_sense_sol2/train-*
- split: validation
path: piqa_Does_this_solution_make_sense_sol2/validation-*
- split: test
path: piqa_Does_this_solution_make_sense_sol2/test-*
- config_name: piqa_choose_the_most_appropriate_solution
data_files:
- split: train
path: piqa_choose_the_most_appropriate_solution/train-*
- split: validation
path: piqa_choose_the_most_appropriate_solution/validation-*
- split: test
path: piqa_choose_the_most_appropriate_solution/test-*
- config_name: piqa_finish_sentence_with_correct_choice
data_files:
- split: train
path: piqa_finish_sentence_with_correct_choice/train-*
- split: validation
path: piqa_finish_sentence_with_correct_choice/validation-*
- split: test
path: piqa_finish_sentence_with_correct_choice/test-*
- config_name: piqa_no_prompt_needed
data_files:
- split: train
path: piqa_no_prompt_needed/train-*
- split: validation
path: piqa_no_prompt_needed/validation-*
- split: test
path: piqa_no_prompt_needed/test-*
- config_name: piqa_pick_correct_choice_index
data_files:
- split: train
path: piqa_pick_correct_choice_index/train-*
- split: validation
path: piqa_pick_correct_choice_index/validation-*
- split: test
path: piqa_pick_correct_choice_index/test-*
- config_name: piqa_pick_correct_choice_with_choice_given_before_goal
data_files:
- split: train
path: piqa_pick_correct_choice_with_choice_given_before_goal/train-*
- split: validation
path: piqa_pick_correct_choice_with_choice_given_before_goal/validation-*
- split: test
path: piqa_pick_correct_choice_with_choice_given_before_goal/test-*
- config_name: piqa_what_is_the_correct_ending
data_files:
- split: train
path: piqa_what_is_the_correct_ending/train-*
- split: validation
path: piqa_what_is_the_correct_ending/validation-*
- split: test
path: piqa_what_is_the_correct_ending/test-*
- config_name: qasc_is_correct_1
data_files:
- split: train
path: qasc_is_correct_1/train-*
- split: validation
path: qasc_is_correct_1/validation-*
- split: test
path: qasc_is_correct_1/test-*
- config_name: qasc_is_correct_2
data_files:
- split: train
path: qasc_is_correct_2/train-*
- split: validation
path: qasc_is_correct_2/validation-*
- split: test
path: qasc_is_correct_2/test-*
- config_name: qasc_qa_with_combined_facts_1
data_files:
- split: train
path: qasc_qa_with_combined_facts_1/train-*
- split: validation
path: qasc_qa_with_combined_facts_1/validation-*
- split: test
path: qasc_qa_with_combined_facts_1/test-*
- config_name: qasc_qa_with_separated_facts_1
data_files:
- split: train
path: qasc_qa_with_separated_facts_1/train-*
- split: validation
path: qasc_qa_with_separated_facts_1/validation-*
- split: test
path: qasc_qa_with_separated_facts_1/test-*
- config_name: qasc_qa_with_separated_facts_2
data_files:
- split: train
path: qasc_qa_with_separated_facts_2/train-*
- split: validation
path: qasc_qa_with_separated_facts_2/validation-*
- split: test
path: qasc_qa_with_separated_facts_2/test-*
- config_name: qasc_qa_with_separated_facts_3
data_files:
- split: train
path: qasc_qa_with_separated_facts_3/train-*
- split: validation
path: qasc_qa_with_separated_facts_3/validation-*
- split: test
path: qasc_qa_with_separated_facts_3/test-*
- config_name: qasc_qa_with_separated_facts_4
data_files:
- split: train
path: qasc_qa_with_separated_facts_4/train-*
- split: validation
path: qasc_qa_with_separated_facts_4/validation-*
- split: test
path: qasc_qa_with_separated_facts_4/test-*
- config_name: qasc_qa_with_separated_facts_5
data_files:
- split: train
path: qasc_qa_with_separated_facts_5/train-*
- split: validation
path: qasc_qa_with_separated_facts_5/validation-*
- split: test
path: qasc_qa_with_separated_facts_5/test-*
- config_name: quail_context_description_question_answer_id
data_files:
- split: train
path: quail_context_description_question_answer_id/train-*
- split: validation
path: quail_context_description_question_answer_id/validation-*
- split: challenge
path: quail_context_description_question_answer_id/challenge-*
- config_name: quail_context_description_question_answer_text
data_files:
- split: train
path: quail_context_description_question_answer_text/train-*
- split: validation
path: quail_context_description_question_answer_text/validation-*
- split: challenge
path: quail_context_description_question_answer_text/challenge-*
- config_name: quail_context_description_question_text
data_files:
- split: train
path: quail_context_description_question_text/train-*
- split: validation
path: quail_context_description_question_text/validation-*
- split: challenge
path: quail_context_description_question_text/challenge-*
- config_name: quail_context_question_answer_description_id
data_files:
- split: train
path: quail_context_question_answer_description_id/train-*
- split: validation
path: quail_context_question_answer_description_id/validation-*
- split: challenge
path: quail_context_question_answer_description_id/challenge-*
- config_name: quail_context_question_answer_description_text
data_files:
- split: train
path: quail_context_question_answer_description_text/train-*
- split: validation
path: quail_context_question_answer_description_text/validation-*
- split: challenge
path: quail_context_question_answer_description_text/challenge-*
- config_name: quail_context_question_description_answer_id
data_files:
- split: train
path: quail_context_question_description_answer_id/train-*
- split: validation
path: quail_context_question_description_answer_id/validation-*
- split: challenge
path: quail_context_question_description_answer_id/challenge-*
- config_name: quail_context_question_description_answer_text
data_files:
- split: train
path: quail_context_question_description_answer_text/train-*
- split: validation
path: quail_context_question_description_answer_text/validation-*
- split: challenge
path: quail_context_question_description_answer_text/challenge-*
- config_name: quail_context_question_description_text
data_files:
- split: train
path: quail_context_question_description_text/train-*
- split: validation
path: quail_context_question_description_text/validation-*
- split: challenge
path: quail_context_question_description_text/challenge-*
- config_name: quail_description_context_question_answer_id
data_files:
- split: train
path: quail_description_context_question_answer_id/train-*
- split: validation
path: quail_description_context_question_answer_id/validation-*
- split: challenge
path: quail_description_context_question_answer_id/challenge-*
- config_name: quail_description_context_question_answer_text
data_files:
- split: train
path: quail_description_context_question_answer_text/train-*
- split: validation
path: quail_description_context_question_answer_text/validation-*
- split: challenge
path: quail_description_context_question_answer_text/challenge-*
- config_name: quail_description_context_question_text
data_files:
- split: train
path: quail_description_context_question_text/train-*
- split: validation
path: quail_description_context_question_text/validation-*
- split: challenge
path: quail_description_context_question_text/challenge-*
- config_name: quail_no_prompt_id
data_files:
- split: train
path: quail_no_prompt_id/train-*
- split: validation
path: quail_no_prompt_id/validation-*
- split: challenge
path: quail_no_prompt_id/challenge-*
- config_name: quail_no_prompt_text
data_files:
- split: train
path: quail_no_prompt_text/train-*
- split: validation
path: quail_no_prompt_text/validation-*
- split: challenge
path: quail_no_prompt_text/challenge-*
- config_name: quarel_choose_between
data_files:
- split: train
path: quarel_choose_between/train-*
- split: validation
path: quarel_choose_between/validation-*
- split: test
path: quarel_choose_between/test-*
- config_name: quarel_do_not_use
data_files:
- split: train
path: quarel_do_not_use/train-*
- split: validation
path: quarel_do_not_use/validation-*
- split: test
path: quarel_do_not_use/test-*
- config_name: quarel_heres_a_story
data_files:
- split: train
path: quarel_heres_a_story/train-*
- split: validation
path: quarel_heres_a_story/validation-*
- split: test
path: quarel_heres_a_story/test-*
- config_name: quarel_logic_test
data_files:
- split: train
path: quarel_logic_test/train-*
- split: validation
path: quarel_logic_test/validation-*
- split: test
path: quarel_logic_test/test-*
- config_name: quarel_testing_students
data_files:
- split: train
path: quarel_testing_students/train-*
- split: validation
path: quarel_testing_students/validation-*
- split: test
path: quarel_testing_students/test-*
- config_name: quartz_answer_question_based_on
data_files:
- split: train
path: quartz_answer_question_based_on/train-*
- split: validation
path: quartz_answer_question_based_on/validation-*
- split: test
path: quartz_answer_question_based_on/test-*
- config_name: quartz_answer_question_below
data_files:
- split: train
path: quartz_answer_question_below/train-*
- split: validation
path: quartz_answer_question_below/validation-*
- split: test
path: quartz_answer_question_below/test-*
- config_name: quartz_given_the_fact_answer_the_q
data_files:
- split: train
path: quartz_given_the_fact_answer_the_q/train-*
- split: validation
path: quartz_given_the_fact_answer_the_q/validation-*
- split: test
path: quartz_given_the_fact_answer_the_q/test-*
- config_name: quartz_having_read_above_passage
data_files:
- split: train
path: quartz_having_read_above_passage/train-*
- split: validation
path: quartz_having_read_above_passage/validation-*
- split: test
path: quartz_having_read_above_passage/test-*
- config_name: quartz_paragraph_question_plain_concat
data_files:
- split: train
path: quartz_paragraph_question_plain_concat/train-*
- split: validation
path: quartz_paragraph_question_plain_concat/validation-*
- split: test
path: quartz_paragraph_question_plain_concat/test-*
- config_name: quartz_read_passage_below_choose
data_files:
- split: train
path: quartz_read_passage_below_choose/train-*
- split: validation
path: quartz_read_passage_below_choose/validation-*
- split: test
path: quartz_read_passage_below_choose/test-*
- config_name: quartz_use_info_from_paragraph_question
data_files:
- split: train
path: quartz_use_info_from_paragraph_question/train-*
- split: validation
path: quartz_use_info_from_paragraph_question/validation-*
- split: test
path: quartz_use_info_from_paragraph_question/test-*
- config_name: quartz_use_info_from_question_paragraph
data_files:
- split: train
path: quartz_use_info_from_question_paragraph/train-*
- split: validation
path: quartz_use_info_from_question_paragraph/validation-*
- split: test
path: quartz_use_info_from_question_paragraph/test-*
- config_name: quoref_Answer_Friend_Question
data_files:
- split: train
path: quoref_Answer_Friend_Question/train-*
- split: validation
path: quoref_Answer_Friend_Question/validation-*
- config_name: quoref_Answer_Question_Given_Context
data_files:
- split: train
path: quoref_Answer_Question_Given_Context/train-*
- split: validation
path: quoref_Answer_Question_Given_Context/validation-*
- config_name: quoref_Answer_Test
data_files:
- split: train
path: quoref_Answer_Test/train-*
- split: validation
path: quoref_Answer_Test/validation-*
- config_name: quoref_Context_Contains_Answer
data_files:
- split: train
path: quoref_Context_Contains_Answer/train-*
- split: validation
path: quoref_Context_Contains_Answer/validation-*
- config_name: quoref_Find_Answer
data_files:
- split: train
path: quoref_Find_Answer/train-*
- split: validation
path: quoref_Find_Answer/validation-*
- config_name: quoref_Found_Context_Online
data_files:
- split: train
path: quoref_Found_Context_Online/train-*
- split: validation
path: quoref_Found_Context_Online/validation-*
- config_name: quoref_Given_Context_Answer_Question
data_files:
- split: train
path: quoref_Given_Context_Answer_Question/train-*
- split: validation
path: quoref_Given_Context_Answer_Question/validation-*
- config_name: quoref_Guess_Answer
data_files:
- split: train
path: quoref_Guess_Answer/train-*
- split: validation
path: quoref_Guess_Answer/validation-*
- config_name: quoref_Guess_Title_For_Context
data_files:
- split: train
path: quoref_Guess_Title_For_Context/train-*
- split: validation
path: quoref_Guess_Title_For_Context/validation-*
- config_name: quoref_Read_And_Extract_
data_files:
- split: train
path: quoref_Read_And_Extract_/train-*
- split: validation
path: quoref_Read_And_Extract_/validation-*
- config_name: quoref_What_Is_The_Answer
data_files:
- split: train
path: quoref_What_Is_The_Answer/train-*
- split: validation
path: quoref_What_Is_The_Answer/validation-*
- config_name: race_high_Is_this_the_right_answer
data_files:
- split: train
path: race_high_Is_this_the_right_answer/train-*
- split: validation
path: race_high_Is_this_the_right_answer/validation-*
- split: test
path: race_high_Is_this_the_right_answer/test-*
- config_name: race_high_Read_the_article_and_answer_the_question_no_option_
data_files:
- split: train
path: race_high_Read_the_article_and_answer_the_question_no_option_/train-*
- split: validation
path: race_high_Read_the_article_and_answer_the_question_no_option_/validation-*
- split: test
path: race_high_Read_the_article_and_answer_the_question_no_option_/test-*
- config_name: race_high_Select_the_best_answer
data_files:
- split: train
path: race_high_Select_the_best_answer/train-*
- split: validation
path: race_high_Select_the_best_answer/validation-*
- split: test
path: race_high_Select_the_best_answer/test-*
- config_name: race_high_Select_the_best_answer_generate_span_
data_files:
- split: train
path: race_high_Select_the_best_answer_generate_span_/train-*
- split: validation
path: race_high_Select_the_best_answer_generate_span_/validation-*
- split: test
path: race_high_Select_the_best_answer_generate_span_/test-*
- config_name: race_high_Select_the_best_answer_no_instructions_
data_files:
- split: train
path: race_high_Select_the_best_answer_no_instructions_/train-*
- split: validation
path: race_high_Select_the_best_answer_no_instructions_/validation-*
- split: test
path: race_high_Select_the_best_answer_no_instructions_/test-*
- config_name: race_high_Taking_a_test
data_files:
- split: train
path: race_high_Taking_a_test/train-*
- split: validation
path: race_high_Taking_a_test/validation-*
- split: test
path: race_high_Taking_a_test/test-*
- config_name: race_high_Write_a_multi_choice_question_for_the_following_article
data_files:
- split: train
path: race_high_Write_a_multi_choice_question_for_the_following_article/train-*
- split: validation
path: race_high_Write_a_multi_choice_question_for_the_following_article/validation-*
- split: test
path: race_high_Write_a_multi_choice_question_for_the_following_article/test-*
- config_name: race_high_Write_a_multi_choice_question_options_given_
data_files:
- split: train
path: race_high_Write_a_multi_choice_question_options_given_/train-*
- split: validation
path: race_high_Write_a_multi_choice_question_options_given_/validation-*
- split: test
path: race_high_Write_a_multi_choice_question_options_given_/test-*
- config_name: race_middle_Is_this_the_right_answer
data_files:
- split: train
path: race_middle_Is_this_the_right_answer/train-*
- split: validation
path: race_middle_Is_this_the_right_answer/validation-*
- split: test
path: race_middle_Is_this_the_right_answer/test-*
- config_name: race_middle_Read_the_article_and_answer_the_question_no_option_
data_files:
- split: train
path: race_middle_Read_the_article_and_answer_the_question_no_option_/train-*
- split: validation
path: race_middle_Read_the_article_and_answer_the_question_no_option_/validation-*
- split: test
path: race_middle_Read_the_article_and_answer_the_question_no_option_/test-*
- config_name: race_middle_Select_the_best_answer
data_files:
- split: train
path: race_middle_Select_the_best_answer/train-*
- split: validation
path: race_middle_Select_the_best_answer/validation-*
- split: test
path: race_middle_Select_the_best_answer/test-*
- config_name: race_middle_Select_the_best_answer_generate_span_
data_files:
- split: train
path: race_middle_Select_the_best_answer_generate_span_/train-*
- split: validation
path: race_middle_Select_the_best_answer_generate_span_/validation-*
- split: test
path: race_middle_Select_the_best_answer_generate_span_/test-*
- config_name: race_middle_Select_the_best_answer_no_instructions_
data_files:
- split: train
path: race_middle_Select_the_best_answer_no_instructions_/train-*
- split: validation
path: race_middle_Select_the_best_answer_no_instructions_/validation-*
- split: test
path: race_middle_Select_the_best_answer_no_instructions_/test-*
- config_name: race_middle_Taking_a_test
data_files:
- split: train
path: race_middle_Taking_a_test/train-*
- split: validation
path: race_middle_Taking_a_test/validation-*
- split: test
path: race_middle_Taking_a_test/test-*
- config_name: race_middle_Write_a_multi_choice_question_for_the_following_article
data_files:
- split: train
path: race_middle_Write_a_multi_choice_question_for_the_following_article/train-*
- split: validation
path: race_middle_Write_a_multi_choice_question_for_the_following_article/validation-*
- split: test
path: race_middle_Write_a_multi_choice_question_for_the_following_article/test-*
- config_name: race_middle_Write_a_multi_choice_question_options_given_
data_files:
- split: train
path: race_middle_Write_a_multi_choice_question_options_given_/train-*
- split: validation
path: race_middle_Write_a_multi_choice_question_options_given_/validation-*
- split: test
path: race_middle_Write_a_multi_choice_question_options_given_/test-*
- config_name: ropes_background_new_situation_answer
data_files:
- split: train
path: ropes_background_new_situation_answer/train-*
- split: validation
path: ropes_background_new_situation_answer/validation-*
- config_name: ropes_background_situation_middle
data_files:
- split: train
path: ropes_background_situation_middle/train-*
- split: validation
path: ropes_background_situation_middle/validation-*
- config_name: ropes_given_background_situation
data_files:
- split: train
path: ropes_given_background_situation/train-*
- split: validation
path: ropes_given_background_situation/validation-*
- config_name: ropes_new_situation_background_answer
data_files:
- split: train
path: ropes_new_situation_background_answer/train-*
- split: validation
path: ropes_new_situation_background_answer/validation-*
- config_name: ropes_plain_background_situation
data_files:
- split: train
path: ropes_plain_background_situation/train-*
- split: validation
path: ropes_plain_background_situation/validation-*
- config_name: ropes_plain_bottom_hint
data_files:
- split: train
path: ropes_plain_bottom_hint/train-*
- split: validation
path: ropes_plain_bottom_hint/validation-*
- config_name: ropes_plain_no_background
data_files:
- split: train
path: ropes_plain_no_background/train-*
- split: validation
path: ropes_plain_no_background/validation-*
- config_name: ropes_prompt_beginning
data_files:
- split: train
path: ropes_prompt_beginning/train-*
- split: validation
path: ropes_prompt_beginning/validation-*
- config_name: ropes_prompt_bottom_hint_beginning
data_files:
- split: train
path: ropes_prompt_bottom_hint_beginning/train-*
- split: validation
path: ropes_prompt_bottom_hint_beginning/validation-*
- config_name: ropes_prompt_bottom_no_hint
data_files:
- split: train
path: ropes_prompt_bottom_no_hint/train-*
- split: validation
path: ropes_prompt_bottom_no_hint/validation-*
- config_name: ropes_prompt_mix
data_files:
- split: train
path: ropes_prompt_mix/train-*
- split: validation
path: ropes_prompt_mix/validation-*
- config_name: ropes_read_background_situation
data_files:
- split: train
path: ropes_read_background_situation/train-*
- split: validation
path: ropes_read_background_situation/validation-*
- config_name: rotten_tomatoes_Movie_Expressed_Sentiment
data_files:
- split: train
path: rotten_tomatoes_Movie_Expressed_Sentiment/train-*
- split: validation
path: rotten_tomatoes_Movie_Expressed_Sentiment/validation-*
- split: test
path: rotten_tomatoes_Movie_Expressed_Sentiment/test-*
- config_name: rotten_tomatoes_Movie_Expressed_Sentiment_2
data_files:
- split: train
path: rotten_tomatoes_Movie_Expressed_Sentiment_2/train-*
- split: validation
path: rotten_tomatoes_Movie_Expressed_Sentiment_2/validation-*
- split: test
path: rotten_tomatoes_Movie_Expressed_Sentiment_2/test-*
- config_name: rotten_tomatoes_Reviewer_Enjoyment
data_files:
- split: train
path: rotten_tomatoes_Reviewer_Enjoyment/train-*
- split: validation
path: rotten_tomatoes_Reviewer_Enjoyment/validation-*
- split: test
path: rotten_tomatoes_Reviewer_Enjoyment/test-*
- config_name: rotten_tomatoes_Reviewer_Enjoyment_Yes_No
data_files:
- split: train
path: rotten_tomatoes_Reviewer_Enjoyment_Yes_No/train-*
- split: validation
path: rotten_tomatoes_Reviewer_Enjoyment_Yes_No/validation-*
- split: test
path: rotten_tomatoes_Reviewer_Enjoyment_Yes_No/test-*
- config_name: rotten_tomatoes_Reviewer_Expressed_Sentiment
data_files:
- split: train
path: rotten_tomatoes_Reviewer_Expressed_Sentiment/train-*
- split: validation
path: rotten_tomatoes_Reviewer_Expressed_Sentiment/validation-*
- split: test
path: rotten_tomatoes_Reviewer_Expressed_Sentiment/test-*
- config_name: rotten_tomatoes_Reviewer_Opinion_bad_good_choices
data_files:
- split: train
path: rotten_tomatoes_Reviewer_Opinion_bad_good_choices/train-*
- split: validation
path: rotten_tomatoes_Reviewer_Opinion_bad_good_choices/validation-*
- split: test
path: rotten_tomatoes_Reviewer_Opinion_bad_good_choices/test-*
- config_name: rotten_tomatoes_Reviewer_Sentiment_Feeling
data_files:
- split: train
path: rotten_tomatoes_Reviewer_Sentiment_Feeling/train-*
- split: validation
path: rotten_tomatoes_Reviewer_Sentiment_Feeling/validation-*
- split: test
path: rotten_tomatoes_Reviewer_Sentiment_Feeling/test-*
- config_name: rotten_tomatoes_Sentiment_with_choices_
data_files:
- split: train
path: rotten_tomatoes_Sentiment_with_choices_/train-*
- split: validation
path: rotten_tomatoes_Sentiment_with_choices_/validation-*
- split: test
path: rotten_tomatoes_Sentiment_with_choices_/test-*
- config_name: rotten_tomatoes_Text_Expressed_Sentiment
data_files:
- split: train
path: rotten_tomatoes_Text_Expressed_Sentiment/train-*
- split: validation
path: rotten_tomatoes_Text_Expressed_Sentiment/validation-*
- split: test
path: rotten_tomatoes_Text_Expressed_Sentiment/test-*
- config_name: rotten_tomatoes_Writer_Expressed_Sentiment
data_files:
- split: train
path: rotten_tomatoes_Writer_Expressed_Sentiment/train-*
- split: validation
path: rotten_tomatoes_Writer_Expressed_Sentiment/validation-*
- split: test
path: rotten_tomatoes_Writer_Expressed_Sentiment/test-*
- config_name: samsum_Generate_a_summary_for_this_dialogue
data_files:
- split: train
path: samsum_Generate_a_summary_for_this_dialogue/train-*
- split: validation
path: samsum_Generate_a_summary_for_this_dialogue/validation-*
- split: test
path: samsum_Generate_a_summary_for_this_dialogue/test-*
- config_name: samsum_Given_the_above_dialogue_write_a_summary
data_files:
- split: train
path: samsum_Given_the_above_dialogue_write_a_summary/train-*
- split: validation
path: samsum_Given_the_above_dialogue_write_a_summary/validation-*
- split: test
path: samsum_Given_the_above_dialogue_write_a_summary/test-*
- config_name: samsum_Sum_up_the_following_dialogue
data_files:
- split: train
path: samsum_Sum_up_the_following_dialogue/train-*
- split: validation
path: samsum_Sum_up_the_following_dialogue/validation-*
- split: test
path: samsum_Sum_up_the_following_dialogue/test-*
- config_name: samsum_Summarize_
data_files:
- split: train
path: samsum_Summarize_/train-*
- split: validation
path: samsum_Summarize_/validation-*
- split: test
path: samsum_Summarize_/test-*
- config_name: samsum_Summarize_this_dialogue_
data_files:
- split: train
path: samsum_Summarize_this_dialogue_/train-*
- split: validation
path: samsum_Summarize_this_dialogue_/validation-*
- split: test
path: samsum_Summarize_this_dialogue_/test-*
- config_name: samsum_To_sum_up_this_dialog
data_files:
- split: train
path: samsum_To_sum_up_this_dialog/train-*
- split: validation
path: samsum_To_sum_up_this_dialog/validation-*
- split: test
path: samsum_To_sum_up_this_dialog/test-*
- config_name: samsum_Write_a_dialogue_that_match_this_summary
data_files:
- split: train
path: samsum_Write_a_dialogue_that_match_this_summary/train-*
- split: validation
path: samsum_Write_a_dialogue_that_match_this_summary/validation-*
- split: test
path: samsum_Write_a_dialogue_that_match_this_summary/test-*
- config_name: sciq_Direct_Question
data_files:
- split: train
path: sciq_Direct_Question/train-*
- split: validation
path: sciq_Direct_Question/validation-*
- split: test
path: sciq_Direct_Question/test-*
- config_name: sciq_Direct_Question_Closed_Book_
data_files:
- split: train
path: sciq_Direct_Question_Closed_Book_/train-*
- split: validation
path: sciq_Direct_Question_Closed_Book_/validation-*
- split: test
path: sciq_Direct_Question_Closed_Book_/test-*
- config_name: sciq_Multiple_Choice
data_files:
- split: train
path: sciq_Multiple_Choice/train-*
- split: validation
path: sciq_Multiple_Choice/validation-*
- split: test
path: sciq_Multiple_Choice/test-*
- config_name: sciq_Multiple_Choice_Closed_Book_
data_files:
- split: train
path: sciq_Multiple_Choice_Closed_Book_/train-*
- split: validation
path: sciq_Multiple_Choice_Closed_Book_/validation-*
- split: test
path: sciq_Multiple_Choice_Closed_Book_/test-*
- config_name: sciq_Multiple_Choice_Question_First
data_files:
- split: train
path: sciq_Multiple_Choice_Question_First/train-*
- split: validation
path: sciq_Multiple_Choice_Question_First/validation-*
- split: test
path: sciq_Multiple_Choice_Question_First/test-*
- config_name: social_i_qa_Check_if_a_random_answer_is_valid_or_not
data_files:
- split: train
path: social_i_qa_Check_if_a_random_answer_is_valid_or_not/train-*
- split: validation
path: social_i_qa_Check_if_a_random_answer_is_valid_or_not/validation-*
- config_name: social_i_qa_Generate_answer
data_files:
- split: train
path: social_i_qa_Generate_answer/train-*
- split: validation
path: social_i_qa_Generate_answer/validation-*
- config_name: social_i_qa_Generate_the_question_from_the_answer
data_files:
- split: train
path: social_i_qa_Generate_the_question_from_the_answer/train-*
- split: validation
path: social_i_qa_Generate_the_question_from_the_answer/validation-*
- config_name: social_i_qa_I_was_wondering
data_files:
- split: train
path: social_i_qa_I_was_wondering/train-*
- split: validation
path: social_i_qa_I_was_wondering/validation-*
- config_name: social_i_qa_Show_choices_and_generate_answer
data_files:
- split: train
path: social_i_qa_Show_choices_and_generate_answer/train-*
- split: validation
path: social_i_qa_Show_choices_and_generate_answer/validation-*
- config_name: social_i_qa_Show_choices_and_generate_index
data_files:
- split: train
path: social_i_qa_Show_choices_and_generate_index/train-*
- split: validation
path: social_i_qa_Show_choices_and_generate_index/validation-*
- config_name: squad_v2_Jeopardy_with_Context
data_files:
- split: train
path: squad_v2_Jeopardy_with_Context/train-*
- split: validation
path: squad_v2_Jeopardy_with_Context/validation-*
- config_name: squad_v2_Jeopardy_without_Context
data_files:
- split: train
path: squad_v2_Jeopardy_without_Context/train-*
- split: validation
path: squad_v2_Jeopardy_without_Context/validation-*
- config_name: squad_v2_Questions_with_Context
data_files:
- split: train
path: squad_v2_Questions_with_Context/train-*
- split: validation
path: squad_v2_Questions_with_Context/validation-*
- config_name: squad_v2_Questions_with_Context_Without_Prompt_Keywords
data_files:
- split: train
path: squad_v2_Questions_with_Context_Without_Prompt_Keywords/train-*
- split: validation
path: squad_v2_Questions_with_Context_Without_Prompt_Keywords/validation-*
- config_name: squad_v2_Questions_with_Context_Without_Prompt_Keywords_unanswerable
data_files:
- split: train
path: squad_v2_Questions_with_Context_Without_Prompt_Keywords_unanswerable/train-*
- split: validation
path: squad_v2_Questions_with_Context_Without_Prompt_Keywords_unanswerable/validation-*
- config_name: squad_v2_Questions_with_Context_unanswerable
data_files:
- split: train
path: squad_v2_Questions_with_Context_unanswerable/train-*
- split: validation
path: squad_v2_Questions_with_Context_unanswerable/validation-*
- config_name: squad_v2_Topic_Prediction_Context
data_files:
- split: train
path: squad_v2_Topic_Prediction_Context/train-*
- split: validation
path: squad_v2_Topic_Prediction_Context/validation-*
- config_name: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options
data_files:
- split: train
path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options/train-*
- split: validation
path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options/validation-*
- config_name: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options_placed_in_the_end
data_files:
- split: train
path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options_placed_in_the_end/train-*
- split: validation
path: squad_v2_Topic_Prediction_Context_with_randomized_prompt_options_placed_in_the_end/validation-*
- config_name: squad_v2_Topic_Prediction_Question_and_Answer_Pair
data_files:
- split: train
path: squad_v2_Topic_Prediction_Question_and_Answer_Pair/train-*
- split: validation
path: squad_v2_Topic_Prediction_Question_and_Answer_Pair/validation-*
- config_name: squad_v2_Trivia
data_files:
- split: train
path: squad_v2_Trivia/train-*
- split: validation
path: squad_v2_Trivia/validation-*
- config_name: squad_v2_Unanwerable_question
data_files:
- split: train
path: squad_v2_Unanwerable_question/train-*
- split: validation
path: squad_v2_Unanwerable_question/validation-*
- config_name: super_glue_boolq_GPT_3_Style
data_files:
- split: train
path: super_glue_boolq_GPT_3_Style/train-*
- split: validation
path: super_glue_boolq_GPT_3_Style/validation-*
- split: test
path: super_glue_boolq_GPT_3_Style/test-*
- config_name: super_glue_boolq_I_wonder_
data_files:
- split: train
path: super_glue_boolq_I_wonder_/train-*
- split: validation
path: super_glue_boolq_I_wonder_/validation-*
- split: test
path: super_glue_boolq_I_wonder_/test-*
- config_name: super_glue_boolq_after_reading
data_files:
- split: train
path: super_glue_boolq_after_reading/train-*
- split: validation
path: super_glue_boolq_after_reading/validation-*
- split: test
path: super_glue_boolq_after_reading/test-*
- config_name: super_glue_boolq_based_on_the_following_passage
data_files:
- split: train
path: super_glue_boolq_based_on_the_following_passage/train-*
- split: validation
path: super_glue_boolq_based_on_the_following_passage/validation-*
- split: test
path: super_glue_boolq_based_on_the_following_passage/test-*
- config_name: super_glue_boolq_based_on_the_previous_passage
data_files:
- split: train
path: super_glue_boolq_based_on_the_previous_passage/train-*
- split: validation
path: super_glue_boolq_based_on_the_previous_passage/validation-*
- split: test
path: super_glue_boolq_based_on_the_previous_passage/test-*
- config_name: super_glue_boolq_could_you_tell_me_
data_files:
- split: train
path: super_glue_boolq_could_you_tell_me_/train-*
- split: validation
path: super_glue_boolq_could_you_tell_me_/validation-*
- split: test
path: super_glue_boolq_could_you_tell_me_/test-*
- config_name: super_glue_boolq_exam
data_files:
- split: train
path: super_glue_boolq_exam/train-*
- split: validation
path: super_glue_boolq_exam/validation-*
- split: test
path: super_glue_boolq_exam/test-*
- config_name: super_glue_boolq_exercise
data_files:
- split: train
path: super_glue_boolq_exercise/train-*
- split: validation
path: super_glue_boolq_exercise/validation-*
- split: test
path: super_glue_boolq_exercise/test-*
- config_name: super_glue_boolq_valid_binary
data_files:
- split: train
path: super_glue_boolq_valid_binary/train-*
- split: validation
path: super_glue_boolq_valid_binary/validation-*
- split: test
path: super_glue_boolq_valid_binary/test-*
- config_name: super_glue_boolq_yes_no_question
data_files:
- split: train
path: super_glue_boolq_yes_no_question/train-*
- split: validation
path: super_glue_boolq_yes_no_question/validation-*
- split: test
path: super_glue_boolq_yes_no_question/test-*
- config_name: super_glue_cb_GPT_3_style
data_files:
- split: train
path: super_glue_cb_GPT_3_style/train-*
- split: validation
path: super_glue_cb_GPT_3_style/validation-*
- split: test
path: super_glue_cb_GPT_3_style/test-*
- config_name: super_glue_cb_GPT_3_style_score_eval
data_files:
- split: train
path: super_glue_cb_GPT_3_style_score_eval/train-*
- split: validation
path: super_glue_cb_GPT_3_style_score_eval/validation-*
- split: test
path: super_glue_cb_GPT_3_style_score_eval/test-*
- config_name: super_glue_cb_MNLI_crowdsource
data_files:
- split: train
path: super_glue_cb_MNLI_crowdsource/train-*
- split: validation
path: super_glue_cb_MNLI_crowdsource/validation-*
- split: test
path: super_glue_cb_MNLI_crowdsource/test-*
- config_name: super_glue_cb_MNLI_crowdsource_score_eval
data_files:
- split: train
path: super_glue_cb_MNLI_crowdsource_score_eval/train-*
- split: validation
path: super_glue_cb_MNLI_crowdsource_score_eval/validation-*
- split: test
path: super_glue_cb_MNLI_crowdsource_score_eval/test-*
- config_name: super_glue_cb_always_sometimes_never
data_files:
- split: train
path: super_glue_cb_always_sometimes_never/train-*
- split: validation
path: super_glue_cb_always_sometimes_never/validation-*
- split: test
path: super_glue_cb_always_sometimes_never/test-*
- config_name: super_glue_cb_always_sometimes_never_score_eval
data_files:
- split: train
path: super_glue_cb_always_sometimes_never_score_eval/train-*
- split: validation
path: super_glue_cb_always_sometimes_never_score_eval/validation-*
- split: test
path: super_glue_cb_always_sometimes_never_score_eval/test-*
- config_name: super_glue_cb_based_on_the_previous_passage
data_files:
- split: train
path: super_glue_cb_based_on_the_previous_passage/train-*
- split: validation
path: super_glue_cb_based_on_the_previous_passage/validation-*
- split: test
path: super_glue_cb_based_on_the_previous_passage/test-*
- config_name: super_glue_cb_based_on_the_previous_passage_score_eval
data_files:
- split: train
path: super_glue_cb_based_on_the_previous_passage_score_eval/train-*
- split: validation
path: super_glue_cb_based_on_the_previous_passage_score_eval/validation-*
- split: test
path: super_glue_cb_based_on_the_previous_passage_score_eval/test-*
- config_name: super_glue_cb_can_we_infer
data_files:
- split: train
path: super_glue_cb_can_we_infer/train-*
- split: validation
path: super_glue_cb_can_we_infer/validation-*
- split: test
path: super_glue_cb_can_we_infer/test-*
- config_name: super_glue_cb_can_we_infer_score_eval
data_files:
- split: train
path: super_glue_cb_can_we_infer_score_eval/train-*
- split: validation
path: super_glue_cb_can_we_infer_score_eval/validation-*
- split: test
path: super_glue_cb_can_we_infer_score_eval/test-*
- config_name: super_glue_cb_claim_true_false_inconclusive
data_files:
- split: train
path: super_glue_cb_claim_true_false_inconclusive/train-*
- split: validation
path: super_glue_cb_claim_true_false_inconclusive/validation-*
- split: test
path: super_glue_cb_claim_true_false_inconclusive/test-*
- config_name: super_glue_cb_claim_true_false_inconclusive_score_eval
data_files:
- split: train
path: super_glue_cb_claim_true_false_inconclusive_score_eval/train-*
- split: validation
path: super_glue_cb_claim_true_false_inconclusive_score_eval/validation-*
- split: test
path: super_glue_cb_claim_true_false_inconclusive_score_eval/test-*
- config_name: super_glue_cb_consider_always_sometimes_never
data_files:
- split: train
path: super_glue_cb_consider_always_sometimes_never/train-*
- split: validation
path: super_glue_cb_consider_always_sometimes_never/validation-*
- split: test
path: super_glue_cb_consider_always_sometimes_never/test-*
- config_name: super_glue_cb_consider_always_sometimes_never_score_eval
data_files:
- split: train
path: super_glue_cb_consider_always_sometimes_never_score_eval/train-*
- split: validation
path: super_glue_cb_consider_always_sometimes_never_score_eval/validation-*
- split: test
path: super_glue_cb_consider_always_sometimes_never_score_eval/test-*
- config_name: super_glue_cb_does_it_follow_that
data_files:
- split: train
path: super_glue_cb_does_it_follow_that/train-*
- split: validation
path: super_glue_cb_does_it_follow_that/validation-*
- split: test
path: super_glue_cb_does_it_follow_that/test-*
- config_name: super_glue_cb_does_it_follow_that_score_eval
data_files:
- split: train
path: super_glue_cb_does_it_follow_that_score_eval/train-*
- split: validation
path: super_glue_cb_does_it_follow_that_score_eval/validation-*
- split: test
path: super_glue_cb_does_it_follow_that_score_eval/test-*
- config_name: super_glue_cb_does_this_imply
data_files:
- split: train
path: super_glue_cb_does_this_imply/train-*
- split: validation
path: super_glue_cb_does_this_imply/validation-*
- split: test
path: super_glue_cb_does_this_imply/test-*
- config_name: super_glue_cb_does_this_imply_score_eval
data_files:
- split: train
path: super_glue_cb_does_this_imply_score_eval/train-*
- split: validation
path: super_glue_cb_does_this_imply_score_eval/validation-*
- split: test
path: super_glue_cb_does_this_imply_score_eval/test-*
- config_name: super_glue_cb_guaranteed_possible_impossible
data_files:
- split: train
path: super_glue_cb_guaranteed_possible_impossible/train-*
- split: validation
path: super_glue_cb_guaranteed_possible_impossible/validation-*
- split: test
path: super_glue_cb_guaranteed_possible_impossible/test-*
- config_name: super_glue_cb_guaranteed_possible_impossible_score_eval
data_files:
- split: train
path: super_glue_cb_guaranteed_possible_impossible_score_eval/train-*
- split: validation
path: super_glue_cb_guaranteed_possible_impossible_score_eval/validation-*
- split: test
path: super_glue_cb_guaranteed_possible_impossible_score_eval/test-*
- config_name: super_glue_cb_guaranteed_true
data_files:
- split: train
path: super_glue_cb_guaranteed_true/train-*
- split: validation
path: super_glue_cb_guaranteed_true/validation-*
- split: test
path: super_glue_cb_guaranteed_true/test-*
- config_name: super_glue_cb_guaranteed_true_score_eval
data_files:
- split: train
path: super_glue_cb_guaranteed_true_score_eval/train-*
- split: validation
path: super_glue_cb_guaranteed_true_score_eval/validation-*
- split: test
path: super_glue_cb_guaranteed_true_score_eval/test-*
- config_name: super_glue_cb_justified_in_saying
data_files:
- split: train
path: super_glue_cb_justified_in_saying/train-*
- split: validation
path: super_glue_cb_justified_in_saying/validation-*
- split: test
path: super_glue_cb_justified_in_saying/test-*
- config_name: super_glue_cb_justified_in_saying_score_eval
data_files:
- split: train
path: super_glue_cb_justified_in_saying_score_eval/train-*
- split: validation
path: super_glue_cb_justified_in_saying_score_eval/validation-*
- split: test
path: super_glue_cb_justified_in_saying_score_eval/test-*
- config_name: super_glue_cb_must_be_true
data_files:
- split: train
path: super_glue_cb_must_be_true/train-*
- split: validation
path: super_glue_cb_must_be_true/validation-*
- split: test
path: super_glue_cb_must_be_true/test-*
- config_name: super_glue_cb_must_be_true_score_eval
data_files:
- split: train
path: super_glue_cb_must_be_true_score_eval/train-*
- split: validation
path: super_glue_cb_must_be_true_score_eval/validation-*
- split: test
path: super_glue_cb_must_be_true_score_eval/test-*
- config_name: super_glue_cb_should_assume
data_files:
- split: train
path: super_glue_cb_should_assume/train-*
- split: validation
path: super_glue_cb_should_assume/validation-*
- split: test
path: super_glue_cb_should_assume/test-*
- config_name: super_glue_cb_should_assume_score_eval
data_files:
- split: train
path: super_glue_cb_should_assume_score_eval/train-*
- split: validation
path: super_glue_cb_should_assume_score_eval/validation-*
- split: test
path: super_glue_cb_should_assume_score_eval/test-*
- config_name: super_glue_cb_take_the_following_as_truth
data_files:
- split: train
path: super_glue_cb_take_the_following_as_truth/train-*
- split: validation
path: super_glue_cb_take_the_following_as_truth/validation-*
- split: test
path: super_glue_cb_take_the_following_as_truth/test-*
- config_name: super_glue_cb_take_the_following_as_truth_score_eval
data_files:
- split: train
path: super_glue_cb_take_the_following_as_truth_score_eval/train-*
- split: validation
path: super_glue_cb_take_the_following_as_truth_score_eval/validation-*
- split: test
path: super_glue_cb_take_the_following_as_truth_score_eval/test-*
- config_name: super_glue_copa_C1_or_C2_premise_so_because_
data_files:
- split: train
path: super_glue_copa_C1_or_C2_premise_so_because_/train-*
- split: validation
path: super_glue_copa_C1_or_C2_premise_so_because_/validation-*
- split: test
path: super_glue_copa_C1_or_C2_premise_so_because_/test-*
- config_name: super_glue_copa_C1_or_C2_premise_so_because__score_eval
data_files:
- split: train
path: super_glue_copa_C1_or_C2_premise_so_because__score_eval/train-*
- split: validation
path: super_glue_copa_C1_or_C2_premise_so_because__score_eval/validation-*
- split: test
path: super_glue_copa_C1_or_C2_premise_so_because__score_eval/test-*
- config_name: super_glue_copa__As_a_result_C1_or_C2_
data_files:
- split: train
path: super_glue_copa__As_a_result_C1_or_C2_/train-*
- split: validation
path: super_glue_copa__As_a_result_C1_or_C2_/validation-*
- split: test
path: super_glue_copa__As_a_result_C1_or_C2_/test-*
- config_name: super_glue_copa__As_a_result_C1_or_C2__score_eval
data_files:
- split: train
path: super_glue_copa__As_a_result_C1_or_C2__score_eval/train-*
- split: validation
path: super_glue_copa__As_a_result_C1_or_C2__score_eval/validation-*
- split: test
path: super_glue_copa__As_a_result_C1_or_C2__score_eval/test-*
- config_name: super_glue_copa__What_could_happen_next_C1_or_C2_
data_files:
- split: train
path: super_glue_copa__What_could_happen_next_C1_or_C2_/train-*
- split: validation
path: super_glue_copa__What_could_happen_next_C1_or_C2_/validation-*
- split: test
path: super_glue_copa__What_could_happen_next_C1_or_C2_/test-*
- config_name: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval
data_files:
- split: train
path: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval/train-*
- split: validation
path: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval/validation-*
- split: test
path: super_glue_copa__What_could_happen_next_C1_or_C2__score_eval/test-*
- config_name: super_glue_copa__which_may_be_caused_by
data_files:
- split: train
path: super_glue_copa__which_may_be_caused_by/train-*
- split: validation
path: super_glue_copa__which_may_be_caused_by/validation-*
- split: test
path: super_glue_copa__which_may_be_caused_by/test-*
- config_name: super_glue_copa__which_may_be_caused_by_score_eval
data_files:
- split: train
path: super_glue_copa__which_may_be_caused_by_score_eval/train-*
- split: validation
path: super_glue_copa__which_may_be_caused_by_score_eval/validation-*
- split: test
path: super_glue_copa__which_may_be_caused_by_score_eval/test-*
- config_name: super_glue_copa__why_C1_or_C2
data_files:
- split: train
path: super_glue_copa__why_C1_or_C2/train-*
- split: validation
path: super_glue_copa__why_C1_or_C2/validation-*
- split: test
path: super_glue_copa__why_C1_or_C2/test-*
- config_name: super_glue_copa__why_C1_or_C2_score_eval
data_files:
- split: train
path: super_glue_copa__why_C1_or_C2_score_eval/train-*
- split: validation
path: super_glue_copa__why_C1_or_C2_score_eval/validation-*
- split: test
path: super_glue_copa__why_C1_or_C2_score_eval/test-*
- config_name: super_glue_copa_best_option
data_files:
- split: train
path: super_glue_copa_best_option/train-*
- split: validation
path: super_glue_copa_best_option/validation-*
- split: test
path: super_glue_copa_best_option/test-*
- config_name: super_glue_copa_best_option_score_eval
data_files:
- split: train
path: super_glue_copa_best_option_score_eval/train-*
- split: validation
path: super_glue_copa_best_option_score_eval/validation-*
- split: test
path: super_glue_copa_best_option_score_eval/test-*
- config_name: super_glue_copa_cause_effect
data_files:
- split: train
path: super_glue_copa_cause_effect/train-*
- split: validation
path: super_glue_copa_cause_effect/validation-*
- split: test
path: super_glue_copa_cause_effect/test-*
- config_name: super_glue_copa_cause_effect_score_eval
data_files:
- split: train
path: super_glue_copa_cause_effect_score_eval/train-*
- split: validation
path: super_glue_copa_cause_effect_score_eval/validation-*
- split: test
path: super_glue_copa_cause_effect_score_eval/test-*
- config_name: super_glue_copa_choose
data_files:
- split: train
path: super_glue_copa_choose/train-*
- split: validation
path: super_glue_copa_choose/validation-*
- split: test
path: super_glue_copa_choose/test-*
- config_name: super_glue_copa_choose_score_eval
data_files:
- split: train
path: super_glue_copa_choose_score_eval/train-*
- split: validation
path: super_glue_copa_choose_score_eval/validation-*
- split: test
path: super_glue_copa_choose_score_eval/test-*
- config_name: super_glue_copa_exercise
data_files:
- split: train
path: super_glue_copa_exercise/train-*
- split: validation
path: super_glue_copa_exercise/validation-*
- split: test
path: super_glue_copa_exercise/test-*
- config_name: super_glue_copa_exercise_score_eval
data_files:
- split: train
path: super_glue_copa_exercise_score_eval/train-*
- split: validation
path: super_glue_copa_exercise_score_eval/validation-*
- split: test
path: super_glue_copa_exercise_score_eval/test-*
- config_name: super_glue_copa_i_am_hesitating
data_files:
- split: train
path: super_glue_copa_i_am_hesitating/train-*
- split: validation
path: super_glue_copa_i_am_hesitating/validation-*
- split: test
path: super_glue_copa_i_am_hesitating/test-*
- config_name: super_glue_copa_i_am_hesitating_score_eval
data_files:
- split: train
path: super_glue_copa_i_am_hesitating_score_eval/train-*
- split: validation
path: super_glue_copa_i_am_hesitating_score_eval/validation-*
- split: test
path: super_glue_copa_i_am_hesitating_score_eval/test-*
- config_name: super_glue_copa_more_likely
data_files:
- split: train
path: super_glue_copa_more_likely/train-*
- split: validation
path: super_glue_copa_more_likely/validation-*
- split: test
path: super_glue_copa_more_likely/test-*
- config_name: super_glue_copa_more_likely_score_eval
data_files:
- split: train
path: super_glue_copa_more_likely_score_eval/train-*
- split: validation
path: super_glue_copa_more_likely_score_eval/validation-*
- split: test
path: super_glue_copa_more_likely_score_eval/test-*
- config_name: super_glue_copa_plausible_alternatives
data_files:
- split: train
path: super_glue_copa_plausible_alternatives/train-*
- split: validation
path: super_glue_copa_plausible_alternatives/validation-*
- split: test
path: super_glue_copa_plausible_alternatives/test-*
- config_name: super_glue_copa_plausible_alternatives_score_eval
data_files:
- split: train
path: super_glue_copa_plausible_alternatives_score_eval/train-*
- split: validation
path: super_glue_copa_plausible_alternatives_score_eval/validation-*
- split: test
path: super_glue_copa_plausible_alternatives_score_eval/test-*
- config_name: super_glue_multirc_I_was_going_to_say_
data_files:
- split: train
path: super_glue_multirc_I_was_going_to_say_/train-*
- split: validation
path: super_glue_multirc_I_was_going_to_say_/validation-*
- split: test
path: super_glue_multirc_I_was_going_to_say_/test-*
- config_name: super_glue_multirc_Would_it_be_good_to_answer_
data_files:
- split: train
path: super_glue_multirc_Would_it_be_good_to_answer_/train-*
- split: validation
path: super_glue_multirc_Would_it_be_good_to_answer_/validation-*
- split: test
path: super_glue_multirc_Would_it_be_good_to_answer_/test-*
- config_name: super_glue_multirc_confirm
data_files:
- split: train
path: super_glue_multirc_confirm/train-*
- split: validation
path: super_glue_multirc_confirm/validation-*
- split: test
path: super_glue_multirc_confirm/test-*
- config_name: super_glue_multirc_correct
data_files:
- split: train
path: super_glue_multirc_correct/train-*
- split: validation
path: super_glue_multirc_correct/validation-*
- split: test
path: super_glue_multirc_correct/test-*
- config_name: super_glue_multirc_decide_valid
data_files:
- split: train
path: super_glue_multirc_decide_valid/train-*
- split: validation
path: super_glue_multirc_decide_valid/validation-*
- split: test
path: super_glue_multirc_decide_valid/test-*
- config_name: super_glue_multirc_found_this_answer
data_files:
- split: train
path: super_glue_multirc_found_this_answer/train-*
- split: validation
path: super_glue_multirc_found_this_answer/validation-*
- split: test
path: super_glue_multirc_found_this_answer/test-*
- config_name: super_glue_multirc_grading
data_files:
- split: train
path: super_glue_multirc_grading/train-*
- split: validation
path: super_glue_multirc_grading/validation-*
- split: test
path: super_glue_multirc_grading/test-*
- config_name: super_glue_multirc_is_a_correct_answer_
data_files:
- split: train
path: super_glue_multirc_is_a_correct_answer_/train-*
- split: validation
path: super_glue_multirc_is_a_correct_answer_/validation-*
- split: test
path: super_glue_multirc_is_a_correct_answer_/test-*
- config_name: super_glue_multirc_is_the_correct_answer_
data_files:
- split: train
path: super_glue_multirc_is_the_correct_answer_/train-*
- split: validation
path: super_glue_multirc_is_the_correct_answer_/validation-*
- split: test
path: super_glue_multirc_is_the_correct_answer_/test-*
- config_name: super_glue_multirc_paragraph_question_is_it_
data_files:
- split: train
path: super_glue_multirc_paragraph_question_is_it_/train-*
- split: validation
path: super_glue_multirc_paragraph_question_is_it_/validation-*
- split: test
path: super_glue_multirc_paragraph_question_is_it_/test-*
- config_name: super_glue_record_Add_sentence_after_after_continuation_choices_
data_files:
- split: train
path: super_glue_record_Add_sentence_after_after_continuation_choices_/train-*
- split: validation
path: super_glue_record_Add_sentence_after_after_continuation_choices_/validation-*
- split: test
path: super_glue_record_Add_sentence_after_after_continuation_choices_/test-*
- config_name: super_glue_record_Add_sentence_after_continuation_choices_
data_files:
- split: train
path: super_glue_record_Add_sentence_after_continuation_choices_/train-*
- split: validation
path: super_glue_record_Add_sentence_after_continuation_choices_/validation-*
- split: test
path: super_glue_record_Add_sentence_after_continuation_choices_/test-*
- config_name: super_glue_record_Can_you_figure_out_
data_files:
- split: train
path: super_glue_record_Can_you_figure_out_/train-*
- split: validation
path: super_glue_record_Can_you_figure_out_/validation-*
- split: test
path: super_glue_record_Can_you_figure_out_/test-*
- config_name: super_glue_record_GPT_3_style_continuation_choices_
data_files:
- split: train
path: super_glue_record_GPT_3_style_continuation_choices_/train-*
- split: validation
path: super_glue_record_GPT_3_style_continuation_choices_/validation-*
- split: test
path: super_glue_record_GPT_3_style_continuation_choices_/test-*
- config_name: super_glue_record_GPT_3_style_summary_only_continuation_choices_
data_files:
- split: train
path: super_glue_record_GPT_3_style_summary_only_continuation_choices_/train-*
- split: validation
path: super_glue_record_GPT_3_style_summary_only_continuation_choices_/validation-*
- split: test
path: super_glue_record_GPT_3_style_summary_only_continuation_choices_/test-*
- config_name: super_glue_record_GPT_3_style_with_labels_continuation_choices_
data_files:
- split: train
path: super_glue_record_GPT_3_style_with_labels_continuation_choices_/train-*
- split: validation
path: super_glue_record_GPT_3_style_with_labels_continuation_choices_/validation-*
- split: test
path: super_glue_record_GPT_3_style_with_labels_continuation_choices_/test-*
- config_name: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_
data_files:
- split: train
path: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_/train-*
- split: validation
path: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_/validation-*
- split: test
path: super_glue_record_GPT_3_style_with_labels_without_hyphens_continuation_choices_/test-*
- config_name: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_
data_files:
- split: train
path: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_/train-*
- split: validation
path: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_/validation-*
- split: test
path: super_glue_record_GPT_3_style_without_hyphens_continuation_choices_/test-*
- config_name: super_glue_record_In_the_question_above_the_placeholder_stands_for
data_files:
- split: train
path: super_glue_record_In_the_question_above_the_placeholder_stands_for/train-*
- split: validation
path: super_glue_record_In_the_question_above_the_placeholder_stands_for/validation-*
- split: test
path: super_glue_record_In_the_question_above_the_placeholder_stands_for/test-*
- config_name: super_glue_record_New_highlight_continuation_choices_
data_files:
- split: train
path: super_glue_record_New_highlight_continuation_choices_/train-*
- split: validation
path: super_glue_record_New_highlight_continuation_choices_/validation-*
- split: test
path: super_glue_record_New_highlight_continuation_choices_/test-*
- config_name: super_glue_record_News_article_continuation_choices_
data_files:
- split: train
path: super_glue_record_News_article_continuation_choices_/train-*
- split: validation
path: super_glue_record_News_article_continuation_choices_/validation-*
- split: test
path: super_glue_record_News_article_continuation_choices_/test-*
- config_name: super_glue_record_Summary_first_continuation_choices_
data_files:
- split: train
path: super_glue_record_Summary_first_continuation_choices_/train-*
- split: validation
path: super_glue_record_Summary_first_continuation_choices_/validation-*
- split: test
path: super_glue_record_Summary_first_continuation_choices_/test-*
- config_name: super_glue_record_What_could_the_placeholder_be_
data_files:
- split: train
path: super_glue_record_What_could_the_placeholder_be_/train-*
- split: validation
path: super_glue_record_What_could_the_placeholder_be_/validation-*
- split: test
path: super_glue_record_What_could_the_placeholder_be_/test-*
- config_name: super_glue_record_Which_one_is_the_placeholder_
data_files:
- split: train
path: super_glue_record_Which_one_is_the_placeholder_/train-*
- split: validation
path: super_glue_record_Which_one_is_the_placeholder_/validation-*
- split: test
path: super_glue_record_Which_one_is_the_placeholder_/test-*
- config_name: super_glue_record_choose_between
data_files:
- split: train
path: super_glue_record_choose_between/train-*
- split: validation
path: super_glue_record_choose_between/validation-*
- split: test
path: super_glue_record_choose_between/test-*
- config_name: super_glue_record_corrupted
data_files:
- split: train
path: super_glue_record_corrupted/train-*
- split: validation
path: super_glue_record_corrupted/validation-*
- split: test
path: super_glue_record_corrupted/test-*
- config_name: super_glue_record_exercise
data_files:
- split: train
path: super_glue_record_exercise/train-*
- split: validation
path: super_glue_record_exercise/validation-*
- split: test
path: super_glue_record_exercise/test-*
- config_name: super_glue_record_pick_one_option
data_files:
- split: train
path: super_glue_record_pick_one_option/train-*
- split: validation
path: super_glue_record_pick_one_option/validation-*
- split: test
path: super_glue_record_pick_one_option/test-*
- config_name: super_glue_record_the_placeholder_refers_to_
data_files:
- split: train
path: super_glue_record_the_placeholder_refers_to_/train-*
- split: validation
path: super_glue_record_the_placeholder_refers_to_/validation-*
- split: test
path: super_glue_record_the_placeholder_refers_to_/test-*
- config_name: super_glue_record_trying_to_decide
data_files:
- split: train
path: super_glue_record_trying_to_decide/train-*
- split: validation
path: super_glue_record_trying_to_decide/validation-*
- split: test
path: super_glue_record_trying_to_decide/test-*
- config_name: super_glue_rte_GPT_3_style
data_files:
- split: train
path: super_glue_rte_GPT_3_style/train-*
- split: validation
path: super_glue_rte_GPT_3_style/validation-*
- split: test
path: super_glue_rte_GPT_3_style/test-*
- config_name: super_glue_rte_GPT_3_style_score_eval
data_files:
- split: train
path: super_glue_rte_GPT_3_style_score_eval/train-*
- split: validation
path: super_glue_rte_GPT_3_style_score_eval/validation-*
- split: test
path: super_glue_rte_GPT_3_style_score_eval/test-*
- config_name: super_glue_rte_MNLI_crowdsource
data_files:
- split: train
path: super_glue_rte_MNLI_crowdsource/train-*
- split: validation
path: super_glue_rte_MNLI_crowdsource/validation-*
- split: test
path: super_glue_rte_MNLI_crowdsource/test-*
- config_name: super_glue_rte_MNLI_crowdsource_score_eval
data_files:
- split: train
path: super_glue_rte_MNLI_crowdsource_score_eval/train-*
- split: validation
path: super_glue_rte_MNLI_crowdsource_score_eval/validation-*
- split: test
path: super_glue_rte_MNLI_crowdsource_score_eval/test-*
- config_name: super_glue_rte_based_on_the_previous_passage
data_files:
- split: train
path: super_glue_rte_based_on_the_previous_passage/train-*
- split: validation
path: super_glue_rte_based_on_the_previous_passage/validation-*
- split: test
path: super_glue_rte_based_on_the_previous_passage/test-*
- config_name: super_glue_rte_based_on_the_previous_passage_score_eval
data_files:
- split: train
path: super_glue_rte_based_on_the_previous_passage_score_eval/train-*
- split: validation
path: super_glue_rte_based_on_the_previous_passage_score_eval/validation-*
- split: test
path: super_glue_rte_based_on_the_previous_passage_score_eval/test-*
- config_name: super_glue_rte_can_we_infer
data_files:
- split: train
path: super_glue_rte_can_we_infer/train-*
- split: validation
path: super_glue_rte_can_we_infer/validation-*
- split: test
path: super_glue_rte_can_we_infer/test-*
- config_name: super_glue_rte_can_we_infer_score_eval
data_files:
- split: train
path: super_glue_rte_can_we_infer_score_eval/train-*
- split: validation
path: super_glue_rte_can_we_infer_score_eval/validation-*
- split: test
path: super_glue_rte_can_we_infer_score_eval/test-*
- config_name: super_glue_rte_does_it_follow_that
data_files:
- split: train
path: super_glue_rte_does_it_follow_that/train-*
- split: validation
path: super_glue_rte_does_it_follow_that/validation-*
- split: test
path: super_glue_rte_does_it_follow_that/test-*
- config_name: super_glue_rte_does_it_follow_that_score_eval
data_files:
- split: train
path: super_glue_rte_does_it_follow_that_score_eval/train-*
- split: validation
path: super_glue_rte_does_it_follow_that_score_eval/validation-*
- split: test
path: super_glue_rte_does_it_follow_that_score_eval/test-*
- config_name: super_glue_rte_does_this_imply
data_files:
- split: train
path: super_glue_rte_does_this_imply/train-*
- split: validation
path: super_glue_rte_does_this_imply/validation-*
- split: test
path: super_glue_rte_does_this_imply/test-*
- config_name: super_glue_rte_does_this_imply_score_eval
data_files:
- split: train
path: super_glue_rte_does_this_imply_score_eval/train-*
- split: validation
path: super_glue_rte_does_this_imply_score_eval/validation-*
- split: test
path: super_glue_rte_does_this_imply_score_eval/test-*
- config_name: super_glue_rte_guaranteed_true
data_files:
- split: train
path: super_glue_rte_guaranteed_true/train-*
- split: validation
path: super_glue_rte_guaranteed_true/validation-*
- split: test
path: super_glue_rte_guaranteed_true/test-*
- config_name: super_glue_rte_guaranteed_true_score_eval
data_files:
- split: train
path: super_glue_rte_guaranteed_true_score_eval/train-*
- split: validation
path: super_glue_rte_guaranteed_true_score_eval/validation-*
- split: test
path: super_glue_rte_guaranteed_true_score_eval/test-*
- config_name: super_glue_rte_justified_in_saying
data_files:
- split: train
path: super_glue_rte_justified_in_saying/train-*
- split: validation
path: super_glue_rte_justified_in_saying/validation-*
- split: test
path: super_glue_rte_justified_in_saying/test-*
- config_name: super_glue_rte_justified_in_saying_score_eval
data_files:
- split: train
path: super_glue_rte_justified_in_saying_score_eval/train-*
- split: validation
path: super_glue_rte_justified_in_saying_score_eval/validation-*
- split: test
path: super_glue_rte_justified_in_saying_score_eval/test-*
- config_name: super_glue_rte_must_be_true
data_files:
- split: train
path: super_glue_rte_must_be_true/train-*
- split: validation
path: super_glue_rte_must_be_true/validation-*
- split: test
path: super_glue_rte_must_be_true/test-*
- config_name: super_glue_rte_must_be_true_score_eval
data_files:
- split: train
path: super_glue_rte_must_be_true_score_eval/train-*
- split: validation
path: super_glue_rte_must_be_true_score_eval/validation-*
- split: test
path: super_glue_rte_must_be_true_score_eval/test-*
- config_name: super_glue_rte_should_assume
data_files:
- split: train
path: super_glue_rte_should_assume/train-*
- split: validation
path: super_glue_rte_should_assume/validation-*
- split: test
path: super_glue_rte_should_assume/test-*
- config_name: super_glue_rte_should_assume_score_eval
data_files:
- split: train
path: super_glue_rte_should_assume_score_eval/train-*
- split: validation
path: super_glue_rte_should_assume_score_eval/validation-*
- split: test
path: super_glue_rte_should_assume_score_eval/test-*
- config_name: super_glue_wic_GPT_3_prompt
data_files:
- split: train
path: super_glue_wic_GPT_3_prompt/train-*
- split: validation
path: super_glue_wic_GPT_3_prompt/validation-*
- split: test
path: super_glue_wic_GPT_3_prompt/test-*
- config_name: super_glue_wic_GPT_3_prompt_score_eval
data_files:
- split: train
path: super_glue_wic_GPT_3_prompt_score_eval/train-*
- split: validation
path: super_glue_wic_GPT_3_prompt_score_eval/validation-*
- split: test
path: super_glue_wic_GPT_3_prompt_score_eval/test-*
- config_name: super_glue_wic_GPT_3_prompt_with_label
data_files:
- split: train
path: super_glue_wic_GPT_3_prompt_with_label/train-*
- split: validation
path: super_glue_wic_GPT_3_prompt_with_label/validation-*
- split: test
path: super_glue_wic_GPT_3_prompt_with_label/test-*
- config_name: super_glue_wic_GPT_3_prompt_with_label_score_eval
data_files:
- split: train
path: super_glue_wic_GPT_3_prompt_with_label_score_eval/train-*
- split: validation
path: super_glue_wic_GPT_3_prompt_with_label_score_eval/validation-*
- split: test
path: super_glue_wic_GPT_3_prompt_with_label_score_eval/test-*
- config_name: super_glue_wic_affirmation_true_or_false
data_files:
- split: train
path: super_glue_wic_affirmation_true_or_false/train-*
- split: validation
path: super_glue_wic_affirmation_true_or_false/validation-*
- split: test
path: super_glue_wic_affirmation_true_or_false/test-*
- config_name: super_glue_wic_affirmation_true_or_false_score_eval
data_files:
- split: train
path: super_glue_wic_affirmation_true_or_false_score_eval/train-*
- split: validation
path: super_glue_wic_affirmation_true_or_false_score_eval/validation-*
- split: test
path: super_glue_wic_affirmation_true_or_false_score_eval/test-*
- config_name: super_glue_wic_grammar_homework
data_files:
- split: train
path: super_glue_wic_grammar_homework/train-*
- split: validation
path: super_glue_wic_grammar_homework/validation-*
- split: test
path: super_glue_wic_grammar_homework/test-*
- config_name: super_glue_wic_grammar_homework_score_eval
data_files:
- split: train
path: super_glue_wic_grammar_homework_score_eval/train-*
- split: validation
path: super_glue_wic_grammar_homework_score_eval/validation-*
- split: test
path: super_glue_wic_grammar_homework_score_eval/test-*
- config_name: super_glue_wic_polysemous
data_files:
- split: train
path: super_glue_wic_polysemous/train-*
- split: validation
path: super_glue_wic_polysemous/validation-*
- split: test
path: super_glue_wic_polysemous/test-*
- config_name: super_glue_wic_polysemous_score_eval
data_files:
- split: train
path: super_glue_wic_polysemous_score_eval/train-*
- split: validation
path: super_glue_wic_polysemous_score_eval/validation-*
- split: test
path: super_glue_wic_polysemous_score_eval/test-*
- config_name: super_glue_wic_question_context
data_files:
- split: train
path: super_glue_wic_question_context/train-*
- split: validation
path: super_glue_wic_question_context/validation-*
- split: test
path: super_glue_wic_question_context/test-*
- config_name: super_glue_wic_question_context_meaning
data_files:
- split: train
path: super_glue_wic_question_context_meaning/train-*
- split: validation
path: super_glue_wic_question_context_meaning/validation-*
- split: test
path: super_glue_wic_question_context_meaning/test-*
- config_name: super_glue_wic_question_context_meaning_score_eval
data_files:
- split: train
path: super_glue_wic_question_context_meaning_score_eval/train-*
- split: validation
path: super_glue_wic_question_context_meaning_score_eval/validation-*
- split: test
path: super_glue_wic_question_context_meaning_score_eval/test-*
- config_name: super_glue_wic_question_context_meaning_with_label
data_files:
- split: train
path: super_glue_wic_question_context_meaning_with_label/train-*
- split: validation
path: super_glue_wic_question_context_meaning_with_label/validation-*
- split: test
path: super_glue_wic_question_context_meaning_with_label/test-*
- config_name: super_glue_wic_question_context_meaning_with_label_score_eval
data_files:
- split: train
path: super_glue_wic_question_context_meaning_with_label_score_eval/train-*
- split: validation
path: super_glue_wic_question_context_meaning_with_label_score_eval/validation-*
- split: test
path: super_glue_wic_question_context_meaning_with_label_score_eval/test-*
- config_name: super_glue_wic_question_context_score_eval
data_files:
- split: train
path: super_glue_wic_question_context_score_eval/train-*
- split: validation
path: super_glue_wic_question_context_score_eval/validation-*
- split: test
path: super_glue_wic_question_context_score_eval/test-*
- config_name: super_glue_wic_same_sense
data_files:
- split: train
path: super_glue_wic_same_sense/train-*
- split: validation
path: super_glue_wic_same_sense/validation-*
- split: test
path: super_glue_wic_same_sense/test-*
- config_name: super_glue_wic_same_sense_score_eval
data_files:
- split: train
path: super_glue_wic_same_sense_score_eval/train-*
- split: validation
path: super_glue_wic_same_sense_score_eval/validation-*
- split: test
path: super_glue_wic_same_sense_score_eval/test-*
- config_name: super_glue_wic_similar_sense
data_files:
- split: train
path: super_glue_wic_similar_sense/train-*
- split: validation
path: super_glue_wic_similar_sense/validation-*
- split: test
path: super_glue_wic_similar_sense/test-*
- config_name: super_glue_wic_similar_sense_score_eval
data_files:
- split: train
path: super_glue_wic_similar_sense_score_eval/train-*
- split: validation
path: super_glue_wic_similar_sense_score_eval/validation-*
- split: test
path: super_glue_wic_similar_sense_score_eval/test-*
- config_name: super_glue_wsc.fixed_GPT_3_Style
data_files:
- split: train
path: super_glue_wsc.fixed_GPT_3_Style/train-*
- split: validation
path: super_glue_wsc.fixed_GPT_3_Style/validation-*
- split: test
path: super_glue_wsc.fixed_GPT_3_Style/test-*
- config_name: super_glue_wsc.fixed_GPT_3_Style_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_GPT_3_Style_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_GPT_3_Style_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_GPT_3_Style_score_eval/test-*
- config_name: super_glue_wsc.fixed_I_think_they_mean
data_files:
- split: train
path: super_glue_wsc.fixed_I_think_they_mean/train-*
- split: validation
path: super_glue_wsc.fixed_I_think_they_mean/validation-*
- split: test
path: super_glue_wsc.fixed_I_think_they_mean/test-*
- config_name: super_glue_wsc.fixed_I_think_they_mean_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_I_think_they_mean_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_I_think_they_mean_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_I_think_they_mean_score_eval/test-*
- config_name: super_glue_wsc.fixed_Who_or_what_is_are
data_files:
- split: train
path: super_glue_wsc.fixed_Who_or_what_is_are/train-*
- split: validation
path: super_glue_wsc.fixed_Who_or_what_is_are/validation-*
- split: test
path: super_glue_wsc.fixed_Who_or_what_is_are/test-*
- config_name: super_glue_wsc.fixed_Who_or_what_is_are_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_Who_or_what_is_are_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_Who_or_what_is_are_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_Who_or_what_is_are_score_eval/test-*
- config_name: super_glue_wsc.fixed_by_p_they_mean
data_files:
- split: train
path: super_glue_wsc.fixed_by_p_they_mean/train-*
- split: validation
path: super_glue_wsc.fixed_by_p_they_mean/validation-*
- split: test
path: super_glue_wsc.fixed_by_p_they_mean/test-*
- config_name: super_glue_wsc.fixed_by_p_they_mean_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_by_p_they_mean_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_by_p_they_mean_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_by_p_they_mean_score_eval/test-*
- config_name: super_glue_wsc.fixed_does_p_stand_for
data_files:
- split: train
path: super_glue_wsc.fixed_does_p_stand_for/train-*
- split: validation
path: super_glue_wsc.fixed_does_p_stand_for/validation-*
- split: test
path: super_glue_wsc.fixed_does_p_stand_for/test-*
- config_name: super_glue_wsc.fixed_does_p_stand_for_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_does_p_stand_for_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_does_p_stand_for_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_does_p_stand_for_score_eval/test-*
- config_name: super_glue_wsc.fixed_does_the_pronoun_refer_to
data_files:
- split: train
path: super_glue_wsc.fixed_does_the_pronoun_refer_to/train-*
- split: validation
path: super_glue_wsc.fixed_does_the_pronoun_refer_to/validation-*
- split: test
path: super_glue_wsc.fixed_does_the_pronoun_refer_to/test-*
- config_name: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_does_the_pronoun_refer_to_score_eval/test-*
- config_name: super_glue_wsc.fixed_in_other_words
data_files:
- split: train
path: super_glue_wsc.fixed_in_other_words/train-*
- split: validation
path: super_glue_wsc.fixed_in_other_words/validation-*
- split: test
path: super_glue_wsc.fixed_in_other_words/test-*
- config_name: super_glue_wsc.fixed_in_other_words_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_in_other_words_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_in_other_words_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_in_other_words_score_eval/test-*
- config_name: super_glue_wsc.fixed_p_is_are_r
data_files:
- split: train
path: super_glue_wsc.fixed_p_is_are_r/train-*
- split: validation
path: super_glue_wsc.fixed_p_is_are_r/validation-*
- split: test
path: super_glue_wsc.fixed_p_is_are_r/test-*
- config_name: super_glue_wsc.fixed_p_is_are_r_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_p_is_are_r_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_p_is_are_r_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_p_is_are_r_score_eval/test-*
- config_name: super_glue_wsc.fixed_replaced_with
data_files:
- split: train
path: super_glue_wsc.fixed_replaced_with/train-*
- split: validation
path: super_glue_wsc.fixed_replaced_with/validation-*
- split: test
path: super_glue_wsc.fixed_replaced_with/test-*
- config_name: super_glue_wsc.fixed_replaced_with_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_replaced_with_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_replaced_with_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_replaced_with_score_eval/test-*
- config_name: super_glue_wsc.fixed_the_pronoun_refers_to
data_files:
- split: train
path: super_glue_wsc.fixed_the_pronoun_refers_to/train-*
- split: validation
path: super_glue_wsc.fixed_the_pronoun_refers_to/validation-*
- split: test
path: super_glue_wsc.fixed_the_pronoun_refers_to/test-*
- config_name: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval
data_files:
- split: train
path: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval/train-*
- split: validation
path: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval/validation-*
- split: test
path: super_glue_wsc.fixed_the_pronoun_refers_to_score_eval/test-*
- config_name: trec_fine_grained_ABBR
data_files:
- split: train
path: trec_fine_grained_ABBR/train-*
- split: test
path: trec_fine_grained_ABBR/test-*
- config_name: trec_fine_grained_ABBR_context_first
data_files:
- split: train
path: trec_fine_grained_ABBR_context_first/train-*
- split: test
path: trec_fine_grained_ABBR_context_first/test-*
- config_name: trec_fine_grained_DESC
data_files:
- split: train
path: trec_fine_grained_DESC/train-*
- split: test
path: trec_fine_grained_DESC/test-*
- config_name: trec_fine_grained_DESC_context_first
data_files:
- split: train
path: trec_fine_grained_DESC_context_first/train-*
- split: test
path: trec_fine_grained_DESC_context_first/test-*
- config_name: trec_fine_grained_ENTY
data_files:
- split: train
path: trec_fine_grained_ENTY/train-*
- split: test
path: trec_fine_grained_ENTY/test-*
- config_name: trec_fine_grained_HUM
data_files:
- split: train
path: trec_fine_grained_HUM/train-*
- split: test
path: trec_fine_grained_HUM/test-*
- config_name: trec_fine_grained_HUM_context_first
data_files:
- split: train
path: trec_fine_grained_HUM_context_first/train-*
- split: test
path: trec_fine_grained_HUM_context_first/test-*
- config_name: trec_fine_grained_LOC
data_files:
- split: train
path: trec_fine_grained_LOC/train-*
- split: test
path: trec_fine_grained_LOC/test-*
- config_name: trec_fine_grained_LOC_context_first
data_files:
- split: train
path: trec_fine_grained_LOC_context_first/train-*
- split: test
path: trec_fine_grained_LOC_context_first/test-*
- config_name: trec_fine_grained_NUM
data_files:
- split: train
path: trec_fine_grained_NUM/train-*
- split: test
path: trec_fine_grained_NUM/test-*
- config_name: trec_fine_grained_NUM_context_first
data_files:
- split: train
path: trec_fine_grained_NUM_context_first/train-*
- split: test
path: trec_fine_grained_NUM_context_first/test-*
- config_name: trec_fine_grained_open
data_files:
- split: train
path: trec_fine_grained_open/train-*
- split: test
path: trec_fine_grained_open/test-*
- config_name: trec_fine_grained_open_context_first
data_files:
- split: train
path: trec_fine_grained_open_context_first/train-*
- split: test
path: trec_fine_grained_open_context_first/test-*
- config_name: trec_pick_the_best_descriptor
data_files:
- split: train
path: trec_pick_the_best_descriptor/train-*
- split: test
path: trec_pick_the_best_descriptor/test-*
- config_name: trec_trec1
data_files:
- split: train
path: trec_trec1/train-*
- split: test
path: trec_trec1/test-*
- config_name: trec_trec2
data_files:
- split: train
path: trec_trec2/train-*
- split: test
path: trec_trec2/test-*
- config_name: trec_what_category_best_describe
data_files:
- split: train
path: trec_what_category_best_describe/train-*
- split: test
path: trec_what_category_best_describe/test-*
- config_name: trec_which_category_best_describes
data_files:
- split: train
path: trec_which_category_best_describes/train-*
- split: test
path: trec_which_category_best_describes/test-*
- config_name: trivia_qa_unfiltered_first_person_context
data_files:
- split: train
path: trivia_qa_unfiltered_first_person_context/train-*
- split: validation
path: trivia_qa_unfiltered_first_person_context/validation-*
- split: test
path: trivia_qa_unfiltered_first_person_context/test-*
- config_name: trivia_qa_unfiltered_formal_description
data_files:
- split: train
path: trivia_qa_unfiltered_formal_description/train-*
- split: validation
path: trivia_qa_unfiltered_formal_description/validation-*
- split: test
path: trivia_qa_unfiltered_formal_description/test-*
- config_name: trivia_qa_unfiltered_guess_question
data_files:
- split: train
path: trivia_qa_unfiltered_guess_question/train-*
- split: validation
path: trivia_qa_unfiltered_guess_question/validation-*
- config_name: trivia_qa_unfiltered_question_answer
data_files:
- split: train
path: trivia_qa_unfiltered_question_answer/train-*
- split: validation
path: trivia_qa_unfiltered_question_answer/validation-*
- split: test
path: trivia_qa_unfiltered_question_answer/test-*
- config_name: trivia_qa_unfiltered_question_with_instruction
data_files:
- split: train
path: trivia_qa_unfiltered_question_with_instruction/train-*
- split: validation
path: trivia_qa_unfiltered_question_with_instruction/validation-*
- split: test
path: trivia_qa_unfiltered_question_with_instruction/test-*
- config_name: web_questions_get_the_answer
data_files:
- split: train
path: web_questions_get_the_answer/train-*
- split: test
path: web_questions_get_the_answer/test-*
- config_name: web_questions_potential_correct_answer
data_files:
- split: train
path: web_questions_potential_correct_answer/train-*
- split: test
path: web_questions_potential_correct_answer/test-*
- config_name: web_questions_question_answer
data_files:
- split: train
path: web_questions_question_answer/train-*
- split: test
path: web_questions_question_answer/test-*
- config_name: web_questions_short_general_knowledge_q
data_files:
- split: train
path: web_questions_short_general_knowledge_q/train-*
- split: test
path: web_questions_short_general_knowledge_q/test-*
- config_name: web_questions_whats_the_answer
data_files:
- split: train
path: web_questions_whats_the_answer/train-*
- split: test
path: web_questions_whats_the_answer/test-*
- config_name: wiki_bio_comprehension
data_files:
- split: train
path: wiki_bio_comprehension/train-*
- split: test
path: wiki_bio_comprehension/test-*
- split: val
path: wiki_bio_comprehension/val-*
- config_name: wiki_bio_guess_person
data_files:
- split: train
path: wiki_bio_guess_person/train-*
- split: test
path: wiki_bio_guess_person/test-*
- split: val
path: wiki_bio_guess_person/val-*
- config_name: wiki_bio_key_content
data_files:
- split: train
path: wiki_bio_key_content/train-*
- split: test
path: wiki_bio_key_content/test-*
- split: val
path: wiki_bio_key_content/val-*
- config_name: wiki_bio_what_content
data_files:
- split: train
path: wiki_bio_what_content/train-*
- split: test
path: wiki_bio_what_content/test-*
- split: val
path: wiki_bio_what_content/val-*
- config_name: wiki_bio_who
data_files:
- split: train
path: wiki_bio_who/train-*
- split: test
path: wiki_bio_who/test-*
- split: val
path: wiki_bio_who/val-*
- config_name: wiki_hop_original_choose_best_object_affirmative_1
data_files:
- split: train
path: wiki_hop_original_choose_best_object_affirmative_1/train-*
- split: validation
path: wiki_hop_original_choose_best_object_affirmative_1/validation-*
- config_name: wiki_hop_original_choose_best_object_affirmative_2
data_files:
- split: train
path: wiki_hop_original_choose_best_object_affirmative_2/train-*
- split: validation
path: wiki_hop_original_choose_best_object_affirmative_2/validation-*
- config_name: wiki_hop_original_choose_best_object_affirmative_3
data_files:
- split: train
path: wiki_hop_original_choose_best_object_affirmative_3/train-*
- split: validation
path: wiki_hop_original_choose_best_object_affirmative_3/validation-*
- config_name: wiki_hop_original_choose_best_object_interrogative_1
data_files:
- split: train
path: wiki_hop_original_choose_best_object_interrogative_1/train-*
- split: validation
path: wiki_hop_original_choose_best_object_interrogative_1/validation-*
- config_name: wiki_hop_original_choose_best_object_interrogative_2
data_files:
- split: train
path: wiki_hop_original_choose_best_object_interrogative_2/train-*
- split: validation
path: wiki_hop_original_choose_best_object_interrogative_2/validation-*
- config_name: wiki_hop_original_explain_relation
data_files:
- split: train
path: wiki_hop_original_explain_relation/train-*
- split: validation
path: wiki_hop_original_explain_relation/validation-*
- config_name: wiki_hop_original_generate_object
data_files:
- split: train
path: wiki_hop_original_generate_object/train-*
- split: validation
path: wiki_hop_original_generate_object/validation-*
- config_name: wiki_hop_original_generate_subject
data_files:
- split: train
path: wiki_hop_original_generate_subject/train-*
- split: validation
path: wiki_hop_original_generate_subject/validation-*
- config_name: wiki_hop_original_generate_subject_and_object
data_files:
- split: train
path: wiki_hop_original_generate_subject_and_object/train-*
- split: validation
path: wiki_hop_original_generate_subject_and_object/validation-*
- config_name: wiki_qa_Decide_good_answer
data_files:
- split: train
path: wiki_qa_Decide_good_answer/train-*
- split: validation
path: wiki_qa_Decide_good_answer/validation-*
- split: test
path: wiki_qa_Decide_good_answer/test-*
- config_name: wiki_qa_Direct_Answer_to_Question
data_files:
- split: train
path: wiki_qa_Direct_Answer_to_Question/train-*
- split: validation
path: wiki_qa_Direct_Answer_to_Question/validation-*
- split: test
path: wiki_qa_Direct_Answer_to_Question/test-*
- config_name: wiki_qa_Generate_Question_from_Topic
data_files:
- split: train
path: wiki_qa_Generate_Question_from_Topic/train-*
- split: validation
path: wiki_qa_Generate_Question_from_Topic/validation-*
- split: test
path: wiki_qa_Generate_Question_from_Topic/test-*
- config_name: wiki_qa_Is_This_True_
data_files:
- split: train
path: wiki_qa_Is_This_True_/train-*
- split: validation
path: wiki_qa_Is_This_True_/validation-*
- split: test
path: wiki_qa_Is_This_True_/test-*
- config_name: wiki_qa_Jeopardy_style
data_files:
- split: train
path: wiki_qa_Jeopardy_style/train-*
- split: validation
path: wiki_qa_Jeopardy_style/validation-*
- split: test
path: wiki_qa_Jeopardy_style/test-*
- config_name: wiki_qa_Topic_Prediction_Answer_Only
data_files:
- split: train
path: wiki_qa_Topic_Prediction_Answer_Only/train-*
- split: validation
path: wiki_qa_Topic_Prediction_Answer_Only/validation-*
- split: test
path: wiki_qa_Topic_Prediction_Answer_Only/test-*
- config_name: wiki_qa_Topic_Prediction_Question_Only
data_files:
- split: train
path: wiki_qa_Topic_Prediction_Question_Only/train-*
- split: validation
path: wiki_qa_Topic_Prediction_Question_Only/validation-*
- split: test
path: wiki_qa_Topic_Prediction_Question_Only/test-*
- config_name: wiki_qa_Topic_Prediction_Question_and_Answer_Pair
data_files:
- split: train
path: wiki_qa_Topic_Prediction_Question_and_Answer_Pair/train-*
- split: validation
path: wiki_qa_Topic_Prediction_Question_and_Answer_Pair/validation-*
- split: test
path: wiki_qa_Topic_Prediction_Question_and_Answer_Pair/test-*
- config_name: wiki_qa_automatic_system
data_files:
- split: train
path: wiki_qa_automatic_system/train-*
- split: validation
path: wiki_qa_automatic_system/validation-*
- split: test
path: wiki_qa_automatic_system/test-*
- config_name: wiki_qa_exercise
data_files:
- split: train
path: wiki_qa_exercise/train-*
- split: validation
path: wiki_qa_exercise/validation-*
- split: test
path: wiki_qa_exercise/test-*
- config_name: wiki_qa_found_on_google
data_files:
- split: train
path: wiki_qa_found_on_google/train-*
- split: validation
path: wiki_qa_found_on_google/validation-*
- split: test
path: wiki_qa_found_on_google/test-*
- config_name: winogrande_winogrande_debiased_Replace
data_files:
- split: train
path: winogrande_winogrande_debiased_Replace/train-*
- split: validation
path: winogrande_winogrande_debiased_Replace/validation-*
- split: test
path: winogrande_winogrande_debiased_Replace/test-*
- config_name: winogrande_winogrande_debiased_Replace_score_eval
data_files:
- split: train
path: winogrande_winogrande_debiased_Replace_score_eval/train-*
- split: validation
path: winogrande_winogrande_debiased_Replace_score_eval/validation-*
- split: test
path: winogrande_winogrande_debiased_Replace_score_eval/test-*
- config_name: winogrande_winogrande_debiased_does_underscore_refer_to
data_files:
- split: train
path: winogrande_winogrande_debiased_does_underscore_refer_to/train-*
- split: validation
path: winogrande_winogrande_debiased_does_underscore_refer_to/validation-*
- split: test
path: winogrande_winogrande_debiased_does_underscore_refer_to/test-*
- config_name: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval
data_files:
- split: train
path: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval/train-*
- split: validation
path: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval/validation-*
- split: test
path: winogrande_winogrande_debiased_does_underscore_refer_to_score_eval/test-*
- config_name: winogrande_winogrande_debiased_fill_in_the_blank
data_files:
- split: train
path: winogrande_winogrande_debiased_fill_in_the_blank/train-*
- split: validation
path: winogrande_winogrande_debiased_fill_in_the_blank/validation-*
- split: test
path: winogrande_winogrande_debiased_fill_in_the_blank/test-*
- config_name: winogrande_winogrande_debiased_fill_in_the_blank_score_eval
data_files:
- split: train
path: winogrande_winogrande_debiased_fill_in_the_blank_score_eval/train-*
- split: validation
path: winogrande_winogrande_debiased_fill_in_the_blank_score_eval/validation-*
- split: test
path: winogrande_winogrande_debiased_fill_in_the_blank_score_eval/test-*
- config_name: winogrande_winogrande_debiased_stand_for
data_files:
- split: train
path: winogrande_winogrande_debiased_stand_for/train-*
- split: validation
path: winogrande_winogrande_debiased_stand_for/validation-*
- split: test
path: winogrande_winogrande_debiased_stand_for/test-*
- config_name: winogrande_winogrande_debiased_stand_for_score_eval
data_files:
- split: train
path: winogrande_winogrande_debiased_stand_for_score_eval/train-*
- split: validation
path: winogrande_winogrande_debiased_stand_for_score_eval/validation-*
- split: test
path: winogrande_winogrande_debiased_stand_for_score_eval/test-*
- config_name: winogrande_winogrande_debiased_underscore_refer_to
data_files:
- split: train
path: winogrande_winogrande_debiased_underscore_refer_to/train-*
- split: validation
path: winogrande_winogrande_debiased_underscore_refer_to/validation-*
- split: test
path: winogrande_winogrande_debiased_underscore_refer_to/test-*
- config_name: winogrande_winogrande_debiased_underscore_refer_to_score_eval
data_files:
- split: train
path: winogrande_winogrande_debiased_underscore_refer_to_score_eval/train-*
- split: validation
path: winogrande_winogrande_debiased_underscore_refer_to_score_eval/validation-*
- split: test
path: winogrande_winogrande_debiased_underscore_refer_to_score_eval/test-*
- config_name: winogrande_winogrande_xl_Replace
data_files:
- split: train
path: winogrande_winogrande_xl_Replace/train-*
- split: validation
path: winogrande_winogrande_xl_Replace/validation-*
- split: test
path: winogrande_winogrande_xl_Replace/test-*
- config_name: winogrande_winogrande_xl_Replace_score_eval
data_files:
- split: train
path: winogrande_winogrande_xl_Replace_score_eval/train-*
- split: validation
path: winogrande_winogrande_xl_Replace_score_eval/validation-*
- split: test
path: winogrande_winogrande_xl_Replace_score_eval/test-*
- config_name: winogrande_winogrande_xl_does_underscore_refer_to
data_files:
- split: train
path: winogrande_winogrande_xl_does_underscore_refer_to/train-*
- split: validation
path: winogrande_winogrande_xl_does_underscore_refer_to/validation-*
- split: test
path: winogrande_winogrande_xl_does_underscore_refer_to/test-*
- config_name: winogrande_winogrande_xl_does_underscore_refer_to_score_eval
data_files:
- split: train
path: winogrande_winogrande_xl_does_underscore_refer_to_score_eval/train-*
- split: validation
path: winogrande_winogrande_xl_does_underscore_refer_to_score_eval/validation-*
- split: test
path: winogrande_winogrande_xl_does_underscore_refer_to_score_eval/test-*
- config_name: winogrande_winogrande_xl_fill_in_the_blank
data_files:
- split: train
path: winogrande_winogrande_xl_fill_in_the_blank/train-*
- split: validation
path: winogrande_winogrande_xl_fill_in_the_blank/validation-*
- split: test
path: winogrande_winogrande_xl_fill_in_the_blank/test-*
- config_name: winogrande_winogrande_xl_fill_in_the_blank_score_eval
data_files:
- split: train
path: winogrande_winogrande_xl_fill_in_the_blank_score_eval/train-*
- split: validation
path: winogrande_winogrande_xl_fill_in_the_blank_score_eval/validation-*
- split: test
path: winogrande_winogrande_xl_fill_in_the_blank_score_eval/test-*
- config_name: winogrande_winogrande_xl_stand_for
data_files:
- split: train
path: winogrande_winogrande_xl_stand_for/train-*
- split: validation
path: winogrande_winogrande_xl_stand_for/validation-*
- split: test
path: winogrande_winogrande_xl_stand_for/test-*
- config_name: winogrande_winogrande_xl_stand_for_score_eval
data_files:
- split: train
path: winogrande_winogrande_xl_stand_for_score_eval/train-*
- split: validation
path: winogrande_winogrande_xl_stand_for_score_eval/validation-*
- split: test
path: winogrande_winogrande_xl_stand_for_score_eval/test-*
- config_name: winogrande_winogrande_xl_underscore_refer_to
data_files:
- split: train
path: winogrande_winogrande_xl_underscore_refer_to/train-*
- split: validation
path: winogrande_winogrande_xl_underscore_refer_to/validation-*
- split: test
path: winogrande_winogrande_xl_underscore_refer_to/test-*
- config_name: winogrande_winogrande_xl_underscore_refer_to_score_eval
data_files:
- split: train
path: winogrande_winogrande_xl_underscore_refer_to_score_eval/train-*
- split: validation
path: winogrande_winogrande_xl_underscore_refer_to_score_eval/validation-*
- split: test
path: winogrande_winogrande_xl_underscore_refer_to_score_eval/test-*
- config_name: wiqa_does_the_supposed_perturbation_have_an_effect
data_files:
- split: train
path: wiqa_does_the_supposed_perturbation_have_an_effect/train-*
- split: validation
path: wiqa_does_the_supposed_perturbation_have_an_effect/validation-*
- split: test
path: wiqa_does_the_supposed_perturbation_have_an_effect/test-*
- config_name: wiqa_effect_with_label_answer
data_files:
- split: train
path: wiqa_effect_with_label_answer/train-*
- split: validation
path: wiqa_effect_with_label_answer/validation-*
- split: test
path: wiqa_effect_with_label_answer/test-*
- config_name: wiqa_effect_with_string_answer
data_files:
- split: train
path: wiqa_effect_with_string_answer/train-*
- split: validation
path: wiqa_effect_with_string_answer/validation-*
- split: test
path: wiqa_effect_with_string_answer/test-*
- config_name: wiqa_what_is_the_final_step_of_the_following_process
data_files:
- split: train
path: wiqa_what_is_the_final_step_of_the_following_process/train-*
- split: validation
path: wiqa_what_is_the_final_step_of_the_following_process/validation-*
- split: test
path: wiqa_what_is_the_final_step_of_the_following_process/test-*
- config_name: wiqa_what_is_the_missing_first_step
data_files:
- split: train
path: wiqa_what_is_the_missing_first_step/train-*
- split: validation
path: wiqa_what_is_the_missing_first_step/validation-*
- split: test
path: wiqa_what_is_the_missing_first_step/test-*
- config_name: wiqa_what_might_be_the_first_step_of_the_process
data_files:
- split: train
path: wiqa_what_might_be_the_first_step_of_the_process/train-*
- split: validation
path: wiqa_what_might_be_the_first_step_of_the_process/validation-*
- split: test
path: wiqa_what_might_be_the_first_step_of_the_process/test-*
- config_name: wiqa_what_might_be_the_last_step_of_the_process
data_files:
- split: train
path: wiqa_what_might_be_the_last_step_of_the_process/train-*
- split: validation
path: wiqa_what_might_be_the_last_step_of_the_process/validation-*
- split: test
path: wiqa_what_might_be_the_last_step_of_the_process/test-*
- config_name: wiqa_which_of_the_following_is_the_supposed_perturbation
data_files:
- split: train
path: wiqa_which_of_the_following_is_the_supposed_perturbation/train-*
- split: validation
path: wiqa_which_of_the_following_is_the_supposed_perturbation/validation-*
- split: test
path: wiqa_which_of_the_following_is_the_supposed_perturbation/test-*
- config_name: xsum_DOC_boils_down_to_simple_idea_that
data_files:
- split: train
path: xsum_DOC_boils_down_to_simple_idea_that/train-*
- split: validation
path: xsum_DOC_boils_down_to_simple_idea_that/validation-*
- split: test
path: xsum_DOC_boils_down_to_simple_idea_that/test-*
- config_name: xsum_DOC_given_above_write_one_sentence
data_files:
- split: train
path: xsum_DOC_given_above_write_one_sentence/train-*
- split: validation
path: xsum_DOC_given_above_write_one_sentence/validation-*
- split: test
path: xsum_DOC_given_above_write_one_sentence/test-*
- config_name: xsum_DOC_how_would_you_rephrase_few_words
data_files:
- split: train
path: xsum_DOC_how_would_you_rephrase_few_words/train-*
- split: validation
path: xsum_DOC_how_would_you_rephrase_few_words/validation-*
- split: test
path: xsum_DOC_how_would_you_rephrase_few_words/test-*
- config_name: xsum_DOC_tldr
data_files:
- split: train
path: xsum_DOC_tldr/train-*
- split: validation
path: xsum_DOC_tldr/validation-*
- split: test
path: xsum_DOC_tldr/test-*
- config_name: xsum_DOC_write_summary_of_above
data_files:
- split: train
path: xsum_DOC_write_summary_of_above/train-*
- split: validation
path: xsum_DOC_write_summary_of_above/validation-*
- split: test
path: xsum_DOC_write_summary_of_above/test-*
- config_name: xsum_article_DOC_summary
data_files:
- split: train
path: xsum_article_DOC_summary/train-*
- split: validation
path: xsum_article_DOC_summary/validation-*
- split: test
path: xsum_article_DOC_summary/test-*
- config_name: xsum_college_roommate_asked_DOC_so_I_recap
data_files:
- split: train
path: xsum_college_roommate_asked_DOC_so_I_recap/train-*
- split: validation
path: xsum_college_roommate_asked_DOC_so_I_recap/validation-*
- split: test
path: xsum_college_roommate_asked_DOC_so_I_recap/test-*
- config_name: xsum_read_below_DOC_write_abstract
data_files:
- split: train
path: xsum_read_below_DOC_write_abstract/train-*
- split: validation
path: xsum_read_below_DOC_write_abstract/validation-*
- split: test
path: xsum_read_below_DOC_write_abstract/test-*
- config_name: xsum_summarize_DOC
data_files:
- split: train
path: xsum_summarize_DOC/train-*
- split: validation
path: xsum_summarize_DOC/validation-*
- split: test
path: xsum_summarize_DOC/test-*
- config_name: xsum_summarize_this_DOC_summary
data_files:
- split: train
path: xsum_summarize_this_DOC_summary/train-*
- split: validation
path: xsum_summarize_this_DOC_summary/validation-*
- split: test
path: xsum_summarize_this_DOC_summary/test-*
- config_name: yelp_review_full_based_on_that
data_files:
- split: train
path: yelp_review_full_based_on_that/train-*
- split: test
path: yelp_review_full_based_on_that/test-*
- config_name: yelp_review_full_format_rating
data_files:
- split: train
path: yelp_review_full_format_rating/train-*
- split: test
path: yelp_review_full_format_rating/test-*
- config_name: yelp_review_full_format_score
data_files:
- split: train
path: yelp_review_full_format_score/train-*
- split: test
path: yelp_review_full_format_score/test-*
- config_name: yelp_review_full_format_star
data_files:
- split: train
path: yelp_review_full_format_star/train-*
- split: test
path: yelp_review_full_format_star/test-*
- config_name: yelp_review_full_on_a_scale
data_files:
- split: train
path: yelp_review_full_on_a_scale/train-*
- split: test
path: yelp_review_full_on_a_scale/test-*
- config_name: yelp_review_full_so_i_would
data_files:
- split: train
path: yelp_review_full_so_i_would/train-*
- split: test
path: yelp_review_full_so_i_would/test-*
- config_name: yelp_review_full_this_place
data_files:
- split: train
path: yelp_review_full_this_place/train-*
- split: test
path: yelp_review_full_this_place/test-*
---
# Dataset Card for P3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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
- **Homepage:** https://bigscience.huggingface.co/promptsource
- **Repository:** https://github.com/bigscience-workshop/promptsource/
- **Paper:** [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207)
- **Point of Contact:** [Victor Sanh](mailto:victor@huggingface.co)
### Dataset Summary
P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).
Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource).
To train [T0*](https://huggingface.co/bigscience/T0pp), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) which represent only a subset of the datasets for which there is at least one prompt in Promptsource.**
### Supported Tasks and Leaderboards
The tasks represented in P3 cover a diverse set of NLP tasks including multiple-choice QA, sentiment analysis or natural language inference. We detail the full list of datasets in [Source Data](#source-data).
### Languages
The data in P3 are in English (BCP-47 `en`).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```bash
{
'answer_choices': ['safe', 'trolley'],
'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 1346, 42, 31682, 58, 37, 3, 929, 9, 3042, 63, 2765, 808, 8, 2045, 6448, 326, 13, 8, 31682, 11, 3, 24052, 135, 16, 8, 1346, 552, 8, 3, 834, 47, 6364, 5], 'inputs_pretokenized': 'In the sentence below, does the _ stand for safe or trolley?\nThe treasury workers took the gold bars off of the trolley and stacked them in the safe until the _ was empty.',
'targets': [31682, 1],
'targets_pretokenized': '\ntrolley'
}
```
In the case of rank classification (letting the model select its the prediction the option with the highest log-likelihood), an example looks as follows:
```bash
{
'idx': [5, 0],
'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 19454, 42, 22227, 58, 19454, 744, 31, 17, 2112, 4553, 17742, 7, 12, 1953, 6, 298, 22227, 966, 373, 405, 5, 3, 834, 19, 72, 952, 12, 619, 16, 3, 9, 17742, 3298, 5],
'inputs_pretokenized': "In the sentence below, does the _ stand for Kyle or Logan?\nKyle doesn't wear leg warmers to bed, while Logan almost always does. _ is more likely to live in a warmer climate.",
'is_correct': True,
'targets': [19454, 1],
'targets_pretokenized': 'Kyle',
'weight': 1.0
}
```
To check all the prompted examples, you can use the [Promptsource hosted tool](http://bigscience.huggingface.co/promptsource) and choose the `Prompted dataset viewer` mode in the left panel.
### Data Fields
The data fields are the same among all splits:
- `answer_choices`: the choices (in natural language) available to the model
- `inputs_pretokenized`: the natural language input fed to the model
- `targets_pretokenized`: the natural language target that the model has to generate
- `inputs`: the tokenized input with [T5](https://huggingface.co/google/t5-v1_1-base)'s tokenizer
- `targets`: the tokenized target with [T5](https://huggingface.co/google/t5-v1_1-base)'s tokenizer
- `idx`: identifier of the (example, answer_option_id) in the case of rank classification
- `weight`: a weight for the example produced by seqio (always set to 1.0 in practise)
- `is_correct`: whether the (example, answer_option_id) is the correct one
### Data Splits
The list of data splits and their respective sizes is very long. You'll find the whole list in this [file](https://huggingface.co/datasets/bigscience/P3/blob/main/tasks_splits_and_features.py).
## Dataset Creation
### Curation Rationale
The Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.
We conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes.
### Source Data
Here's the full list of the datasets present in the materialized version of P3:
- Multiple-Choice QA
- CommonsenseQA
- DREAM
- QUAIL
- QuaRTz
- Social IQA
- WiQA
- Cosmos
- QASC
- Quarel
- SciQ
- Wiki Hop
- ARC
- OpenBookQA
- MultiRC
- PIQA
- RACE
- HellaSwag
- BoolQ
- Extractive QA
- Adversarial QA
- Quoref
- DuoRC
- ROPES
- SQuAD v2
- ReCoRD
- Close-book QA
- Hotpot QA
- Wiki QA
- Trivia QA
- Web Questions
- Structure-to-text
- Common Gen
- Wiki Bio
- Sentiment
- Amazon
- App Reviews
- IMDB
- Rotten Tomatoes
- Yelp
- Summarization
- CNN Daily Mail
- Gigaword
- MultiNews
- SamSum
- XSum
- Topic Classification
- AG News
- DBPedia
- TREC
- Paraphrase Identification
- MRPC
- PAWS
- QQP
- Natural Language Inference
- ANLI
- CB
- RTE
- Coreference Resolution
- WSC
- Winogrande
- Word Sense disambiguation
- WiC
- Sentence Completion
- COPA
- HellaSwag
- Story Cloze
### Annotations
The prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers.
The main annotation guideline was that prompts needed to be grammatical and understandable by a native English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants, e.g., instead of predicting indices of a span to extract (e.g. in extractive question answering), the model was expected to copy the span's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data. Most of the prompts correspond directly to a version of the original proposed task, although we also allowed prompts that permuted the original task (for instance, generating a document from its summary) or allowed for ambiguous output (for instance, not indicating a list of available choices).
The full annotation given to the contributors can be found [here](https://github.com/bigscience-workshop/promptsource/blob/main/CONTRIBUTING.md). *Note to self: the link is currently being updated with the)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding this dataset.
|
jinzhuoran/RWKU | jinzhuoran | "2024-06-18T02:25:48Z" | 16,299 | 3 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.10890",
"doi:10.57967/hf/2448",
"region:us",
"unlearning",
"knowledge unlearning",
"NLP",
"LLM"
] | [
"text-generation",
"fill-mask",
"question-answering"
] | "2024-06-02T12:02:18Z" | ---
language:
- en
license: cc-by-4.0
pretty_name: RWKU
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- fill-mask
- question-answering
tags:
- unlearning
- knowledge unlearning
- NLP
- LLM
configs:
- config_name: forget_target
data_files:
- split: train
path:
- "All/intro.json"
- config_name: forget_level1
data_files:
- split: test
path:
- "All/forget_level1.json"
- config_name: forget_level2
data_files:
- split: test
path:
- "All/forget_level2.json"
- config_name: forget_level3
data_files:
- split: test
path:
- "All/forget_level3.json"
- config_name: neighbor_level1
data_files:
- split: test
path:
- "All/neighbor_level1.json"
- config_name: neighbor_level2
data_files:
- split: test
path:
- "All/neighbor_level2.json"
- config_name: mia_forget
data_files:
- split: test
path:
- "All/forget_mia.json"
- config_name: mia_retain
data_files:
- split: test
path:
- "All/retain_mia.json"
- config_name: utility_general
data_files:
- split: test
path:
- "All/retain_mmlu.json"
- config_name: utility_general
data_files:
- split: test
path:
- "All/retain_mmlu.json"
- config_name: utility_reason
data_files:
- split: test
path:
- "All/retain_bbh.json"
- config_name: utility_truthfulness
data_files:
- split: test
path:
- "All/truthful.json"
- config_name: utility_factuality
data_files:
- split: test
path:
- "All/triviaqa.json"
- config_name: utility_fluency
data_files:
- split: test
path:
- "All/fluency.json"
- config_name: train_original_passage
data_files:
- split: train
path:
- "All/passage.json"
- config_name: train_positive_llama3
data_files:
- split: train
path:
- "All/positive.json"
- config_name: train_negative_llama3
data_files:
- split: train
path:
- "All/negative.json"
- config_name: train_pair_llama3
data_files:
- split: train
path:
- "All/pair.json"
- config_name: train_refusal_llama3
data_files:
- split: train
path:
- "All/reject.json"
- config_name: train_positive_phi3
data_files:
- split: train
path:
- "All/positive_phi.json"
- config_name: train_negative_phi3
data_files:
- split: train
path:
- "All/negative_phi.json"
- config_name: train_pair_phi3
data_files:
- split: train
path:
- "All/pair_phi.json"
- config_name: train_refusal_phi3
data_files:
- split: train
path:
- "All/reject_phi.json"
---
# Dataset Card for Real-World Knowledge Unlearning Benchmark (RWKU)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://rwku-bench.github.io
- **Repository:** https://github.com/jinzhuoran/RWKU
- **Paper:** https://arxiv.org/abs/2406.10890
### Dataset Summary
**RWKU is a real-world knowledge unlearning benchmark specifically designed for large language models (LLMs).**
This benchmark contains 200 real-world unlearning targets and 13,131 multi-level forget probes, including 3,268 fill-in-the-blank probes, 2,879 question-answer probes, and 6,984 adversarial-attack probes.
RWKU is designed based on the following three key factors:
1. For the **task setting**, we consider a more practical and challenging setting, similar to _zero-shot knowledge unlearning_.
We provide only the unlearning target and the original model, without offering any forget corpus or retain corpus.
In this way, it avoids secondary information leakage caused by the forget corpus and is not affected by the distribution bias of the retain corpus.
2. For the **knowledge source**, we choose real-world famous people from Wikipedia as the unlearning targets and demonstrate that such popular knowledge is widely present in various LLMs through memorization quantification, making it more suitable for knowledge unlearning.
Additionally, choosing entities as unlearning targets can well clearly define the unlearning boundaries.
3. For the **evaluation framework**, we carefully design the forget set and the retain set to evaluate the model's capabilities from multiple real-world applications.
Regarding the forget set, we evaluate the **efficacy** of knowledge unlearning at both the knowledge memorization (fill-in-the-blank style) and knowledge manipulation (question-answer style) abilities.
Specifically, we also evaluate these two abilities through **adversarial attacks** to induce forgotten knowledge in the model.
We adopt four membership inference attack (MIA) methods for knowledge memorization on our collected MIA set.
We meticulously designed nine types of adversarial-attack probes for knowledge manipulation, including prefix injection, affirmative suffix, role playing, reverse query, and others.
Regarding the retain set, we design a neighbor set to test the impact of neighbor perturbation, specifically focusing on the **locality** of unlearning.
In addition, we assess the **model utility** on various capabilities, including general ability, reasoning ability, truthfulness, factuality, and fluency.
### Supported Tasks
Knowledge unlearning for LLMs.
### Languages
English.
## Dataset Structure
To evaluate the unlearning efficacy:
```python
from datasets import load_dataset
forget_level1 = load_dataset("jinzhuoran/RWKU", 'forget_level1')
forget_level2 = load_dataset("jinzhuoran/RWKU", 'forget_level2')
forget_level2 = load_dataset("jinzhuoran/RWKU", 'forget_level2')
```
To evaluate the locality:
```python
from datasets import load_dataset
neighbor_level1 = load_dataset("jinzhuoran/RWKU", 'neighbor_level1')
neighbor_level2 = load_dataset("jinzhuoran/RWKU", 'neighbor_level2')
```
To evaluate the model utility:
```python
from datasets import load_dataset
utility_general = load_dataset("jinzhuoran/RWKU", 'utility_general')
utility_reason = load_dataset("jinzhuoran/RWKU", 'utility_reason')
utility_truthfulness = load_dataset("jinzhuoran/RWKU", 'utility_truthfulness')
utility_factuality = load_dataset("jinzhuoran/RWKU", 'utility_factuality')
utility_fluency = load_dataset("jinzhuoran/RWKU", 'utility_fluency')
```
To conduct membership inference attacks:
```python
from datasets import load_dataset
mia_forget = load_dataset("jinzhuoran/RWKU", 'mia_forget')
mia_retain = load_dataset("jinzhuoran/RWKU", 'mia_retain')
```
To load the forget corpus:
```python
from datasets import load_dataset
train_original_passage = load_dataset("jinzhuoran/RWKU", 'train_original_passage')
train_positive_llama3 = load_dataset("jinzhuoran/RWKU", 'train_positive_llama3')
```
## 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]
### Citing Our Work
If you find our codebase and dataset beneficial, please cite our work:
```bibtex
@misc{jin2024rwku,
title={RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models},
author={Zhuoran Jin and Pengfei Cao and Chenhao Wang and Zhitao He and Hongbang Yuan and Jiachun Li and Yubo Chen and Kang Liu and Jun Zhao},
year={2024},
eprint={2406.10890},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
airtrain-ai/fineweb-edu-fortified | airtrain-ai | "2024-08-08T18:04:44Z" | 16,296 | 55 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.17557",
"arxiv:2109.07445",
"region:us"
] | [
"text-generation"
] | "2024-07-22T14:22:31Z" | ---
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- en
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---
# Fineweb-Edu-Fortified
<figure>
<img src="https://cdn-uploads.huggingface.co/production/uploads/646516d2200b583e1e50faf8/79yPdK79m9mA0cCz-3h4v.png" width="500" style="margin-left:auto; margin-right: auto"/>
<figcaption style="text-align: center; margin-left: auto; margin-right: auto; font-style: italic;">
The composition of fineweb-edu-fortified, produced by automatically clustering a 500k row sample in
<a href="https://app.airtrain.ai/dataset/c232b33f-4f4a-49a7-ba55-8167a5f433da/null/1/0"> Airtrain </a>
</figcaption>
</figure>
## What is it?
Fineweb-Edu-Fortified is a dataset derived from
[Fineweb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) by applying exact-match
deduplication across the whole dataset and producing an embedding for each row. The number of times
the text from each row appears is also included as a `count` column. The embeddings were produced
using [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro)
Fineweb and Fineweb-Edu were obtained by processing data from 95 crawls of
[Common Crawl](https://commoncrawl.org/), covering a time period from 2013 to 2024.
More information about the original datasets can be found by consulting:
- [Fineweb-edu dataset card](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
- [Fineweb dataset card](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- [Fineweb release blog post](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1)
- [Fineweb paper](https://arxiv.org/abs/2406.17557)
The contents of a randomly selected 500k rows from this dataset can be interactively
explored in this
[Airtrain](https://app.airtrain.ai/dataset/c232b33f-4f4a-49a7-ba55-8167a5f433da/null/1/0)
dashboard.
## Deduplication
### Deduplication in original Fineweb and Fineweb-Edu
During creation of the original Fineweb dataset, a variety of deduplication strategies were
explored. The evaluation criteria used to assess deduplication strategies was to train ablation models
on randomly selected subsets of the data, using a subset of up to ~350 billion tokens.
Using this mechanism, the Fineweb authors selected a MinHash algorithm, using parameters
considering documents with approximately 75% similarity or higher to be duplicates. This deduplication was
performed *within* each Common Crawl crawl. For example, it would have removed all approximate duplicates
from the 20th crawl from 2013, but would have retained an identical record that showed up
in both the 2013-20 crawl and the 2013-48 crawl. The authors note that applying the
deduplication *across crawls* reduced the evaluation performance of the ablation models used
for assessment. The proposed reason for this performance degredation is that data
duplicated across crawls is more likely to be high-quality compared to data that is not,
so leaving in the duplicates effectively upsamples the higer-quality data.
Following deduplication in Fineweb, Fineweb-Edu was extracted using a model-based quality classifier
targeting educational content. It thus inherited the same inter-crawl deduplication strategy of Fineweb.
### Deduplication in this dataset
#### Motivation
Given the findings that cross-crawl deduplication reduced ablation model performance, one might ask
what the motivation is for producing a dataset that uses it. Our motivation was threefold:
- Reduce the number of rows that needed to be embedded by avoiding embedding of exact-match content
- Enable easier filtering of the dataset for subsets-of-interest
- Provide a version of the dataset for users whose training goals include avoiding training on non-unique
tokens.
For use cases that would benefit from "re-hydrating" or filtering the rows based on how frequently
the text appeared in the original dataset, the new `count` column retains the number of appearances
of the associated text.
#### Procedure
The overall procedure was to remove exact matches that appeared in multiple crawls (also referred to
as "dumps"). This was achieved by performing an md5 hash on the text column and removing rows with
duplicate hashes. To make this tractable at scale, we first grouped all rows by the first two hex
digits of their hashes, then looked for exact hash matches within each of the resulting 256
buckets of data. Note that unlike the intra-crawl deduplication, we only eliminated exact matches
across crawls. For duplicated rows, a strong preference was given to keep the metadata
(ex: dump, url) from the oldest crawl where the text appeared. Following deduplication and
embedding, the data were grouped by the "dump" column, mirroring the organization of the original
Fineweb-Edu dataset.
### Deduplication stats
Deduplication removed approximately 74.7% of rows from the original dataset
(from 1.279 billion in Fineweb-Edu to 0.324 billion rows in Fineweb-Edu-Fortified).
This indicates that a substantial amount of data in Fineweb-Edu is present across multiple crawls.
The total token count in the deduplicated dataset is approximately 375 billion, compared to the
1,320 billion tokens in Fineweb-Edu.
<figure>
<img src="https://cdn-uploads.huggingface.co/production/uploads/646516d2200b583e1e50faf8/mUFyO1fUWJEXbYwiteR9e.png" width="750" style="margin-left:auto; margin-right: auto"/>
<figcaption style="text-align: center; margin-left: auto; margin-right: auto; font-style: italic;">
A histogram of the `count` column. Histogram was generated using a 500k row sample after
performing global per-row text duplication counting.
</figcaption>
</figure>
## Embeddings
To support use cases with Fineweb-Edu such as classification, clustering, semantic search, etc.,
we have produced an embedding vector for each row in the dataset. The embedding model
[TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro)
was selected for its tradeoff of strong performance on [MTEB](https://huggingface.co/spaces/mteb/leaderboard)
benchmarks relative to its size (17 million parameters). The model's embedding space
has 384 dimensions. The context-window of the model is 512 tokens (roughly several paragraphs of text);
each row is embedded by using the first 512 tokens in its text field. Producing the embeddings took approximately
412 GPU-hours on Nvidia T4 GPUs.
## Using via `datasets`
```python
from datasets import load_dataset
fw = load_dataset("airtrain-ai/fineweb-edu-fortified", name="CC-MAIN-2024-10", split="train", streaming=True)
```
## Considerations for Using the Data
This "Considerations" section is copied from the parent dataset:
[FineWeb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu).
### Social Impact of Dataset
With the release of this dataset we aim to make model training more accessible to the machine learning community at large.
While multiple open-weights models with strong performance have been publicly released in the past, more often than not these releases are not accompanied by the corresponding training dataset. This is unfortunate as the dataset specificities and characteristics have been demonstrated to have a very large impact and role in the performances of the models. As the creation of a high quality training dataset is a fundamental requirement to training an LLM capable of excelling at downstream tasks, with 🍷 FineWeb we (a) not only make the dataset creation process more transparent, by sharing our entire processing setup including the codebase used, we also (b) help alleviate the costs of dataset curation, both in time and in compute, for model creators by publicly releasing our dataset with the community.
### Discussion of Biases
Efforts were made to minimize the amount of NSFW and toxic content present in the dataset by employing filtering on the URL level. However, there are still a significant number of documents present in the final dataset that could be considered toxic or contain harmful content. As 🍷 FineWeb was sourced from the web as a whole, any harmful biases typically present in it may be reproduced on our dataset.
We deliberately avoided using machine learning filtering methods that define text quality based on the similarity to a “gold” source such as wikipedia or toxicity classifiers as these methods have been known to [disproportionately remove content in specific dialects](https://aclanthology.org/D16-1120/) and [overclassify as toxic text related to specific social identities](https://arxiv.org/pdf/2109.07445.pdf), respectively.
### Other Known Limitations
As a consequence of some of the filtering steps applied, it is likely that code content is not prevalent in our dataset. If you are training a model that should also perform code tasks, we recommend you use 🍷 FineWeb with a code dataset, such as [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2). You should also probably consider complementing 🍷 FineWeb with specialized curated sources (such as Wikipedia, for example) as they will likely have better formatting than the wikipedia content included in 🍷 FineWeb (we did not tailor the processing to individual websites).
## Additional Information
### Acknowledgements
Airtrain would like to thank the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets,
as well as for their support during work on Fineweb-Edu-Fortified.
We'd also like to thank [@underspirit](https://huggingface.co/underspirit) for
[pointing out](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu/discussions/7)
the amount of reduction in dataset size that could be achieved via deduplication.
We owe gratitude to [TaylorAI](https://huggingface.co/TaylorAI) for the `bge-micro` embedding model.
Finally, thank you to the Hugging Face community for fostering a thriving ecosystem of models, datasets, and tools
to support open-source AI.
### Licensing Information
The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use).
|
opentensor/openvalidators-test | opentensor | "2023-06-20T14:21:16Z" | 16,264 | 0 | [
"license:mit",
"size_categories:1M<n<10M",
"region:us"
] | null | "2023-06-09T15:42:16Z" | ---
license: mit
viewer: False
size_categories:
- 1M<n<10M
---
# Dataset Card for Openvalidators dataset
## Dataset Description
- **Repository:** https://github.com/opentensor/validators
- **Homepage:** https://bittensor.com/
### Dataset Summary
The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the [OpenValidators](https://github.com/opentensor/validators) project in [W&B](https://wandb.ai/opentensor-dev/openvalidators/table). It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing.
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale.
The OpenValidators dataset gives you the granularity of extracting data by ************run_id************, by ************************************OpenValidators version************************************ and by ******************************************************************multiple OpenValidators versions.****************************************************************** The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
**Downloading by run id**
For example, to download the data for a specific run, simply specify the corresponding ********************************************OpenValidators version******************************************** and the ************************wandb run id************************ in the format `version/raw_data/run_id.parquet`:
```python
from datasets import load_dataset
version = '1.0.4' # OpenValidators version
run_id = '0plco3n0' # WandB run id
run_id_dataset = load_dataset('opentensor/openvalidators-test', data_files=f'{version}/raw_data/{run_id}.parquet')
```
_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._
**Downloading by OpenValidators version**
One can also leverage the `datasets` library to download all the runs within a determined ****************************OpenValidators**************************** version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific ****************************OpenValidators**************************** version state.
```python
from datasets import load_dataset
version = '1.0.4' # Openvalidators version
version_dataset = load_dataset('opentensor/openvalidators-test', data_files=f'{version}/raw_data/*')
```
**Downloading by multiple OpenValidators version**
Utilizing the `datasets` library, users can efficiently download runs from multiple **OpenValidators** versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.
```python
from datasets import load_dataset
versions = ['1.0.0', '1.0.1', '1.0.2', '1.0.4'] # Desired versions for extraction
data_files = [f'{version}/raw_data/*' for version in versions] # Set data files directories
dataset = load_dataset('opentensor/openvalidators-test', data_files={ 'test': data_files })
```
**Analyzing metadata**
All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.
```python
import pandas as pd
version = '1.0.4' # OpenValidators version for metadata analysis
df = pd.read_csv(f'hf://datasets/opentensor/openvalidators-test/{version}/metadata.csv')
```
## Dataset Structure
### Data Instances
**versioned raw_data**
The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at `version/raw_data` where each file is a wandb run.
**metadata**
This dataset defines the current state of the wandb data ingestion by **run id**.
### Data Fields
**Raw data**
The versioned raw_data collected from W&B follows the following schema:
- `_runtime`: (float64) Runtime of the event
- `_step`: (int64) Step of the event
- `_timestamp`: (float64) Timestamp of the event
- `answer_completions`: (list(string)) Completions of the answer_prompt
- `answer_prompt`: (string) Prompt used to generate the answer
- `answer_rewards`: (list(float64)) Rewards of the answer responses
- `answer_times`: (list(float64)) Elapsed time of answer responses
- `answer_uids`: (list(int32)) UIDs of nodes that answered the answer_prompt
- `base_prompt`: (string) Bootstrap prompt
- `best_answer`: (string) Best answer response
- `best_followup`: (string) Best followup response
- `block`: (float64) Subtensor current block
- `followup_completions`: (list(string)) Completions of the base_prompt
- `followup_rewards`: (list(float64)) Rewards of the followup responses
- `followup_times`: (list(float64)) Ellapsed time of followup responses
- `followup_uids`: (list(int64)) UIDs of nodes that answered the base_prompt
- `gating_loss`: (float64) Gating model loss
- `gating_scorings`: (list(float64)) Gating model scores
- `moving_averaged_scores`: (list(float64)) Moving averaged scores at the time of the event
- `set_weights`: (list(list(float64))) Processed weights of nodes by uid
- `step_length`: (float64) Time difference from beginning of forward call to event logging
**Metadata**
- `run_id`: (string) Wandb Run Id
- `completed`: (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- `downloaded`: (boolean) Flag indicating if the run_id data has been downloaded
- `last_checkpoint`: (string) Last checkpoint of the run_id
- `hotkey`: (string) Hotkey associated with the run_id
- `openvalidators_version`: (string) Version of OpenValidators associated with the run_id
- `problematic`: (boolean) Flag indicating if the run_id data had problems to be ingested
- `problematic_reason`: (string) Reason for the run_id being problematic (Exception message)
- `wandb_json_config`: (string) JSON configuration associated with the run_id in Wandb
- `wandb_run_name`: (string) Name of the Wandb run
- `wandb_user_info`: (string) Username information associated with the Wandb run
- `wandb_tags`: (list) List of tags associated with the Wandb run
- `wandb_createdAt`: (string) Timestamp of the run creation in Wandb
## Dataset Creation
### Curation Rationale
This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network.
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining.
### Source Data
#### Initial Data Collection and Normalization
The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a `metadata.csv` file is included to manage the collection state, while the raw data of each run is saved in the `.parquet` format with the file name corresponding to the run ID (e.g., `run_id.parquet`). Please note that the code for this data collection process will be released for transparency and reproducibility.
#### Who are the source language producers?
The language producers for this dataset are all the openvalidators that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at https://wandb.ai/opentensor-dev/openvalidators/table.
### Licensing Information
The dataset is licensed under the [MIT License](https://github.com/opentensor/validators/blob/main/LICENSE)
### Supported Tasks and Leaderboards
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
sal4ahm/RealCQA | sal4ahm | "2024-09-09T18:14:20Z" | 16,220 | 5 | [
"license:mit",
"size_categories:10K<n<100K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2308.01979",
"region:us"
] | null | "2024-02-01T17:18:07Z" | ---
license: mit
---
# RealCQA: Real-World Complex Question Answering Dataset
This repository contains the dataset used in the paper "[RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic](https://arxiv.org/pdf/2308.01979)" (ICDAR 2023). The dataset is designed to facilitate research in complex question answering, involving a diverse set of real-world images and associated textual question-answer pairs.
## Dataset Overview
The RealCQA dataset consists of 28,266 images, and corresponding 2 million question-answer pairs organized into three complementary subsets. Each image is accompanied by a JSON file containing one or more question blocks. The dataset is structured to address a range of question-answering tasks that require an understanding of the visual content.
### Dataset Structure
The dataset is organized into the following folders:
- **Images**
- `images`: Contains the first 10,000 images.
- `images2`: Contains the next 10,000 images.
- `images3`: Contains the remaining 8,266 images.
- **JSON Files**
- `jsons`: Contains the JSON files corresponding to the images in the `images` folder.
- `jsons2`: Contains the JSON files corresponding to the images in the `images2` folder.
- `jsons3`: Contains the JSON files corresponding to the images in the `images3` folder.
- **QA Files**
These are the QA created in our proposed dataset.
- `qa`: Contains the QA files corresponding to the images in the `images` folder.
- `qa2`: Contains the QA files corresponding to the images in the `images2` folder.
- `qa3`: Contains the QA files corresponding to the images in the `images3` folder.
### File Details
- **Images**: JPEG files named in the format `PMCxxxxxx_abc.jpg`, where `xxxxxx` represents the PubMed Central ID and `abc` represents an identifier specific to the image.
- **JSON Files**: JSON files named in the same format as the images. These are groundtruth annotations from the https://chartinfo.github.io challenge, they provide annotations for chart type, text(OCR), text location, text type (axis/tick/legend), data used to plot the chart.
- **QA Files**: QA files named in the same format as the images. Each QA file is a list of question blocks associated with the corresponding image we created in our proposed dataset.
#### QA Structure
Each QA file contains a list of question blocks in the following format:
```json
[
{
"taxonomy id": "2j",
"QID": "16",
"question": "Are all the bars in the chart visually horizontal?",
"answer": "no",
"answer_type": "Binary",
"qa_id": "XbUzFtjqsEOF",
"PMC_ID": "PMC8439477___g003"
},
{
"taxonomy id": "1a",
"QID": "7a",
"question": "What is the type of chart?",
"answer": "Vertical Bar chart",
"answer_type": "String",
"qa_id": "wzcdDijkrHtt",
"PMC_ID": "PMC8439477___g003"
}
]
```
### Dataset Loader
To facilitate loading and using the dataset, we provide a custom dataset loader script, `dataset.py`. This script defines a PyTorch `Dataset` class to handle loading, preprocessing, and batching of the images and question-answer pairs.
#### How to Use the Dataset Loader
1. **Setup and Requirements**
Ensure you have the following Python packages installed:
```bash
pip install torch torchvision Pillow
```
2. **Dataset Loader Script**
Use the provided `dataset.py` to load the dataset. The script is designed to load the dataset efficiently and handle both training and testing cases.
```python
from dataset import RQADataset
from torch.utils.data import DataLoader
dataset = RQADataset(data_dir='.', split='train') # split='test' for RQA9357 split used in the paper
# Test loading a single item
print(f"Number of samples in dataset: {len(dataset)}")
sample = dataset[0]
print("Sample data:", sample)
# Initialize DataLoader
dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate)
# Test DataLoader
for batch in dataloader:
print("Batch data:", batch)
break # Load only one batch for testing
```
### Citation
If you use this dataset in your research, please cite the following paper:
```bibtex
@InProceedings{10.1007/978-3-031-41682-8_5,
author="Ahmed, Saleem
and Jawade, Bhavin
and Pandey, Shubham
and Setlur, Srirangaraj
and Govindaraju, Venu",
editor="Fink, Gernot A.
and Jain, Rajiv
and Kise, Koichi
and Zanibbi, Richard",
title="RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic",
booktitle="Document Analysis and Recognition - ICDAR 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="66--83",
abstract="We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. Despite efforts to tackle this problem using synthetic charts, solutions are limited by the shortage of annotated real-world data. To fill this gap, we introduce a benchmark and dataset for chart visual QA on real-world charts, offering a systematic analysis of the task and a novel taxonomy for template-based chart question creation. Our contribution includes the introduction of a new answer type, `list', with both ranked and unranked variations. Our study is conducted on a real-world chart dataset from scientific literature, showcasing higher visual complexity compared to other works. Our focus is on template-based QA and how it can serve as a standard for evaluating the first-order logic capabilities of models. The results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust evaluation of large-scale pre-trained models and advance the field of chart visual QA and formal logic verification for neural networks in general. Our code and dataset is publicly available (https://github.com/cse-ai-lab/RealCQA).",
isbn="978-3-031-41682-8"
}
}
```
### License
This dataset is licensed under the [MIT License](LICENSE). By using this dataset, you agree to abide by its terms and conditions.
### Contact
For any questions or issues, please contact the authors of the paper or open an issue in this repository. |
ncoop57/mmmlu | ncoop57 | "2023-02-01T07:02:32Z" | 16,196 | 2 | [
"license:mit",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | null | "2023-01-24T23:50:14Z" | ---
license: mit
---
|
japanese-asr/whisper_transcriptions.mls.wer_10.0.vectorized | japanese-asr | "2024-09-15T01:35:08Z" | 16,192 | 1 | [
"size_categories:1M<n<10M",
"format:parquet",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-09-11T12:32:36Z" | ---
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---
|
OpenDILabCommunity/LMDrive | OpenDILabCommunity | "2023-12-25T13:28:07Z" | 16,178 | 14 | [
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2312.07488",
"region:us"
] | null | "2023-11-30T08:56:30Z" | ---
configs:
- config_name: default
data_files:
- split: train
path: navigation_instruction_list.txt
sep: " "
default: true
license: apache-2.0
language:
- en
size_categories:
- n>1T
---
# LMDrive 64K Dataset Card
LMDrive Dataset consists of 64K instruction-sensor-control data clips collected in the CARLA simulator, where each clip includes one navigation instruction, several notice instructions, a sequence of multi-modal multi-view sensor data, and control signals. The duration of the clip spans from 2 to 20 seconds.
## Dataset details
- `data/`: dataset folder, the entire dataset contains about 2T of data.
- `data/Town01`: sub dataset folder, which only consists of the data folder for the Town01
- `data/Town02`: sub dataset folder, which only consists of the data folder for the Town02
- ...
- `dataset_index.txt`: the data list for pretraining the vision encoder
- `navigation_instruction_list.txt`: the data list for instruction finetuning
- `notice_instruction_list.json`: the data list for instruction finetuning (optional if the notice instruction data is not engaged in the training)
**Dataset date:**
LMDrive-1.0 Dataset was collected in September 2023.
**Paper or resources for more information:**
Github: https://github.com/opendilab/LMDrive/README.md
Paper: https://arxiv.org/abs/2312.07488
**License:**
Attribution-NonCommercial 4.0 International
**Where to send questions or comments about the model:**
https://github.com/opendilab/LMDrive/issues
## Intended use
**Primary intended uses:**
The primary use of LMDrive is research on large multimodal models for autonomous driving.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence. |
anon8231489123/ShareGPT_Vicuna_unfiltered | anon8231489123 | "2023-04-12T05:23:59Z" | 16,121 | 768 | [
"language:en",
"license:apache-2.0",
"region:us"
] | null | "2023-04-02T05:30:31Z" | ---
license: apache-2.0
language:
- en
---
**Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.**
**Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c**
Two choices:
- Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
- Has instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json
The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training.
~100k ShareGPT conversations narrowed down to 53k by:
* Removing non-english conversations
* Removing excessive unicode (indicative of Chinese or Korean text, usually)
* Removing excessive repeated characters
* Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here):
"text-based AI language model",
"domestic violence",
"please refrain",
"derogatory",
"inappropriate",
"offensive",
"racism",
"racist",
"racial",
"discriminate",
"discriminatory",
"discrimination",
"sexist",
"sexism",
"unacceptable",
"inclusive workplace",
"lgbt",
"morals",
"ethics",
"ethical",
"legality",
"illegal",
"illegality",
"hateful",
"harmful",
"it is never okay",
"It is important to",
"It's important to",
"real-world consequences",
"hate speech",
"glorify",
"not be appropriate",
"supremacist",
"extremist",
"responsible AI",
"AI principles",
"AI assistant",
"an AI language",
"ableist",
"hurtful",
"gender stereotype",
"gender inequality",
"underrepresentation",
"safe spaces",
"gender-based",
"inclusivity",
"feminist",
"feminism",
"transgender",
"empowerment",
"communist",
"capitalism",
"stereotypes",
"biases",
"bias",
"Microaggression",
"prioritize human safety",
"as a language model",
"as an AI language model",
"As a large language model",
"As an AI",
"ethical principles",
"consensual",
"it is not appropriate",
"it's not appropriate",
"I cannot fulfill your request",
"harmful to human beings",
"ethical guidelines",
"my guidelines",
"prioritize user safety",
"adhere to ethical guidelines",
"harmful consequences",
"potentially harmful",
"dangerous activities",
"promote safety",
"well-being of all users",
"responsible information sharing",
"jeopardize the safety",
"illegal actions or intentions",
"undermine the stability",
"promote the well-being",
"illegal activities or actions",
"adherence to the law",
"potentially be harmful",
"illegal substances or activities",
"committed to promoting",
"safe information",
"lawful information",
"cannot provide guidance",
"cannot provide information",
"unable to offer assistance",
"cannot engage in discussions",
"programming prohibits",
"follow ethical guidelines",
"ensure the safety",
"involves an illegal subject",
"prioritize safety",
"illegal subject",
"prioritize user well-being",
"cannot support or promote",
"activities that could harm",
"pose a risk to others",
"against my programming",
"activities that could undermine",
"potentially dangerous",
"not within the scope",
"designed to prioritize safety",
"not able to provide",
"maintain user safety",
"adhere to safety guidelines",
"dangerous or harmful",
"cannot provide any information",
"focus on promoting safety"
* Conversations split into 2048 token chunks as described here: https://github.com/lm-sys/FastChat/blob/main/docs/commands/data_cleaning.md
This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: https://github.com/lm-sys/FastChat/ |
mteb/sickr-sts | mteb | "2022-09-27T19:13:22Z" | 15,983 | 4 | [
"language:en",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2022-04-19T14:28:03Z" | ---
language:
- en
--- |
laion/strategic_game_chess | laion | "2023-10-20T04:14:20Z" | 15,868 | 29 | [
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"game"
] | null | "2023-06-06T02:09:13Z" | ---
tags:
- game
pretty_name: The Chess Dataset
license: cc-by-4.0
---
# 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. |
yahma/alpaca-cleaned | yahma | "2023-04-10T20:29:06Z" | 15,864 | 634 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-finetuning"
] | [
"text-generation"
] | "2023-03-24T18:27:58Z" | ---
license: cc-by-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: Alpaca-Cleaned
task_categories:
- text-generation
---
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
```
"instruction":"Summarize the given article in 200 Words.",
"input": "https://www.bbc.com/news/world-51461830",
"output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state."
"instruction": "Classify the following shape according to simple geometry.",
"input": "<Picture Attached>",
"output": "Triangle"
"instruction": "Given a recent news article, write a headline for it.",
"input": "https://www.news.example.com/article-1",
"output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\""
```
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
```
"instruction": "Should I invest in stocks?",
"input": "",
"output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple"
```
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
```
"instruction": "Calculate the area of the following shape in square centimeters.",
"input": "rectangle of size 4 cm x 5 cm",
"output": ""
```
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
```
"instruction": "Create a graphic or logo that visually represents the word \"courage\".",
"input": "",
"output": "<No Output>"
```
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
```
"input":"<no input>"
"input":"No input"
"input":"noinput"
"input":"<noinput>"
```
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
```
"instruction": "Calculate the median of the following data set.",
"input": "1, 2, 4, 5, 8, 9",
"output": "5"
"instruction": "Convert 25m to km.",
"input": "",
"output": "25km"
```
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
```
"instruction": "Freeze the following sample of yogurt for 10 minutes.",
"input": "Yogurt sample",
"output": "<noinput>"
"instruction": "Increase the font size to 12 points.",
"input": "",
"output": "The font size has been increased to 12 points."
```
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.
### Original Alpaca Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The data in Alpaca are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| alpaca | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed] |
asahi417/seamless-align-enA-esA.speaker-embedding.xlsr-2b | asahi417 | "2024-06-24T08:03:42Z" | 15,815 | 0 | [
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-15T02:25:12Z" | ---
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---
|
databricks/databricks-dolly-15k | databricks | "2023-06-30T18:34:13Z" | 15,771 | 791 | [
"task_categories:question-answering",
"task_categories:summarization",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2203.02155",
"region:us"
] | [
"question-answering",
"summarization"
] | "2023-04-11T16:43:13Z" | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
language:
- en
size_categories:
- 10K<n<100K
---
# Summary
`databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several
of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification,
closed QA, generation, information extraction, open QA, and summarization.
This dataset can be used for any purpose, whether academic or commercial, under the terms of the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: English
Version: 1.0
**Owner: Databricks, Inc.**
# Dataset Overview
`databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language
models to exhibit the magical interactivity of ChatGPT.
Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including
the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using
information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly
instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the
types of questions and instructions appropriate to each category.
Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors.
They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly.
For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context`
field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications.
# Intended Uses
While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts,
this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper.
For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a
corpus of millions of examples of instructions in each of the respective InstructGPT categories.
Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to
restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might
provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from
these synthetic datasets.
# Dataset
## Purpose of Collection
As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source,
human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT.
Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including
academic or commercial applications.
## Sources
- **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories.
- **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization)
contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the
target passages.
## Annotator Guidelines
To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical
of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of
rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor.
The annotation guidelines for each of the categories are as follows:
- **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better.
- **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form.
- **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation.
- **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form.
- **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form.
- **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better.
- **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas.
## Personal or Sensitive Data
This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information.
## Language
American English
# Known Limitations
- Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia
- Some annotators may not be native English speakers
- Annotator demographics and subject matter may reflect the makeup of Databricks employees
# Citation
```
@online{DatabricksBlog2023DollyV2,
author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
urldate = {2023-06-30}
}
```
# License/Attribution
**Copyright (2023) Databricks, Inc.**
This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license.
Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:
Wikipedia (various pages) - https://www.wikipedia.org/
Copyright © Wikipedia editors and contributors. |
philschmid/llama2-german-corpus-tokenized-llama-chunk-4096 | philschmid | "2023-08-18T17:33:00Z" | 15,730 | 0 | [
"size_categories:10M<n<100M",
"format:parquet",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-08-18T10:51:09Z" | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1190392538880
num_examples: 20753008
download_size: 307400657843
dataset_size: 1190392538880
---
# Dataset Card for "llama2-german-corpus-tokenized-llama-chunk-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HuggingFaceM4/OBELICS | HuggingFaceM4 | "2023-08-22T20:50:09Z" | 15,709 | 148 | [
"language:en",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.16527",
"region:us"
] | null | "2023-05-30T23:06:14Z" | ---
language:
- en
license: cc-by-4.0
size_categories:
- 100M<n<1B
pretty_name: OBELICS
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: opt_out_docs_removed_2023_07_12
data_files:
- split: train
path: opt_out_docs_removed_2023_07_12/train-*
dataset_info:
- config_name: default
features:
- name: images
sequence: string
- name: metadata
dtype: string
- name: general_metadata
dtype: string
- name: texts
sequence: string
splits:
- name: train
num_bytes: 715724717192
num_examples: 141047697
download_size: 71520629655
dataset_size: 715724717192
- config_name: opt_out_docs_removed_2023_07_12
features:
- name: images
sequence: string
- name: metadata
dtype: string
- name: general_metadata
dtype: string
- name: texts
sequence: string
splits:
- name: train
num_bytes: 684638314215
num_examples: 134648855
download_size: 266501092920
dataset_size: 684638314215
---
# Dataset Card for OBELICS
## Dataset Description
- **Visualization of OBELICS web documents:** https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization
- **Paper:** [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527)
- **Repository:** https://github.com/huggingface/OBELICS
- **Point of Contact: hugo@huggingface.co**
`OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527).
Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs.
We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents.
[![OBELICS Nomic map](assets/nomic_map.png)](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f)
## Data Fields
An example of a sample looks as follows:
```
# The example has been cropped
{
'images': [
'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg',
None
],
'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]',
'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}',
'texts': [
None,
'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.'
]
}
```
Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`.
The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset).
`metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc.
`general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots.
## Size and Data Splits
There is only one split, `train`, that contains 141,047,697 documents.
`OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format.
## Considerations for Using the Data
### Discussion of Biases
A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric
> nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is.
> nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021).
> You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times.
> The resulting ranked words are those that co-occur with both identity terms.
> The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term.
While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.
![Data Measurement Tool Associations Eval](assets/DMT_eval.png)
We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations.
## Opted-out content
To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices.
However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`.
We recommend users of OBELICS to regularly check every document against the API.
## Content warnings
Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents.
## Terms of Use
By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model.
### Licensing Information
License CC-BY-4.0.
### Citation Information
If you are using this dataset, please cite
```
@misc{laurencon2023obelics,
title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh},
year={2023},
eprint={2306.16527},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
|
livecodebench/code_generation_lite | livecodebench | "2025-01-14T18:03:07Z" | 15,410 | 19 | [
"license:cc",
"size_categories:n<1K",
"arxiv:2403.07974",
"region:us",
"code",
"code generation"
] | null | "2024-04-16T04:46:53Z" | ---
license: cc
tags:
- code
- code generation
pretty_name: LiveCodeBench
size_categories:
- n<1K
---
## LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
<p align="center">
<a href="https://livecodebench.github.io/">🏠 Home Page</a> •
<a href="https://github.com/LiveCodeBench/LiveCodeBench">💻 GitHub Repository </a> •
<a href="https://livecodebench.github.io/leaderboard.html">🏆 Leaderboard</a> •
<a href="https://arxiv.org/abs/2403.07974">📄 Paper </a>
</p>
![LiveCodeBench](images/lcb.png)
## Change Log
Since LiveCodeBench is a continuously updated benchmark, we provide different versions of the dataset. Particularly, we provide the following versions of the dataset:
- `release_v1`: The initial release of the dataset with problems released between May 2023 and Mar 2024 containing 400 problems.
- `release_v2`: The updated release of the dataset with problems released between May 2023 and May 2024 containing 511 problems.
- `release_v3`: The updated release of the dataset with problems released between May 2023 and Jul 2024 containing 612 problems.
- `release_v4`: The updated release of the dataset with problems released between May 2023 and Sep 2024 containing 713 problems.
- `release_v5`: The updated release of the dataset with problems released between May 2023 and Jan 2025 containing 880 problems.
You can use the `version_tag` argument to load the desired version of the dataset. Additionally, you can use version tags like `v1`, `v2`, `v1_v3`, `v4_v5` to get the problems released in a specific version.
## Dataset Description
LiveCodeBench is a "live" updating benchmark for holistically evaluating code related capabilities of LLMs.
Particularly, it evaluates LLMs across a range of capabilties including code generation, self-repair, test output prediction, and code execution.
This is the code generation scenario of LiveCodeBench. It is also used for evaluating self-repair using test case feedback.
LiveCodeBench problems are collected from competition programming websites with particular focus on maintaining problem quality, test case quality, and problem difficulty diversity.
This scenario currently hosts over 500 problems from LeetCode, AtCoder, and Codeforces.
Each problem instance is consists of problem description, input/output examples, and hidden test cases.
Additionally, every problem is tagged with its difficulty level and release date which allows measuring model performance across different time windows.
The goal is to generate a correct and efficient solution for each problem instance.
The initial code_generation dataset included larger number of test cases which leads to substantially large dataset size. This (lite) version has pruned and sampled tests while trying to ensure similar performances with the original dataset. Going forward, livecodebench will be using this lite version for code generation evaluations.
## Usage
You can use the dataset by loading it from the Hugging Face datasets library. Additionally, the version tag "release_v1" is used to specify the (temporal) version of the dataset. "v1" corresponds to the initial release of the dataset and "release_v2" is the second version.
```python
from datasets import load_dataset
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
``` |
codeparrot/github-code | codeparrot | "2022-10-20T15:01:14Z" | 15,399 | 307 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"region:us"
] | [
"text-generation"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: github-code
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# GitHub Code Dataset
## Dataset Description
The GitHub Code dataset consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in 1TB of data. The dataset was created from the public GitHub dataset on Google BiqQuery.
### How to use it
The GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following two lines of code:
```python
from datasets import load_dataset
ds = load_dataset("codeparrot/github-code", streaming=True, split="train")
print(next(iter(ds)))
#OUTPUT:
{
'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
'repo_name': 'MirekSz/webpack-es6-ts',
'path': 'app/mods/mod190.js',
'language': 'JavaScript',
'license': 'isc',
'size': 73
}
```
You can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration:
```python
ds = load_dataset("codeparrot/github-code", streaming=True, split="train", languages=["Dockerfile"])
print(next(iter(ds))["code"])
#OUTPUT:
"""\
FROM rockyluke/ubuntu:precise
ENV DEBIAN_FRONTEND="noninteractive" \
TZ="Europe/Amsterdam"
...
"""
```
We also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages:
```python
ds = load_dataset("codeparrot/github-code", streaming=True, split="train", licenses=["mit", "isc"])
licenses = []
for element in iter(ds).take(10_000):
licenses.append(element["license"])
print(Counter(licenses))
#OUTPUT:
Counter({'mit': 9896, 'isc': 104})
```
Naturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage:
```python
ds = load_dataset("codeparrot/github-code", split="train")
```
## Data Structure
### Data Instances
```python
{
'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
'repo_name': 'MirekSz/webpack-es6-ts',
'path': 'app/mods/mod190.js',
'language': 'JavaScript',
'license': 'isc',
'size': 73
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|code|string|content of source file|
|repo_name|string|name of the GitHub repository|
|path|string|path of file in GitHub repository|
|language|string|programming language as inferred by extension|
|license|string|license of GitHub repository|
|size|int|size of source file in bytes|
### Data Splits
The dataset only contains a train split.
## Languages
The dataset contains 30 programming languages with over 60 extensions:
```python
{
"Assembly": [".asm"],
"Batchfile": [".bat", ".cmd"],
"C": [".c", ".h"],
"C#": [".cs"],
"C++": [".cpp", ".hpp", ".c++", ".h++", ".cc", ".hh", ".C", ".H"],
"CMake": [".cmake"],
"CSS": [".css"],
"Dockerfile": [".dockerfile", "Dockerfile"],
"FORTRAN": ['.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp'],
"GO": [".go"],
"Haskell": [".hs"],
"HTML":[".html"],
"Java": [".java"],
"JavaScript": [".js"],
"Julia": [".jl"],
"Lua": [".lua"],
"Makefile": ["Makefile"],
"Markdown": [".md", ".markdown"],
"PHP": [".php", ".php3", ".php4", ".php5", ".phps", ".phpt"],
"Perl": [".pl", ".pm", ".pod", ".perl"],
"PowerShell": ['.ps1', '.psd1', '.psm1'],
"Python": [".py"],
"Ruby": [".rb"],
"Rust": [".rs"],
"SQL": [".sql"],
"Scala": [".scala"],
"Shell": [".sh", ".bash", ".command", ".zsh"],
"TypeScript": [".ts", ".tsx"],
"TeX": [".tex"],
"Visual Basic": [".vb"]
}
```
## Licenses
Each example is also annotated with the license of the associated repository. There are in total 15 licenses:
```python
[
'mit',
'apache-2.0',
'gpl-3.0',
'gpl-2.0',
'bsd-3-clause',
'agpl-3.0',
'lgpl-3.0',
'lgpl-2.1',
'bsd-2-clause',
'cc0-1.0',
'epl-1.0',
'mpl-2.0',
'unlicense',
'isc',
'artistic-2.0'
]
```
## Dataset Statistics
The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:
![dataset-statistics](https://huggingface.co/datasets/codeparrot/github-code/resolve/main/github-code-stats-alpha.png)
| | Language |File Count| Size (GB)|
|---:|:-------------|---------:|-------:|
| 0 | Java | 19548190 | 107.70 |
| 1 | C | 14143113 | 183.83 |
| 2 | JavaScript | 11839883 | 87.82 |
| 3 | HTML | 11178557 | 118.12 |
| 4 | PHP | 11177610 | 61.41 |
| 5 | Markdown | 8464626 | 23.09 |
| 6 | C++ | 7380520 | 87.73 |
| 7 | Python | 7226626 | 52.03 |
| 8 | C# | 6811652 | 36.83 |
| 9 | Ruby | 4473331 | 10.95 |
| 10 | GO | 2265436 | 19.28 |
| 11 | TypeScript | 1940406 | 24.59 |
| 12 | CSS | 1734406 | 22.67 |
| 13 | Shell | 1385648 | 3.01 |
| 14 | Scala | 835755 | 3.87 |
| 15 | Makefile | 679430 | 2.92 |
| 16 | SQL | 656671 | 5.67 |
| 17 | Lua | 578554 | 2.81 |
| 18 | Perl | 497949 | 4.70 |
| 19 | Dockerfile | 366505 | 0.71 |
| 20 | Haskell | 340623 | 1.85 |
| 21 | Rust | 322431 | 2.68 |
| 22 | TeX | 251015 | 2.15 |
| 23 | Batchfile | 236945 | 0.70 |
| 24 | CMake | 175282 | 0.54 |
| 25 | Visual Basic | 155652 | 1.91 |
| 26 | FORTRAN | 142038 | 1.62 |
| 27 | PowerShell | 136846 | 0.69 |
| 28 | Assembly | 82905 | 0.78 |
| 29 | Julia | 58317 | 0.29 |
## Dataset Creation
The dataset was created in two steps:
1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/query.sql)). The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_.
2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/github_preprocessing.py)).
## Considerations for Using the Data
The dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames.
## Releases
You can load any older version of the dataset with the `revision` argument:
```Python
ds = load_dataset("codeparrot/github-code", revision="v1.0")
```
### v1.0
- Initial release of dataset
- The query was executed on _Feb 14, 2022, 12:03:16 PM UTC+1_
### v1.1
- Fix missing Scala/TypeScript
- Fix deduplication issue with inconsistent Python `hash`
- The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_
|
openai/MMMLU | openai | "2024-10-16T18:39:00Z" | 15,169 | 453 | [
"task_categories:question-answering",
"language:ar",
"language:bn",
"language:de",
"language:es",
"language:fr",
"language:hi",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:pt",
"language:sw",
"language:yo",
"language:zh",
"license:mit",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2009.03300",
"region:us"
] | [
"question-answering"
] | "2024-09-13T16:37:19Z" | ---
task_categories:
- question-answering
configs:
- config_name: default
data_files:
- split: test
path: test/*.csv
- config_name: AR_XY
data_files:
- split: test
path: test/mmlu_AR-XY.csv
- config_name: BN_BD
data_files:
- split: test
path: test/mmlu_BN-BD.csv
- config_name: DE_DE
data_files:
- split: test
path: test/mmlu_DE-DE.csv
- config_name: ES_LA
data_files:
- split: test
path: test/mmlu_ES-LA.csv
- config_name: FR_FR
data_files:
- split: test
path: test/mmlu_FR-FR.csv
- config_name: HI_IN
data_files:
- split: test
path: test/mmlu_HI-IN.csv
- config_name: ID_ID
data_files:
- split: test
path: test/mmlu_ID-ID.csv
- config_name: IT_IT
data_files:
- split: test
path: test/mmlu_IT-IT.csv
- config_name: JA_JP
data_files:
- split: test
path: test/mmlu_JA-JP.csv
- config_name: KO_KR
data_files:
- split: test
path: test/mmlu_KO-KR.csv
- config_name: PT_BR
data_files:
- split: test
path: test/mmlu_PT-BR.csv
- config_name: SW_KE
data_files:
- split: test
path: test/mmlu_SW-KE.csv
- config_name: YO_NG
data_files:
- split: test
path: test/mmlu_YO-NG.csv
- config_name: ZH_CN
data_files:
- split: test
path: test/mmlu_ZH-CN.csv
language:
- ar
- bn
- de
- es
- fr
- hi
- id
- it
- ja
- ko
- pt
- sw
- yo
- zh
license: mit
---
# Multilingual Massive Multitask Language Understanding (MMMLU)
The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science.
We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases confidence in the accuracy of the translations, especially for low-resource languages like Yoruba. We are publishing the professional human translations and the code we use to run the evaluations.
This effort reflects our commitment to improving the multilingual capabilities of AI models, ensuring they perform accurately across languages, particularly for underrepresented communities. By prioritizing high-quality translations, we aim to make AI technology more inclusive and effective for users worldwide.
## Locales
MMMLU contains the MMLU test set translated into the following locales:
* AR_XY (Arabic)
* BN_BD (Bengali)
* DE_DE (German)
* ES_LA (Spanish)
* FR_FR (French)
* HI_IN (Hindi)
* ID_ID (Indonesian)
* IT_IT (Italian)
* JA_JP (Japanese)
* KO_KR (Korean)
* PT_BR (Brazilian Portuguese)
* SW_KE (Swahili)
* YO_NG (Yoruba)
* ZH_CN (Simplified Chinese)
## Sources
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). [*Measuring Massive Multitask Language Understanding*](https://arxiv.org/abs/2009.03300).
[OpenAI Simple Evals GitHub Repository](https://github.com/openai/simple-evals) |
fsicoli/common_voice_16_0 | fsicoli | "2023-12-22T19:58:33Z" | 15,009 | 2 | [
"task_categories:automatic-speech-recognition",
"language:ab",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:dv",
"language:dyu",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:gl",
"language:gn",
"language:ha",
"language:he",
"language:hi",
"language:hsb",
"language:hu",
"language:ia",
"language:id",
"language:ig",
"language:is",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:kk",
"language:kmr",
"language:ko",
"language:ky",
"language:lg",
"language:lo",
"language:lt",
"language:lv",
"language:mdf",
"language:mhr",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:mt",
"language:myv",
"language:nl",
"language:oc",
"language:or",
"language:pl",
"language:ps",
"language:pt",
"language:quy",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sat",
"language:sc",
"language:sk",
"language:skr",
"language:sl",
"language:sq",
"language:sr",
"language:sw",
"language:ta",
"language:th",
"language:ti",
"language:tig",
"language:tk",
"language:tok",
"language:tr",
"language:tt",
"language:tw",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yue",
"language:zgh",
"language:zh",
"language:yo",
"license:cc0-1.0",
"size_categories:100B<n<1T",
"region:us",
"mozilla",
"foundation"
] | [
"automatic-speech-recognition"
] | "2023-12-19T17:26:21Z" | ---
license: cc0-1.0
language:
- ab
- af
- am
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- gl
- gn
- ha
- he
- hi
- hsb
- hu
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mrj
- mt
- myv
- nl
- oc
- or
- pl
- ps
- pt
- quy
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- skr
- sl
- sq
- sr
- sw
- ta
- th
- ti
- tig
- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yue
- zgh
- zh
- yo
task_categories:
- automatic-speech-recognition
pretty_name: Common Voice Corpus 16.0
size_categories:
- 100B<n<1T
tags:
- mozilla
- foundation
---
# Dataset Card for Common Voice Corpus 16.0
<!-- Provide a quick summary of the dataset. -->
This dataset is an unofficial version of the Mozilla Common Voice Corpus 16. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/.
## Languages
```
Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese):
```
from datasets import load_dataset
cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```
from datasets import load_dataset
cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train", streaming=True)
print(next(iter(cv_16)))
```
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
### Local
```
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_16), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_16, batch_sampler=batch_sampler)
```
### Streaming
```
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train")
dataloader = DataLoader(cv_16, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
### Dataset Structure
Data Instances
A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
### Licensing Information
Public Domain, CC-0
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
---
|
jacobbieker/eumetsat-cloudmask-0deg | jacobbieker | "2024-11-09T20:17:38Z" | 14,946 | 0 | [
"license:mit",
"doi:10.57967/hf/1643",
"region:us"
] | null | "2024-01-12T18:50:32Z" | ---
license: mit
---
|
asahi417/seamless-align-enA-viA.speaker-embedding.xlsr-2b | asahi417 | "2024-06-25T01:27:26Z" | 14,939 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-11T14:33:04Z" | ---
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|
regent-research/regent-subset-of-jat-dataset-tokenized | regent-research | "2024-10-20T18:10:12Z" | 14,903 | 0 | [
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"format:parquet",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-10-04T01:39:53Z" | ---
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- config_name: babyai-go-to_newdata
data_files:
- split: train
path: babyai-go-to_newdata/train-*
- config_name: babyai-go-to_subset
data_files:
- split: train
path: babyai-go-to_subset/train-*
- config_name: babyai-key-corridor_newdata
data_files:
- split: train
path: babyai-key-corridor_newdata/train-*
- config_name: babyai-key-corridor_subset
data_files:
- split: train
path: babyai-key-corridor_subset/train-*
- config_name: babyai-mini-boss-level_newdata
data_files:
- split: train
path: babyai-mini-boss-level_newdata/train-*
- config_name: babyai-mini-boss-level_subset
data_files:
- split: train
path: babyai-mini-boss-level_subset/train-*
- config_name: babyai-move-two-across-s8n9_newdata
data_files:
- split: train
path: babyai-move-two-across-s8n9_newdata/train-*
- config_name: babyai-move-two-across-s8n9_subset
data_files:
- split: train
path: babyai-move-two-across-s8n9_subset/train-*
- config_name: babyai-one-room-s8_newdata
data_files:
- split: train
path: babyai-one-room-s8_newdata/train-*
- config_name: babyai-one-room-s8_subset
data_files:
- split: train
path: babyai-one-room-s8_subset/train-*
- config_name: babyai-open-door_newdata
data_files:
- split: train
path: babyai-open-door_newdata/train-*
- config_name: babyai-open-door_subset
data_files:
- split: train
path: babyai-open-door_subset/train-*
- config_name: babyai-open-doors-order-n4_newdata
data_files:
- split: train
path: babyai-open-doors-order-n4_newdata/train-*
- config_name: babyai-open-doors-order-n4_subset
data_files:
- split: train
path: babyai-open-doors-order-n4_subset/train-*
- config_name: babyai-open-red-door_newdata
data_files:
- split: train
path: babyai-open-red-door_newdata/train-*
- config_name: babyai-open-red-door_subset
data_files:
- split: train
path: babyai-open-red-door_subset/train-*
- config_name: babyai-open-two-doors_newdata
data_files:
- split: train
path: babyai-open-two-doors_newdata/train-*
- config_name: babyai-open-two-doors_subset
data_files:
- split: train
path: babyai-open-two-doors_subset/train-*
- config_name: babyai-open_newdata
data_files:
- split: train
path: babyai-open_newdata/train-*
- config_name: babyai-open_subset
data_files:
- split: train
path: babyai-open_subset/train-*
- config_name: babyai-pickup-above_newdata
data_files:
- split: train
path: babyai-pickup-above_newdata/train-*
- config_name: babyai-pickup-above_subset
data_files:
- split: train
path: babyai-pickup-above_subset/train-*
- config_name: babyai-pickup-dist_newdata
data_files:
- split: train
path: babyai-pickup-dist_newdata/train-*
- config_name: babyai-pickup-dist_subset
data_files:
- split: train
path: babyai-pickup-dist_subset/train-*
- config_name: babyai-pickup-loc_newdata
data_files:
- split: train
path: babyai-pickup-loc_newdata/train-*
- config_name: babyai-pickup-loc_subset
data_files:
- split: train
path: babyai-pickup-loc_subset/train-*
- config_name: babyai-pickup_newdata
data_files:
- split: train
path: babyai-pickup_newdata/train-*
- config_name: babyai-pickup_subset
data_files:
- split: train
path: babyai-pickup_subset/train-*
- config_name: babyai-put-next-local_newdata
data_files:
- split: train
path: babyai-put-next-local_newdata/train-*
- config_name: babyai-put-next-local_subset
data_files:
- split: train
path: babyai-put-next-local_subset/train-*
- config_name: babyai-put-next_newdata
data_files:
- split: train
path: babyai-put-next_newdata/train-*
- config_name: babyai-put-next_subset
data_files:
- split: train
path: babyai-put-next_subset/train-*
- config_name: babyai-synth-loc_newdata
data_files:
- split: train
path: babyai-synth-loc_newdata/train-*
- config_name: babyai-synth-loc_subset
data_files:
- split: train
path: babyai-synth-loc_subset/train-*
- config_name: babyai-synth-seq_newdata
data_files:
- split: train
path: babyai-synth-seq_newdata/train-*
- config_name: babyai-synth-seq_subset
data_files:
- split: train
path: babyai-synth-seq_subset/train-*
- config_name: babyai-synth_newdata
data_files:
- split: train
path: babyai-synth_newdata/train-*
- config_name: babyai-synth_subset
data_files:
- split: train
path: babyai-synth_subset/train-*
- config_name: babyai-unblock-pickup_newdata
data_files:
- split: train
path: babyai-unblock-pickup_newdata/train-*
- config_name: babyai-unblock-pickup_subset
data_files:
- split: train
path: babyai-unblock-pickup_subset/train-*
- config_name: babyai-unlock-local_newdata
data_files:
- split: train
path: babyai-unlock-local_newdata/train-*
- config_name: babyai-unlock-local_subset
data_files:
- split: train
path: babyai-unlock-local_subset/train-*
- config_name: babyai-unlock-pickup_newdata
data_files:
- split: train
path: babyai-unlock-pickup_newdata/train-*
- config_name: babyai-unlock-pickup_subset
data_files:
- split: train
path: babyai-unlock-pickup_subset/train-*
- config_name: babyai-unlock-to-unlock_newdata
data_files:
- split: train
path: babyai-unlock-to-unlock_newdata/train-*
- config_name: babyai-unlock-to-unlock_subset
data_files:
- split: train
path: babyai-unlock-to-unlock_subset/train-*
- config_name: babyai-unlock_newdata
data_files:
- split: train
path: babyai-unlock_newdata/train-*
- config_name: babyai-unlock_subset
data_files:
- split: train
path: babyai-unlock_subset/train-*
- config_name: metaworld-assembly_newdata
data_files:
- split: train
path: metaworld-assembly_newdata/train-*
- config_name: metaworld-assembly_subset
data_files:
- split: train
path: metaworld-assembly_subset/train-*
- config_name: metaworld-basketball_newdata
data_files:
- split: train
path: metaworld-basketball_newdata/train-*
- config_name: metaworld-basketball_subset
data_files:
- split: train
path: metaworld-basketball_subset/train-*
- config_name: metaworld-bin-picking_newdata
data_files:
- split: train
path: metaworld-bin-picking_newdata/train-*
- config_name: metaworld-box-close_newdata
data_files:
- split: train
path: metaworld-box-close_newdata/train-*
- config_name: metaworld-button-press-topdown-wall_newdata
data_files:
- split: train
path: metaworld-button-press-topdown-wall_newdata/train-*
- config_name: metaworld-button-press-topdown-wall_subset
data_files:
- split: train
path: metaworld-button-press-topdown-wall_subset/train-*
- config_name: metaworld-button-press-topdown_newdata
data_files:
- split: train
path: metaworld-button-press-topdown_newdata/train-*
- config_name: metaworld-button-press-topdown_subset
data_files:
- split: train
path: metaworld-button-press-topdown_subset/train-*
- config_name: metaworld-button-press-wall_newdata
data_files:
- split: train
path: metaworld-button-press-wall_newdata/train-*
- config_name: metaworld-button-press-wall_subset
data_files:
- split: train
path: metaworld-button-press-wall_subset/train-*
- config_name: metaworld-button-press_newdata
data_files:
- split: train
path: metaworld-button-press_newdata/train-*
- config_name: metaworld-button-press_subset
data_files:
- split: train
path: metaworld-button-press_subset/train-*
- config_name: metaworld-coffee-button_newdata
data_files:
- split: train
path: metaworld-coffee-button_newdata/train-*
- config_name: metaworld-coffee-button_subset
data_files:
- split: train
path: metaworld-coffee-button_subset/train-*
- config_name: metaworld-coffee-pull_newdata
data_files:
- split: train
path: metaworld-coffee-pull_newdata/train-*
- config_name: metaworld-coffee-pull_subset
data_files:
- split: train
path: metaworld-coffee-pull_subset/train-*
- config_name: metaworld-coffee-push_newdata
data_files:
- split: train
path: metaworld-coffee-push_newdata/train-*
- config_name: metaworld-coffee-push_subset
data_files:
- split: train
path: metaworld-coffee-push_subset/train-*
- config_name: metaworld-dial-turn_newdata
data_files:
- split: train
path: metaworld-dial-turn_newdata/train-*
- config_name: metaworld-dial-turn_subset
data_files:
- split: train
path: metaworld-dial-turn_subset/train-*
- config_name: metaworld-disassemble_newdata
data_files:
- split: train
path: metaworld-disassemble_newdata/train-*
- config_name: metaworld-disassemble_subset
data_files:
- split: train
path: metaworld-disassemble_subset/train-*
- config_name: metaworld-door-close_newdata
data_files:
- split: train
path: metaworld-door-close_newdata/train-*
- config_name: metaworld-door-close_subset
data_files:
- split: train
path: metaworld-door-close_subset/train-*
- config_name: metaworld-door-lock_newdata
data_files:
- split: train
path: metaworld-door-lock_newdata/train-*
- config_name: metaworld-door-open_newdata
data_files:
- split: train
path: metaworld-door-open_newdata/train-*
- config_name: metaworld-door-open_subset
data_files:
- split: train
path: metaworld-door-open_subset/train-*
- config_name: metaworld-door-unlock_newdata
data_files:
- split: train
path: metaworld-door-unlock_newdata/train-*
- config_name: metaworld-drawer-close_newdata
data_files:
- split: train
path: metaworld-drawer-close_newdata/train-*
- config_name: metaworld-drawer-close_subset
data_files:
- split: train
path: metaworld-drawer-close_subset/train-*
- config_name: metaworld-drawer-open_newdata
data_files:
- split: train
path: metaworld-drawer-open_newdata/train-*
- config_name: metaworld-drawer-open_subset
data_files:
- split: train
path: metaworld-drawer-open_subset/train-*
- config_name: metaworld-faucet-close_newdata
data_files:
- split: train
path: metaworld-faucet-close_newdata/train-*
- config_name: metaworld-faucet-close_subset
data_files:
- split: train
path: metaworld-faucet-close_subset/train-*
- config_name: metaworld-faucet-open_newdata
data_files:
- split: train
path: metaworld-faucet-open_newdata/train-*
- config_name: metaworld-faucet-open_subset
data_files:
- split: train
path: metaworld-faucet-open_subset/train-*
- config_name: metaworld-hammer_newdata
data_files:
- split: train
path: metaworld-hammer_newdata/train-*
- config_name: metaworld-hammer_subset
data_files:
- split: train
path: metaworld-hammer_subset/train-*
- config_name: metaworld-hand-insert_newdata
data_files:
- split: train
path: metaworld-hand-insert_newdata/train-*
- config_name: metaworld-handle-press-side_newdata
data_files:
- split: train
path: metaworld-handle-press-side_newdata/train-*
- config_name: metaworld-handle-press-side_subset
data_files:
- split: train
path: metaworld-handle-press-side_subset/train-*
- config_name: metaworld-handle-press_newdata
data_files:
- split: train
path: metaworld-handle-press_newdata/train-*
- config_name: metaworld-handle-press_subset
data_files:
- split: train
path: metaworld-handle-press_subset/train-*
- config_name: metaworld-handle-pull-side_newdata
data_files:
- split: train
path: metaworld-handle-pull-side_newdata/train-*
- config_name: metaworld-handle-pull-side_subset
data_files:
- split: train
path: metaworld-handle-pull-side_subset/train-*
- config_name: metaworld-handle-pull_newdata
data_files:
- split: train
path: metaworld-handle-pull_newdata/train-*
- config_name: metaworld-handle-pull_subset
data_files:
- split: train
path: metaworld-handle-pull_subset/train-*
- config_name: metaworld-lever-pull_newdata
data_files:
- split: train
path: metaworld-lever-pull_newdata/train-*
- config_name: metaworld-lever-pull_subset
data_files:
- split: train
path: metaworld-lever-pull_subset/train-*
- config_name: metaworld-peg-insert-side_newdata
data_files:
- split: train
path: metaworld-peg-insert-side_newdata/train-*
- config_name: metaworld-peg-insert-side_subset
data_files:
- split: train
path: metaworld-peg-insert-side_subset/train-*
- config_name: metaworld-peg-unplug-side_newdata
data_files:
- split: train
path: metaworld-peg-unplug-side_newdata/train-*
- config_name: metaworld-peg-unplug-side_subset
data_files:
- split: train
path: metaworld-peg-unplug-side_subset/train-*
- config_name: metaworld-pick-out-of-hole_newdata
data_files:
- split: train
path: metaworld-pick-out-of-hole_newdata/train-*
- config_name: metaworld-pick-out-of-hole_subset
data_files:
- split: train
path: metaworld-pick-out-of-hole_subset/train-*
- config_name: metaworld-pick-place-wall_newdata
data_files:
- split: train
path: metaworld-pick-place-wall_newdata/train-*
- config_name: metaworld-pick-place-wall_subset
data_files:
- split: train
path: metaworld-pick-place-wall_subset/train-*
- config_name: metaworld-pick-place_newdata
data_files:
- split: train
path: metaworld-pick-place_newdata/train-*
- config_name: metaworld-pick-place_subset
data_files:
- split: train
path: metaworld-pick-place_subset/train-*
- config_name: metaworld-plate-slide-back-side_newdata
data_files:
- split: train
path: metaworld-plate-slide-back-side_newdata/train-*
- config_name: metaworld-plate-slide-back-side_subset
data_files:
- split: train
path: metaworld-plate-slide-back-side_subset/train-*
- config_name: metaworld-plate-slide-back_newdata
data_files:
- split: train
path: metaworld-plate-slide-back_newdata/train-*
- config_name: metaworld-plate-slide-back_subset
data_files:
- split: train
path: metaworld-plate-slide-back_subset/train-*
- config_name: metaworld-plate-slide-side_newdata
data_files:
- split: train
path: metaworld-plate-slide-side_newdata/train-*
- config_name: metaworld-plate-slide-side_subset
data_files:
- split: train
path: metaworld-plate-slide-side_subset/train-*
- config_name: metaworld-plate-slide_newdata
data_files:
- split: train
path: metaworld-plate-slide_newdata/train-*
- config_name: metaworld-plate-slide_subset
data_files:
- split: train
path: metaworld-plate-slide_subset/train-*
- config_name: metaworld-push-back_newdata
data_files:
- split: train
path: metaworld-push-back_newdata/train-*
- config_name: metaworld-push-back_subset
data_files:
- split: train
path: metaworld-push-back_subset/train-*
- config_name: metaworld-push-wall_newdata
data_files:
- split: train
path: metaworld-push-wall_newdata/train-*
- config_name: metaworld-push-wall_subset
data_files:
- split: train
path: metaworld-push-wall_subset/train-*
- config_name: metaworld-push_newdata
data_files:
- split: train
path: metaworld-push_newdata/train-*
- config_name: metaworld-push_subset
data_files:
- split: train
path: metaworld-push_subset/train-*
- config_name: metaworld-reach-wall_newdata
data_files:
- split: train
path: metaworld-reach-wall_newdata/train-*
- config_name: metaworld-reach-wall_subset
data_files:
- split: train
path: metaworld-reach-wall_subset/train-*
- config_name: metaworld-reach_newdata
data_files:
- split: train
path: metaworld-reach_newdata/train-*
- config_name: metaworld-reach_subset
data_files:
- split: train
path: metaworld-reach_subset/train-*
- config_name: metaworld-shelf-place_newdata
data_files:
- split: train
path: metaworld-shelf-place_newdata/train-*
- config_name: metaworld-shelf-place_subset
data_files:
- split: train
path: metaworld-shelf-place_subset/train-*
- config_name: metaworld-soccer_newdata
data_files:
- split: train
path: metaworld-soccer_newdata/train-*
- config_name: metaworld-soccer_subset
data_files:
- split: train
path: metaworld-soccer_subset/train-*
- config_name: metaworld-stick-pull_newdata
data_files:
- split: train
path: metaworld-stick-pull_newdata/train-*
- config_name: metaworld-stick-pull_subset
data_files:
- split: train
path: metaworld-stick-pull_subset/train-*
- config_name: metaworld-stick-push_newdata
data_files:
- split: train
path: metaworld-stick-push_newdata/train-*
- config_name: metaworld-stick-push_subset
data_files:
- split: train
path: metaworld-stick-push_subset/train-*
- config_name: metaworld-sweep-into_newdata
data_files:
- split: train
path: metaworld-sweep-into_newdata/train-*
- config_name: metaworld-sweep-into_subset
data_files:
- split: train
path: metaworld-sweep-into_subset/train-*
- config_name: metaworld-sweep_newdata
data_files:
- split: train
path: metaworld-sweep_newdata/train-*
- config_name: metaworld-sweep_subset
data_files:
- split: train
path: metaworld-sweep_subset/train-*
- config_name: metaworld-window-close_newdata
data_files:
- split: train
path: metaworld-window-close_newdata/train-*
- config_name: metaworld-window-close_subset
data_files:
- split: train
path: metaworld-window-close_subset/train-*
- config_name: metaworld-window-open_newdata
data_files:
- split: train
path: metaworld-window-open_newdata/train-*
- config_name: metaworld-window-open_subset
data_files:
- split: train
path: metaworld-window-open_subset/train-*
- config_name: mujoco-ant_newdata
data_files:
- split: train
path: mujoco-ant_newdata/train-*
- config_name: mujoco-ant_subset
data_files:
- split: train
path: mujoco-ant_subset/train-*
- config_name: mujoco-doublependulum_newdata
data_files:
- split: train
path: mujoco-doublependulum_newdata/train-*
- config_name: mujoco-doublependulum_subset
data_files:
- split: train
path: mujoco-doublependulum_subset/train-*
- config_name: mujoco-halfcheetah_newdata
data_files:
- split: train
path: mujoco-halfcheetah_newdata/train-*
- config_name: mujoco-hopper_newdata
data_files:
- split: train
path: mujoco-hopper_newdata/train-*
- config_name: mujoco-humanoid_newdata
data_files:
- split: train
path: mujoco-humanoid_newdata/train-*
- config_name: mujoco-humanoid_subset
data_files:
- split: train
path: mujoco-humanoid_subset/train-*
- config_name: mujoco-pendulum_newdata
data_files:
- split: train
path: mujoco-pendulum_newdata/train-*
- config_name: mujoco-pendulum_subset
data_files:
- split: train
path: mujoco-pendulum_subset/train-*
- config_name: mujoco-pusher_newdata
data_files:
- split: train
path: mujoco-pusher_newdata/train-*
- config_name: mujoco-pusher_subset
data_files:
- split: train
path: mujoco-pusher_subset/train-*
- config_name: mujoco-reacher_newdata
data_files:
- split: train
path: mujoco-reacher_newdata/train-*
- config_name: mujoco-reacher_subset
data_files:
- split: train
path: mujoco-reacher_subset/train-*
- config_name: mujoco-standup_newdata
data_files:
- split: train
path: mujoco-standup_newdata/train-*
- config_name: mujoco-standup_subset
data_files:
- split: train
path: mujoco-standup_subset/train-*
- config_name: mujoco-swimmer_newdata
data_files:
- split: train
path: mujoco-swimmer_newdata/train-*
- config_name: mujoco-swimmer_subset
data_files:
- split: train
path: mujoco-swimmer_subset/train-*
- config_name: mujoco-walker_newdata
data_files:
- split: train
path: mujoco-walker_newdata/train-*
- config_name: mujoco-walker_subset
data_files:
- split: train
path: mujoco-walker_subset/train-*
---
This is the dataset for `REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context In New Environments`.
The REGENT dataset includes a subset of the JAT (tokenized) dataset (from https://huggingface.co/datasets/jat-project) for the REGENT training environments.
It has around a 100k transitions from each of the 145 training environments (45 metaworld, 52 atari, 9 mujoco, 39 babyai).
Please find this in the *_subset folders.
![depiction of 145 training environments and 12 unseen environments](https://kaustubhsridhar.github.io/regent-research/static/images/problem_setting_jat.png)
It also has distance values for input sequences used in the REGENT model for both the above 145 training environments and 12 unseen environments.
These values are used for computing the distance normalization value in every environment.
These values, along with the indices of the input sequences, in the training environments alone, are used for pre-training REGENT.
We detail this REGENT subset of the JAT dataset at https://github.com/regent-research/regent/blob/main/scripts_preprocessing/README.md. |
poloclub/diffusiondb | poloclub | "2024-01-22T22:17:47Z" | 14,835 | 485 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"size_categories:n>1T",
"arxiv:2210.14896",
"region:us",
"stable diffusion",
"prompt engineering",
"prompts",
"research paper"
] | [
"text-to-image",
"image-to-text"
] | "2022-10-25T02:25:28Z" | ---
layout: default
title: Home
nav_order: 1
has_children: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: DiffusionDB
size_categories:
- n>1T
source_datasets:
- original
tags:
- stable diffusion
- prompt engineering
- prompts
- research paper
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
# DiffusionDB
<img width="100%" src="https://user-images.githubusercontent.com/15007159/201762588-f24db2b8-dbb2-4a94-947b-7de393fc3d33.gif">
## Table of Contents
- [DiffusionDB](#diffusiondb)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Two Subsets](#two-subsets)
- [Key Differences](#key-differences)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Metadata](#dataset-metadata)
- [Metadata Schema](#metadata-schema)
- [Data Splits](#data-splits)
- [Loading Data Subsets](#loading-data-subsets)
- [Method 1: Using Hugging Face Datasets Loader](#method-1-using-hugging-face-datasets-loader)
- [Method 2. Use the PoloClub Downloader](#method-2-use-the-poloclub-downloader)
- [Usage/Examples](#usageexamples)
- [Downloading a single file](#downloading-a-single-file)
- [Downloading a range of files](#downloading-a-range-of-files)
- [Downloading to a specific directory](#downloading-to-a-specific-directory)
- [Setting the files to unzip once they've been downloaded](#setting-the-files-to-unzip-once-theyve-been-downloaded)
- [Method 3. Use `metadata.parquet` (Text Only)](#method-3-use-metadataparquet-text-only)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [DiffusionDB homepage](https://poloclub.github.io/diffusiondb)
- **Repository:** [DiffusionDB repository](https://github.com/poloclub/diffusiondb)
- **Distribution:** [DiffusionDB Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb)
- **Paper:** [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://arxiv.org/abs/2210.14896)
- **Point of Contact:** [Jay Wang](mailto:jayw@gatech.edu)
### Dataset Summary
DiffusionDB is the first large-scale text-to-image prompt dataset. It contains **14 million** images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
DiffusionDB is publicly available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb).
### Supported Tasks and Leaderboards
The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.
### Languages
The text in the dataset is mostly English. It also contains other languages such as Spanish, Chinese, and Russian.
### Two Subsets
DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs.
|Subset|Num of Images|Num of Unique Prompts|Size|Image Directory|Metadata Table|
|:--|--:|--:|--:|--:|--:|
|DiffusionDB 2M|2M|1.5M|1.6TB|`images/`|`metadata.parquet`|
|DiffusionDB Large|14M|1.8M|6.5TB|`diffusiondb-large-part-1/` `diffusiondb-large-part-2/`|`metadata-large.parquet`|
##### Key Differences
1. Two subsets have a similar number of unique prompts, but DiffusionDB Large has much more images. DiffusionDB Large is a superset of DiffusionDB 2M.
2. Images in DiffusionDB 2M are stored in `png` format; images in DiffusionDB Large use a lossless `webp` format.
## Dataset Structure
We use a modularized file structure to distribute DiffusionDB. The 2 million images in DiffusionDB 2M are split into 2,000 folders, where each folder contains 1,000 images and a JSON file that links these 1,000 images to their prompts and hyperparameters. Similarly, the 14 million images in DiffusionDB Large are split into 14,000 folders.
```bash
# DiffusionDB 2M
./
├── images
│ ├── part-000001
│ │ ├── 3bfcd9cf-26ea-4303-bbe1-b095853f5360.png
│ │ ├── 5f47c66c-51d4-4f2c-a872-a68518f44adb.png
│ │ ├── 66b428b9-55dc-4907-b116-55aaa887de30.png
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-000002
│ ├── part-000003
│ ├── [...]
│ └── part-002000
└── metadata.parquet
```
```bash
# DiffusionDB Large
./
├── diffusiondb-large-part-1
│ ├── part-000001
│ │ ├── 0a8dc864-1616-4961-ac18-3fcdf76d3b08.webp
│ │ ├── 0a25cacb-5d91-4f27-b18a-bd423762f811.webp
│ │ ├── 0a52d584-4211-43a0-99ef-f5640ee2fc8c.webp
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-000002
│ ├── part-000003
│ ├── [...]
│ └── part-010000
├── diffusiondb-large-part-2
│ ├── part-010001
│ │ ├── 0a68f671-3776-424c-91b6-c09a0dd6fc2d.webp
│ │ ├── 0a0756e9-1249-4fe2-a21a-12c43656c7a3.webp
│ │ ├── 0aa48f3d-f2d9-40a8-a800-c2c651ebba06.webp
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-010002
│ ├── part-010003
│ ├── [...]
│ └── part-014000
└── metadata-large.parquet
```
These sub-folders have names `part-0xxxxx`, and each image has a unique name generated by [UUID Version 4](https://en.wikipedia.org/wiki/Universally_unique_identifier). The JSON file in a sub-folder has the same name as the sub-folder. Each image is a `PNG` file (DiffusionDB 2M) or a lossless `WebP` file (DiffusionDB Large). The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters.
### Data Instances
For example, below is the image of `f3501e05-aef7-4225-a9e9-f516527408ac.png` and its key-value pair in `part-000001.json`.
<img width="300" src="https://i.imgur.com/gqWcRs2.png">
```json
{
"f3501e05-aef7-4225-a9e9-f516527408ac.png": {
"p": "geodesic landscape, john chamberlain, christopher balaskas, tadao ando, 4 k, ",
"se": 38753269,
"c": 12.0,
"st": 50,
"sa": "k_lms"
},
}
```
### Data Fields
- key: Unique image name
- `p`: Prompt
- `se`: Random seed
- `c`: CFG Scale (guidance scale)
- `st`: Steps
- `sa`: Sampler
### Dataset Metadata
To help you easily access prompts and other attributes of images without downloading all the Zip files, we include two metadata tables `metadata.parquet` and `metadata-large.parquet` for DiffusionDB 2M and DiffusionDB Large, respectively.
The shape of `metadata.parquet` is (2000000, 13) and the shape of `metatable-large.parquet` is (14000000, 13). Two tables share the same schema, and each row represents an image. We store these tables in the Parquet format because Parquet is column-based: you can efficiently query individual columns (e.g., prompts) without reading the entire table.
Below are three random rows from `metadata.parquet`.
| image_name | prompt | part_id | seed | step | cfg | sampler | width | height | user_name | timestamp | image_nsfw | prompt_nsfw |
|:-----------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------:|-----------:|-------:|------:|----------:|--------:|---------:|:-----------------------------------------------------------------|:--------------------------|-------------:|--------------:|
| 0c46f719-1679-4c64-9ba9-f181e0eae811.png | a small liquid sculpture, corvette, viscous, reflective, digital art | 1050 | 2026845913 | 50 | 7 | 8 | 512 | 512 | c2f288a2ba9df65c38386ffaaf7749106fed29311835b63d578405db9dbcafdb | 2022-08-11 09:05:00+00:00 | 0.0845108 | 0.00383462 |
| a00bdeaa-14eb-4f6c-a303-97732177eae9.png | human sculpture of lanky tall alien on a romantic date at italian restaurant with smiling woman, nice restaurant, photography, bokeh | 905 | 1183522603 | 50 | 10 | 8 | 512 | 768 | df778e253e6d32168eb22279a9776b3cde107cc82da05517dd6d114724918651 | 2022-08-19 17:55:00+00:00 | 0.692934 | 0.109437 |
| 6e5024ce-65ed-47f3-b296-edb2813e3c5b.png | portrait of barbaric spanish conquistador, symmetrical, by yoichi hatakenaka, studio ghibli and dan mumford | 286 | 1713292358 | 50 | 7 | 8 | 512 | 640 | 1c2e93cfb1430adbd956be9c690705fe295cbee7d9ac12de1953ce5e76d89906 | 2022-08-12 03:26:00+00:00 | 0.0773138 | 0.0249675 |
#### Metadata Schema
`metadata.parquet` and `metatable-large.parquet` share the same schema.
|Column|Type|Description|
|:---|:---|:---|
|`image_name`|`string`|Image UUID filename.|
|`prompt`|`string`|The text prompt used to generate this image.|
|`part_id`|`uint16`|Folder ID of this image.|
|`seed`|`uint32`| Random seed used to generate this image.|
|`step`|`uint16`| Step count (hyperparameter).|
|`cfg`|`float32`| Guidance scale (hyperparameter).|
|`sampler`|`uint8`| Sampler method (hyperparameter). Mapping: `{1: "ddim", 2: "plms", 3: "k_euler", 4: "k_euler_ancestral", 5: "k_heun", 6: "k_dpm_2", 7: "k_dpm_2_ancestral", 8: "k_lms", 9: "others"}`.
|`width`|`uint16`|Image width.|
|`height`|`uint16`|Image height.|
|`user_name`|`string`|The unique discord ID's SHA256 hash of the user who generated this image. For example, the hash for `xiaohk#3146` is `e285b7ef63be99e9107cecd79b280bde602f17e0ca8363cb7a0889b67f0b5ed0`. "deleted_account" refer to users who have deleted their accounts. None means the image has been deleted before we scrape it for the second time.|
|`timestamp`|`timestamp`|UTC Timestamp when this image was generated. None means the image has been deleted before we scrape it for the second time. Note that timestamp is not accurate for duplicate images that have the same prompt, hypareparameters, width, height.|
|`image_nsfw`|`float32`|Likelihood of an image being NSFW. Scores are predicted by [LAION's state-of-art NSFW detector](https://github.com/LAION-AI/LAION-SAFETY) (range from 0 to 1). A score of 2.0 means the image has already been flagged as NSFW and blurred by Stable Diffusion.|
|`prompt_nsfw`|`float32`|Likelihood of a prompt being NSFW. Scores are predicted by the library [Detoxicy](https://github.com/unitaryai/detoxify). Each score represents the maximum of `toxicity` and `sexual_explicit` (range from 0 to 1).|
> **Warning**
> Although the Stable Diffusion model has an NSFW filter that automatically blurs user-generated NSFW images, this NSFW filter is not perfect—DiffusionDB still contains some NSFW images. Therefore, we compute and provide the NSFW scores for images and prompts using the state-of-the-art models. The distribution of these scores is shown below. Please decide an appropriate NSFW score threshold to filter out NSFW images before using DiffusionDB in your projects.
<img src="https://i.imgur.com/1RiGAXL.png" width="100%">
### Data Splits
For DiffusionDB 2M, we split 2 million images into 2,000 folders where each folder contains 1,000 images and a JSON file. For DiffusionDB Large, we split 14 million images into 14,000 folders where each folder contains 1,000 images and a JSON file.
### Loading Data Subsets
DiffusionDB is large (1.6TB or 6.5 TB)! However, with our modularized file structure, you can easily load a desirable number of images and their prompts and hyperparameters. In the [`example-loading.ipynb`](https://github.com/poloclub/diffusiondb/blob/main/notebooks/example-loading.ipynb) notebook, we demonstrate three methods to load a subset of DiffusionDB. Below is a short summary.
#### Method 1: Using Hugging Face Datasets Loader
You can use the Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart) library to easily load prompts and images from DiffusionDB. We pre-defined 16 DiffusionDB subsets (configurations) based on the number of instances. You can see all subsets in the [Dataset Preview](https://huggingface.co/datasets/poloclub/diffusiondb/viewer/all/train).
```python
import numpy as np
from datasets import load_dataset
# Load the dataset with the `large_random_1k` subset
dataset = load_dataset('poloclub/diffusiondb', 'large_random_1k')
```
#### Method 2. Use the PoloClub Downloader
This repo includes a Python downloader [`download.py`](https://github.com/poloclub/diffusiondb/blob/main/scripts/download.py) that allows you to download and load DiffusionDB. You can use it from your command line. Below is an example of loading a subset of DiffusionDB.
##### Usage/Examples
The script is run using command-line arguments as follows:
- `-i` `--index` - File to download or lower bound of a range of files if `-r` is also set.
- `-r` `--range` - Upper bound of range of files to download if `-i` is set.
- `-o` `--output` - Name of custom output directory. Defaults to the current directory if not set.
- `-z` `--unzip` - Unzip the file/files after downloading
- `-l` `--large` - Download from Diffusion DB Large. Defaults to Diffusion DB 2M.
###### Downloading a single file
The specific file to download is supplied as the number at the end of the file on HuggingFace. The script will automatically pad the number out and generate the URL.
```bash
python download.py -i 23
```
###### Downloading a range of files
The upper and lower bounds of the set of files to download are set by the `-i` and `-r` flags respectively.
```bash
python download.py -i 1 -r 2000
```
Note that this range will download the entire dataset. The script will ask you to confirm that you have 1.7Tb free at the download destination.
###### Downloading to a specific directory
The script will default to the location of the dataset's `part` .zip files at `images/`. If you wish to move the download location, you should move these files as well or use a symbolic link.
```bash
python download.py -i 1 -r 2000 -o /home/$USER/datahoarding/etc
```
Again, the script will automatically add the `/` between the directory and the file when it downloads.
###### Setting the files to unzip once they've been downloaded
The script is set to unzip the files _after_ all files have downloaded as both can be lengthy processes in certain circumstances.
```bash
python download.py -i 1 -r 2000 -z
```
#### Method 3. Use `metadata.parquet` (Text Only)
If your task does not require images, then you can easily access all 2 million prompts and hyperparameters in the `metadata.parquet` table.
```python
from urllib.request import urlretrieve
import pandas as pd
# Download the parquet table
table_url = f'https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/metadata.parquet'
urlretrieve(table_url, 'metadata.parquet')
# Read the table using Pandas
metadata_df = pd.read_parquet('metadata.parquet')
```
## Dataset Creation
### Curation Rationale
Recent diffusion models have gained immense popularity by enabling high-quality and controllable image generation based on text prompts written in natural language. Since the release of these models, people from different domains have quickly applied them to create award-winning artworks, synthetic radiology images, and even hyper-realistic videos.
However, generating images with desired details is difficult, as it requires users to write proper prompts specifying the exact expected results. Developing such prompts requires trial and error, and can often feel random and unprincipled. Simon Willison analogizes writing prompts to wizards learning “magical spells”: users do not understand why some prompts work, but they will add these prompts to their “spell book.” For example, to generate highly-detailed images, it has become a common practice to add special keywords such as “trending on artstation” and “unreal engine” in the prompt.
Prompt engineering has become a field of study in the context of text-to-text generation, where researchers systematically investigate how to construct prompts to effectively solve different down-stream tasks. As large text-to-image models are relatively new, there is a pressing need to understand how these models react to prompts, how to write effective prompts, and how to design tools to help users generate images.
To help researchers tackle these critical challenges, we create DiffusionDB, the first large-scale prompt dataset with 14 million real prompt-image pairs.
### Source Data
#### Initial Data Collection and Normalization
We construct DiffusionDB by scraping user-generated images on the official Stable Diffusion Discord server. We choose Stable Diffusion because it is currently the only open-source large text-to-image generative model, and all generated images have a CC0 1.0 Universal Public Domain Dedication license that waives all copyright and allows uses for any purpose. We choose the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion) because it is public, and it has strict rules against generating and sharing illegal, hateful, or NSFW (not suitable for work, such as sexual and violent content) images. The server also disallows users to write or share prompts with personal information.
#### Who are the source language producers?
The language producers are users of the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion).
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The authors removed the discord usernames from the dataset.
We decide to anonymize the dataset because some prompts might include sensitive information: explicitly linking them to their creators can cause harm to creators.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better understanding of large text-to-image generative models.
The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.
It should note that we collect images and their prompts from the Stable Diffusion Discord server. The Discord server has rules against users generating or sharing harmful or NSFW (not suitable for work, such as sexual and violent content) images. The Stable Diffusion model used in the server also has an NSFW filter that blurs the generated images if it detects NSFW content. However, it is still possible that some users had generated harmful images that were not detected by the NSFW filter or removed by the server moderators. Therefore, DiffusionDB can potentially contain these images. To mitigate the potential harm, we provide a [Google Form](https://forms.gle/GbYaSpRNYqxCafMZ9) on the [DiffusionDB website](https://poloclub.github.io/diffusiondb/) where users can report harmful or inappropriate images and prompts. We will closely monitor this form and remove reported images and prompts from DiffusionDB.
### Discussion of Biases
The 14 million images in DiffusionDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DiffusionDB might not represent novice users. Similarly, the prompts in DiffusionDB might not generalize to domains that require specific knowledge, such as medical images.
### Other Known Limitations
**Generalizability.** Previous research has shown a prompt that works well on one generative model might not give the optimal result when used in other models.
Therefore, different models can need users to write different prompts. For example, many Stable Diffusion prompts use commas to separate keywords, while this pattern is less seen in prompts for DALL-E 2 or Midjourney. Thus, we caution researchers that some research findings from DiffusionDB might not be generalizable to other text-to-image generative models.
## Additional Information
### Dataset Curators
DiffusionDB is created by [Jay Wang](https://zijie.wang), [Evan Montoya](https://www.linkedin.com/in/evan-montoya-b252391b4/), [David Munechika](https://www.linkedin.com/in/dmunechika/), [Alex Yang](https://alexanderyang.me), [Ben Hoover](https://www.bhoov.com), [Polo Chau](https://faculty.cc.gatech.edu/~dchau/).
### Licensing Information
The DiffusionDB dataset is available under the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/).
The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
### Citation Information
```bibtex
@article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:2210.14896 [cs]},
url = {https://arxiv.org/abs/2210.14896}
}
```
### Contributions
If you have any questions, feel free to [open an issue](https://github.com/poloclub/diffusiondb/issues/new) or contact [Jay Wang](https://zijie.wang).
|
isp-uv-es/WorldFloodsv2 | isp-uv-es | "2024-01-10T11:38:21Z" | 14,746 | 6 | [
"license:cc-by-nc-4.0",
"modality:geospatial",
"modality:image",
"doi:10.57967/hf/3149",
"region:us",
"remote sensing",
"sentinel2",
"landsat",
"floods"
] | null | "2023-12-13T17:43:27Z" | ---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- remote sensing
- sentinel2
- landsat
- floods
---
# *WorldFloodsv2* dataset
This repository contains the *WorldFloodsv2* dataset released with the publication:
> E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7). _Scientific Reports 13, 20316_ (2023). DOI: 10.1038/s41598-023-47595-7.
The [*WorldFloodsv2* database](https://www.nature.com/articles/s41598-023-47595-7) contains 509 pairs of Sentinel-2 images and flood segmentation masks. Splitted in train, val and test sets.
It requires approximately 76GB of hard-disk storage.
<img src="worldfloods_v2.png" alt="licence" width="65%"/>
## Download the dataset
```
huggingface-cli download --cache-dir /path/to/cachedir --local-dir /path/to/localdir/WorldFloodsv2 --repo-type dataset isp-uv-es/WorldFloodsv2
```
## Explore the dataset
The [exploring *WorldFloodsv2*](https://spaceml-org.github.io/ml4floods/content/prep/exploring_worldfloods.html) tutorial in the [ml4floods](https://github.com/spaceml-org/ml4floods) package shows how to
process the dataset and plot the images and masks.
## Licence
The *WorldFloods* database and all pre-trained models are released under a [Creative Commons non-commercial licence](https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt)
## Cite
If you find this work useful, please cite:
```
@article{portales-julia_global_2023,
title = {Global flood extent segmentation in optical satellite images},
volume = {13},
issn = {2045-2322},
doi = {10.1038/s41598-023-47595-7},
number = {1},
urldate = {2023-11-30},
journal = {Scientific Reports},
author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},
month = nov,
year = {2023},
pages = {20316},
}
```
|
michaelauli/wiki_bio | michaelauli | "2024-01-18T11:18:02Z" | 14,530 | 21 | [
"task_categories:table-to-text",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:100K<n<1M",
"arxiv:1603.07771",
"region:us"
] | [
"table-to-text"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: wikibio
pretty_name: WikiBio
dataset_info:
features:
- name: input_text
struct:
- name: table
sequence:
- name: column_header
dtype: string
- name: row_number
dtype: int16
- name: content
dtype: string
- name: context
dtype: string
- name: target_text
dtype: string
splits:
- name: train
num_bytes: 619269257
num_examples: 582659
- name: test
num_bytes: 77264695
num_examples: 72831
- name: val
num_bytes: 77335069
num_examples: 72831
download_size: 333998704
dataset_size: 773869021
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/DavidGrangier/wikipedia-biography-dataset
- **Paper:** https://arxiv.org/pdf/1603.07771.pdf
- **GitHub:** https://github.com/DavidGrangier/wikipedia-biography-dataset
### Dataset Summary
This Dataset contains 728321 biographies extracted from Wikipedia containing the first paragraph of the biography and the tabular infobox.
### Supported Tasks and Leaderboards
The main purpose of this dataset is developing text generation models.
### Languages
English.
## Dataset Structure
### Data Instances
More Information Needed
### Data Fields
The structure of a single sample is the following:
```json
{
"input_text":{
"context":"pope michael iii of alexandria\n",
"table":{
"column_header":[
"type",
"ended",
"death_date",
"title",
"enthroned",
"name",
"buried",
"religion",
"predecessor",
"nationality",
"article_title",
"feast_day",
"birth_place",
"residence",
"successor"
],
"content":[
"pope",
"16 march 907",
"16 march 907",
"56th of st. mark pope of alexandria & patriarch of the see",
"25 april 880",
"michael iii of alexandria",
"monastery of saint macarius the great",
"coptic orthodox christian",
"shenouda i",
"egyptian",
"pope michael iii of alexandria\n",
"16 -rrb- march -lrb- 20 baramhat in the coptic calendar",
"egypt",
"saint mark 's church",
"gabriel i"
],
"row_number":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
},
"target_text":"pope michael iii of alexandria -lrb- also known as khail iii -rrb- was the coptic pope of alexandria and patriarch of the see of st. mark -lrb- 880 -- 907 -rrb- .\nin 882 , the governor of egypt , ahmad ibn tulun , forced khail to pay heavy contributions , forcing him to sell a church and some attached properties to the local jewish community .\nthis building was at one time believed to have later become the site of the cairo geniza .\n"
}
```
where, in the `"table"` field, all the information of the Wikpedia infobox is stored (the header of the infobox is stored in `"column_header"` and the information in the `"content"` field).
### Data Splits
- Train: 582659 samples.
- Test: 72831 samples.
- Validation: 72831 samples.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
This dataset was announced in the paper <em>Neural Text Generation from Structured Data with Application to the Biography Domain</em> [(arxiv link)](https://arxiv.org/pdf/1603.07771.pdf) and is stored in [this](https://github.com/DavidGrangier/wikipedia-biography-dataset) repo (owned by DavidGrangier).
#### 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
This dataset is ditributed under Creative Comons CC BY-SA 3.0 License.
### Citation Information
For refering the original paper in BibTex format:
```
@article{DBLP:journals/corr/LebretGA16,
author = {R{\'{e}}mi Lebret and
David Grangier and
Michael Auli},
title = {Generating Text from Structured Data with Application to the Biography
Domain},
journal = {CoRR},
volume = {abs/1603.07771},
year = {2016},
url = {http://arxiv.org/abs/1603.07771},
archivePrefix = {arXiv},
eprint = {1603.07771},
timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
biburl = {https://dblp.org/rec/journals/corr/LebretGA16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@alejandrocros](https://github.com/alejandrocros) for adding this dataset. |
2A2I/Arabic_Aya | 2A2I | "2024-03-15T11:08:04Z" | 14,407 | 13 | [
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"task_categories:summarization",
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"modality:tabular",
"modality:text",
"arxiv:2402.06619",
"region:us"
] | [
"text-classification",
"translation",
"summarization"
] | "2024-02-13T17:16:49Z" | ---
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splits:
- name: test
num_bytes: 222650
num_examples: 250
download_size: 120393
dataset_size: 222650
- config_name: CohereForAI-aya_evaluation_suite-dolly_human_edited
features:
- name: id
dtype: int64
- name: inputs
dtype: string
- name: targets
dtype: string
- name: language
dtype: string
- name: script
dtype: string
- name: source_id
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: test
num_bytes: 188495
num_examples: 200
download_size: 100291
dataset_size: 188495
- config_name: CohereForAI-aya_evaluation_suite-dolly_machine_translated
features:
- name: id
dtype: int64
- name: inputs
dtype: string
- name: targets
dtype: string
- name: language
dtype: string
- name: script
dtype: string
- name: source_id
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: test
num_bytes: 3491803
num_examples: 2000
download_size: 1762303
dataset_size: 3491803
configs:
- config_name: CohereForAI-aya_collection-aya_dataset
data_files:
- split: train
path: CohereForAI-aya_collection-aya_dataset/train-*
- config_name: CohereForAI-aya_collection-aya_human_annotated
data_files:
- split: test
path: CohereForAI-aya_collection-aya_human_annotated/test-*
- config_name: CohereForAI-aya_collection-templated_afrisenti
data_files:
- split: train
path: CohereForAI-aya_collection-templated_afrisenti/train-*
- split: test
path: CohereForAI-aya_collection-templated_afrisenti/test-*
- split: validation
path: CohereForAI-aya_collection-templated_afrisenti/validation-*
- config_name: CohereForAI-aya_collection-templated_mintaka
data_files:
- split: train
path: CohereForAI-aya_collection-templated_mintaka/train-*
- split: test
path: CohereForAI-aya_collection-templated_mintaka/test-*
- split: validation
path: CohereForAI-aya_collection-templated_mintaka/validation-*
- config_name: CohereForAI-aya_collection-templated_ntx_llm
data_files:
- split: train
path: CohereForAI-aya_collection-templated_ntx_llm/train-*
- config_name: CohereForAI-aya_collection-templated_xcsqa
data_files:
- split: validation
path: CohereForAI-aya_collection-templated_xcsqa/validation-*
- config_name: CohereForAI-aya_collection-templated_xlel_wd
data_files:
- split: train
path: CohereForAI-aya_collection-templated_xlel_wd/train-*
- split: test
path: CohereForAI-aya_collection-templated_xlel_wd/test-*
- split: validation
path: CohereForAI-aya_collection-templated_xlel_wd/validation-*
- config_name: CohereForAI-aya_collection-translated_adversarial_qa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_adversarial_qa/train-*
- split: test
path: CohereForAI-aya_collection-translated_adversarial_qa/test-*
- split: validation
path: CohereForAI-aya_collection-translated_adversarial_qa/validation-*
- config_name: CohereForAI-aya_collection-translated_cnn_dailymail
data_files:
- split: train
path: CohereForAI-aya_collection-translated_cnn_dailymail/train-*
- split: test
path: CohereForAI-aya_collection-translated_cnn_dailymail/test-*
- split: validation
path: CohereForAI-aya_collection-translated_cnn_dailymail/validation-*
- config_name: CohereForAI-aya_collection-translated_dolly
data_files:
- split: train
path: CohereForAI-aya_collection-translated_dolly/train-*
- config_name: CohereForAI-aya_collection-translated_flan_coqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_coqa/train-*
- config_name: CohereForAI-aya_collection-translated_flan_cot
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_cot/train-*
- config_name: CohereForAI-aya_collection-translated_flan_gem_wiki
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_gem_wiki/train-*
- config_name: CohereForAI-aya_collection-translated_flan_lambada
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_lambada/train-*
- config_name: CohereForAI-aya_collection-translated_flan_qa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_flan_qa/train-*
- config_name: CohereForAI-aya_collection-translated_hotpotqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_hotpotqa/train-*
- split: validation
path: CohereForAI-aya_collection-translated_hotpotqa/validation-*
- config_name: CohereForAI-aya_collection-translated_joke_explaination
data_files:
- split: train
path: CohereForAI-aya_collection-translated_joke_explaination/train-*
- config_name: CohereForAI-aya_collection-translated_mintaka
data_files:
- split: train
path: CohereForAI-aya_collection-translated_mintaka/train-*
- split: test
path: CohereForAI-aya_collection-translated_mintaka/test-*
- split: validation
path: CohereForAI-aya_collection-translated_mintaka/validation-*
- config_name: CohereForAI-aya_collection-translated_mlqa
data_files:
- split: test
path: CohereForAI-aya_collection-translated_mlqa/test-*
- split: validation
path: CohereForAI-aya_collection-translated_mlqa/validation-*
- config_name: CohereForAI-aya_collection-translated_nqopen
data_files:
- split: train
path: CohereForAI-aya_collection-translated_nqopen/train-*
- split: validation
path: CohereForAI-aya_collection-translated_nqopen/validation-*
- config_name: CohereForAI-aya_collection-translated_paws
data_files:
- split: train
path: CohereForAI-aya_collection-translated_paws/train-*
- split: test
path: CohereForAI-aya_collection-translated_paws/test-*
- split: validation
path: CohereForAI-aya_collection-translated_paws/validation-*
- config_name: CohereForAI-aya_collection-translated_piqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_piqa/train-*
- split: validation
path: CohereForAI-aya_collection-translated_piqa/validation-*
- config_name: CohereForAI-aya_collection-translated_soda
data_files:
- split: train
path: CohereForAI-aya_collection-translated_soda/train-*
- split: test
path: CohereForAI-aya_collection-translated_soda/test-*
- split: validation
path: CohereForAI-aya_collection-translated_soda/validation-*
- config_name: CohereForAI-aya_collection-translated_wiki_split
data_files:
- split: train
path: CohereForAI-aya_collection-translated_wiki_split/train-*
- split: test
path: CohereForAI-aya_collection-translated_wiki_split/test-*
- split: validation
path: CohereForAI-aya_collection-translated_wiki_split/validation-*
- config_name: CohereForAI-aya_collection-translated_wikiqa
data_files:
- split: train
path: CohereForAI-aya_collection-translated_wikiqa/train-*
- split: test
path: CohereForAI-aya_collection-translated_wikiqa/test-*
- split: validation
path: CohereForAI-aya_collection-translated_wikiqa/validation-*
- config_name: CohereForAI-aya_collection-translated_xlel_wd
data_files:
- split: train
path: CohereForAI-aya_collection-translated_xlel_wd/train-*
- split: test
path: CohereForAI-aya_collection-translated_xlel_wd/test-*
- split: validation
path: CohereForAI-aya_collection-translated_xlel_wd/validation-*
- config_name: CohereForAI-aya_dataset
data_files:
- split: train
path: CohereForAI-aya_dataset/train-*
- split: test
path: CohereForAI-aya_dataset/test-*
- config_name: CohereForAI-aya_evaluation_suite-aya_human_annotated
data_files:
- split: test
path: CohereForAI-aya_evaluation_suite-aya_human_annotated/test-*
- config_name: CohereForAI-aya_evaluation_suite-dolly_human_edited
data_files:
- split: test
path: CohereForAI-aya_evaluation_suite-dolly_human_edited/test-*
- config_name: CohereForAI-aya_evaluation_suite-dolly_machine_translated
data_files:
- split: test
path: CohereForAI-aya_evaluation_suite-dolly_machine_translated/test-*
---
# Dataset Card for : Arabic Aya (2A)
<!-- Provide a quick summary of the dataset. -->
<!-- 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).-->
## **Arabic Aya (2A) : A Curated Subset of the Aya Collection for Arabic Language Processing**
### Dataset Sources & Infos
- **Data Origin**: Derived from 69 subsets of the original Aya datasets : [CohereForAI/aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), and [CohereForAI/aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite).
- **Languages**: Modern Standard Arabic (MSA) and a variety of Arabic dialects ( 'arb', 'arz', 'ary', 'ars', 'knc', 'acm', 'apc', 'aeb', 'ajp', 'acq' )
- **Applications**: `Language Modeling`, `Text Classification`, `Sentiment Analysis`, `Dialect Identification`, `Translation`
- **Paper:** [2402.06619](https://huggingface.co/papers/2402.06619)
- **Maintainer:** [Elfilali Ali](https://huggingface.co/Ali-C137)
- **License:** Apache-2.0
### Overview
`Arabic Aya` is a meticulously curated dataset derived from the comprehensive Aya collection by [CohereForAI](https://huggingface.co/CohereForAI), specifically focusing on Arabic text data. This dataset aggregates content from the [CohereForAI/aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), and [CohereForAI/aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite), filtering out all but the Arabic content, including both Modern Standard Arabic (MSA) and various regional dialects.
### Purpose
The aim of 'Arabic Aya' is to provide researchers, technologists, and linguists with a ready-to-use Arabic text resource, significantly reducing the time and effort required for data preprocessing in NLP and AI projects focused on the Arabic language.
- Use the Aya datasets out of the box for your Arabic applications and research 😀
### Usage
This dataset serves as a foundational tool for those embarking on Arabic language projects, from academic research to commercial applications. By providing a pre-filtered source of Arabic text, 'Arabic Aya' enables users to dive straight into model training, analysis, and application development without the preliminary hassle of data cleaning and language filtering.
#### Use with HuggingFace's datasets library
To load this dataset with Datasets, you'll need to install Datasets as `pip install datasets --upgrade` and then use a similar code to the following:
```python
from datasets import load_dataset
dataset = load_dataset("2A2I/Arabic_Aya", "CohereForAI-aya_collection-templated_mintaka")
```
In the above code snippet, "CohereForAI-aya_collection-templated_mintaka" refers to the arabic version (100k rows) of the original "templated_mintaka" subset (780k rows) of the aya_collection. You can load other subsets by specifying its name at the time of loading the dataset.
### Access and Contribution
Available on the Hugging Face Hub under [2A2I/Arabic_Aya](https://huggingface.co/datasets/2A2I/Arabic_Aya), 'Arabic Aya' invites contributions from the community. Users are encouraged to offer feedback, suggest improvements.
### Support and Collaboration
We are committed to fostering an inclusive and supportive environment around Arabic AI and NLP research. For support, collaboration, or queries regarding the dataset, please reach out through the Hugging Face Hub's discussion section or reach out at [2A2I Contact Email](arabic.ai.initiative@gmail.com).
# Original Dataset Card of Aya by CohereForAI
![Aya Header](https://huggingface.co/datasets/CohereForAI/aya_collection/resolve/main/aya_header.png)
# Dataset Summary
The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks.
This collection incorporates instruction-style templates from fluent speakers and applies them to a curated list of datasets, as well as translations of instruction-style datasets into 101 languages. Aya Dataset, a human-curated multilingual instruction and response dataset, is also part of this collection. See our paper for more details regarding the collection.
- **Curated by:** Contributors of [Aya Open Science Intiative](https://cohere.com/research/aya)
- **Language(s):** 115 languages
- **License:** [Apache 2.0](https://opensource.org/license/apache-2-0)
- **Aya Datasets Family:**
| Name | Explanation |
|------|--------------|
| [aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) | Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages. |
| [aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection) | Created by applying instruction-style templates from fluent speakers to 44 datasets, including translations of 19 instruction-style datasets into 101 languages.|
| [aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite) | A diverse evaluation set for multilingual open-ended generation, featuring 250 culturally grounded prompts in 7 languages, 200 translated prompts in 24 languages, and human-edited versions selected for cross-cultural relevance from English Dolly in 6 languages.|
# Dataset
The `Aya Collection` is a comprehensive, large corpus of datasets that can be used by researchers around the world to train multilingual models. Our goal is only to include datasets with permissive licensing for manipulation and redistribution.
The `Aya Collection` consists of three different sources of data:
1. Templated data: We collaborated with fluent speakers to create templates that allowed for the automatic expansion of existing datasets into various languages.
2. Translated data: We translated a hand-selected subset of 19 datasets into 101 languages (114 dialects) using the NLLB 3.3B parameter machine translation model.
3. Aya Dataset: We release the [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) as a subset of the overall collection. This is the only dataset in the collection that is human-annotated in its entirety.
## Load with Datasets
To load this dataset with Datasets, you'll need to install Datasets as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("CohereForAI/aya_collection", "templated_mintaka")
```
In the above code snippet, "templated_mintaka" refers to a subset of the aya_collection. You can load other subsets by specifying its name at the time of loading the dataset.
## Data Instances
An example of a `train` instance looks as follows:
```json
{'id': 246001,
'inputs': 'The following query in English is taken from the geography category. What could be the answer to the question?\nWhat is the seventh tallest mountain in North America?',
'targets': 'The answer is Mount Lucania.',
'dataset_name': 'Mintaka-inst',
'sub_dataset_name': '-',
'task_type': 'question-answering',
'template_id': 3,
'language': 'eng',
'split': 'train',
'script': 'Latn'
}
```
## Data Fields
The data fields are the same among all splits:
- `id:` Unique id of the data point
- `inputs:` Prompt or input to the language model.
- `targets:` Completion or output of the language model.
- `dataset_name:` The name of the source dataset that the data point was taken from
- `sub_dataset_name:` If the source is a collection, this field indicates which part of that collection the data point was taken from. If it is not a collection, this field is left blank.
- `task_type:` The task type that this conversation belongs to.
- `template_id`: The id of the template applied to this data point.
- `language:` The ISO code of the dialect of the conversation.
- `script:` The script of the language.
- `split:` Indicates whether the data point is part of the `train` or the `test` split.
### Statistics
The total number of data points, including the Aya Dataset` is 513,758,189. To view the breakdown of dialect codes and the respective templated and translated data point counts in the Aya Collection , refer to the toggled table below.
<details>
<summary> <b> Breakdown of Aya Collection data point counts grouped by dialects </b> </summary>
|dialect code|language|translated data point count|templated data point count|total count |
|------------|--------|---------------------------|--------------------------|---------------|
|ace |Achinese|8240684 |2000 |8242684 |
|acm |Arabic |4120342 |0 |4120342 |
|acq |Arabic |4120342 |0 |4120342 |
|aeb |Arabic |4120342 |0 |4120342 |
|afr |Afrikaans|4120342 |6108 |4126450 |
|ajp |Arabic |4120342 |0 |4120342 |
|als |Albanian|4120342 |0 |4120342 |
|amh |Amharic |4120342 |25327 |4145669 |
|apc |Arabic |4120342 |0 |4120342 |
|arb |Arabic |6424999 |216430 |6641429 |
|ars |Arabic |4120342 |0 |4120342 |
|ary |Arabic |4120342 |18076 |4138418 |
|arz |Arabic |4120342 |0 |4120342 |
|azb |Azerbaijani|4120342 |0 |4120342 |
|azj |Azerbaijani|4120342 |0 |4120342 |
|bel |Belarusian|4120342 |21273 |4141615 |
|ben |Bengali |4120342 |30661 |4151003 |
|bjn |Banjar |8240684 |2000 |8242684 |
|bul |Bulgarian|4120342 |37722 |4158064 |
|cat |Catalan |4120342 |66900 |4187242 |
|ceb |Cebuano |4120342 |0 |4120342 |
|ces |Czech |4120342 |179604 |4299946 |
|ckb |Kurdish |4120342 |0 |4120342 |
|cym |Welsh |4120342 |0 |4120342 |
|dan |Danish |4120342 |36310 |4156652 |
|deu |German |4120342 |1326722 |5447064 |
|ell |Greek |4120342 |40291 |4160633 |
|eng |English |9771427 |8066678 |17838105 |
|epo |Esperanto|4120342 |0 |4120342 |
|est |Estonian|4120342 |0 |4120342 |
|eus |Basque |4120342 |0 |4120342 |
|fin |Finnish |4120342 |457895 |4578237 |
|fra |French |4120342 |835520 |4955862 |
|gla |Scottish Gaelic|4120342 |0 |4120342 |
|gle |Irish |4120342 |0 |4120342 |
|glg |Galician|4120342 |0 |4120342 |
|guj |Gujarati|4120342 |2157 |4122499 |
|hat |Haitian Creole|4120342 |0 |4120342 |
|hau |Hausa |4120342 |51396 |4171738 |
|heb |Hebrew |4120342 |103466 |4223808 |
|hin |Hindi |4120342 |260387 |4380729 |
|hun |Hungarian|4120342 |82039 |4202381 |
|hye |Armenian|4120342 |7080 |4127422 |
|ibo |Igbo |4120342 |36312 |4156654 |
|ind |Indonesian|4120342 |45709 |4166051 |
|isl |Icelandic|4120342 |0 |4120342 |
|ita |Italian |4120342 |405682 |4526024 |
|jav |Javanese|4120342 |829 |4121171 |
|jpn |Japanese|4120342 |2693177 |6813519 |
|kan |Kannada |4120342 |1156 |4121498 |
|kas |Kashmiri|4120342 |0 |4120342 |
|kat |Georgian|4120342 |0 |4120342 |
|kaz |Kazakh |4120342 |0 |4120342 |
|khk |Mongolian|4120342 |0 |4120342 |
|khm |Khmer |4120342 |0 |4120342 |
|kir |Kyrgyz |4120342 |0 |4120342 |
|kmr |Kurdish |4120342 |0 |4120342 |
|knc |Kanuri |8240684 |0 |8240684 |
|kor |Korean |4120342 |41011 |4161353 |
|lao |Lao |4120342 |0 |4120342 |
|lit |Lithuanian|4120342 |0 |4120342 |
|ltz |Luxembourgish|4120342 |0 |4120342 |
|lvs |Latvian |4120342 |0 |4120342 |
|mal |Malayalam|4120342 |4347 |4124689 |
|mar |Marathi |4120342 |3678 |4124020 |
|min |Minangkabau|6753788 |2000 |6755788 |
|mkd |Macedonian|4120342 |0 |4120342 |
|mlt |Maltese |4120342 |0 |4120342 |
|mni |Manipuri|4120342 |0 |4120342 |
|mri |Maori |4120342 |0 |4120342 |
|mya |Burmese |4120342 |0 |4120342 |
|nld |Dutch |4120342 |220181 |4340523 |
|nno |Norwegian|4120342 |0 |4120342 |
|nob |Norwegian|4120342 |0 |4120342 |
|npi |Nepali |4120342 |0 |4120342 |
|nso |Northern Sotho|4120342 |0 |4120342 |
|pbt |Pashto |4120342 |0 |4120342 |
|pes |Persian |4120342 |245520 |4365862 |
|plt |Malagasy|4120342 |0 |4120342 |
|pol |Polish |4120342 |332503 |4452845 |
|por |Portuguese|4120342 |287432 |4407774 |
|ron |Romanian|4120342 |36359 |4156701 |
|rus |Russian |4120342 |545920 |4666262 |
|sin |Sinhala |4120342 |195 |4120537 |
|slk |Slovak |4120342 |27845 |4148187 |
|slv |Slovenian|4120342 |25731 |4146073 |
|smo |Samoan |4120342 |0 |4120342 |
|sna |Shona |4120342 |3684 |4124026 |
|snd |Sindhi |4120342 |0 |4120342 |
|som |Somali |4120342 |2926 |4123268 |
|sot |Southern Sotho|4120342 |0 |4120342 |
|spa |Spanish |4120342 |379194 |4499536 |
|srp |Serbian |4120342 |77124 |4197466 |
|sun |Sundanese|4120342 |2208 |4122550 |
|swe |Swedish |4120342 |76486 |4196828 |
|swh |Swahili |4120342 |12726 |4133068 |
|tam |Tamil |4120342 |11462 |4131804 |
|taq |Tamasheq|4120342 |0 |4120342 |
|tel |Telugu |4120342 |477821 |4598163 |
|tgk |Tajik |4120342 |0 |4120342 |
|tha |Thai |4120342 |2125180 |6245522 |
|tur |Turkish |4120342 |59932 |4180274 |
|ukr |Ukrainian|4120342 |189384 |4309726 |
|urd |Urdu |4120342 |337739 |4458081 |
|uzn |Uzbek |4120342 |0 |4120342 |
|vie |Vietnamese|4120342 |42232 |4162574 |
|xho |Xhosa |4120342 |2952 |4123294 |
|ydd |Yiddish |4120342 |0 |4120342 |
|yor |Yoruba |4120342 |4907 |4125249 |
|yue |Chinese |4120342 |0 |4120342 |
|zho-Hans |Chinese |4120342 |54528 |4174870 |
|zho-Hant |Chinese |4120342 |0 |4120342 |
|zsm |Malay |4120342 |13950 |4134292 |
|zul |Zulu |4120342 |786 |4121128 |
|arq |Arabic |0 |6046 |6046 |
|ban |Balinese|0 |2000 |2000 |
|bbc |Toba Batak|0 |2000 |2000 |
|bem |Bemba |0 |776 |776 |
|fil |Filipino|0 |220 |220 |
|fon |Fon |0 |845 |845 |
|hrv |Croatian|0 |9007 |9007 |
|kin |Kinyarwanda|0 |11165 |11165 |
|lij |Ligurian|0 |6409 |6409 |
|mad |Madurese|0 |2000 |2000 |
|nij |Ngaju |0 |2000 |2000 |
|nor |Norwegian|0 |72352 |72352 |
|pan |Punjabi |0 |2156 |2156 |
|twi |Twi |0 |10840 |10840 |
|wol |Wolof |0 |785 |785 |
|zho |Chinese |0 |74972 |74972 |
PS: Templated data also includes Mozambican Portuguese, which doesn't have its own ISO language code.
</details>
<br>
# Motivations & Intentions
- **Curation Rationale:** Automatic augmentation of existing datasets serves to enhance the available linguistic resources for multiple languages. The list of languages was initially established from mT5 and aligned with the annotators’ language list and NLLB translation model. The datasets were translated directly from English for all languages.
# Additional Information
## Provenance
- **Methods Used:** A combination of crowd-sourced templating and automatic translation was employed to source this dataset.
- **Methodology Details:**
- *Source:* Existing NLP datasets
- *Dates of Collection:* May 2023 - Dec 2023
## Dataset Version and Maintenance
- **Maintenance Status:** Actively Maintained
- **Version Details:**
- *Current version:* 1.0
- *Last Update:* 02/2024
- *First Release:* 02/2024
## Authorship
- **Publishing Organization:** [Cohere For AI](https://cohere.com/research)
- **Industry Type:** Not-for-profit - Tech
- **Contact Details:** https://cohere.com/research/aya
## Licensing Information
This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License.
## Citation Information
```bibtex
@misc{singh2024aya,
title={Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning},
author={Shivalika Singh and Freddie Vargus and Daniel Dsouza and Börje F. Karlsson and Abinaya Mahendiran and Wei-Yin Ko and Herumb Shandilya and Jay Patel and Deividas Mataciunas and Laura OMahony and Mike Zhang and Ramith Hettiarachchi and Joseph Wilson and Marina Machado and Luisa Souza Moura and Dominik Krzemiński and Hakimeh Fadaei and Irem Ergün and Ifeoma Okoh and Aisha Alaagib and Oshan Mudannayake and Zaid Alyafeai and Vu Minh Chien and Sebastian Ruder and Surya Guthikonda and Emad A. Alghamdi and Sebastian Gehrmann and Niklas Muennighoff and Max Bartolo and Julia Kreutzer and Ahmet Üstün and Marzieh Fadaee and Sara Hooker},
year={2024},
eprint={2402.06619},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
fsicoli/common_voice_15_0 | fsicoli | "2023-12-20T18:55:52Z" | 14,400 | 5 | [
"task_categories:automatic-speech-recognition",
"language:ab",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
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"language:tk",
"language:tok",
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"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yue",
"language:zgh",
"language:zh",
"language:yo",
"license:cc",
"size_categories:100B<n<1T",
"region:us",
"mozilla",
"foundation"
] | [
"automatic-speech-recognition"
] | "2023-11-13T13:27:04Z" | ---
license: cc
language:
- ab
- af
- am
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- gl
- gn
- ha
- he
- hi
- hsb
- hu
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
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- mk
- ml
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- myv
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- sw
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- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yue
- zgh
- zh
- yo
task_categories:
- automatic-speech-recognition
pretty_name: Common Voice Corpus 15.0
size_categories:
- 100B<n<1T
tags:
- mozilla
- foundation
---
# Dataset Card for Common Voice Corpus 15.0
<!-- Provide a quick summary of the dataset. -->
This dataset is an unofficial version of the Mozilla Common Voice Corpus 15. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/.
## Languages
```
Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese):
```
from datasets import load_dataset
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```
from datasets import load_dataset
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train", streaming=True)
print(next(iter(cv_15)))
```
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
### Local
```
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_15), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_15, batch_sampler=batch_sampler)
```
### Streaming
```
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
dataloader = DataLoader(cv_15, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
### Dataset Structure
Data Instances
A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
### Licensing Information
Public Domain, CC-0
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
``` |
asahi417/seamless-align-enA-esA.speaker-embedding.w2vbert-600m | asahi417 | "2024-06-24T06:04:42Z" | 14,368 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-14T10:40:09Z" | ---
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data_files:
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data_files:
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data_files:
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data_files:
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data_files:
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- config_name: subset_99
data_files:
- split: train
path: subset_99/train-*
---
|
L4NLP/LEval | L4NLP | "2023-10-11T03:56:48Z" | 14,305 | 18 | [
"task_categories:summarization",
"task_categories:question-answering",
"task_categories:multiple-choice",
"language:en",
"license:gpl-3.0",
"size_categories:n<1K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"Long_context"
] | [
"summarization",
"question-answering",
"multiple-choice"
] | "2023-06-14T11:51:39Z" | ---
license: gpl-3.0
task_categories:
- summarization
- question-answering
- multiple-choice
language:
- en
size_categories:
- 1K<n<10K
viewer: true
tags:
- Long_context
---
### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*
L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.
We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.
Dataset list:
```
["coursera", "gsm100", "quality", "topic_retrieval_longchat", "tpo", "financial_qa", "gov_report_summ", "legal_contract_qa", "meeting_summ", "multidoc_qa", "narrative_qa", "natural_question", "news_summ", "paper_assistant", "patent_summ", "review_summ", "scientific_qa", "tv_show_summ"]
```
Detailed descriptions and how we collect the data can be found [https://github.com/OpenLMLab/LEval](https://github.com/OpenLMLab/LEval). |
allenai/sciq | allenai | "2024-01-04T16:23:51Z" | 14,237 | 99 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-nc-3.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"question-answering"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: sciq
pretty_name: SciQ
dataset_info:
features:
- name: question
dtype: string
- name: distractor3
dtype: string
- name: distractor1
dtype: string
- name: distractor2
dtype: string
- name: correct_answer
dtype: string
- name: support
dtype: string
splits:
- name: train
num_bytes: 6546183
num_examples: 11679
- name: validation
num_bytes: 554120
num_examples: 1000
- name: test
num_bytes: 563927
num_examples: 1000
download_size: 4674410
dataset_size: 7664230
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "sciq"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://allenai.org/data/sciq](https://allenai.org/data/sciq)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 2.82 MB
- **Size of the generated dataset:** 7.68 MB
- **Total amount of disk used:** 10.50 MB
### Dataset Summary
The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 2.82 MB
- **Size of the generated dataset:** 7.68 MB
- **Total amount of disk used:** 10.50 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"correct_answer": "coriolis effect",
"distractor1": "muon effect",
"distractor2": "centrifugal effect",
"distractor3": "tropical effect",
"question": "What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?",
"support": "\"Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to..."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question`: a `string` feature.
- `distractor3`: a `string` feature.
- `distractor1`: a `string` feature.
- `distractor2`: a `string` feature.
- `correct_answer`: a `string` feature.
- `support`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|11679| 1000|1000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is licensed under the [Creative Commons Attribution-NonCommercial 3.0 Unported License](http://creativecommons.org/licenses/by-nc/3.0/).
### Citation Information
```
@inproceedings{SciQ,
title={Crowdsourcing Multiple Choice Science Questions},
author={Johannes Welbl, Nelson F. Liu, Matt Gardner},
year={2017},
journal={arXiv:1707.06209v1}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
MartinKu/wikipedia_stage2_coverage_20230402 | MartinKu | "2023-04-06T11:09:20Z" | 14,234 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-04-03T01:51:56Z" | ---
dataset_info:
features:
- name: text
dtype: string
- name: S_V_position
sequence: int64
- name: O_C_position
sequence: int64
- name: start_point_list
sequence: int64
splits:
- name: train
num_bytes: 113325298194
num_examples: 3295240
download_size: 33360668694
dataset_size: 113325298194
---
# Dataset Card for "wikipedia_stage2_coverage_20230402"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
QingyiSi/Alpaca-CoT | QingyiSi | "2023-09-14T08:52:10Z" | 14,226 | 724 | [
"language:en",
"language:zh",
"language:ml",
"license:apache-2.0",
"region:us",
"Instruction",
"Cot"
] | null | "2023-03-25T14:58:30Z" | ---
language:
- en
- zh
- ml
tags:
- Instruction
- Cot
license: apache-2.0
datasets:
- dataset1
- dataset2
---
# Instruction-Finetuning Dataset Collection (Alpaca-CoT)
This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the [code](https://github.com/PhoebusSi/alpaca-CoT) of Alpaca model.
We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in [https://github.com/PhoebusSi/alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT).
If you think this dataset collection is helpful to you, please `like` this dataset and `star` our [github project](https://github.com/PhoebusSi/alpaca-CoT)!
You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints.
# Contribute
Welcome to join us and become a contributor to this project!
If you want to share some datasets, adjust the data in the following format:
```
example.json
[
{"instruction": instruction string,
"input": input string, # (may be empty)
"output": output string}
]
```
Folder should be like this:
```
Alpaca-CoT
|
|----example
| |
| |----example.json
| |
| ----example_context.json
...
```
Create a new pull request in [Community
](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/discussions) and publish your branch when you are ready. We will merge it as soon as we can.
# Data Usage and Resources
## Data Format
All data in this folder is formatted into the same templates, where each sample is as follows:
```
[
{"instruction": instruction string,
"input": input string, # (may be empty)
"output": output string}
]
```
## alpaca
#### alpaca_data.json
> This dataset is published by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). It contains 52K English instruction-following samples obtained by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques.
#### alpaca_data_cleaned.json
> This dataset is obtained [here](https://github.com/tloen/alpaca-lora). It is a revised version of `alpaca_data.json` by stripping of various tokenization artifacts.
## alpacaGPT4
#### alpaca_gpt4_data.json
> This dataset is published by [Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
#### alpaca_gpt4_data_zh.json
> This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT.
<!-- ## belle_cn
#### belle_data_cn.json
This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 0.5M Chinese instruction-following samples, which is also generated by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques.
#### belle_data1M_cn.json
This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 1M Chinese instruction-following samples. The data of `belle_data_cn.json` and `belle_data1M_cn.json` are not duplicated. -->
## Chain-of-Thought
#### CoT_data.json
> This dataset is obtained by formatting the combination of 9 CoT datasets published by [FLAN](https://github.com/google-research/FLAN). It contains 9 CoT tasks involving 74771 samples.
#### CoT_CN_data.json
> This dataset is obtained by tranlating `CoT_data.json` into Chinese, using Google Translate(en2cn).
#### formatted_cot_data folder
> This folder contains the formatted English data for each CoT dataset.
#### formatted_cot_data folder
> This folder contains the formatted Chinese data for each CoT dataset.
## CodeAlpaca
#### code_alpaca.json
> This dataset is published by [codealpaca](https://github.com/sahil280114/codealpaca). It contains code generation task involving 20022 samples.
## finance
#### finance_en.json
> This dataset is collected from [here](https://huggingface.co/datasets/gbharti/finance-alpaca). It contains 68912 financial related instructions in English.
## firefly
#### firefly.json
> his dataset is collected from [here](https://github.com/yangjianxin1/Firefly). It contains 1649398 chinese instructions in 23 nlp tasks.
## GPT4all
#### gpt4all.json
> This dataset is collected from [here](https://github.com/nomic-ai/gpt4all). It contains 806199 en instructions in code, storys and dialogs tasks.
#### gpt4all_without_p3.json
> gpt4all without Bigscience/P3, contains 437605 samples.
## GPTeacher
#### GPTeacher.json
> This dataset is collected from [here](https://github.com/teknium1/GPTeacher). It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer.
## Guanaco
#### GuanacoDataset.json
> This dataset is collected from [here](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition.
#### Guanaco_additional_Dataset.json
> A new additional larger dataset for different languages.
## HC3
#### HC3_ChatGPT.json/HC3_Human.json
> This dataset is collected from [here](https://huggingface.co/datasets/Hello-SimpleAI/HC3). It contains 37175 en/zh instructions generated by ChatGPT and human.
#### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json
> HC3 dataset without deduplication instructions.
## instinwild
#### instinwild_en.json & instinwild_cn.json
> The two datasets are obtained [here](https://github.com/XueFuzhao/InstructionWild). It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.)
## instruct
#### instruct.json
> The two datasets are obtained [here](https://huggingface.co/datasets/swype/instruct). It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI.
## Natural Instructions
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://github.com/allenai/natural-instructions). It contains 5040134 instructions, which are collected from diverse nlp tasks
## prosocial dialog
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://huggingface.co/datasets/allenai/prosocial-dialog). It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback
## xP3
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://huggingface.co/datasets/bigscience/xP3). It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks
## Chinese-instruction-collection
> all datasets of Chinese instruction collection
## combination
#### alcapa_plus_belle_data.json
> This dataset is the combination of English `alpaca_data.json` and Chinese `belle_data_cn.json`.
#### alcapa_plus_cot_data.json
> This dataset is the combination of English `alpaca_data.json` and CoT `CoT_data.json`.
#### alcapa_plus_belle_cot_data.json
> This dataset is the combination of English `alpaca_data.json`, Chinese `belle_data_cn.json` and CoT `CoT_data.json`.
## Citation
Please cite the repo if you use the data collection, code, and experimental findings in this repo.
```
@misc{alpaca-cot,
author = {Qingyi Si, Zheng Lin },
school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China},
title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}},
}
```
Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.
|
sentence-transformers/parallel-sentences-ccmatrix | sentence-transformers | "2024-06-18T19:49:55Z" | 14,097 | 4 | [
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"sentence-transformers"
] | [
"feature-extraction",
"sentence-similarity"
] | "2024-05-25T08:10:49Z" | ---
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- config_name: en-el
data_files:
- split: train
path: en-el/train-*
- config_name: en-eo
data_files:
- split: train
path: en-eo/train-*
- config_name: en-es
data_files:
- split: train
path: en-es/train-*
- config_name: en-et
data_files:
- split: train
path: en-et/train-*
- config_name: en-eu
data_files:
- split: train
path: en-eu/train-*
- config_name: en-fa
data_files:
- split: train
path: en-fa/train-*
- config_name: en-fi
data_files:
- split: train
path: en-fi/train-*
- config_name: en-fr
data_files:
- split: train
path: en-fr/train-*
- config_name: en-fy
data_files:
- split: train
path: en-fy/train-*
- config_name: en-ga
data_files:
- split: train
path: en-ga/train-*
- config_name: en-gd
data_files:
- split: train
path: en-gd/train-*
- config_name: en-gl
data_files:
- split: train
path: en-gl/train-*
- config_name: en-ha
data_files:
- split: train
path: en-ha/train-*
- config_name: en-he
data_files:
- split: train
path: en-he/train-*
- config_name: en-hi
data_files:
- split: train
path: en-hi/train-*
- config_name: en-hr
data_files:
- split: train
path: en-hr/train-*
- config_name: en-hu
data_files:
- split: train
path: en-hu/train-*
- config_name: en-id
data_files:
- split: train
path: en-id/train-*
- config_name: en-ig
data_files:
- split: train
path: en-ig/train-*
- config_name: en-ilo
data_files:
- split: train
path: en-ilo/train-*
- config_name: en-is
data_files:
- split: train
path: en-is/train-*
- config_name: en-it
data_files:
- split: train
path: en-it/train-*
- config_name: en-ja
data_files:
- split: train
path: en-ja/train-*
- config_name: en-jv
data_files:
- split: train
path: en-jv/train-*
- config_name: en-ko
data_files:
- split: train
path: en-ko/train-*
- config_name: en-la
data_files:
- split: train
path: en-la/train-*
- config_name: en-lb
data_files:
- split: train
path: en-lb/train-*
- config_name: en-lt
data_files:
- split: train
path: en-lt/train-*
- config_name: en-lv
data_files:
- split: train
path: en-lv/train-*
- config_name: en-mg
data_files:
- split: train
path: en-mg/train-*
- config_name: en-mk
data_files:
- split: train
path: en-mk/train-*
- config_name: en-ml
data_files:
- split: train
path: en-ml/train-*
- config_name: en-mr
data_files:
- split: train
path: en-mr/train-*
- config_name: en-ms
data_files:
- split: train
path: en-ms/train-*
- config_name: en-ne
data_files:
- split: train
path: en-ne/train-*
- config_name: en-nl
data_files:
- split: train
path: en-nl/train-*
- config_name: en-no
data_files:
- split: train
path: en-no/train-*
- config_name: en-oc
data_files:
- split: train
path: en-oc/train-*
- config_name: en-or
data_files:
- split: train
path: en-or/train-*
- config_name: en-pl
data_files:
- split: train
path: en-pl/train-*
- config_name: en-pt
data_files:
- split: train
path: en-pt/train-*
- config_name: en-ro
data_files:
- split: train
path: en-ro/train-*
- config_name: en-ru
data_files:
- split: train
path: en-ru/train-*
- config_name: en-sd
data_files:
- split: train
path: en-sd/train-*
- config_name: en-si
data_files:
- split: train
path: en-si/train-*
- config_name: en-sk
data_files:
- split: train
path: en-sk/train-*
- config_name: en-sl
data_files:
- split: train
path: en-sl/train-*
- config_name: en-so
data_files:
- split: train
path: en-so/train-*
- config_name: en-sq
data_files:
- split: train
path: en-sq/train-*
- config_name: en-sr
data_files:
- split: train
path: en-sr/train-*
- config_name: en-su
data_files:
- split: train
path: en-su/train-*
- config_name: en-sv
data_files:
- split: train
path: en-sv/train-*
- config_name: en-sw
data_files:
- split: train
path: en-sw/train-*
- config_name: en-ta
data_files:
- split: train
path: en-ta/train-*
- config_name: en-tl
data_files:
- split: train
path: en-tl/train-*
- config_name: en-tr
data_files:
- split: train
path: en-tr/train-*
- config_name: en-uk
data_files:
- split: train
path: en-uk/train-*
- config_name: en-ur
data_files:
- split: train
path: en-ur/train-*
- config_name: en-vi
data_files:
- split: train
path: en-vi/train-*
- config_name: en-xh
data_files:
- split: train
path: en-xh/train-*
- config_name: en-yi
data_files:
- split: train
path: en-yi/train-*
- config_name: en-zh
data_files:
- split: train
path: en-zh/train-*
language:
- af
- ar
- ast
- az
- be
- bg
- bn
- br
- ca
- ceb
- cs
- da
- de
- el
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- ha
- he
- hi
- hr
- hu
- id
- ig
- ilo
- is
- it
- ja
- jv
- ko
- la
- lb
- lt
- lv
- mg
- mk
- ml
- mr
- ms
- ne
- nl
- 'no'
- oc
- or
- pl
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- tl
- tr
- uk
- ur
- vi
- xh
- yi
- zh
---
# Dataset Card for Parallel Sentences - CCMatrix
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. The texts originate from the [CCMatrix](https://ai.meta.com/blog/ccmatrix-a-billion-scale-bitext-data-set-for-training-translation-models/) dataset.
## Related Datasets
The following datasets are also a part of the Parallel Sentences collection:
* [parallel-sentences-europarl](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-europarl)
* [parallel-sentences-global-voices](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-global-voices)
* [parallel-sentences-muse](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-muse)
* [parallel-sentences-jw300](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-jw300)
* [parallel-sentences-news-commentary](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-news-commentary)
* [parallel-sentences-opensubtitles](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-opensubtitles)
* [parallel-sentences-talks](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks)
* [parallel-sentences-tatoeba](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-tatoeba)
* [parallel-sentences-wikimatrix](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikimatrix)
* [parallel-sentences-wikititles](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikititles)
* [parallel-sentences-ccmatrix](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-ccmatrix)
These datasets can be used to train multilingual sentence embedding models. For more information, see [sbert.net - Multilingual Models](https://www.sbert.net/examples/training/multilingual/README.html).
## Dataset Subsets
### `en-...` subsets
* Columns: "english", "non_english"
* Column types: `str`, `str`
* Examples:
```python
{
"english": "He and his mother will be standing vigil there.”",
"non_english": "Él y su madre estarán de vigilia allí”.",
}
```
* Collection strategy: Processing the data from [yhavinga/ccmatrix](https://huggingface.co/datasets/yhavinga/ccmatrix) and reformatting it in Parquet and with "english" and "non_english" columns.
* Deduplified: No |
legacy-datasets/mc4 | legacy-datasets | "2024-03-05T08:45:03Z" | 14,023 | 151 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:am",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:ca",
"language:ceb",
"language:co",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fil",
"language:fr",
"language:fy",
"language:ga",
"language:gd",
"language:gl",
"language:gu",
"language:ha",
"language:haw",
"language:he",
"language:hi",
"language:hmn",
"language:ht",
"language:hu",
"language:hy",
"language:id",
"language:ig",
"language:is",
"language:it",
"language:iw",
"language:ja",
"language:jv",
"language:ka",
"language:kk",
"language:km",
"language:kn",
"language:ko",
"language:ku",
"language:ky",
"language:la",
"language:lb",
"language:lo",
"language:lt",
"language:lv",
"language:mg",
"language:mi",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:ms",
"language:mt",
"language:my",
"language:ne",
"language:nl",
"language:no",
"language:ny",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:ro",
"language:ru",
"language:sd",
"language:si",
"language:sk",
"language:sl",
"language:sm",
"language:sn",
"language:so",
"language:sq",
"language:sr",
"language:st",
"language:su",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:tg",
"language:th",
"language:tr",
"language:uk",
"language:und",
"language:ur",
"language:uz",
"language:vi",
"language:xh",
"language:yi",
"language:yo",
"language:zh",
"language:zu",
"license:odc-by",
"size_categories:n<1K",
"arxiv:1910.10683",
"region:us"
] | [
"text-generation",
"fill-mask"
] | "2022-03-02T23:29:22Z" | ---
pretty_name: mC4
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- he
- hi
- hmn
- ht
- hu
- hy
- id
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- 'no'
- ny
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tr
- uk
- und
- ur
- uz
- vi
- xh
- yi
- yo
- zh
- zu
language_bcp47:
- bg-Latn
- el-Latn
- hi-Latn
- ja-Latn
- ru-Latn
- zh-Latn
license:
- odc-by
multilinguality:
- multilingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
- 1B<n<10B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: mc4
viewer: false
---
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> Dataset "mc4" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/allenai/c4">allenai/c4</a>" instead.</p>
</div>
# Dataset Card for mC4
## Table of Contents
- [Dataset Card for mC4](#dataset-card-for-mc4)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/allenai/c4
- **Paper:** https://arxiv.org/abs/1910.10683
### Dataset Summary
A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org".
This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4
108 languages are available and are reported in the table below.
Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script.
| language code | language name |
|:----------------|:---------------------|
| af | Afrikaans |
| am | Amharic |
| ar | Arabic |
| az | Azerbaijani |
| be | Belarusian |
| bg | Bulgarian |
| bg-Latn | Bulgarian (Latin) |
| bn | Bangla |
| ca | Catalan |
| ceb | Cebuano |
| co | Corsican |
| cs | Czech |
| cy | Welsh |
| da | Danish |
| de | German |
| el | Greek |
| el-Latn | Greek (Latin) |
| en | English |
| eo | Esperanto |
| es | Spanish |
| et | Estonian |
| eu | Basque |
| fa | Persian |
| fi | Finnish |
| fil | Filipino |
| fr | French |
| fy | Western Frisian |
| ga | Irish |
| gd | Scottish Gaelic |
| gl | Galician |
| gu | Gujarati |
| ha | Hausa |
| haw | Hawaiian |
| hi | Hindi |
| hi-Latn | Hindi (Latin script) |
| hmn | Hmong, Mong |
| ht | Haitian |
| hu | Hungarian |
| hy | Armenian |
| id | Indonesian |
| ig | Igbo |
| is | Icelandic |
| it | Italian |
| iw | former Hebrew |
| ja | Japanese |
| ja-Latn | Japanese (Latin) |
| jv | Javanese |
| ka | Georgian |
| kk | Kazakh |
| km | Khmer |
| kn | Kannada |
| ko | Korean |
| ku | Kurdish |
| ky | Kyrgyz |
| la | Latin |
| lb | Luxembourgish |
| lo | Lao |
| lt | Lithuanian |
| lv | Latvian |
| mg | Malagasy |
| mi | Maori |
| mk | Macedonian |
| ml | Malayalam |
| mn | Mongolian |
| mr | Marathi |
| ms | Malay |
| mt | Maltese |
| my | Burmese |
| ne | Nepali |
| nl | Dutch |
| no | Norwegian |
| ny | Nyanja |
| pa | Punjabi |
| pl | Polish |
| ps | Pashto |
| pt | Portuguese |
| ro | Romanian |
| ru | Russian |
| ru-Latn | Russian (Latin) |
| sd | Sindhi |
| si | Sinhala |
| sk | Slovak |
| sl | Slovenian |
| sm | Samoan |
| sn | Shona |
| so | Somali |
| sq | Albanian |
| sr | Serbian |
| st | Southern Sotho |
| su | Sundanese |
| sv | Swedish |
| sw | Swahili |
| ta | Tamil |
| te | Telugu |
| tg | Tajik |
| th | Thai |
| tr | Turkish |
| uk | Ukrainian |
| und | Unknown language |
| ur | Urdu |
| uz | Uzbek |
| vi | Vietnamese |
| xh | Xhosa |
| yi | Yiddish |
| yo | Yoruba |
| zh | Chinese |
| zh-Latn | Chinese (Latin) |
| zu | Zulu |
You can load the mC4 subset of any language like this:
```python
from datasets import load_dataset
en_mc4 = load_dataset("mc4", "en")
```
And if you can even specify a list of languages:
```python
from datasets import load_dataset
mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"])
```
### Supported Tasks and Leaderboards
mC4 is mainly intended to pretrain language models and word representations.
### Languages
The dataset supports 108 languages.
## Dataset Structure
### Data Instances
An example form the `en` config is:
```
{'timestamp': '2018-06-24T01:32:39Z',
'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County',
'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'}
```
### Data Fields
The data have several fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp as a string
### Data Splits
To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table:
| config | train | validation |
|:---------|:--------|:-------------|
| af | ? | ? |
| am | ? | ? |
| ar | ? | ? |
| az | ? | ? |
| be | ? | ? |
| bg | ? | ? |
| bg-Latn | ? | ? |
| bn | ? | ? |
| ca | ? | ? |
| ceb | ? | ? |
| co | ? | ? |
| cs | ? | ? |
| cy | ? | ? |
| da | ? | ? |
| de | ? | ? |
| el | ? | ? |
| el-Latn | ? | ? |
| en | ? | ? |
| eo | ? | ? |
| es | ? | ? |
| et | ? | ? |
| eu | ? | ? |
| fa | ? | ? |
| fi | ? | ? |
| fil | ? | ? |
| fr | ? | ? |
| fy | ? | ? |
| ga | ? | ? |
| gd | ? | ? |
| gl | ? | ? |
| gu | ? | ? |
| ha | ? | ? |
| haw | ? | ? |
| hi | ? | ? |
| hi-Latn | ? | ? |
| hmn | ? | ? |
| ht | ? | ? |
| hu | ? | ? |
| hy | ? | ? |
| id | ? | ? |
| ig | ? | ? |
| is | ? | ? |
| it | ? | ? |
| iw | ? | ? |
| ja | ? | ? |
| ja-Latn | ? | ? |
| jv | ? | ? |
| ka | ? | ? |
| kk | ? | ? |
| km | ? | ? |
| kn | ? | ? |
| ko | ? | ? |
| ku | ? | ? |
| ky | ? | ? |
| la | ? | ? |
| lb | ? | ? |
| lo | ? | ? |
| lt | ? | ? |
| lv | ? | ? |
| mg | ? | ? |
| mi | ? | ? |
| mk | ? | ? |
| ml | ? | ? |
| mn | ? | ? |
| mr | ? | ? |
| ms | ? | ? |
| mt | ? | ? |
| my | ? | ? |
| ne | ? | ? |
| nl | ? | ? |
| no | ? | ? |
| ny | ? | ? |
| pa | ? | ? |
| pl | ? | ? |
| ps | ? | ? |
| pt | ? | ? |
| ro | ? | ? |
| ru | ? | ? |
| ru-Latn | ? | ? |
| sd | ? | ? |
| si | ? | ? |
| sk | ? | ? |
| sl | ? | ? |
| sm | ? | ? |
| sn | ? | ? |
| so | ? | ? |
| sq | ? | ? |
| sr | ? | ? |
| st | ? | ? |
| su | ? | ? |
| sv | ? | ? |
| sw | ? | ? |
| ta | ? | ? |
| te | ? | ? |
| tg | ? | ? |
| th | ? | ? |
| tr | ? | ? |
| uk | ? | ? |
| und | ? | ? |
| ur | ? | ? |
| uz | ? | ? |
| vi | ? | ? |
| xh | ? | ? |
| yi | ? | ? |
| yo | ? | ? |
| zh | ? | ? |
| zh-Latn | ? | ? |
| zu | ? | ? |
## 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
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
### Contributions
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
Sterzhang/PVIT-3M | Sterzhang | "2024-11-02T07:41:57Z" | 14,006 | 17 | [
"task_categories:visual-question-answering",
"task_categories:image-text-to-text",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2410.07113",
"region:us",
"multi-modal",
"personalized"
] | [
"visual-question-answering",
"image-text-to-text"
] | "2024-10-07T09:28:17Z" | ---
configs:
- config_name: PVIT-3M
data_files:
- split: all_data
path: PVIT-3M.json
language:
- en
task_categories:
- visual-question-answering
- image-text-to-text
tags:
- multi-modal
- personalized
license: apache-2.0
pretty_name: personalized visual instruction tuning
size_categories:
- 1M<n<10M
---
# PVIT-3M
The paper titled "[**Personalized Visual Instruction Tuning**](https://arxiv.org/abs/2410.07113)" introduces a novel dataset called PVIT-3M. This dataset is specifically designed for tuning MLLMs in the context of personalized visual instruction tasks. The dataset consists of 3 million image-text pairs that aim to improve MLLMs' abilities to generate responses based on personalized visual inputs, making them more tailored and adaptable to individual user needs and preferences.
Here’s the PVIT-3M statistics:
<img width="900" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/65d8b0f0661492b25c6623de/BLF2Uo72nqpvjPv_yplbt.png">
## Directory
The images in the PVIT-3M dataset have been organized into 40 separate folders, each named pvit-3m_0 to pvit-3m_39. After downloading, you can combine all the subfolders for a unified dataset structure.
```
Sterzhang/PVIT-3M
└─ image
└─ pvit-3m_0
└─ aug-face
└─ coco
└─ concat-person
└─ crop-face
└─ flickr30k
└─ object365
└─ vg
└─ pvit-3m_1
└─ ...
└─ ...
└─ PVIT-3M.json
└─ README.md
```
## Json Structure
The PVIT-3M.json file contains structured data for each conversation instance in the PVIT-3M dataset. Below is an outline of the key fields:
- `image`: A list of image file paths related to the conversation.
- `conversations`: A list of dictionaries, each representing a turn in the conversation.
- `from`: `human` or `gpt`.
- `value`: The content of the conversation.
- `type`: A string indicating the type.
## Example
Here is an example from the PVIT-3M:
```json
{
"image": [
"crop-face/object365/objects365_v2_01169026/2.jpg",
"object365/objects365_v2_01169026.jpg"
],
"conversations": [
{
"from": "human",
"value": "<image>\nThis is a photo of Erin.\n<image>\nHow does Erin look in this photo?"
},
{
"from": "gpt",
"value": "Erin is a woman with short, brown hair. She is wearing a dark blue dress with short sleeves and a ruffled design. She has a pearl necklace and a watch on her left wrist. She is also wearing a ring on her left hand."
}
],
"type": "crop description"
}
```
"crop-face/object365/objects365_v2_01169026/2.jpg": <img width="100" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/65d8b0f0661492b25c6623de/lJi0aDYE44wyGP2QMZ13W.png">
"object365/objects365_v2_01169026.jpg": <img width="400" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/65d8b0f0661492b25c6623de/RY_80A5rSOO1vv6A6CuJy.png">
## Script
The script processes conversation data in the **PVIT-3M** dataset by adding personalized wrapper tokens (`<person_s>` and `<person_e>`) around specific segments. This helps the model correctly associate personalized text and images with each individual, reducing ambiguity in multimodal training.
```python
import json
def process_image_description(text):
segments = text.split('<image>\n')
processed_segments = []
for i, segment in enumerate(segments):
if i == 0:
processed_segments.append(segment)
elif i == len(segments) - 1:
continue
else:
last_newline_index = segment.rfind('\n')
if last_newline_index != -1:
segment = segment[:last_newline_index] + '<person_e>' + segment[last_newline_index:]
else:
segment += '<person_e>'
processed_segments.append(f'<person_s><image>\n{segment}')
processed_segments.append(f"<image>\n{segments[-1]}")
return ''.join(processed_segments)
def process_conversation_data(input_path, output_path):
with open(input_path, 'r', encoding='utf-8') as f:
data = json.load(f)
for item in data:
conversation_value = item["conversations"][0]["value"]
item["conversations"][0]["value"] = process_image_description(conversation_value)
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
input_file = ""
output_file = ""
process_conversation_data(input_file, output_file)
```
# Code
Our code will be released in [PVIT](https://github.com/sterzhang/PVIT), containing scripts for generating PVIT dataset as well as our code for training.
# Case Study
<img width="1000" alt="image" src="https://github.com/user-attachments/assets/d50fa03f-fdb6-41ff-ab25-806578d29f3e">
# Citation
Our paper is now available at: [https://arxiv.org/abs/2410.07113](https://arxiv.org/abs/2410.07113)
```bibtex
@misc{pi2024personalizedvisualinstructiontuning,
title={Personalized Visual Instruction Tuning},
author={Renjie Pi and Jianshu Zhang and Tianyang Han and Jipeng Zhang and Rui Pan and Tong Zhang},
year={2024},
eprint={2410.07113},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.07113},
} |
HuggingFaceGECLM/StackExchange_Mar2023 | HuggingFaceGECLM | "2023-03-16T19:06:45Z" | 13,803 | 4 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-03-13T17:04:30Z" | ---
dataset_info:
features:
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dtype: int64
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---
# Dataset Card for "StackExchange_Mar2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hoskinson-center/proof-pile | hoskinson-center | "2023-08-19T03:24:11Z" | 13,762 | 58 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"math",
"mathematics",
"formal-mathematics"
] | [
"text-generation"
] | "2022-08-08T20:57:56Z" | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: [apache-2.0]
multilinguality:
- monolingual
pretty_name: proof-pile
size_categories: []
source_datasets: []
tags:
- math
- mathematics
- formal-mathematics
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Description
The `proof-pile` is a 13GB pre-training dataset of mathematical text that comprises 8.3 billion tokens (using the `gpt-neox` tokenizer). Models trained on this dataset are coming soon :) The dataset is composed of diverse sources of both informal and formal mathematics, namely
- ArXiv.math (10GB)
- Open-source math textbooks (50MB)
- Formal mathematics libraries (500MB)
- Lean mathlib and other Lean repositories
- Isabelle AFP
- Coq mathematical components and other Coq repositories
- HOL Light
- set.mm
- Mizar Mathematical Library
- Math Overflow and Math Stack Exchange (2.5GB)
- Wiki-style sources (50MB)
- ProofWiki
- Wikipedia math articles
- MATH dataset (6MB)
The construction of the dataset is reproducible using the code and instructions in the [proof-pile Github
repo](https://github.com/zhangir-azerbayev/proof-pile).
# Supported Tasks
This dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the `proof-pile` will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization.
# Languages
All informal mathematics in the `proof-pile` is written in English and LaTeX (arXiv articles in other languages are filtered out using [languagedetect](https://github.com/shuyo/language-detection/blob/wiki/ProjectHome.md)). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.
# Evaluation
The version of `set.mm` in this dataset has 10% of proofs replaced with the `?` character in order to preserve a validation and test set for Metamath provers pre-trained on the `proof-pile`. The precise split can be found here: [validation](https://github.com/zhangir-azerbayev/mm-extract/blob/main/valid_decls.json) and [test](https://github.com/zhangir-azerbayev/mm-extract/blob/main/test_decls.json).
The Lean mathlib commit used in this dataset is `6313863`. Theorems created in subsequent commits can be used for evaluating Lean theorem provers.
This dataset contains only the training set of the [MATH dataset](https://github.com/hendrycks/math). However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the [NaturalProofs dataset](https://github.com/wellecks/naturalproofs).
# Data Preprocessing
This section describes any significant filtering and transformations made to various subsets of the data.
**arXiv.math.**
The arXiv.math dataset is large, heterogeneous, and contains a great deal of noise. We used the following heuristics
when choosing which files from arXiv.math source folders to include in the dataset:
- Keep only files with a `.tex` extension.
- Only include files that use either a `utf-8/16/32` or `latin-1` text encoding.
- Discard files that do not contain a part, chapter, section, sub...section, paragraph, or subparagraph heading.
- Delete files that contain the keyword `gnuplot`. Gnuplot-latex is an old command line utility that generates blocks
of entirely unintelligible source.
- Include only articles in English, as determined by the [langdetect library](https://pypi.org/project/langdetect/). \n",
"\n",
- Exclude files shorter than 280 characters (characters counted after substring removal described below).
In addition, we apply the following transformations to arXiv.math texts:
- Delete everything outside of `\begin{document}` and `\end{document}`.
- Delete everything including or after `\Refs`, `\begin{thebibliography}`, or `\begin{bibdiv}`
- Delete comments.
- Any more than three consecutive newlines are replaced by three consecutive newlines.
In [this notebook](https://github.com/zhangir-azerbayev/proof-pile/blob/main/analysis/arxiv_noisedetection.ipynb), we provide an analysis of the prevalence of noisy documents in the arXiv.math subset of the
proof-pile.
**Stack Exchange.**
We only include questions that have at least 5 upvotes and an answer. We format Stack Exchange posts as follows
```
QUESTION [{num_upvotes} upvotes]: {text of question}
REPLY [{num_upvotes} votes]: {text of reply}
REPLY [{num_upvotes} votes]: {text of reply}
.
.
.
```
**set.mm.**
We converted `set.mm` into human-readable form by following the instructions in the [mm-extract repo](https://github.com/zhangir-azerbayev/mm-extract)
## Contributions
Authors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski.
We would like to thank Jeremy Avigad, Albert Jiang, and Wenda Li for their invaluable guidance, and the Hoskinson Center for Formal Mathematics for its support.
|
Jackmin108/xtreme | Jackmin108 | "2023-10-21T20:14:19Z" | 13,761 | 0 | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-10-20T13:16:37Z" | ---
license: apache-2.0
configs:
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features:
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path:
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features:
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path: xnli/de/test.parquet
features:
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_type: Value
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label:
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idx:
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path: paws-x/fr/validation.parquet
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path: paws-x/fr/test.parquet
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label:
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- split: validation
path: paws-x/ja/validation.parquet
- split: test
path: paws-x/ja/test.parquet
features:
sentence1:
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label:
names:
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- split: validation
path: paws-x/ko/validation.parquet
- split: test
path: paws-x/ko/test.parquet
features:
sentence1:
dtype: string
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sentence2:
dtype: string
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label:
names:
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data_files:
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path: paws-x/zh/train.parquet
- split: validation
path: paws-x/zh/validation.parquet
- split: test
path: paws-x/zh/test.parquet
features:
sentence1:
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---
|
laion/LAION-Audio-300M | laion | "2025-01-10T21:33:57Z" | 13,673 | 21 | [
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:webdataset",
"modality:audio",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | null | "2024-12-29T09:50:41Z" | ---
license: apache-2.0
---
|
bigbio/pubmed_qa | bigbio | "2024-03-23T19:06:35Z" | 13,662 | 41 | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"region:us"
] | null | "2022-11-13T22:11:45Z" |
---
language:
- en
bigbio_language:
- English
license: mit
multilinguality: monolingual
bigbio_license_shortname: MIT
pretty_name: PubMedQA
homepage: https://github.com/pubmedqa/pubmedqa
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for PubMedQA
## Dataset Description
- **Homepage:** https://github.com/pubmedqa/pubmedqa
- **Pubmed:** True
- **Public:** True
- **Tasks:** QA
PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts.
PubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A).
Each PubMedQA instance is composed of:
(1) a question which is either an existing research article title or derived from one,
(2) a context which is the corresponding PubMed abstract without its conclusion,
(3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and
(4) a yes/no/maybe answer which summarizes the conclusion.
PubMedQA is the first QA dataset where reasoning over biomedical research texts,
especially their quantitative contents, is required to answer the questions.
PubMedQA datasets comprise of 3 different subsets:
(1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles.
(2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic.
(3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles.
## Citation Information
```
@inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={2567--2577},
year={2019}
}
```
|
ilsp/mmlu_greek | ilsp | "2024-05-20T12:36:54Z" | 13,578 | 3 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-04-01T14:53:41Z" | ---
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- config_name: nutrition
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- config_name: philosophy
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- config_name: professional_law
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- config_name: professional_medicine
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- config_name: professional_psychology
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- config_name: public_relations
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- config_name: security_studies
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- config_name: sociology
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- config_name: us_foreign_policy
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- config_name: virology
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- config_name: world_religions
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dataset_size: 87808
configs:
- config_name: abstract_algebra
data_files:
- split: test
path: abstract_algebra/test-*
- split: validation
path: abstract_algebra/validation-*
- split: dev
path: abstract_algebra/dev-*
- config_name: all
data_files:
- split: test
path: all/test-*
- split: validation
path: all/validation-*
- split: dev
path: all/dev-*
- config_name: anatomy
data_files:
- split: test
path: anatomy/test-*
- split: validation
path: anatomy/validation-*
- split: dev
path: anatomy/dev-*
- config_name: astronomy
data_files:
- split: test
path: astronomy/test-*
- split: validation
path: astronomy/validation-*
- split: dev
path: astronomy/dev-*
- config_name: business_ethics
data_files:
- split: test
path: business_ethics/test-*
- split: validation
path: business_ethics/validation-*
- split: dev
path: business_ethics/dev-*
- config_name: clinical_knowledge
data_files:
- split: test
path: clinical_knowledge/test-*
- split: validation
path: clinical_knowledge/validation-*
- split: dev
path: clinical_knowledge/dev-*
- config_name: college_biology
data_files:
- split: test
path: college_biology/test-*
- split: validation
path: college_biology/validation-*
- split: dev
path: college_biology/dev-*
- config_name: college_chemistry
data_files:
- split: test
path: college_chemistry/test-*
- split: validation
path: college_chemistry/validation-*
- split: dev
path: college_chemistry/dev-*
- config_name: college_computer_science
data_files:
- split: test
path: college_computer_science/test-*
- split: validation
path: college_computer_science/validation-*
- split: dev
path: college_computer_science/dev-*
- config_name: college_mathematics
data_files:
- split: test
path: college_mathematics/test-*
- split: validation
path: college_mathematics/validation-*
- split: dev
path: college_mathematics/dev-*
- config_name: college_medicine
data_files:
- split: test
path: college_medicine/test-*
- split: validation
path: college_medicine/validation-*
- split: dev
path: college_medicine/dev-*
- config_name: college_physics
data_files:
- split: test
path: college_physics/test-*
- split: validation
path: college_physics/validation-*
- split: dev
path: college_physics/dev-*
- config_name: computer_security
data_files:
- split: test
path: computer_security/test-*
- split: validation
path: computer_security/validation-*
- split: dev
path: computer_security/dev-*
- config_name: conceptual_physics
data_files:
- split: test
path: conceptual_physics/test-*
- split: validation
path: conceptual_physics/validation-*
- split: dev
path: conceptual_physics/dev-*
- config_name: econometrics
data_files:
- split: test
path: econometrics/test-*
- split: validation
path: econometrics/validation-*
- split: dev
path: econometrics/dev-*
- config_name: electrical_engineering
data_files:
- split: test
path: electrical_engineering/test-*
- split: validation
path: electrical_engineering/validation-*
- split: dev
path: electrical_engineering/dev-*
- config_name: elementary_mathematics
data_files:
- split: test
path: elementary_mathematics/test-*
- split: validation
path: elementary_mathematics/validation-*
- split: dev
path: elementary_mathematics/dev-*
- config_name: formal_logic
data_files:
- split: test
path: formal_logic/test-*
- split: validation
path: formal_logic/validation-*
- split: dev
path: formal_logic/dev-*
- config_name: global_facts
data_files:
- split: test
path: global_facts/test-*
- split: validation
path: global_facts/validation-*
- split: dev
path: global_facts/dev-*
- config_name: high_school_biology
data_files:
- split: test
path: high_school_biology/test-*
- split: validation
path: high_school_biology/validation-*
- split: dev
path: high_school_biology/dev-*
- config_name: high_school_chemistry
data_files:
- split: test
path: high_school_chemistry/test-*
- split: validation
path: high_school_chemistry/validation-*
- split: dev
path: high_school_chemistry/dev-*
- config_name: high_school_computer_science
data_files:
- split: test
path: high_school_computer_science/test-*
- split: validation
path: high_school_computer_science/validation-*
- split: dev
path: high_school_computer_science/dev-*
- config_name: high_school_european_history
data_files:
- split: test
path: high_school_european_history/test-*
- split: validation
path: high_school_european_history/validation-*
- split: dev
path: high_school_european_history/dev-*
- config_name: high_school_geography
data_files:
- split: test
path: high_school_geography/test-*
- split: validation
path: high_school_geography/validation-*
- split: dev
path: high_school_geography/dev-*
- config_name: high_school_government_and_politics
data_files:
- split: test
path: high_school_government_and_politics/test-*
- split: validation
path: high_school_government_and_politics/validation-*
- split: dev
path: high_school_government_and_politics/dev-*
- config_name: high_school_macroeconomics
data_files:
- split: test
path: high_school_macroeconomics/test-*
- split: validation
path: high_school_macroeconomics/validation-*
- split: dev
path: high_school_macroeconomics/dev-*
- config_name: high_school_mathematics
data_files:
- split: test
path: high_school_mathematics/test-*
- split: validation
path: high_school_mathematics/validation-*
- split: dev
path: high_school_mathematics/dev-*
- config_name: high_school_microeconomics
data_files:
- split: test
path: high_school_microeconomics/test-*
- split: validation
path: high_school_microeconomics/validation-*
- split: dev
path: high_school_microeconomics/dev-*
- config_name: high_school_physics
data_files:
- split: test
path: high_school_physics/test-*
- split: validation
path: high_school_physics/validation-*
- split: dev
path: high_school_physics/dev-*
- config_name: high_school_psychology
data_files:
- split: test
path: high_school_psychology/test-*
- split: validation
path: high_school_psychology/validation-*
- split: dev
path: high_school_psychology/dev-*
- config_name: high_school_statistics
data_files:
- split: test
path: high_school_statistics/test-*
- split: validation
path: high_school_statistics/validation-*
- split: dev
path: high_school_statistics/dev-*
- config_name: high_school_us_history
data_files:
- split: test
path: high_school_us_history/test-*
- split: validation
path: high_school_us_history/validation-*
- split: dev
path: high_school_us_history/dev-*
- config_name: high_school_world_history
data_files:
- split: test
path: high_school_world_history/test-*
- split: validation
path: high_school_world_history/validation-*
- split: dev
path: high_school_world_history/dev-*
- config_name: human_aging
data_files:
- split: test
path: human_aging/test-*
- split: validation
path: human_aging/validation-*
- split: dev
path: human_aging/dev-*
- config_name: human_sexuality
data_files:
- split: test
path: human_sexuality/test-*
- split: validation
path: human_sexuality/validation-*
- split: dev
path: human_sexuality/dev-*
- config_name: international_law
data_files:
- split: test
path: international_law/test-*
- split: validation
path: international_law/validation-*
- split: dev
path: international_law/dev-*
- config_name: jurisprudence
data_files:
- split: test
path: jurisprudence/test-*
- split: validation
path: jurisprudence/validation-*
- split: dev
path: jurisprudence/dev-*
- config_name: logical_fallacies
data_files:
- split: test
path: logical_fallacies/test-*
- split: validation
path: logical_fallacies/validation-*
- split: dev
path: logical_fallacies/dev-*
- config_name: machine_learning
data_files:
- split: test
path: machine_learning/test-*
- split: validation
path: machine_learning/validation-*
- split: dev
path: machine_learning/dev-*
- config_name: management
data_files:
- split: test
path: management/test-*
- split: validation
path: management/validation-*
- split: dev
path: management/dev-*
- config_name: marketing
data_files:
- split: test
path: marketing/test-*
- split: validation
path: marketing/validation-*
- split: dev
path: marketing/dev-*
- config_name: medical_genetics
data_files:
- split: test
path: medical_genetics/test-*
- split: validation
path: medical_genetics/validation-*
- split: dev
path: medical_genetics/dev-*
- config_name: miscellaneous
data_files:
- split: test
path: miscellaneous/test-*
- split: validation
path: miscellaneous/validation-*
- split: dev
path: miscellaneous/dev-*
- config_name: moral_disputes
data_files:
- split: test
path: moral_disputes/test-*
- split: validation
path: moral_disputes/validation-*
- split: dev
path: moral_disputes/dev-*
- config_name: moral_scenarios
data_files:
- split: test
path: moral_scenarios/test-*
- split: validation
path: moral_scenarios/validation-*
- split: dev
path: moral_scenarios/dev-*
- config_name: nutrition
data_files:
- split: test
path: nutrition/test-*
- split: validation
path: nutrition/validation-*
- split: dev
path: nutrition/dev-*
- config_name: philosophy
data_files:
- split: test
path: philosophy/test-*
- split: validation
path: philosophy/validation-*
- split: dev
path: philosophy/dev-*
- config_name: prehistory
data_files:
- split: test
path: prehistory/test-*
- split: validation
path: prehistory/validation-*
- split: dev
path: prehistory/dev-*
- config_name: professional_accounting
data_files:
- split: test
path: professional_accounting/test-*
- split: validation
path: professional_accounting/validation-*
- split: dev
path: professional_accounting/dev-*
- config_name: professional_law
data_files:
- split: test
path: professional_law/test-*
- split: validation
path: professional_law/validation-*
- split: dev
path: professional_law/dev-*
- config_name: professional_medicine
data_files:
- split: test
path: professional_medicine/test-*
- split: validation
path: professional_medicine/validation-*
- split: dev
path: professional_medicine/dev-*
- config_name: professional_psychology
data_files:
- split: test
path: professional_psychology/test-*
- split: validation
path: professional_psychology/validation-*
- split: dev
path: professional_psychology/dev-*
- config_name: public_relations
data_files:
- split: test
path: public_relations/test-*
- split: validation
path: public_relations/validation-*
- split: dev
path: public_relations/dev-*
- config_name: security_studies
data_files:
- split: test
path: security_studies/test-*
- split: validation
path: security_studies/validation-*
- split: dev
path: security_studies/dev-*
- config_name: sociology
data_files:
- split: test
path: sociology/test-*
- split: validation
path: sociology/validation-*
- split: dev
path: sociology/dev-*
- config_name: us_foreign_policy
data_files:
- split: test
path: us_foreign_policy/test-*
- split: validation
path: us_foreign_policy/validation-*
- split: dev
path: us_foreign_policy/dev-*
- config_name: virology
data_files:
- split: test
path: virology/test-*
- split: validation
path: virology/validation-*
- split: dev
path: virology/dev-*
- config_name: world_religions
data_files:
- split: test
path: world_religions/test-*
- split: validation
path: world_religions/validation-*
- split: dev
path: world_religions/dev-*
---
# Dataset Card for MMLU Greek
The MMLU Greek dataset is a set of 15858 examples from the MMLU dataset [available from here and here], machine-translated into Greek. The original dataset consists of multiple-choice questions from 57 tasks including elementary mathematics, US history, computer science, law, etc.
## Dataset Details
### Dataset Description
- **Curated by:** ILSP/Athena RC
- **Language(s) (NLP):** el
- **License:** cc-by-nc-sa-4.0
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This dataset is the result of machine translation.
## Dataset Card Contact
https://www.athenarc.gr/en/ilsp
|
mteb/amazon_massive_scenario | mteb | "2024-05-07T21:23:34Z" | 13,531 | 2 | [
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | null | "2022-05-15T20:30:23Z" | ---
configs:
- config_name: default
data_files:
- path: train/*.json.gz
split: train
- path: test/*.json.gz
split: test
- path: validation/*.json.gz
split: validation
- config_name: ta
data_files:
- path: train/ta.json.gz
split: train
- path: test/ta.json.gz
split: test
- path: validation/ta.json.gz
split: validation
- config_name: is
data_files:
- path: train/is.json.gz
split: train
- path: test/is.json.gz
split: test
- path: validation/is.json.gz
split: validation
- config_name: pl
data_files:
- path: train/pl.json.gz
split: train
- path: test/pl.json.gz
split: test
- path: validation/pl.json.gz
split: validation
- config_name: zh-CN
data_files:
- path: train/zh-CN.json.gz
split: train
- path: test/zh-CN.json.gz
split: test
- path: validation/zh-CN.json.gz
split: validation
- config_name: el
data_files:
- path: train/el.json.gz
split: train
- path: test/el.json.gz
split: test
- path: validation/el.json.gz
split: validation
- config_name: ru
data_files:
- path: train/ru.json.gz
split: train
- path: test/ru.json.gz
split: test
- path: validation/ru.json.gz
split: validation
- config_name: te
data_files:
- path: train/te.json.gz
split: train
- path: test/te.json.gz
split: test
- path: validation/te.json.gz
split: validation
- config_name: cy
data_files:
- path: train/cy.json.gz
split: train
- path: test/cy.json.gz
split: test
- path: validation/cy.json.gz
split: validation
- config_name: he
data_files:
- path: train/he.json.gz
split: train
- path: test/he.json.gz
split: test
- path: validation/he.json.gz
split: validation
- config_name: de
data_files:
- path: train/de.json.gz
split: train
- path: test/de.json.gz
split: test
- path: validation/de.json.gz
split: validation
- config_name: af
data_files:
- path: train/af.json.gz
split: train
- path: test/af.json.gz
split: test
- path: validation/af.json.gz
split: validation
- config_name: ml
data_files:
- path: train/ml.json.gz
split: train
- path: test/ml.json.gz
split: test
- path: validation/ml.json.gz
split: validation
- config_name: sl
data_files:
- path: train/sl.json.gz
split: train
- path: test/sl.json.gz
split: test
- path: validation/sl.json.gz
split: validation
- config_name: vi
data_files:
- path: train/vi.json.gz
split: train
- path: test/vi.json.gz
split: test
- path: validation/vi.json.gz
split: validation
- config_name: mn
data_files:
- path: train/mn.json.gz
split: train
- path: test/mn.json.gz
split: test
- path: validation/mn.json.gz
split: validation
- config_name: tl
data_files:
- path: train/tl.json.gz
split: train
- path: test/tl.json.gz
split: test
- path: validation/tl.json.gz
split: validation
- config_name: it
data_files:
- path: train/it.json.gz
split: train
- path: test/it.json.gz
split: test
- path: validation/it.json.gz
split: validation
- config_name: jv
data_files:
- path: train/jv.json.gz
split: train
- path: test/jv.json.gz
split: test
- path: validation/jv.json.gz
split: validation
- config_name: sq
data_files:
- path: train/sq.json.gz
split: train
- path: test/sq.json.gz
split: test
- path: validation/sq.json.gz
split: validation
- config_name: fa
data_files:
- path: train/fa.json.gz
split: train
- path: test/fa.json.gz
split: test
- path: validation/fa.json.gz
split: validation
- config_name: nb
data_files:
- path: train/nb.json.gz
split: train
- path: test/nb.json.gz
split: test
- path: validation/nb.json.gz
split: validation
- config_name: km
data_files:
- path: train/km.json.gz
split: train
- path: test/km.json.gz
split: test
- path: validation/km.json.gz
split: validation
- config_name: th
data_files:
- path: train/th.json.gz
split: train
- path: test/th.json.gz
split: test
- path: validation/th.json.gz
split: validation
- config_name: ja
data_files:
- path: train/ja.json.gz
split: train
- path: test/ja.json.gz
split: test
- path: validation/ja.json.gz
split: validation
- config_name: hi
data_files:
- path: train/hi.json.gz
split: train
- path: test/hi.json.gz
split: test
- path: validation/hi.json.gz
split: validation
- config_name: id
data_files:
- path: train/id.json.gz
split: train
- path: test/id.json.gz
split: test
- path: validation/id.json.gz
split: validation
- config_name: kn
data_files:
- path: train/kn.json.gz
split: train
- path: test/kn.json.gz
split: test
- path: validation/kn.json.gz
split: validation
- config_name: fi
data_files:
- path: train/fi.json.gz
split: train
- path: test/fi.json.gz
split: test
- path: validation/fi.json.gz
split: validation
- config_name: ur
data_files:
- path: train/ur.json.gz
split: train
- path: test/ur.json.gz
split: test
- path: validation/ur.json.gz
split: validation
- config_name: my
data_files:
- path: train/my.json.gz
split: train
- path: test/my.json.gz
split: test
- path: validation/my.json.gz
split: validation
- config_name: lv
data_files:
- path: train/lv.json.gz
split: train
- path: test/lv.json.gz
split: test
- path: validation/lv.json.gz
split: validation
- config_name: fr
data_files:
- path: train/fr.json.gz
split: train
- path: test/fr.json.gz
split: test
- path: validation/fr.json.gz
split: validation
- config_name: ko
data_files:
- path: train/ko.json.gz
split: train
- path: test/ko.json.gz
split: test
- path: validation/ko.json.gz
split: validation
- config_name: sw
data_files:
- path: train/sw.json.gz
split: train
- path: test/sw.json.gz
split: test
- path: validation/sw.json.gz
split: validation
- config_name: sv
data_files:
- path: train/sv.json.gz
split: train
- path: test/sv.json.gz
split: test
- path: validation/sv.json.gz
split: validation
- config_name: nl
data_files:
- path: train/nl.json.gz
split: train
- path: test/nl.json.gz
split: test
- path: validation/nl.json.gz
split: validation
- config_name: da
data_files:
- path: train/da.json.gz
split: train
- path: test/da.json.gz
split: test
- path: validation/da.json.gz
split: validation
- config_name: ar
data_files:
- path: train/ar.json.gz
split: train
- path: test/ar.json.gz
split: test
- path: validation/ar.json.gz
split: validation
- config_name: ms
data_files:
- path: train/ms.json.gz
split: train
- path: test/ms.json.gz
split: test
- path: validation/ms.json.gz
split: validation
- config_name: en
data_files:
- path: train/en.json.gz
split: train
- path: test/en.json.gz
split: test
- path: validation/en.json.gz
split: validation
- config_name: am
data_files:
- path: train/am.json.gz
split: train
- path: test/am.json.gz
split: test
- path: validation/am.json.gz
split: validation
- config_name: pt
data_files:
- path: train/pt.json.gz
split: train
- path: test/pt.json.gz
split: test
- path: validation/pt.json.gz
split: validation
- config_name: ka
data_files:
- path: train/ka.json.gz
split: train
- path: test/ka.json.gz
split: test
- path: validation/ka.json.gz
split: validation
- config_name: ro
data_files:
- path: train/ro.json.gz
split: train
- path: test/ro.json.gz
split: test
- path: validation/ro.json.gz
split: validation
- config_name: tr
data_files:
- path: train/tr.json.gz
split: train
- path: test/tr.json.gz
split: test
- path: validation/tr.json.gz
split: validation
- config_name: hu
data_files:
- path: train/hu.json.gz
split: train
- path: test/hu.json.gz
split: test
- path: validation/hu.json.gz
split: validation
- config_name: zh-TW
data_files:
- path: train/zh-TW.json.gz
split: train
- path: test/zh-TW.json.gz
split: test
- path: validation/zh-TW.json.gz
split: validation
- config_name: bn
data_files:
- path: train/bn.json.gz
split: train
- path: test/bn.json.gz
split: test
- path: validation/bn.json.gz
split: validation
- config_name: hy
data_files:
- path: train/hy.json.gz
split: train
- path: test/hy.json.gz
split: test
- path: validation/hy.json.gz
split: validation
- config_name: es
data_files:
- path: train/es.json.gz
split: train
- path: test/es.json.gz
split: test
- path: validation/es.json.gz
split: validation
- config_name: az
data_files:
- path: train/az.json.gz
split: train
- path: test/az.json.gz
split: test
- path: validation/az.json.gz
split: validation
---
|
pico-lm/pretokenized-dolma | pico-lm | "2025-02-04T10:08:48Z" | 13,514 | 2 | [
"language:en",
"license:apache-2.0",
"size_categories:100M<n<1B",
"format:parquet",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-02-04T04:58:16Z" | ---
license: apache-2.0
language:
- en
pretty_name: 'Pico Dataset: Pre-tokenized, Pre-shuffled Dolma'
size_categories:
- 100B<n<1T
---
## The Pico Dataset
A pre-tokenized, pre-shuffled version of [Dolma](https://huggingface.co/datasets/allenai/dolma), the high-quality text corpus from AI2.
### Overview
The Pico dataset simplifies training by providing:
- Pre-tokenized text in chunks of 2048 tokens, using the [OLMo Tokenizer](https://huggingface.co/allenai/OLMo-7B-0724-hf/blob/main/tokenizer_config.json)
- Pre-shuffled data for consistent training
- Streaming-friendly format
- 420B tokens total (perfect for 200K steps at batch size 1024)
### Benefits
- **Storage Efficient**: No need to download the full 10TB Dolma dataset
- **Memory Efficient**: Stream data directly with `load_dataset(..., streaming=True)`
- **Reproducible**: All models see identical data in identical order
- **Fast**: Skip tokenization during training
- **Simple**: Minimal boilerplate code needed
### Usage
```
from datasets import load_dataset
dataset = load_dataset("pico-lm/pretokenized-dolma", streaming=True)
``` |
asahi417/seamless-align-deA-enA.speaker-embedding.xlsr-2b | asahi417 | "2024-06-24T01:12:19Z" | 13,411 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-17T10:18:56Z" | ---
dataset_info:
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- name: deA.audio.speaker_embedding
sequence: float32
- name: deA.audio.speaker_embedding.full
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- name: enA.audio.speaker_embedding.full
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sequence: float32
splits:
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---
|
facebook/xnli | facebook | "2024-01-05T08:30:52Z" | 13,368 | 53 | [
"language:ar",
"language:bg",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:ru",
"language:sw",
"language:th",
"language:tr",
"language:ur",
"language:vi",
"language:zh",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2022-03-02T23:29:22Z" | ---
language:
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- ru
- sw
- th
- tr
- ur
- vi
- zh
paperswithcode_id: xnli
pretty_name: Cross-lingual Natural Language Inference
dataset_info:
- config_name: all_languages
features:
- name: premise
dtype:
translation:
languages:
- ar
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- de
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data_files:
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path: all_languages/train-*
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path: all_languages/test-*
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data_files:
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path: fr/test-*
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path: ru/train-*
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path: ru/test-*
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path: sw/test-*
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path: th/train-*
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path: th/test-*
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path: th/validation-*
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data_files:
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path: tr/train-*
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path: tr/test-*
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path: tr/validation-*
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data_files:
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path: ur/train-*
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path: ur/test-*
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path: ur/validation-*
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data_files:
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path: vi/train-*
- split: test
path: vi/test-*
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path: vi/validation-*
- config_name: zh
data_files:
- split: train
path: zh/train-*
- split: test
path: zh/test-*
- split: validation
path: zh/validation-*
---
# Dataset Card for "xnli"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.nyu.edu/projects/bowman/xnli/](https://www.nyu.edu/projects/bowman/xnli/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 7.74 GB
- **Size of the generated dataset:** 3.23 GB
- **Total amount of disk used:** 10.97 GB
### Dataset Summary
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### all_languages
- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 1.61 GB
- **Total amount of disk used:** 2.09 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...",
"label": 0,
"premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..."
}
```
#### ar
- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 109.32 MB
- **Total amount of disk used:** 593.29 MB
An example of 'validation' looks as follows.
```
{
"hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.",
"label": 1,
"premise": "وقال، ماما، لقد عدت للمنزل."
}
```
#### bg
- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 128.32 MB
- **Total amount of disk used:** 612.28 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...",
"label": 0,
"premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..."
}
```
#### de
- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 86.17 MB
- **Total amount of disk used:** 570.14 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .",
"label": 0,
"premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..."
}
```
#### el
- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 142.30 MB
- **Total amount of disk used:** 626.26 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...",
"label": 1,
"premise": "Και είπε, Μαμά, έφτασα στο σπίτι."
}
```
### Data Fields
The data fields are the same among all splits.
#### all_languages
- `premise`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`.
- `hypothesis`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### ar
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### bg
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### de
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### el
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
### Data Splits
| name |train |validation|test|
|-------------|-----:|---------:|---:|
|all_languages|392702| 2490|5010|
|ar |392702| 2490|5010|
|bg |392702| 2490|5010|
|de |392702| 2490|5010|
|el |392702| 2490|5010|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{conneau2018xnli,
author = {Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin},
title = {XNLI: Evaluating Cross-lingual Sentence Representations},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing},
year = {2018},
publisher = {Association for Computational Linguistics},
location = {Brussels, Belgium},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
ptb-text-only/ptb_text_only | ptb-text-only | "2024-01-18T11:13:39Z" | 13,364 | 16 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:10K<n<100K",
"region:us"
] | [
"text-generation",
"fill-mask"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
license_details: LDC User Agreement for Non-Members
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Penn Treebank
dataset_info:
features:
- name: sentence
dtype: string
config_name: penn_treebank
splits:
- name: train
num_bytes: 5143706
num_examples: 42068
- name: test
num_bytes: 453710
num_examples: 3761
- name: validation
num_bytes: 403156
num_examples: 3370
download_size: 5951345
dataset_size: 6000572
---
# Dataset Card for Penn Treebank
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://catalog.ldc.upenn.edu/LDC99T42
- **Repository:** 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt',
'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt',
'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt'
- **Paper:** https://www.aclweb.org/anthology/J93-2004.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material.
The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token.
### Supported Tasks and Leaderboards
Language Modelling
### Languages
The text in the dataset is in American English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Dataset provided for research purposes only. Please check dataset license for additional information.
### Citation Information
@article{marcus-etal-1993-building,
title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank",
author = "Marcus, Mitchell P. and
Santorini, Beatrice and
Marcinkiewicz, Mary Ann",
journal = "Computational Linguistics",
volume = "19",
number = "2",
year = "1993",
url = "https://www.aclweb.org/anthology/J93-2004",
pages = "313--330",
}
### Contributions
Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset. |
asahi417/seamless-align-enA-zhA.speaker-embedding.xlsr-2b | asahi417 | "2024-06-17T08:52:20Z" | 13,363 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-14T10:18:38Z" | ---
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---
|
hails/bigbench | hails | "2023-11-17T16:05:10Z" | 13,185 | 4 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-10-03T19:55:51Z" | ---
dataset_info:
- config_name: abstract_narrative_understanding_zero_shot
features:
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download_size: 0
dataset_size: 13120138
- config_name: anachronisms_zero_shot
features:
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download_size: 0
dataset_size: 97652
- config_name: analogical_similarity_zero_shot
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dataset_size: 2747630
- config_name: analytic_entailment_zero_shot
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download_size: 0
dataset_size: 34632
- config_name: arithmetic_zero_shot
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- config_name: tense_zero_shot
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- config_name: topical_chat_zero_shot
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- config_name: tracking_shuffled_objects_zero_shot
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- config_name: understanding_fables_zero_shot
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- config_name: undo_permutation_zero_shot
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- config_name: unit_conversion_zero_shot
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- config_name: vitaminc_fact_verification_zero_shot
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- config_name: what_is_the_tao_zero_shot
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- config_name: which_wiki_edit_zero_shot
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- config_name: winowhy_zero_shot
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- config_name: word_sorting_zero_shot
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- config_name: word_unscrambling_zero_shot
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configs:
- config_name: abstract_narrative_understanding_zero_shot
data_files:
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path: abstract_narrative_understanding_zero_shot/default-*
- split: train
path: abstract_narrative_understanding_zero_shot/train-*
- split: validation
path: abstract_narrative_understanding_zero_shot/validation-*
- config_name: anachronisms_zero_shot
data_files:
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path: anachronisms_zero_shot/default-*
- split: train
path: anachronisms_zero_shot/train-*
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path: anachronisms_zero_shot/validation-*
- config_name: analogical_similarity_zero_shot
data_files:
- split: default
path: analogical_similarity_zero_shot/default-*
- split: train
path: analogical_similarity_zero_shot/train-*
- split: validation
path: analogical_similarity_zero_shot/validation-*
- config_name: analytic_entailment_zero_shot
data_files:
- split: default
path: analytic_entailment_zero_shot/default-*
- split: train
path: analytic_entailment_zero_shot/train-*
- split: validation
path: analytic_entailment_zero_shot/validation-*
- config_name: arithmetic_zero_shot
data_files:
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path: arithmetic_zero_shot/default-*
- split: train
path: arithmetic_zero_shot/train-*
- split: validation
path: arithmetic_zero_shot/validation-*
- config_name: ascii_word_recognition_zero_shot
data_files:
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path: ascii_word_recognition_zero_shot/default-*
- split: train
path: ascii_word_recognition_zero_shot/train-*
- split: validation
path: ascii_word_recognition_zero_shot/validation-*
- config_name: authorship_verification_zero_shot
data_files:
- split: default
path: authorship_verification_zero_shot/default-*
- split: train
path: authorship_verification_zero_shot/train-*
- split: validation
path: authorship_verification_zero_shot/validation-*
- config_name: auto_categorization_zero_shot
data_files:
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path: auto_categorization_zero_shot/default-*
- split: train
path: auto_categorization_zero_shot/train-*
- split: validation
path: auto_categorization_zero_shot/validation-*
- config_name: auto_debugging_zero_shot
data_files:
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path: auto_debugging_zero_shot/default-*
- split: train
path: auto_debugging_zero_shot/train-*
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path: auto_debugging_zero_shot/validation-*
- config_name: bbq_lite_json_zero_shot
data_files:
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path: bbq_lite_json_zero_shot/default-*
- split: train
path: bbq_lite_json_zero_shot/train-*
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path: bbq_lite_json_zero_shot/validation-*
- config_name: bridging_anaphora_resolution_barqa_zero_shot
data_files:
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path: bridging_anaphora_resolution_barqa_zero_shot/default-*
- split: train
path: bridging_anaphora_resolution_barqa_zero_shot/train-*
- split: validation
path: bridging_anaphora_resolution_barqa_zero_shot/validation-*
- config_name: causal_judgment_zero_shot
data_files:
- split: default
path: causal_judgment_zero_shot/default-*
- split: train
path: causal_judgment_zero_shot/train-*
- split: validation
path: causal_judgment_zero_shot/validation-*
- config_name: cause_and_effect_zero_shot
data_files:
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path: cause_and_effect_zero_shot/default-*
- split: train
path: cause_and_effect_zero_shot/train-*
- split: validation
path: cause_and_effect_zero_shot/validation-*
- config_name: checkmate_in_one_zero_shot
data_files:
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path: checkmate_in_one_zero_shot/default-*
- split: train
path: checkmate_in_one_zero_shot/train-*
- split: validation
path: checkmate_in_one_zero_shot/validation-*
- config_name: chess_state_tracking_zero_shot
data_files:
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path: chess_state_tracking_zero_shot/default-*
- split: train
path: chess_state_tracking_zero_shot/train-*
- split: validation
path: chess_state_tracking_zero_shot/validation-*
- config_name: chinese_remainder_theorem_zero_shot
data_files:
- split: default
path: chinese_remainder_theorem_zero_shot/default-*
- split: train
path: chinese_remainder_theorem_zero_shot/train-*
- split: validation
path: chinese_remainder_theorem_zero_shot/validation-*
- config_name: cifar10_classification_zero_shot
data_files:
- split: default
path: cifar10_classification_zero_shot/default-*
- split: train
path: cifar10_classification_zero_shot/train-*
- split: validation
path: cifar10_classification_zero_shot/validation-*
- config_name: code_line_description_zero_shot
data_files:
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path: code_line_description_zero_shot/default-*
- split: train
path: code_line_description_zero_shot/train-*
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path: code_line_description_zero_shot/validation-*
- config_name: codenames_zero_shot
data_files:
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path: codenames_zero_shot/default-*
- split: train
path: codenames_zero_shot/train-*
- split: validation
path: codenames_zero_shot/validation-*
- config_name: color_zero_shot
data_files:
- split: default
path: color_zero_shot/default-*
- split: train
path: color_zero_shot/train-*
- split: validation
path: color_zero_shot/validation-*
- config_name: common_morpheme_zero_shot
data_files:
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path: common_morpheme_zero_shot/default-*
- split: train
path: common_morpheme_zero_shot/train-*
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path: common_morpheme_zero_shot/validation-*
- config_name: conceptual_combinations_zero_shot
data_files:
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path: conceptual_combinations_zero_shot/default-*
- split: train
path: conceptual_combinations_zero_shot/train-*
- split: validation
path: conceptual_combinations_zero_shot/validation-*
- config_name: conlang_translation_zero_shot
data_files:
- split: default
path: conlang_translation_zero_shot/default-*
- split: train
path: conlang_translation_zero_shot/train-*
- split: validation
path: conlang_translation_zero_shot/validation-*
- config_name: contextual_parametric_knowledge_conflicts_zero_shot
data_files:
- split: default
path: contextual_parametric_knowledge_conflicts_zero_shot/default-*
- split: train
path: contextual_parametric_knowledge_conflicts_zero_shot/train-*
- split: validation
path: contextual_parametric_knowledge_conflicts_zero_shot/validation-*
- config_name: crash_blossom_zero_shot
data_files:
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path: crash_blossom_zero_shot/default-*
- split: train
path: crash_blossom_zero_shot/train-*
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path: crash_blossom_zero_shot/validation-*
- config_name: crass_ai_zero_shot
data_files:
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path: crass_ai_zero_shot/default-*
- split: train
path: crass_ai_zero_shot/train-*
- split: validation
path: crass_ai_zero_shot/validation-*
- config_name: cryobiology_spanish_zero_shot
data_files:
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path: cryobiology_spanish_zero_shot/default-*
- split: train
path: cryobiology_spanish_zero_shot/train-*
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path: cryobiology_spanish_zero_shot/validation-*
- config_name: cryptonite_zero_shot
data_files:
- split: default
path: cryptonite_zero_shot/default-*
- split: train
path: cryptonite_zero_shot/train-*
- split: validation
path: cryptonite_zero_shot/validation-*
- config_name: cs_algorithms_zero_shot
data_files:
- split: default
path: cs_algorithms_zero_shot/default-*
- split: train
path: cs_algorithms_zero_shot/train-*
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path: cs_algorithms_zero_shot/validation-*
- config_name: dark_humor_detection_zero_shot
data_files:
- split: default
path: dark_humor_detection_zero_shot/default-*
- split: train
path: dark_humor_detection_zero_shot/train-*
- split: validation
path: dark_humor_detection_zero_shot/validation-*
- config_name: date_understanding_zero_shot
data_files:
- split: default
path: date_understanding_zero_shot/default-*
- split: train
path: date_understanding_zero_shot/train-*
- split: validation
path: date_understanding_zero_shot/validation-*
- config_name: disambiguation_qa_zero_shot
data_files:
- split: default
path: disambiguation_qa_zero_shot/default-*
- split: train
path: disambiguation_qa_zero_shot/train-*
- split: validation
path: disambiguation_qa_zero_shot/validation-*
- config_name: discourse_marker_prediction_zero_shot
data_files:
- split: default
path: discourse_marker_prediction_zero_shot/default-*
- split: train
path: discourse_marker_prediction_zero_shot/train-*
- split: validation
path: discourse_marker_prediction_zero_shot/validation-*
- config_name: disfl_qa_zero_shot
data_files:
- split: default
path: disfl_qa_zero_shot/default-*
- split: train
path: disfl_qa_zero_shot/train-*
- split: validation
path: disfl_qa_zero_shot/validation-*
- config_name: dyck_languages_zero_shot
data_files:
- split: default
path: dyck_languages_zero_shot/default-*
- split: train
path: dyck_languages_zero_shot/train-*
- split: validation
path: dyck_languages_zero_shot/validation-*
- config_name: elementary_math_qa_zero_shot
data_files:
- split: default
path: elementary_math_qa_zero_shot/default-*
- split: train
path: elementary_math_qa_zero_shot/train-*
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path: elementary_math_qa_zero_shot/validation-*
- config_name: emoji_movie_zero_shot
data_files:
- split: default
path: emoji_movie_zero_shot/default-*
- split: train
path: emoji_movie_zero_shot/train-*
- split: validation
path: emoji_movie_zero_shot/validation-*
- config_name: emojis_emotion_prediction_zero_shot
data_files:
- split: default
path: emojis_emotion_prediction_zero_shot/default-*
- split: train
path: emojis_emotion_prediction_zero_shot/train-*
- split: validation
path: emojis_emotion_prediction_zero_shot/validation-*
- config_name: empirical_judgments_zero_shot
data_files:
- split: default
path: empirical_judgments_zero_shot/default-*
- split: train
path: empirical_judgments_zero_shot/train-*
- split: validation
path: empirical_judgments_zero_shot/validation-*
- config_name: english_proverbs_zero_shot
data_files:
- split: default
path: english_proverbs_zero_shot/default-*
- split: train
path: english_proverbs_zero_shot/train-*
- split: validation
path: english_proverbs_zero_shot/validation-*
- config_name: english_russian_proverbs_zero_shot
data_files:
- split: default
path: english_russian_proverbs_zero_shot/default-*
- split: train
path: english_russian_proverbs_zero_shot/train-*
- split: validation
path: english_russian_proverbs_zero_shot/validation-*
- config_name: entailed_polarity_hindi_zero_shot
data_files:
- split: default
path: entailed_polarity_hindi_zero_shot/default-*
- split: train
path: entailed_polarity_hindi_zero_shot/train-*
- split: validation
path: entailed_polarity_hindi_zero_shot/validation-*
- config_name: entailed_polarity_zero_shot
data_files:
- split: default
path: entailed_polarity_zero_shot/default-*
- split: train
path: entailed_polarity_zero_shot/train-*
- split: validation
path: entailed_polarity_zero_shot/validation-*
- config_name: epistemic_reasoning_zero_shot
data_files:
- split: default
path: epistemic_reasoning_zero_shot/default-*
- split: train
path: epistemic_reasoning_zero_shot/train-*
- split: validation
path: epistemic_reasoning_zero_shot/validation-*
- config_name: evaluating_information_essentiality_zero_shot
data_files:
- split: default
path: evaluating_information_essentiality_zero_shot/default-*
- split: train
path: evaluating_information_essentiality_zero_shot/train-*
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path: evaluating_information_essentiality_zero_shot/validation-*
- config_name: fact_checker_zero_shot
data_files:
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path: fact_checker_zero_shot/default-*
- split: train
path: fact_checker_zero_shot/train-*
- split: validation
path: fact_checker_zero_shot/validation-*
- config_name: fantasy_reasoning_zero_shot
data_files:
- split: default
path: fantasy_reasoning_zero_shot/default-*
- split: train
path: fantasy_reasoning_zero_shot/train-*
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path: fantasy_reasoning_zero_shot/validation-*
- config_name: few_shot_nlg_zero_shot
data_files:
- split: default
path: few_shot_nlg_zero_shot/default-*
- split: train
path: few_shot_nlg_zero_shot/train-*
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path: few_shot_nlg_zero_shot/validation-*
- config_name: figure_of_speech_detection_zero_shot
data_files:
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path: figure_of_speech_detection_zero_shot/default-*
- split: train
path: figure_of_speech_detection_zero_shot/train-*
- split: validation
path: figure_of_speech_detection_zero_shot/validation-*
- config_name: formal_fallacies_syllogisms_negation_zero_shot
data_files:
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path: formal_fallacies_syllogisms_negation_zero_shot/default-*
- split: train
path: formal_fallacies_syllogisms_negation_zero_shot/train-*
- split: validation
path: formal_fallacies_syllogisms_negation_zero_shot/validation-*
- config_name: gem_zero_shot
data_files:
- split: default
path: gem_zero_shot/default-*
- split: train
path: gem_zero_shot/train-*
- split: validation
path: gem_zero_shot/validation-*
- config_name: gender_inclusive_sentences_german_zero_shot
data_files:
- split: default
path: gender_inclusive_sentences_german_zero_shot/default-*
- split: train
path: gender_inclusive_sentences_german_zero_shot/train-*
- split: validation
path: gender_inclusive_sentences_german_zero_shot/validation-*
- config_name: general_knowledge_zero_shot
data_files:
- split: default
path: general_knowledge_zero_shot/default-*
- split: train
path: general_knowledge_zero_shot/train-*
- split: validation
path: general_knowledge_zero_shot/validation-*
- config_name: geometric_shapes_zero_shot
data_files:
- split: default
path: geometric_shapes_zero_shot/default-*
- split: train
path: geometric_shapes_zero_shot/train-*
- split: validation
path: geometric_shapes_zero_shot/validation-*
- config_name: goal_step_wikihow_zero_shot
data_files:
- split: default
path: goal_step_wikihow_zero_shot/default-*
- split: train
path: goal_step_wikihow_zero_shot/train-*
- split: validation
path: goal_step_wikihow_zero_shot/validation-*
- config_name: gre_reading_comprehension_zero_shot
data_files:
- split: default
path: gre_reading_comprehension_zero_shot/default-*
- split: train
path: gre_reading_comprehension_zero_shot/train-*
- split: validation
path: gre_reading_comprehension_zero_shot/validation-*
- config_name: hhh_alignment_zero_shot
data_files:
- split: default
path: hhh_alignment_zero_shot/default-*
- split: train
path: hhh_alignment_zero_shot/train-*
- split: validation
path: hhh_alignment_zero_shot/validation-*
- config_name: hindi_question_answering_zero_shot
data_files:
- split: default
path: hindi_question_answering_zero_shot/default-*
- split: train
path: hindi_question_answering_zero_shot/train-*
- split: validation
path: hindi_question_answering_zero_shot/validation-*
- config_name: hindu_knowledge_zero_shot
data_files:
- split: default
path: hindu_knowledge_zero_shot/default-*
- split: train
path: hindu_knowledge_zero_shot/train-*
- split: validation
path: hindu_knowledge_zero_shot/validation-*
- config_name: hinglish_toxicity_zero_shot
data_files:
- split: default
path: hinglish_toxicity_zero_shot/default-*
- split: train
path: hinglish_toxicity_zero_shot/train-*
- split: validation
path: hinglish_toxicity_zero_shot/validation-*
- config_name: human_organs_senses_zero_shot
data_files:
- split: default
path: human_organs_senses_zero_shot/default-*
- split: train
path: human_organs_senses_zero_shot/train-*
- split: validation
path: human_organs_senses_zero_shot/validation-*
- config_name: hyperbaton_zero_shot
data_files:
- split: default
path: hyperbaton_zero_shot/default-*
- split: train
path: hyperbaton_zero_shot/train-*
- split: validation
path: hyperbaton_zero_shot/validation-*
- config_name: identify_math_theorems_zero_shot
data_files:
- split: default
path: identify_math_theorems_zero_shot/default-*
- split: train
path: identify_math_theorems_zero_shot/train-*
- split: validation
path: identify_math_theorems_zero_shot/validation-*
- config_name: identify_odd_metaphor_zero_shot
data_files:
- split: default
path: identify_odd_metaphor_zero_shot/default-*
- split: train
path: identify_odd_metaphor_zero_shot/train-*
- split: validation
path: identify_odd_metaphor_zero_shot/validation-*
- config_name: implicatures_zero_shot
data_files:
- split: default
path: implicatures_zero_shot/default-*
- split: train
path: implicatures_zero_shot/train-*
- split: validation
path: implicatures_zero_shot/validation-*
- config_name: implicit_relations_zero_shot
data_files:
- split: default
path: implicit_relations_zero_shot/default-*
- split: train
path: implicit_relations_zero_shot/train-*
- split: validation
path: implicit_relations_zero_shot/validation-*
- config_name: intent_recognition_zero_shot
data_files:
- split: default
path: intent_recognition_zero_shot/default-*
- split: train
path: intent_recognition_zero_shot/train-*
- split: validation
path: intent_recognition_zero_shot/validation-*
- config_name: international_phonetic_alphabet_nli_zero_shot
data_files:
- split: default
path: international_phonetic_alphabet_nli_zero_shot/default-*
- split: train
path: international_phonetic_alphabet_nli_zero_shot/train-*
- split: validation
path: international_phonetic_alphabet_nli_zero_shot/validation-*
- config_name: international_phonetic_alphabet_transliterate_zero_shot
data_files:
- split: default
path: international_phonetic_alphabet_transliterate_zero_shot/default-*
- split: train
path: international_phonetic_alphabet_transliterate_zero_shot/train-*
- split: validation
path: international_phonetic_alphabet_transliterate_zero_shot/validation-*
- config_name: intersect_geometry_zero_shot
data_files:
- split: default
path: intersect_geometry_zero_shot/default-*
- split: train
path: intersect_geometry_zero_shot/train-*
- split: validation
path: intersect_geometry_zero_shot/validation-*
- config_name: irony_identification_zero_shot
data_files:
- split: default
path: irony_identification_zero_shot/default-*
- split: train
path: irony_identification_zero_shot/train-*
- split: validation
path: irony_identification_zero_shot/validation-*
- config_name: kanji_ascii_zero_shot
data_files:
- split: default
path: kanji_ascii_zero_shot/default-*
- split: train
path: kanji_ascii_zero_shot/train-*
- split: validation
path: kanji_ascii_zero_shot/validation-*
- config_name: kannada_zero_shot
data_files:
- split: default
path: kannada_zero_shot/default-*
- split: train
path: kannada_zero_shot/train-*
- split: validation
path: kannada_zero_shot/validation-*
- config_name: key_value_maps_zero_shot
data_files:
- split: default
path: key_value_maps_zero_shot/default-*
- split: train
path: key_value_maps_zero_shot/train-*
- split: validation
path: key_value_maps_zero_shot/validation-*
- config_name: known_unknowns_zero_shot
data_files:
- split: default
path: known_unknowns_zero_shot/default-*
- split: train
path: known_unknowns_zero_shot/train-*
- split: validation
path: known_unknowns_zero_shot/validation-*
- config_name: language_games_zero_shot
data_files:
- split: default
path: language_games_zero_shot/default-*
- split: train
path: language_games_zero_shot/train-*
- split: validation
path: language_games_zero_shot/validation-*
- config_name: language_identification_zero_shot
data_files:
- split: default
path: language_identification_zero_shot/default-*
- split: train
path: language_identification_zero_shot/train-*
- split: validation
path: language_identification_zero_shot/validation-*
- config_name: linguistic_mappings_zero_shot
data_files:
- split: default
path: linguistic_mappings_zero_shot/default-*
- split: train
path: linguistic_mappings_zero_shot/train-*
- split: validation
path: linguistic_mappings_zero_shot/validation-*
- config_name: linguistics_puzzles_zero_shot
data_files:
- split: default
path: linguistics_puzzles_zero_shot/default-*
- split: train
path: linguistics_puzzles_zero_shot/train-*
- split: validation
path: linguistics_puzzles_zero_shot/validation-*
- config_name: list_functions_zero_shot
data_files:
- split: default
path: list_functions_zero_shot/default-*
- split: train
path: list_functions_zero_shot/train-*
- split: validation
path: list_functions_zero_shot/validation-*
- config_name: logic_grid_puzzle_zero_shot
data_files:
- split: default
path: logic_grid_puzzle_zero_shot/default-*
- split: train
path: logic_grid_puzzle_zero_shot/train-*
- split: validation
path: logic_grid_puzzle_zero_shot/validation-*
- config_name: logical_args_zero_shot
data_files:
- split: default
path: logical_args_zero_shot/default-*
- split: train
path: logical_args_zero_shot/train-*
- split: validation
path: logical_args_zero_shot/validation-*
- config_name: logical_deduction_zero_shot
data_files:
- split: default
path: logical_deduction_zero_shot/default-*
- split: train
path: logical_deduction_zero_shot/train-*
- split: validation
path: logical_deduction_zero_shot/validation-*
- config_name: logical_fallacy_detection_zero_shot
data_files:
- split: default
path: logical_fallacy_detection_zero_shot/default-*
- split: train
path: logical_fallacy_detection_zero_shot/train-*
- split: validation
path: logical_fallacy_detection_zero_shot/validation-*
- config_name: logical_sequence_zero_shot
data_files:
- split: default
path: logical_sequence_zero_shot/default-*
- split: train
path: logical_sequence_zero_shot/train-*
- split: validation
path: logical_sequence_zero_shot/validation-*
- config_name: mathematical_induction_zero_shot
data_files:
- split: default
path: mathematical_induction_zero_shot/default-*
- split: train
path: mathematical_induction_zero_shot/train-*
- split: validation
path: mathematical_induction_zero_shot/validation-*
- config_name: matrixshapes_zero_shot
data_files:
- split: default
path: matrixshapes_zero_shot/default-*
- split: train
path: matrixshapes_zero_shot/train-*
- split: validation
path: matrixshapes_zero_shot/validation-*
- config_name: metaphor_boolean_zero_shot
data_files:
- split: default
path: metaphor_boolean_zero_shot/default-*
- split: train
path: metaphor_boolean_zero_shot/train-*
- split: validation
path: metaphor_boolean_zero_shot/validation-*
- config_name: metaphor_understanding_zero_shot
data_files:
- split: default
path: metaphor_understanding_zero_shot/default-*
- split: train
path: metaphor_understanding_zero_shot/train-*
- split: validation
path: metaphor_understanding_zero_shot/validation-*
- config_name: minute_mysteries_qa_zero_shot
data_files:
- split: default
path: minute_mysteries_qa_zero_shot/default-*
- split: train
path: minute_mysteries_qa_zero_shot/train-*
- split: validation
path: minute_mysteries_qa_zero_shot/validation-*
- config_name: misconceptions_russian_zero_shot
data_files:
- split: default
path: misconceptions_russian_zero_shot/default-*
- split: train
path: misconceptions_russian_zero_shot/train-*
- split: validation
path: misconceptions_russian_zero_shot/validation-*
- config_name: misconceptions_zero_shot
data_files:
- split: default
path: misconceptions_zero_shot/default-*
- split: train
path: misconceptions_zero_shot/train-*
- split: validation
path: misconceptions_zero_shot/validation-*
- config_name: mnist_ascii_zero_shot
data_files:
- split: default
path: mnist_ascii_zero_shot/default-*
- split: train
path: mnist_ascii_zero_shot/train-*
- split: validation
path: mnist_ascii_zero_shot/validation-*
- config_name: modified_arithmetic_zero_shot
data_files:
- split: default
path: modified_arithmetic_zero_shot/default-*
- split: train
path: modified_arithmetic_zero_shot/train-*
- split: validation
path: modified_arithmetic_zero_shot/validation-*
- config_name: moral_permissibility_zero_shot
data_files:
- split: default
path: moral_permissibility_zero_shot/default-*
- split: train
path: moral_permissibility_zero_shot/train-*
- split: validation
path: moral_permissibility_zero_shot/validation-*
- config_name: movie_dialog_same_or_different_zero_shot
data_files:
- split: default
path: movie_dialog_same_or_different_zero_shot/default-*
- split: train
path: movie_dialog_same_or_different_zero_shot/train-*
- split: validation
path: movie_dialog_same_or_different_zero_shot/validation-*
- config_name: movie_recommendation_zero_shot
data_files:
- split: default
path: movie_recommendation_zero_shot/default-*
- split: train
path: movie_recommendation_zero_shot/train-*
- split: validation
path: movie_recommendation_zero_shot/validation-*
- config_name: mult_data_wrangling_zero_shot
data_files:
- split: default
path: mult_data_wrangling_zero_shot/default-*
- split: train
path: mult_data_wrangling_zero_shot/train-*
- split: validation
path: mult_data_wrangling_zero_shot/validation-*
- config_name: multiemo_zero_shot
data_files:
- split: default
path: multiemo_zero_shot/default-*
- split: train
path: multiemo_zero_shot/train-*
- split: validation
path: multiemo_zero_shot/validation-*
- config_name: natural_instructions_zero_shot
data_files:
- split: default
path: natural_instructions_zero_shot/default-*
- split: train
path: natural_instructions_zero_shot/train-*
- split: validation
path: natural_instructions_zero_shot/validation-*
- config_name: navigate_zero_shot
data_files:
- split: default
path: navigate_zero_shot/default-*
- split: train
path: navigate_zero_shot/train-*
- split: validation
path: navigate_zero_shot/validation-*
- config_name: nonsense_words_grammar_zero_shot
data_files:
- split: default
path: nonsense_words_grammar_zero_shot/default-*
- split: train
path: nonsense_words_grammar_zero_shot/train-*
- split: validation
path: nonsense_words_grammar_zero_shot/validation-*
- config_name: novel_concepts_zero_shot
data_files:
- split: default
path: novel_concepts_zero_shot/default-*
- split: train
path: novel_concepts_zero_shot/train-*
- split: validation
path: novel_concepts_zero_shot/validation-*
- config_name: object_counting_zero_shot
data_files:
- split: default
path: object_counting_zero_shot/default-*
- split: train
path: object_counting_zero_shot/train-*
- split: validation
path: object_counting_zero_shot/validation-*
- config_name: odd_one_out_zero_shot
data_files:
- split: default
path: odd_one_out_zero_shot/default-*
- split: train
path: odd_one_out_zero_shot/train-*
- split: validation
path: odd_one_out_zero_shot/validation-*
- config_name: operators_zero_shot
data_files:
- split: default
path: operators_zero_shot/default-*
- split: train
path: operators_zero_shot/train-*
- split: validation
path: operators_zero_shot/validation-*
- config_name: paragraph_segmentation_zero_shot
data_files:
- split: default
path: paragraph_segmentation_zero_shot/default-*
- split: train
path: paragraph_segmentation_zero_shot/train-*
- split: validation
path: paragraph_segmentation_zero_shot/validation-*
- config_name: parsinlu_qa_zero_shot
data_files:
- split: default
path: parsinlu_qa_zero_shot/default-*
- split: train
path: parsinlu_qa_zero_shot/train-*
- split: validation
path: parsinlu_qa_zero_shot/validation-*
- config_name: parsinlu_reading_comprehension_zero_shot
data_files:
- split: default
path: parsinlu_reading_comprehension_zero_shot/default-*
- split: train
path: parsinlu_reading_comprehension_zero_shot/train-*
- split: validation
path: parsinlu_reading_comprehension_zero_shot/validation-*
- config_name: penguins_in_a_table_zero_shot
data_files:
- split: default
path: penguins_in_a_table_zero_shot/default-*
- split: train
path: penguins_in_a_table_zero_shot/train-*
- split: validation
path: penguins_in_a_table_zero_shot/validation-*
- config_name: periodic_elements_zero_shot
data_files:
- split: default
path: periodic_elements_zero_shot/default-*
- split: train
path: periodic_elements_zero_shot/train-*
- split: validation
path: periodic_elements_zero_shot/validation-*
- config_name: persian_idioms_zero_shot
data_files:
- split: default
path: persian_idioms_zero_shot/default-*
- split: train
path: persian_idioms_zero_shot/train-*
- split: validation
path: persian_idioms_zero_shot/validation-*
- config_name: phrase_relatedness_zero_shot
data_files:
- split: default
path: phrase_relatedness_zero_shot/default-*
- split: train
path: phrase_relatedness_zero_shot/train-*
- split: validation
path: phrase_relatedness_zero_shot/validation-*
- config_name: physical_intuition_zero_shot
data_files:
- split: default
path: physical_intuition_zero_shot/default-*
- split: train
path: physical_intuition_zero_shot/train-*
- split: validation
path: physical_intuition_zero_shot/validation-*
- config_name: physics_questions_zero_shot
data_files:
- split: default
path: physics_questions_zero_shot/default-*
- split: train
path: physics_questions_zero_shot/train-*
- split: validation
path: physics_questions_zero_shot/validation-*
- config_name: physics_zero_shot
data_files:
- split: default
path: physics_zero_shot/default-*
- split: train
path: physics_zero_shot/train-*
- split: validation
path: physics_zero_shot/validation-*
- config_name: play_dialog_same_or_different_zero_shot
data_files:
- split: default
path: play_dialog_same_or_different_zero_shot/default-*
- split: train
path: play_dialog_same_or_different_zero_shot/train-*
- split: validation
path: play_dialog_same_or_different_zero_shot/validation-*
- config_name: polish_sequence_labeling_zero_shot
data_files:
- split: default
path: polish_sequence_labeling_zero_shot/default-*
- split: train
path: polish_sequence_labeling_zero_shot/train-*
- split: validation
path: polish_sequence_labeling_zero_shot/validation-*
- config_name: presuppositions_as_nli_zero_shot
data_files:
- split: default
path: presuppositions_as_nli_zero_shot/default-*
- split: train
path: presuppositions_as_nli_zero_shot/train-*
- split: validation
path: presuppositions_as_nli_zero_shot/validation-*
- config_name: qa_wikidata_zero_shot
data_files:
- split: default
path: qa_wikidata_zero_shot/default-*
- split: train
path: qa_wikidata_zero_shot/train-*
- split: validation
path: qa_wikidata_zero_shot/validation-*
- config_name: question_selection_zero_shot
data_files:
- split: default
path: question_selection_zero_shot/default-*
- split: train
path: question_selection_zero_shot/train-*
- split: validation
path: question_selection_zero_shot/validation-*
- config_name: real_or_fake_text_zero_shot
data_files:
- split: default
path: real_or_fake_text_zero_shot/default-*
- split: train
path: real_or_fake_text_zero_shot/train-*
- split: validation
path: real_or_fake_text_zero_shot/validation-*
- config_name: reasoning_about_colored_objects_zero_shot
data_files:
- split: default
path: reasoning_about_colored_objects_zero_shot/default-*
- split: train
path: reasoning_about_colored_objects_zero_shot/train-*
- split: validation
path: reasoning_about_colored_objects_zero_shot/validation-*
- config_name: repeat_copy_logic_zero_shot
data_files:
- split: default
path: repeat_copy_logic_zero_shot/default-*
- split: train
path: repeat_copy_logic_zero_shot/train-*
- split: validation
path: repeat_copy_logic_zero_shot/validation-*
- config_name: rephrase_zero_shot
data_files:
- split: default
path: rephrase_zero_shot/default-*
- split: train
path: rephrase_zero_shot/train-*
- split: validation
path: rephrase_zero_shot/validation-*
- config_name: riddle_sense_zero_shot
data_files:
- split: default
path: riddle_sense_zero_shot/default-*
- split: train
path: riddle_sense_zero_shot/train-*
- split: validation
path: riddle_sense_zero_shot/validation-*
- config_name: ruin_names_zero_shot
data_files:
- split: default
path: ruin_names_zero_shot/default-*
- split: train
path: ruin_names_zero_shot/train-*
- split: validation
path: ruin_names_zero_shot/validation-*
- config_name: salient_translation_error_detection_zero_shot
data_files:
- split: default
path: salient_translation_error_detection_zero_shot/default-*
- split: train
path: salient_translation_error_detection_zero_shot/train-*
- split: validation
path: salient_translation_error_detection_zero_shot/validation-*
- config_name: scientific_press_release_zero_shot
data_files:
- split: default
path: scientific_press_release_zero_shot/default-*
- split: train
path: scientific_press_release_zero_shot/train-*
- split: validation
path: scientific_press_release_zero_shot/validation-*
- config_name: semantic_parsing_in_context_sparc_zero_shot
data_files:
- split: default
path: semantic_parsing_in_context_sparc_zero_shot/default-*
- split: train
path: semantic_parsing_in_context_sparc_zero_shot/train-*
- split: validation
path: semantic_parsing_in_context_sparc_zero_shot/validation-*
- config_name: semantic_parsing_spider_zero_shot
data_files:
- split: default
path: semantic_parsing_spider_zero_shot/default-*
- split: train
path: semantic_parsing_spider_zero_shot/train-*
- split: validation
path: semantic_parsing_spider_zero_shot/validation-*
- config_name: sentence_ambiguity_zero_shot
data_files:
- split: default
path: sentence_ambiguity_zero_shot/default-*
- split: train
path: sentence_ambiguity_zero_shot/train-*
- split: validation
path: sentence_ambiguity_zero_shot/validation-*
- config_name: similarities_abstraction_zero_shot
data_files:
- split: default
path: similarities_abstraction_zero_shot/default-*
- split: train
path: similarities_abstraction_zero_shot/train-*
- split: validation
path: similarities_abstraction_zero_shot/validation-*
- config_name: simp_turing_concept_zero_shot
data_files:
- split: default
path: simp_turing_concept_zero_shot/default-*
- split: train
path: simp_turing_concept_zero_shot/train-*
- split: validation
path: simp_turing_concept_zero_shot/validation-*
- config_name: simple_arithmetic_json_multiple_choice_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_multiple_choice_zero_shot/default-*
- split: train
path: simple_arithmetic_json_multiple_choice_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_multiple_choice_zero_shot/validation-*
- config_name: simple_arithmetic_json_subtasks_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_subtasks_zero_shot/default-*
- split: train
path: simple_arithmetic_json_subtasks_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_subtasks_zero_shot/validation-*
- config_name: simple_arithmetic_json_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_zero_shot/default-*
- split: train
path: simple_arithmetic_json_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_zero_shot/validation-*
- config_name: simple_arithmetic_multiple_targets_json_zero_shot
data_files:
- split: default
path: simple_arithmetic_multiple_targets_json_zero_shot/default-*
- split: train
path: simple_arithmetic_multiple_targets_json_zero_shot/train-*
- split: validation
path: simple_arithmetic_multiple_targets_json_zero_shot/validation-*
- config_name: simple_ethical_questions_zero_shot
data_files:
- split: default
path: simple_ethical_questions_zero_shot/default-*
- split: train
path: simple_ethical_questions_zero_shot/train-*
- split: validation
path: simple_ethical_questions_zero_shot/validation-*
- config_name: simple_text_editing_zero_shot
data_files:
- split: default
path: simple_text_editing_zero_shot/default-*
- split: train
path: simple_text_editing_zero_shot/train-*
- split: validation
path: simple_text_editing_zero_shot/validation-*
- config_name: snarks_zero_shot
data_files:
- split: default
path: snarks_zero_shot/default-*
- split: train
path: snarks_zero_shot/train-*
- split: validation
path: snarks_zero_shot/validation-*
- config_name: social_iqa_zero_shot
data_files:
- split: default
path: social_iqa_zero_shot/default-*
- split: train
path: social_iqa_zero_shot/train-*
- split: validation
path: social_iqa_zero_shot/validation-*
- config_name: social_support_zero_shot
data_files:
- split: default
path: social_support_zero_shot/default-*
- split: train
path: social_support_zero_shot/train-*
- split: validation
path: social_support_zero_shot/validation-*
- config_name: sports_understanding_zero_shot
data_files:
- split: default
path: sports_understanding_zero_shot/default-*
- split: train
path: sports_understanding_zero_shot/train-*
- split: validation
path: sports_understanding_zero_shot/validation-*
- config_name: strange_stories_zero_shot
data_files:
- split: default
path: strange_stories_zero_shot/default-*
- split: train
path: strange_stories_zero_shot/train-*
- split: validation
path: strange_stories_zero_shot/validation-*
- config_name: strategyqa_zero_shot
data_files:
- split: default
path: strategyqa_zero_shot/default-*
- split: train
path: strategyqa_zero_shot/train-*
- split: validation
path: strategyqa_zero_shot/validation-*
- config_name: sufficient_information_zero_shot
data_files:
- split: default
path: sufficient_information_zero_shot/default-*
- split: train
path: sufficient_information_zero_shot/train-*
- split: validation
path: sufficient_information_zero_shot/validation-*
- config_name: suicide_risk_zero_shot
data_files:
- split: default
path: suicide_risk_zero_shot/default-*
- split: train
path: suicide_risk_zero_shot/train-*
- split: validation
path: suicide_risk_zero_shot/validation-*
- config_name: swahili_english_proverbs_zero_shot
data_files:
- split: default
path: swahili_english_proverbs_zero_shot/default-*
- split: train
path: swahili_english_proverbs_zero_shot/train-*
- split: validation
path: swahili_english_proverbs_zero_shot/validation-*
- config_name: swedish_to_german_proverbs_zero_shot
data_files:
- split: default
path: swedish_to_german_proverbs_zero_shot/default-*
- split: train
path: swedish_to_german_proverbs_zero_shot/train-*
- split: validation
path: swedish_to_german_proverbs_zero_shot/validation-*
- config_name: symbol_interpretation_zero_shot
data_files:
- split: default
path: symbol_interpretation_zero_shot/default-*
- split: train
path: symbol_interpretation_zero_shot/train-*
- split: validation
path: symbol_interpretation_zero_shot/validation-*
- config_name: temporal_sequences_zero_shot
data_files:
- split: default
path: temporal_sequences_zero_shot/default-*
- split: train
path: temporal_sequences_zero_shot/train-*
- split: validation
path: temporal_sequences_zero_shot/validation-*
- config_name: tense_zero_shot
data_files:
- split: default
path: tense_zero_shot/default-*
- split: train
path: tense_zero_shot/train-*
- split: validation
path: tense_zero_shot/validation-*
- config_name: timedial_zero_shot
data_files:
- split: default
path: timedial_zero_shot/default-*
- split: train
path: timedial_zero_shot/train-*
- split: validation
path: timedial_zero_shot/validation-*
- config_name: topical_chat_zero_shot
data_files:
- split: default
path: topical_chat_zero_shot/default-*
- split: train
path: topical_chat_zero_shot/train-*
- split: validation
path: topical_chat_zero_shot/validation-*
- config_name: tracking_shuffled_objects_zero_shot
data_files:
- split: default
path: tracking_shuffled_objects_zero_shot/default-*
- split: train
path: tracking_shuffled_objects_zero_shot/train-*
- split: validation
path: tracking_shuffled_objects_zero_shot/validation-*
- config_name: understanding_fables_zero_shot
data_files:
- split: default
path: understanding_fables_zero_shot/default-*
- split: train
path: understanding_fables_zero_shot/train-*
- split: validation
path: understanding_fables_zero_shot/validation-*
- config_name: undo_permutation_zero_shot
data_files:
- split: default
path: undo_permutation_zero_shot/default-*
- split: train
path: undo_permutation_zero_shot/train-*
- split: validation
path: undo_permutation_zero_shot/validation-*
- config_name: unit_conversion_zero_shot
data_files:
- split: default
path: unit_conversion_zero_shot/default-*
- split: train
path: unit_conversion_zero_shot/train-*
- split: validation
path: unit_conversion_zero_shot/validation-*
- config_name: unit_interpretation_zero_shot
data_files:
- split: default
path: unit_interpretation_zero_shot/default-*
- split: train
path: unit_interpretation_zero_shot/train-*
- split: validation
path: unit_interpretation_zero_shot/validation-*
- config_name: unnatural_in_context_learning_zero_shot
data_files:
- split: default
path: unnatural_in_context_learning_zero_shot/default-*
- split: train
path: unnatural_in_context_learning_zero_shot/train-*
- split: validation
path: unnatural_in_context_learning_zero_shot/validation-*
- config_name: vitaminc_fact_verification_zero_shot
data_files:
- split: default
path: vitaminc_fact_verification_zero_shot/default-*
- split: train
path: vitaminc_fact_verification_zero_shot/train-*
- split: validation
path: vitaminc_fact_verification_zero_shot/validation-*
- config_name: what_is_the_tao_zero_shot
data_files:
- split: default
path: what_is_the_tao_zero_shot/default-*
- split: train
path: what_is_the_tao_zero_shot/train-*
- split: validation
path: what_is_the_tao_zero_shot/validation-*
- config_name: which_wiki_edit_zero_shot
data_files:
- split: default
path: which_wiki_edit_zero_shot/default-*
- split: train
path: which_wiki_edit_zero_shot/train-*
- split: validation
path: which_wiki_edit_zero_shot/validation-*
- config_name: winowhy_zero_shot
data_files:
- split: default
path: winowhy_zero_shot/default-*
- split: train
path: winowhy_zero_shot/train-*
- split: validation
path: winowhy_zero_shot/validation-*
- config_name: word_sorting_zero_shot
data_files:
- split: default
path: word_sorting_zero_shot/default-*
- split: train
path: word_sorting_zero_shot/train-*
- split: validation
path: word_sorting_zero_shot/validation-*
- config_name: word_unscrambling_zero_shot
data_files:
- split: default
path: word_unscrambling_zero_shot/default-*
- split: train
path: word_unscrambling_zero_shot/train-*
- split: validation
path: word_unscrambling_zero_shot/validation-*
---
# Dataset Card for "bigbench"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lmms-lab/MMMU | lmms-lab | "2024-03-08T05:09:42Z" | 13,104 | 4 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-01-15T06:32:16Z" | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: options
dtype: string
- name: explanation
dtype: string
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: image_3
dtype: image
- name: image_4
dtype: image
- name: image_5
dtype: image
- name: image_6
dtype: image
- name: image_7
dtype: image
- name: img_type
dtype: string
- name: answer
dtype: string
- name: topic_difficulty
dtype: string
- name: question_type
dtype: string
- name: subfield
dtype: string
splits:
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num_bytes: 57719107.0
num_examples: 150
- name: validation
num_bytes: 347519954.0
num_examples: 900
- name: test
num_bytes: 3271046267.0
num_examples: 10500
download_size: 3377778136
dataset_size: 3676285328.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated.
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{yue2023mmmu,
title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi},
author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others},
journal={arXiv preprint arXiv:2311.16502},
year={2023}
}
``` |
nguha/legalbench | nguha | "2024-09-30T04:35:09Z" | 13,041 | 99 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:other",
"size_categories:10K<n<100K",
"arxiv:2308.11462",
"arxiv:2110.01799",
"arxiv:2103.06268",
"arxiv:2301.00876",
"arxiv:1911.00841",
"arxiv:2105.07903",
"region:us",
"legal",
"law",
"finance"
] | [
"text-classification",
"question-answering",
"text-generation"
] | "2023-03-16T23:03:42Z" | ---
language:
- en
license: other
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- question-answering
- text-generation
tags:
- legal
- law
- finance
dataset_info:
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- name: text
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- config_name: canada_tax_court_outcomes
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- config_name: citation_prediction_classification
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- config_name: citation_prediction_open
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- config_name: consumer_contracts_qa
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---
# Dataset Card for Dataset Name
- **Homepage: https://hazyresearch.stanford.edu/legalbench/**
- **Repository: https://github.com/HazyResearch/legalbench/**
- **Paper: https://arxiv.org/abs/2308.11462**
## Dataset Description
### Dataset Summary
The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors.
Note: Because LegalBench is intended to test zero and few-shot reasoning, the available "train" splits are small. However, if you are interested in finetuning models or studying model performance in a more traditional train/test regime, you can combine and re-partition train and test data.
If you have questions about the project or would like to get involved, please see the website for more information.
### Supported Tasks and Leaderboards
LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process.
### Languages
All LegalBench tasks are in English.
## Dataset Structure
### Data Instances
Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the `abercrombie` task, is provided below:
```
{
"text": "The mark "Ivory" for a product made of elephant tusks.",
"label": "generic"
"idx": 0
}
```
A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No".
### Data Fields
Detailed descriptions of the instances for each task can be found on the Github.
### Data Splits
Each task (except for `rule_qa` and `scalr`) has both a training and evaluation split. Following [RAFT](https://huggingface.co/datasets/ought/raft), train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs.
## Dataset Creation
### Curation Rationale
LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs.
### Source Data
#### Initial Data Collection and Normalization
Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original [CUAD dataset](https://github.com/TheAtticusProject/cuad) contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details.
#### Who are the source language producers?
LegalBench data was created by humans. Demographic information for these individuals is not available.
### Annotations
#### Annotation process
Please see the paper for more information on the annotation process used in the creation of each task.
#### Who are the annotators?
Please see the paper for more information on the identity of annotators for each task.
### Personal and Sensitive Information
Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database).
Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues.
## Considerations for Using the Data
### Social Impact of Dataset
Please see the original paper for a discussion of social impact.
### Discussion of Biases
Please see the original paper for a discussion of social impact.
### Other Known Limitations
LegalBench primarily contains tasks corresponding to American law.
## Additional Information
### Dataset Curators
Please see the website for a full list of participants in the LegalBench project.
### Licensing Information
LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses.
### Citation Information
If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github).
```
@misc{guha2023legalbench,
title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models},
author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li},
year={2023},
eprint={2308.11462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{koreeda2021contractnli,
title={ContractNLI: A dataset for document-level natural language inference for contracts},
author={Koreeda, Yuta and Manning, Christopher D},
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}
@article{hendrycks2021cuad,
title={Cuad: An expert-annotated nlp dataset for legal contract review},
author={Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
@article{wang2023maud,
title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding},
author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan},
journal={arXiv preprint arXiv:2301.00876},
year={2023}
}
@inproceedings{wilson2016creation,
title={The creation and analysis of a website privacy policy corpus},
author={Wilson, Shomir and Schaub, Florian and Dara, Aswarth Abhilash and Liu, Frederick and Cherivirala, Sushain and Leon, Pedro Giovanni and Andersen, Mads Schaarup and Zimmeck, Sebastian and Sathyendra, Kanthashree Mysore and Russell, N Cameron and others},
booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={1330--1340},
year={2016}
}
@inproceedings{zheng2021does,
title={When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings},
author={Zheng, Lucia and Guha, Neel and Anderson, Brandon R and Henderson, Peter and Ho, Daniel E},
booktitle={Proceedings of the eighteenth international conference on artificial intelligence and law},
pages={159--168},
year={2021}
}
@article{zimmeck2019maps,
title={Maps: Scaling privacy compliance analysis to a million apps},
author={Zimmeck, Sebastian and Story, Peter and Smullen, Daniel and Ravichander, Abhilasha and Wang, Ziqi and Reidenberg, Joel R and Russell, N Cameron and Sadeh, Norman},
journal={Proc. Priv. Enhancing Tech.},
volume={2019},
pages={66},
year={2019}
}
@article{ravichander2019question,
title={Question answering for privacy policies: Combining computational and legal perspectives},
author={Ravichander, Abhilasha and Black, Alan W and Wilson, Shomir and Norton, Thomas and Sadeh, Norman},
journal={arXiv preprint arXiv:1911.00841},
year={2019}
}
@article{holzenberger2021factoring,
title={Factoring statutory reasoning as language understanding challenges},
author={Holzenberger, Nils and Van Durme, Benjamin},
journal={arXiv preprint arXiv:2105.07903},
year={2021}
}
@article{lippi2019claudette,
title={CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service},
author={Lippi, Marco and Pa{\l}ka, Przemys{\l}aw and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo},
journal={Artificial Intelligence and Law},
volume={27},
pages={117--139},
year={2019},
publisher={Springer}
}
``` |
HuggingFaceM4/Docmatix | HuggingFaceM4 | "2024-08-26T08:15:21Z" | 13,008 | 249 | [
"task_categories:visual-question-answering",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2408.12637",
"region:us",
"docvqa"
] | [
"visual-question-answering"
] | "2024-07-17T11:33:00Z" | ---
language:
- en
license: mit
size_categories:
- 1M<n<10M
task_categories:
- visual-question-answering
pretty_name: Docmatix
tags:
- docvqa
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---
# Dataset Card for Docmatix
![image/webp](https://cdn-uploads.huggingface.co/production/uploads/65d66b494bbd0d92b641cdbb/P7rIELr2eom_IorBY5DZu.webp)
## Dataset description
Docmatix is part of the Idefics3 release (stay tuned).
It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3.
## Load the dataset
To load the dataset, install the library `datasets` with `pip install datasets`. Then,
```
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/Docmatix")
```
If you want the dataset to link to the pdf files as binaries instead of the images, do:
```
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/Docmatix", "pdf")
```
## Data fields
An example of a sample looks as follows:
```
{
"images" = [PIL.Image]
"texts" = [
{ "user": "What is the purpose of the Confirmation Statement mentioned in the document?",
"assistant": "The purpose of the Confirmation Statement is to confirm that all information required to be delivered by the company to the registrar in relation to the confirmation period concerned has been delivered or is being delivered at the same time as the confirmation statement.",
"source": "PDFA key: 244" },
{ "user": "When was the filing received as per the document?",
"assistant": "The filing was received for filing in Electronic Format on the 23/03/2021.",
"source": "PDFA key: 244" },
]
}
```
In `images`, there is a list of up to 4 images, to be placed before the text.
In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns.
## Comparison to other DocVQA datasets
| Dataset | # images | # Q/A pairs | # tokens |
|----------------------|----------|-------------|------------|
| *Document visual question answering* |
| **Docmatix** | **2,444,750**| **9,500,000** | **390,000,000**|
| DocVQA | 10,189 | 39,463 | 337,829 |
| TextCaps | 21,953 | 21,953 | 389,658 |
| TextVQA | 21,953 | 34,602 | 181,918 |
| ST-VQA | 17,247 | 23,121 | 127,846 |
| OCR-VQA | 165,746 | 801,579 | 6,073,824 |
| VisualMRC | 3,027 | 11,988 | 168,828 |
| IAM | 5,663 | 5,663 | 144,216 |
| InfoVQA | 2,118 | 10,074 | 61,048 |
| Diagram image-to-text| 300 | 300 | 22,196 |
# Citation
**BibTeX:**
```bibtex
@misc{laurençon2024building,
title={Building and better understanding vision-language models: insights and future directions.},
author={Hugo Laurençon and Andrés Marafioti and Victor Sanh and Léo Tronchon},
year={2024},
eprint={2408.12637},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
Helsinki-NLP/news_commentary | Helsinki-NLP | "2024-02-29T15:28:06Z" | 12,941 | 32 | [
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] | [
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] | "2022-03-02T23:29:22Z" | ---
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- found
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- found
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license:
- unknown
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size_categories:
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source_datasets:
- original
task_categories:
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task_ids: []
pretty_name: News-Commentary
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---
# Dataset Card for OPUS News-Commentary
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://opus.nlpl.eu/News-Commentary/corpus/version/News-Commentary
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://aclanthology.org/L12-1246/
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
[More Information Needed]
### 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
Please cite the following article if you use any part of the OPUS corpus in your own work:
```bibtex
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
Weyaxi/huggingface-leaderboard | Weyaxi | "2025-02-16T01:26:36Z" | 12,904 | 10 | [
"region:us"
] | null | "2023-10-19T10:40:57Z" | ---
viewer: false
---
# Huggingface Leaderboard's History Dataset
🏆 This is the history dataset of [Huggingface Leaderboard](https://huggingface.co/spaces/Weyaxi/huggingface-leaderboard).
🗒️ This dataset contains full dataframes in a CSV file for each time lapse.
⌛ This dataset is automatically updated when space restarts. (Which is approximately every 6 hours)
## Leaderboard Link
🔗 [Weyaxi/huggingface-leaderboard](https://huggingface.co/spaces/Weyaxi/huggingface-leaderboard) |
Anthropic/hh-rlhf | Anthropic | "2023-05-26T18:47:34Z" | 12,860 | 1,267 | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | null | "2022-12-08T20:11:33Z" | ---
license: mit
tags:
- human-feedback
---
# Dataset Card for HH-RLHF
## Dataset Summary
This repository provides access to two different kinds of data:
1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.
2. Human-generated and annotated red teaming dialogues from [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.
**Disclaimer**: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior.
Each of these datasets are described further below.
## Human preference data about helpfulness and harmlessness (PM Data)
The data are described in the paper: [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected".
For **helpfulness**, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process.
For **harmlessness**, the data are only collected for our base models, but otherwise formatted in the same way.
Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D.
## Red teaming data (not PM Data)
The data are described in the paper: [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like.
Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper.
Each line of the jsonl file contains a dictionary with the following fields:
- `transcript` a text transcript of a conversation between a human adversary (red team member) and an AI assistant
- `min_harmlessness_score_transcript` a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model
- `num_params` number of parameters in the language model powering the AI assistant
- `model_type` type of model powering the AI assistant
- `rating` the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful)
- `task_description` a short text description written by the red team member about how they tried to red team the AI assistant
- `task_description_harmlessness_score` a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model
- `red_team_member_id` an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks
- `is_upworker` a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk
- `tags` a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.
## Usage
Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the `data_dir` argument of the `load_dataset()` function as follows:
```python
from datasets import load_dataset
# Load all helpfulness/harmless subsets (share the same schema)
dataset = load_dataset("Anthropic/hh-rlhf")
# Load one of the harmless subsets
dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")
# Load the red teaming subset
dataset = load_dataset("Anthropic/hh-rlhf", data_dir="red-team-attempts")
```
## Contact
The original authors host this dataset on GitHub here: https://github.com/anthropics/hh-rlhf
You can submit inquiries to: redteam@anthropic.com |
Salesforce/lotsa_data | Salesforce | "2025-01-21T09:25:00Z" | 12,855 | 64 | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:arrow",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:mlcroissant",
"arxiv:2402.02592",
"region:us"
] | null | "2024-02-22T03:12:11Z" | ---
license: apache-2.0
configs:
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data_files:
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path: "*/*.arrow"
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data_files:
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path: "BEIJING_SUBWAY_30MIN/*.arrow"
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data_files:
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path: "PEMS_BAY/*.arrow"
- config_name: "Q-TRAFFIC"
data_files:
- split: train
path: "Q-TRAFFIC/*.arrow"
- config_name: "SHMETRO"
data_files:
- split: train
path: "SHMETRO/*.arrow"
- config_name: "SZ_TAXI"
data_files:
- split: train
path: "SZ_TAXI/*.arrow"
- config_name: "alibaba_cluster_trace_2018"
data_files:
- split: train
path: "alibaba_cluster_trace_2018/*.arrow"
- config_name: "australian_electricity_demand"
data_files:
- split: train
path: "australian_electricity_demand/*.arrow"
- config_name: "azure_vm_traces_2017"
data_files:
- split: train
path: "azure_vm_traces_2017/*.arrow"
- config_name: "bdg-2_bear"
data_files:
- split: train
path: "bdg-2_bear/*.arrow"
- config_name: "bdg-2_fox"
data_files:
- split: train
path: "bdg-2_fox/*.arrow"
- config_name: "bdg-2_panther"
data_files:
- split: train
path: "bdg-2_panther/*.arrow"
- config_name: "bdg-2_rat"
data_files:
- split: train
path: "bdg-2_rat/*.arrow"
- config_name: "beijing_air_quality"
data_files:
- split: train
path: "beijing_air_quality/*.arrow"
- config_name: "bitcoin_with_missing"
data_files:
- split: train
path: "bitcoin_with_missing/*.arrow"
- config_name: "borealis"
data_files:
- split: train
path: "borealis/*.arrow"
- config_name: "borg_cluster_data_2011"
data_files:
- split: train
path: "borg_cluster_data_2011/*.arrow"
- config_name: "buildings_900k"
data_files:
- split: train
path: "buildings_900k/*.arrow"
- config_name: "bull"
data_files:
- split: train
path: "bull/*.arrow"
- config_name: "car_parts_with_missing"
data_files:
- split: train
path: "car_parts_with_missing/*.arrow"
- config_name: "cdc_fluview_ilinet"
data_files:
- split: train
path: "cdc_fluview_ilinet/*.arrow"
- config_name: "cdc_fluview_who_nrevss"
data_files:
- split: train
path: "cdc_fluview_who_nrevss/*.arrow"
- config_name: "china_air_quality"
data_files:
- split: train
path: "china_air_quality/*.arrow"
- config_name: "cif_2016_12"
data_files:
- split: train
path: "cif_2016_12/*.arrow"
- config_name: "cif_2016_6"
data_files:
- split: train
path: "cif_2016_6/*.arrow"
- config_name: "cmip6"
data_files:
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path: "cmip6_*/*.arrow"
- config_name: "cmip6_1850"
data_files:
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path: "cmip6_1850/*.arrow"
- config_name: "cmip6_1855"
data_files:
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path: "cmip6_1855/*.arrow"
- config_name: "cmip6_1860"
data_files:
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path: "cmip6_1860/*.arrow"
- config_name: "cmip6_1865"
data_files:
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path: "cmip6_1865/*.arrow"
- config_name: "cmip6_1870"
data_files:
- split: train
path: "cmip6_1870/*.arrow"
- config_name: "cmip6_1875"
data_files:
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path: "cmip6_1875/*.arrow"
- config_name: "cmip6_1880"
data_files:
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path: "cmip6_1880/*.arrow"
- config_name: "cmip6_1885"
data_files:
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- config_name: "cmip6_1890"
data_files:
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path: "cmip6_1890/*.arrow"
- config_name: "cmip6_1895"
data_files:
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path: "cmip6_1895/*.arrow"
- config_name: "cmip6_1900"
data_files:
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path: "cmip6_1900/*.arrow"
- config_name: "cmip6_1905"
data_files:
- split: train
path: "cmip6_1905/*.arrow"
- config_name: "cmip6_1910"
data_files:
- split: train
path: "cmip6_1910/*.arrow"
- config_name: "cmip6_1915"
data_files:
- split: train
path: "cmip6_1915/*.arrow"
- config_name: "cmip6_1920"
data_files:
- split: train
path: "cmip6_1920/*.arrow"
- config_name: "cmip6_1925"
data_files:
- split: train
path: "cmip6_1925/*.arrow"
- config_name: "cmip6_1930"
data_files:
- split: train
path: "cmip6_1930/*.arrow"
- config_name: "cmip6_1935"
data_files:
- split: train
path: "cmip6_1935/*.arrow"
- config_name: "cmip6_1940"
data_files:
- split: train
path: "cmip6_1940/*.arrow"
- config_name: "cmip6_1945"
data_files:
- split: train
path: "cmip6_1945/*.arrow"
- config_name: "cmip6_1950"
data_files:
- split: train
path: "cmip6_1950/*.arrow"
- config_name: "cmip6_1955"
data_files:
- split: train
path: "cmip6_1955/*.arrow"
- config_name: "cmip6_1960"
data_files:
- split: train
path: "cmip6_1960/*.arrow"
- config_name: "cmip6_1965"
data_files:
- split: train
path: "cmip6_1965/*.arrow"
- config_name: "cmip6_1970"
data_files:
- split: train
path: "cmip6_1970/*.arrow"
- config_name: "cmip6_1975"
data_files:
- split: train
path: "cmip6_1975/*.arrow"
- config_name: "cmip6_1980"
data_files:
- split: train
path: "cmip6_1980/*.arrow"
- config_name: "cmip6_1985"
data_files:
- split: train
path: "cmip6_1985/*.arrow"
- config_name: "cmip6_1990"
data_files:
- split: train
path: "cmip6_1990/*.arrow"
- config_name: "cmip6_1995"
data_files:
- split: train
path: "cmip6_1995/*.arrow"
- config_name: "cmip6_2000"
data_files:
- split: train
path: "cmip6_2000/*.arrow"
- config_name: "cmip6_2005"
data_files:
- split: train
path: "cmip6_2005/*.arrow"
- config_name: "cmip6_2010"
data_files:
- split: train
path: "cmip6_2010/*.arrow"
- config_name: "cockatoo"
data_files:
- split: train
path: "cockatoo/*.arrow"
- config_name: "covid19_energy"
data_files:
- split: train
path: "covid19_energy/*.arrow"
- config_name: "covid_deaths"
data_files:
- split: train
path: "covid_deaths/*.arrow"
- config_name: "covid_mobility"
data_files:
- split: train
path: "covid_mobility/*.arrow"
- config_name: "elecdemand"
data_files:
- split: train
path: "elecdemand/*.arrow"
- config_name: "elf"
data_files:
- split: train
path: "elf/*.arrow"
- config_name: "era5"
data_files:
- split: train
path: "era5_*/*.arrow"
- config_name: "era5_1989"
data_files:
- split: train
path: "era5_1989/*.arrow"
- config_name: "era5_1990"
data_files:
- split: train
path: "era5_1990/*.arrow"
- config_name: "era5_1991"
data_files:
- split: train
path: "era5_1991/*.arrow"
- config_name: "era5_1992"
data_files:
- split: train
path: "era5_1992/*.arrow"
- config_name: "era5_1993"
data_files:
- split: train
path: "era5_1993/*.arrow"
- config_name: "era5_1994"
data_files:
- split: train
path: "era5_1994/*.arrow"
- config_name: "era5_1995"
data_files:
- split: train
path: "era5_1995/*.arrow"
- config_name: "era5_1996"
data_files:
- split: train
path: "era5_1996/*.arrow"
- config_name: "era5_1997"
data_files:
- split: train
path: "era5_1997/*.arrow"
- config_name: "era5_1998"
data_files:
- split: train
path: "era5_1998/*.arrow"
- config_name: "era5_1999"
data_files:
- split: train
path: "era5_1999/*.arrow"
- config_name: "era5_2000"
data_files:
- split: train
path: "era5_2000/*.arrow"
- config_name: "era5_2001"
data_files:
- split: train
path: "era5_2001/*.arrow"
- config_name: "era5_2002"
data_files:
- split: train
path: "era5_2002/*.arrow"
- config_name: "era5_2003"
data_files:
- split: train
path: "era5_2003/*.arrow"
- config_name: "era5_2004"
data_files:
- split: train
path: "era5_2004/*.arrow"
- config_name: "era5_2005"
data_files:
- split: train
path: "era5_2005/*.arrow"
- config_name: "era5_2006"
data_files:
- split: train
path: "era5_2006/*.arrow"
- config_name: "era5_2007"
data_files:
- split: train
path: "era5_2007/*.arrow"
- config_name: "era5_2008"
data_files:
- split: train
path: "era5_2008/*.arrow"
- config_name: "era5_2009"
data_files:
- split: train
path: "era5_2009/*.arrow"
- config_name: "era5_2010"
data_files:
- split: train
path: "era5_2010/*.arrow"
- config_name: "era5_2011"
data_files:
- split: train
path: "era5_2011/*.arrow"
- config_name: "era5_2012"
data_files:
- split: train
path: "era5_2012/*.arrow"
- config_name: "era5_2013"
data_files:
- split: train
path: "era5_2013/*.arrow"
- config_name: "era5_2014"
data_files:
- split: train
path: "era5_2014/*.arrow"
- config_name: "era5_2015"
data_files:
- split: train
path: "era5_2015/*.arrow"
- config_name: "era5_2016"
data_files:
- split: train
path: "era5_2016/*.arrow"
- config_name: "era5_2017"
data_files:
- split: train
path: "era5_2017/*.arrow"
- config_name: "era5_2018"
data_files:
- split: train
path: "era5_2018/*.arrow"
- config_name: "extended_web_traffic_with_missing"
data_files:
- split: train
path: "extended_web_traffic_with_missing/*.arrow"
- config_name: "favorita_sales"
data_files:
- split: train
path: "favorita_sales/*.arrow"
- config_name: "favorita_transactions"
data_files:
- split: train
path: "favorita_transactions/*.arrow"
- config_name: "fred_md"
data_files:
- split: train
path: "fred_md/*.arrow"
- config_name: "gfc12_load"
data_files:
- split: train
path: "gfc12_load/*.arrow"
- config_name: "gfc14_load"
data_files:
- split: train
path: "gfc14_load/*.arrow"
- config_name: "gfc17_load"
data_files:
- split: train
path: "gfc17_load/*.arrow"
- config_name: "godaddy"
data_files:
- split: train
path: "godaddy/*.arrow"
- config_name: "hierarchical_sales"
data_files:
- split: train
path: "hierarchical_sales/*.arrow"
- config_name: "hog"
data_files:
- split: train
path: "hog/*.arrow"
- config_name: "hospital"
data_files:
- split: train
path: "hospital/*.arrow"
- config_name: "ideal"
data_files:
- split: train
path: "ideal/*.arrow"
- config_name: "kaggle_web_traffic_weekly"
data_files:
- split: train
path: "kaggle_web_traffic_weekly/*.arrow"
- config_name: "kdd2022"
data_files:
- split: train
path: "kdd2022/*.arrow"
- config_name: "kdd_cup_2018_with_missing"
data_files:
- split: train
path: "kdd_cup_2018_with_missing/*.arrow"
- config_name: "largest"
data_files:
- split: train
path: "largest_*/*.arrow"
- config_name: "largest_2017"
data_files:
- split: train
path: "largest_2017/*.arrow"
- config_name: "largest_2018"
data_files:
- split: train
path: "largest_2018/*.arrow"
- config_name: "largest_2019"
data_files:
- split: train
path: "largest_2019/*.arrow"
- config_name: "largest_2020"
data_files:
- split: train
path: "largest_2020/*.arrow"
- config_name: "largest_2021"
data_files:
- split: train
path: "largest_2021/*.arrow"
- config_name: "lcl"
data_files:
- split: train
path: "lcl/*.arrow"
- config_name: "london_smart_meters_with_missing"
data_files:
- split: train
path: "london_smart_meters_with_missing/*.arrow"
- config_name: "m1_monthly"
data_files:
- split: train
path: "m1_monthly/*.arrow"
- config_name: "m1_quarterly"
data_files:
- split: train
path: "m1_quarterly/*.arrow"
- config_name: "m1_yearly"
data_files:
- split: train
path: "m1_yearly/*.arrow"
- config_name: "m4_daily"
data_files:
- split: train
path: "m4_daily/*.arrow"
- config_name: "m4_hourly"
data_files:
- split: train
path: "m4_hourly/*.arrow"
- config_name: "m4_monthly"
data_files:
- split: train
path: "m4_monthly/*.arrow"
- config_name: "m4_quarterly"
data_files:
- split: train
path: "m4_quarterly/*.arrow"
- config_name: "m4_weekly"
data_files:
- split: train
path: "m4_weekly/*.arrow"
- config_name: "m4_yearly"
data_files:
- split: train
path: "m4_yearly/*.arrow"
- config_name: "m5"
data_files:
- split: train
path: "m5/*.arrow"
- config_name: "monash_m3_monthly"
data_files:
- split: train
path: "monash_m3_monthly/*.arrow"
- config_name: "monash_m3_other"
data_files:
- split: train
path: "monash_m3_other/*.arrow"
- config_name: "monash_m3_quarterly"
data_files:
- split: train
path: "monash_m3_quarterly/*.arrow"
- config_name: "monash_m3_yearly"
data_files:
- split: train
path: "monash_m3_yearly/*.arrow"
- config_name: "nn5_daily_with_missing"
data_files:
- split: train
path: "nn5_daily_with_missing/*.arrow"
- config_name: "nn5_weekly"
data_files:
- split: train
path: "nn5_weekly/*.arrow"
- config_name: "oikolab_weather"
data_files:
- split: train
path: "oikolab_weather/*.arrow"
- config_name: "pdb"
data_files:
- split: train
path: "pdb/*.arrow"
- config_name: "pedestrian_counts"
data_files:
- split: train
path: "pedestrian_counts/*.arrow"
- config_name: "project_tycho"
data_files:
- split: train
path: "project_tycho/*.arrow"
- config_name: "residential_load_power"
data_files:
- split: train
path: "residential_load_power/*.arrow"
- config_name: "residential_pv_power"
data_files:
- split: train
path: "residential_pv_power/*.arrow"
- config_name: "restaurant"
data_files:
- split: train
path: "restaurant/*.arrow"
- config_name: "rideshare_with_missing"
data_files:
- split: train
path: "rideshare_with_missing/*.arrow"
- config_name: "saugeenday"
data_files:
- split: train
path: "saugeenday/*.arrow"
- config_name: "sceaux"
data_files:
- split: train
path: "sceaux/*.arrow"
- config_name: "smart"
data_files:
- split: train
path: "smart/*.arrow"
- config_name: "solar_power"
data_files:
- split: train
path: "solar_power/*.arrow"
- config_name: "spain"
data_files:
- split: train
path: "spain/*.arrow"
- config_name: "subseasonal"
data_files:
- split: train
path: "subseasonal/*.arrow"
- config_name: "subseasonal_precip"
data_files:
- split: train
path: "subseasonal_precip/*.arrow"
- config_name: "sunspot_with_missing"
data_files:
- split: train
path: "sunspot_with_missing/*.arrow"
- config_name: "taxi_30min"
data_files:
- split: train
path: "taxi_30min/*.arrow"
- config_name: "temperature_rain_with_missing"
data_files:
- split: train
path: "temperature_rain_with_missing/*.arrow"
- config_name: "tourism_monthly"
data_files:
- split: train
path: "tourism_monthly/*.arrow"
- config_name: "tourism_quarterly"
data_files:
- split: train
path: "tourism_quarterly/*.arrow"
- config_name: "tourism_yearly"
data_files:
- split: train
path: "tourism_yearly/*.arrow"
- config_name: "traffic_hourly"
data_files:
- split: train
path: "traffic_hourly/*.arrow"
- config_name: "traffic_weekly"
data_files:
- split: train
path: "traffic_weekly/*.arrow"
- config_name: "uber_tlc_daily"
data_files:
- split: train
path: "uber_tlc_daily/*.arrow"
- config_name: "uber_tlc_hourly"
data_files:
- split: train
path: "uber_tlc_hourly/*.arrow"
- config_name: "us_births"
data_files:
- split: train
path: "us_births/*.arrow"
- config_name: "vehicle_trips_with_missing"
data_files:
- split: train
path: "vehicle_trips_with_missing/*.arrow"
- config_name: "weather"
data_files:
- split: train
path: "weather/*.arrow"
- config_name: "wiki-rolling_nips"
data_files:
- split: train
path: "wiki-rolling_nips/*.arrow"
- config_name: "wind_farms_with_missing"
data_files:
- split: train
path: "wind_farms_with_missing/*.arrow"
- config_name: "wind_power"
data_files:
- split: train
path: "wind_power/*.arrow"
---
# LOTSA Data
The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting.
It was collected for the purpose of pre-training Large Time Series Models.
See the [paper](https://arxiv.org/abs/2402.02592) and [codebase](https://github.com/SalesforceAIResearch/uni2ts) for more information.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you're using LOTSA data in your research or applications, please cite it using this BibTeX:
**BibTeX:**
```markdown
@article{woo2024unified,
title={Unified Training of Universal Time Series Forecasting Transformers},
author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Xiong, Caiming and Savarese, Silvio and Sahoo, Doyen},
journal={arXiv preprint arXiv:2402.02592},
year={2024}
}
```
## Ethical Considerations
This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP. |
Samsung/samsum | Samsung | "2024-01-18T11:15:13Z" | 12,849 | 321 | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-nc-nd-4.0",
"size_categories:10K<n<100K",
"arxiv:1911.12237",
"region:us",
"conversations-summarization"
] | [
"summarization"
] | "2022-03-02T23:29:22Z" | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: samsum-corpus
pretty_name: SAMSum Corpus
tags:
- conversations-summarization
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
config_name: samsum
splits:
- name: train
num_bytes: 9479141
num_examples: 14732
- name: test
num_bytes: 534492
num_examples: 819
- name: validation
num_bytes: 516431
num_examples: 818
download_size: 2944100
dataset_size: 10530064
train-eval-index:
- config: samsum
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
dialogue: text
summary: target
---
# Dataset Card for SAMSum Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://arxiv.org/abs/1911.12237v2
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/1911.12237v2
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
The first instance in the training set:
{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique id of an example.
### Data Splits
- train: 14732
- val: 818
- test: 819
## Dataset Creation
### Curation Rationale
In paper:
> In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol.
As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app.
### Source Data
#### Initial Data Collection and Normalization
In paper:
> We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora.
#### Who are the source language producers?
linguists
### Annotations
#### Annotation process
In paper:
> Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary.
#### Who are the annotators?
language experts
### Personal and Sensitive Information
None, see above: Initial Data Collection and Normalization
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
### Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
### Contributions
Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset. |