id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
yelp_review_full | 2023-01-25T15:03:32.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1509.01626",
"region:u... | null | The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.
The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset.
It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo... | @inproceedings{zhang2015character,
title={Character-level convolutional networks for text classification},
author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle={Advances in neural information processing systems},
pages={649--657},
year={2015}
} | 38 | 20,703 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: YelpReviewFull
license_details: yelp... | 6,551 | [
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lex_glue | 2023-06-01T14:59:56.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:multiple-choice-qa",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolin... | null | Legal General Language Understanding Evaluation (LexGLUE) benchmark is
a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks | @article{chalkidis-etal-2021-lexglue,
title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},
author={Chalkidis, Ilias and
Jana, Abhik and
Hartung, Dirk and
Bommarito, Michael and
Androutsopoulos, Ion and
Katz, Daniel Martin and
Aletras, Nikola... | 32 | 20,558 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended
task_categories:
- question-answering
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
- mult... | 32,871 | [
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allenai/nllb | 2022-09-29T18:53:15.000Z | [
"arxiv:2207.0467",
"arxiv:2205.12654",
"arxiv:2207.04672",
"region:us"
] | allenai | null | null | 77 | 20,362 | 2022-08-14T02:02:15 | # Dataset Card for No Language Left Behind (NLLB - 200vo)
## 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... | 38,640 | [
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cosmos_qa | 2023-04-05T10:02:42.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.00277",
"region:us"
] | null | Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events tha... | @inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in ... | 9 | 20,192 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CosmosQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: cosmosqa
dataset_inf... | 7,507 | [
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snli | 2023-01-25T14:44:35.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-flicker-30k",
"... | null | The SNLI corpus (version 1.0) is a collection of 570k human-written English
sentence pairs manually labeled for balanced classification with the labels
entailment, contradiction, and neutral, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE). | @inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title ... | 32 | 19,998 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-flicker-30k
- extended|other-visual-genome
task_categories:
- text-classification
task_ids:
- natural-language-infe... | 14,092 | [
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anli | 2023-04-05T09:33:23.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_data... | null | The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test s... | @InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
bookt... | 22 | 19,862 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
- extended|hotpot_qa
task_categories:
- text-classification
task_ids:
- natural-language-inference
- mult... | 7,467 | [
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imagenet-1k | 2023-09-25T19:42:34.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1409.0575",
"a... | null | ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+... | @article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challeng... | 182 | 19,787 | 2022-05-02T16:33:23 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
license_details: imagenet-agreement
multilinguality:
- monolingual
paperswithcode_id: imagenet
pretty_name: ImageNet
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-classification
... | 85,410 | [
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togethercomputer/RedPajama-Data-1T | 2023-06-30T22:06:10.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | togethercomputer | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. | null | 902 | 19,698 | 2023-04-17T06:28:35 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Red Pajama 1T
---
### Getting Started
The dataset consists of 2084 jsonl files.
You can download the dataset using HuggingFace:
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-1T")
```
Or you can directly... | 6,120 | [
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librispeech_asr | 2022-11-18T20:18:42.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"sou... | null | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | 65 | 19,278 | 2022-03-02T23:29:22 | ---
pretty_name: LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-reco... | 10,177 | [
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wiki_qa | 2023-04-05T13:43:16.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Wiki Question Answering corpus from Microsoft | @InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-12... | 17 | 18,900 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: wikiqa
pretty_name: WikiQA
dataset_info:
feat... | 13,584 | [
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jmhessel/newyorker_caption_contest | 2023-10-26T00:38:13.000Z | [
"task_categories:image-to-text",
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:visual-question-answering",
"task_categories:other",
"task_categories:text2text-generation",
"task_ids:multi-class-classification",
"task_ids... | jmhessel | There are 3 caption contest tasks, described in the paper. In the Matching multiple choice task, models must recognize a caption written about a cartoon (vs. options that were not). In the Quality Ranking task, models must evaluate the quality
of that caption by scoring it more highly than a lower quality option from t... | @article{hessel2022androids,
title={Do Androids Laugh at Electric Sheep? Humor" Understanding" Benchmarks from The New Yorker Caption Contest},
author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin},
journal={arXiv prepri... | 29 | 18,251 | 2022-09-29T17:28:05 | ---
annotations_creators:
- expert-generated
- crowdsourced
- found
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: newyorker_caption_contest
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- humor
- caption contest
- new... | 13,097 | [
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opus100 | 2023-06-01T14:59:58.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:extended",
"l... | null | OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side.
The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs.
Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 ha... | @misc{zhang2020improving,
title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation},
author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},
year={2020},
eprint={2004.11867},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 59 | 18,204 | 2022-03-02T23:29:22 | ---
pretty_name: Opus100
task_categories:
- translation
multilinguality:
- translation
task_ids: []
language:
- af
- am
- an
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- dz
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- ig
- is
- it
- ja
... | 46,666 | [
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bigcode/humanevalpack | 2023-08-17T18:45:27.000Z | [
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"code",
"arxiv:2308.07124",
"region:us"
] | bigcode | null | 24 | 18,107 | 2023-03-29T12:00:16 | ---
license: mit
pretty_name: HumanEvalPack
language_creators:
- expert-generated
multilinguality:
- multilingual
language:
- code
tags:
- code
---
# Dataset Card for HumanEvalPack
## Table of C... | 7,586 | [
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EleutherAI/lambada_openai | 2022-12-16T19:53:23.000Z | [
"task_ids:language-modeling",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:lambada",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"license:mit",
"region:us"
] | EleutherAI | The LAMBADA dataset as processed by OpenAI. It is used to evaluate the capabilities
of computational models for text understanding by means of a word prediction task.
LAMBADA is a collection of narrative texts sharing the characteristic that human subjects
are able to guess their last word if they are exposed to the wh... | @misc{
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
title={The LAMBADA dataset},
DOI={10.5281/zenodo.2630551},
publisher={Zenodo},
year={2016},
mo... | 30 | 17,887 | 2022-12-16T16:35:07 | ---
pretty_name: LAMBADA OpenAI
language_creators:
- machine-generated
license: mit
multilinguality:
- translation
task_ids:
- language-modeling
source_datasets:
- lambada
size_categories:
- 1K<n<10K
language:
- de
- en
- es
- fr
- it
dataset_info:
- config_name: default
features:
- name: text
dtype: string
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oscar-corpus/OSCAR-2301 | 2023-04-18T10:08:22.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"license:cc0-1.0",
"arxiv:2212.10440",
"arxiv:2010.14571",
"region:us"
] | oscar-corpus | The Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.\ | @ARTICLE{2022arXiv221210440J,
author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
journal = {arXiv e-prints},
... | 66 | 17,338 | 2023-03-02T10:22:42 | ---
license: cc0-1.0
size_categories:
- n>1T
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
extra_gated_prompt: "By filling the form below, you understand that only the metadata and the annotations of OSCA... | 37,419 | [
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fashion_mnist | 2023-04-17T14:02:05.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1708.07747",
"reg... | null | Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of
60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image,
associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in
replacement for the original MNIST dataset for ... | @article{DBLP:journals/corr/abs-1708-07747,
author = {Han Xiao and
Kashif Rasul and
Roland Vollgraf},
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms},
journal = {CoRR},
volume = {abs/1708.07747},
year = {... | 28 | 16,661 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: fashion-mnist
pretty_name... | 8,832 | [
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mlabonne/guanaco-llama2-1k | 2023-08-25T16:49:41.000Z | [
"region:us"
] | mlabonne | null | null | 55 | 16,543 | 2023-07-23T15:07:50 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is ... | 1,017 | [
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huggingface/cats-image | 2022-02-03T12:31:30.000Z | [
"region:us"
] | huggingface | \\n | \\n | 0 | 16,081 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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exams | 2023-06-01T14:59:56.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",... | null | EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations.
It consists of more than 24,000 high-quality high school exam questions in 16 languages,
covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. | @article{hardalov2020exams,
title={EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering},
author={Hardalov, Momchil and Mihaylov, Todor and Dimitrina Zlatkova and Yoan Dinkov and Ivan Koychev and Preslav Nvakov},
journal={arXiv preprint arXiv:2011.03080},
... | 10 | 16,040 | 2022-03-02T23:29:22 | ---
pretty_name: EXAMS
annotations_creators:
- found
language_creators:
- found
language:
- ar
- bg
- de
- es
- fr
- hr
- hu
- it
- lt
- mk
- pl
- pt
- sq
- sr
- tr
- vi
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
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task... | 31,938 | [
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pubmed_qa | 2023-06-01T14:59:56.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
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"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<... | null | PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative
statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
PubMedQA has 1k expe... | @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 Intern... | 71 | 15,961 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
papers... | 4,590 | [
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mteb/sts22-crosslingual-sts | 2022-09-27T19:10:13.000Z | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:pl",
"language:ru",
"language:tr",
"language:zh",
"region:us"
] | mteb | SemEval 2022 Task 8: Multilingual News Article Similarity | \ | 4 | 15,195 | 2022-05-30T20:19:00 | ---
language:
- ar
- de
- en
- es
- fr
- it
- pl
- ru
- tr
- zh
---
Scores in this dataset have been inverted to be from least to most similar!
The scores in the original STS22 task were from most to least similar. | 220 | [
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HuggingFaceM4/general-pmd-synthetic-testing-with-embeddings | 2023-04-20T13:40:41.000Z | [
"license:bigscience-openrail-m",
"region:us"
] | HuggingFaceM4 | This dataset is designed to be used in testing. It's derived from general-pmd-10k dataset | @InProceedings{huggingface:dataset,
title = {Multimodal synthetic dataset for testing / general PMD},
author={HuggingFace, Inc.},
year={2022}
} | 0 | 15,125 | 2023-04-20T13:12:55 | ---
license: bigscience-openrail-m
---
This dataset is designed to be used in testing. It's derived from general-pmd/localized_narratives__ADE20k dataset
The current splits are: `['100.unique', '100.repeat', '300.unique', '300.repeat', '1k.unique', '1k.repeat', '10k.unique', '10k.repeat']`.
The `unique` ones ensure ... | 854 | [
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bookcorpus | 2023-04-05T09:41:56.000Z | [
"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:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en"... | null | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanation... | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE I... | 152 | 15,018 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BookCorpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
... | 6,481 | [
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togethercomputer/RedPajama-Data-1T-Sample | 2023-07-19T06:59:10.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | togethercomputer | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset. | null | 62 | 14,912 | 2023-04-16T23:12:30 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Red Pajama 1T Sample
---
# Dataset Card for Dataset Name
### Dataset Summary
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
This HuggingFace repo contains a 1B-token sample of the RedPajama dataset.
The full dataset ... | 3,606 | [
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lighteval/siqa | 2023-10-07T08:03:32.000Z | [
"region:us"
] | lighteval | null | null | 3 | 14,901 | 2023-10-07T08:03:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answerA
dtype: string
- name: answerB
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- name:... | 731 | [
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amazon_polarity | 2023-01-25T14:26:12.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1509.01626",
"regi... | null | The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review. | @inproceedings{mcauley2013hidden,
title={Hidden factors and hidden topics: understanding rating dimensions with review text},
author={McAuley, Julian and Leskovec, Jure},
booktitle={Proceedings of the 7th ACM conference on Recommender systems},
pages={165--172},
year={2013}
} | 28 | 14,708 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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pretty_name: Amazon Review Polarity
dataset_i... | 6,641 | [
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Open-Orca/OpenOrca | 2023-10-21T10:09:31.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extra... | Open-Orca | null | null | 843 | 14,679 | 2023-06-15T18:16:11 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
size_categories:
- 10M<n<100M
---
... | 11,960 | [
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mosaicml/dolly_hhrlhf | 2023-10-02T15:48:48.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | mosaicml | null | null | 91 | 14,342 | 2023-05-02T22:27:06 | ---
dataset_info:
features:
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dtype: string
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splits:
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num_bytes: 43781455.002688624
num_examples: 59310
- name: test
num_bytes: 4479286.805304853
num_examples: 5129
download_size: 24882010
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khalidalt/tydiqa-goldp | 2022-07-28T21:49:31.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"language:en",
"language:ar",
"language:bn",
"language:fi",
"l... | khalidalt | TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize a... | @article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of... | 7 | 14,253 | 2022-05-18T14:20:23 | ---
pretty_name: TyDi QA
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
- ar
- bn
- fi
- id
- ja
- sw
- ko
- ru
- te
- th
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
ta... | 9,481 | [
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xquad | 2023-04-05T13:45:22.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:ar",
"language:de",
"language:el",
"language:en",
... | null | XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translat... | @article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprin... | 12 | 14,186 | 2022-03-02T23:29:22 | ---
pretty_name: XQuAD
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ar
- de
- el
- en
- es
- hi
- ro
- ru
- th
- tr
- vi
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-ans... | 14,536 | [
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boolq | 2023-04-05T09:42:01.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
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"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair... | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | 26 | 14,150 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: boolq
pretty_name: BoolQ
da... | 6,600 | [
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reazon-research/reazonspeech | 2023-02-08T02:22:58.000Z | [
"task_categories:automatic-speech-recognition",
"size_categories:10M<n<100M",
"language:ja",
"license:other",
"region:us"
] | reazon-research | null | null | 29 | 13,980 | 2023-01-17T23:03:48 | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- ja
pretty_name: ReazonSpeech
size_categories:
- 10M<n<100M
---
# Dataset Card for ReazonSpeech
## Dataset Description
- **Homepage:** https://research.reazon.jp/projects/ReazonSpeech
- **Repository:** https://github.com/reazon-research/re... | 1,780 | [
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knkarthick/dialogsum | 2023-10-03T10:56:21.000Z | [
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"licens... | knkarthick | null | null | 82 | 13,884 | 2022-06-28T10:17:20 | ---
annotations_creators:
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license: cc-by-nc-sa-4.0
multilinguality:
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- 10K<n<100K
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task_categories:
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pretty_name: DIALOGSu... | 4,654 | [
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iohadrubin/c5 | 2023-10-07T06:13:07.000Z | [
"region:us"
] | iohadrubin | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C5 dataset by AllenAI. | @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 = {2... | 0 | 13,779 | 2023-09-28T18:29:28 | Entry not found | 15 | [
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hf-internal-testing/dummy_image_text_data | 2023-02-08T10:34:38.000Z | [
"region:us"
] | hf-internal-testing | null | null | 0 | 13,737 | 2023-02-08T10:34:30 | ---
dataset_info:
features:
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num_bytes: 1944983.0
num_examples: 20
download_size: 1690123
dataset_size: 1944983.0
---
# Dataset Card for "dummy_image_text_data"
[More Information needed](https://github.com/huggingf... | 398 | [
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agemagician/uniref50 | 2023-10-07T23:04:56.000Z | [
"region:us"
] | agemagician | null | null | 2 | 13,651 | 2022-03-15T11:14:51 | Entry not found | 15 | [
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csebuetnlp/xlsum | 2023-04-18T01:46:20.000Z | [
"task_categories:summarization",
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"annotations_creators:found",
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"lan... | csebuetnlp | We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally
annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics.
The dataset covers 45 languages ranging from low to high-resource, for many of which no
public dataset is currently available. X... | @inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Soh... | 55 | 13,520 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
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- found
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license:
- cc-by-nc-sa-4.0
multil... | 14,594 | [
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quail | 2023-04-05T13:37:16.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
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"size_categories:10K<n<100K",
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"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types.\ | @inproceedings{DBLP:conf/aaai/RogersKDR20,
author = {Anna Rogers and
Olga Kovaleva and
Matthew Downey and
Anna Rumshisky},
title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
Real Tasks},
booktitle = {The Thirty-Fo... | 3 | 13,477 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Question Answering for Artificial Intelligence (QuAIL)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multip... | 8,198 | [
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lvwerra/stack-exchange-paired | 2023-03-13T11:30:17.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:en",
"region:us"
] | lvwerra | null | null | 75 | 13,373 | 2023-03-13T09:32:41 | ---
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: StackExchange Paired
size_categories:
- 10M<n<100M
---
# StackExchange Paired
This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences... | 737 | [
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cifar100 | 2023-01-25T14:27:57.000Z | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-80-Million-Tiny-Images",
"language:en",
"license:unknown",
"region:us"
] | null | The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (act... | @TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
} | 15 | 13,213 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
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license:
- unknown
multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: cifar-100
pretty_name: Cifar... | 9,829 | [
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hendrycks/ethics | 2023-04-19T18:55:00.000Z | [
"language:en",
"license:mit",
"AI Alignment",
"arxiv:2008.02275",
"region:us"
] | hendrycks | A benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. | @article{hendrycks2020aligning,
title={Aligning ai with shared human values},
author={Hendrycks, Dan and Burns, Collin and Basart, Steven and Critch, Andrew and Li, Jerry and Song, Dawn and Steinhardt, Jacob},
journal={arXiv preprint arXiv:2008.02275},
year={2020}
} | 6 | 13,189 | 2023-03-06T15:25:03 | ---
license: mit
language: en
dataset_info:
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features:
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features:
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num_examples: ... | 3,092 | [
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Open-Orca/FLAN | 2023-08-02T15:08:01.000Z | [
"size_categories:1B<n<10B",
"language:en",
"license:cc-by-4.0",
"arxiv:2301.13688",
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"arxiv:2110.08207",
"arxiv:2204.07705",
"region:us"
] | Open-Orca | null | null | 104 | 13,091 | 2023-07-21T13:45:12 | ---
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>
# ... | 6,822 | [
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opus_books | 2022-11-03T16:47:07.000Z | [
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"language:ca",
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"language:el",
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"language:fi",
"language... | null | This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php
Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The ... | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | 20 | 13,017 | 2022-03-02T23:29:22 | ---
annotations_creators:
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license:
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paperswithcode_i... | 20,464 | [
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iohadrubin/c4 | 2023-09-22T09:14:22.000Z | [
"region:us"
] | iohadrubin | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C4 dataset by AllenAI. | @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 = {2... | 0 | 12,937 | 2023-09-22T07:17:57 | Entry not found | 15 | [
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Blablablab/SOCKET | 2023-10-10T20:51:48.000Z | [
"license:cc-by-4.0",
"arxiv:2305.14938",
"region:us"
] | Blablablab | A unified evaluation benchmark dataset for evaludating socialbility of NLP models. | @misc{choi2023llms,
title={Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark},
author={Minje Choi and Jiaxin Pei and Sagar Kumar and Chang Shu and David Jurgens},
year={2023},
eprint={2305.14938},
archivePrefix={arXiv},
pr... | 3 | 12,693 | 2023-05-26T19:56:41 | ---
license: cc-by-4.0
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository: https://github.com/minjechoi/SOCKET
- **Paper: Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark [link](https://arxiv.org/abs/2305.14938)
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mozilla-foundation/common_voice_13_0 | 2023-06-26T15:23:12.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @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 Lang... | 81 | 12,661 | 2023-03-29T07:43:24 | ---
pretty_name: Common Voice Corpus 13.0
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google/fleurs | 2023-02-07T20:51:01.000Z | [
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... | google | null | null | 113 | 12,436 | 2022-04-19T10:25:58 | ---
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- fra
- gle
- glg
- guj
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- h... | 13,336 | [
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AmazonScience/massive | 2022-11-16T15:44:51.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
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"language_creators:found",
"multilinguality:af-ZA",
"multilinguality:am-ET",
"multilinguality:ar-SA",
"multilinguality:az-AZ",
"multilinguality:b... | AmazonScience | MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
for the Natural Language Understanding tasks of intent prediction and slot annotation.
Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
the SLURP dataset, composed... | @misc{fitzgerald2022massive,
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and... | 37 | 12,294 | 2022-04-27T20:48:46 | ---
annotations_creators:
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license:
- cc-by-4.0
multilinguality:
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- bn-BD
- ca-ES
- cy-GB
- da-DK
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- es-ES
- fa-IR
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- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
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- km-KH
- ... | 34,412 | [
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yelp_polarity | 2023-06-27T07:34:43.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"language:en",
"arxiv:1509.01626",
"region:us"
] | null | Large Yelp Review Dataset.
This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing.
ORIGIN
The Yelp reviews dataset consists of reviews from Yelp. It is extracted
from the Yelp Dataset Challenge 2015 data. For more information, p... | @article{zhangCharacterlevelConvolutionalNetworks2015,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1509.01626},
primaryClass = {cs},
title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
abstract = {This article offers an empirical exploration on the use of character... | 7 | 12,266 | 2022-03-02T23:29:22 | ---
language:
- en
pretty_name: YelpPolarity
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: yelp-review-polarity
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
... | 8,778 | [
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clips/mqa | 2022-09-27T12:38:50.000Z | [
"task_categories:question-answering",
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"size_categories:unknown",
"source_datasets:original",
"language:ca",
"language:en",
"language:de",
"language:es",
"language:fr"... | clips | MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages. | @misc{debruyn2021mfaq,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
booktitle={MRQA@EMNLP2021},
} | 28 | 12,078 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- ca
- en
- de
- es
- fr
- ru
- ja
- it
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- pt
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- vi
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- id
- uk
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- fi
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- da
- cs
- ko
- fa
- hi
- hu
- sk
- lt
- et
- hr
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- ms
- bg
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license:
- cc0-1.0
multilinguality:
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math_qa | 2023-04-05T10:09:35.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|aqua_rat",
"language:en",
"licens... | null | Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options. | 41 | 12,036 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- apache-2.0
multilinguality:
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pretty_name: MathQA
size_categories:
- 10K<n<100K
source_datasets:
- extended|aqua_rat
task_categories:
- question-answering
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pa... | 7,438 | [
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Babelscape/SREDFM | 2023-06-20T07:33:28.000Z | [
"task_categories:token-classification",
"size_categories:10M<n<100M",
"language:ar",
"language:ca",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:it",
"language:ja",
"language:ko",
"language:nl",
"language:pl",
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"lan... | Babelscape | Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when... | @InProceedings{REDFM2023,
author = {Huguet Cabot, Pere-Lluis
and Tedeschi, Simone
and Ngonga Ngomo, Axel-Cyrille
and Navigli, Roberto},
title = {RED\textsuperscript{FM}: a Filtered and Multilingual Relation Extraction Dataset},
booktitle = {Proceedings of the 202... | 4 | 11,988 | 2023-06-13T18:35:19 | ---
dataset_info:
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dtype: string
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list:
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THUDM/LongBench | 2023-08-29T04:51:14.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"Long Context",
"arxiv:2308.14508",
"arxiv:2108.00573",
"... | THUDM | LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single... | null | 37 | 11,806 | 2023-07-29T14:33:21 | ---
task_categories:
- question-answering
- text-generation
- summarization
- conversational
- text-classification
language:
- en
- zh
tags:
- Long Context
size_categories:
- 1K<n<10K
---
# Introduction
**LongBench** is the first benchmark for bilingual, multitask, and comprehensive assessment of **long context under... | 16,055 | [
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wiki_dpr | 2023-04-05T13:43:12.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
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"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"lang... | null | This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages. | @misc{karpukhin2020dense,
title={Dense Passage Retrieval for Open-Domain Question Answering},
author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
year={2020},
eprint={2004.04906},
archivePrefix={arXiv},
prima... | 18 | 11,566 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gfdl
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size_categories:
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source_datasets:
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task_categories:
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pret... | 14,594 | [
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bigcode/the-stack-dedup | 2023-08-17T08:21:58.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"arxiv:2211.15533",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | bigcode | null | null | 249 | 11,387 | 2022-10-06T17:49:19 | ---
annotations_creators: []
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language:
- code
license:
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pretty_name: The-Stack
size_categories:
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task_categories:
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task_ids: []
extra_gated_prompt: |-
## Terms of Use for The Stac... | 19,338 | [
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rungalileo/20_Newsgroups_Fixed | 2022-10-25T10:25:50.000Z | [
"task_categories:text-classification",
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"license:unk... | rungalileo | null | null | 1 | 11,318 | 2022-05-19T01:02:07 | ---
annotations_creators:
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language:
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license:
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pretty_name: 20_Newsgroups_Fixed
size_categories:
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- topic-cla... | 5,422 | [
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amazon_us_reviews | 2023-11-02T14:57:03.000Z | [
"task_categories:summarization",
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"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"ta... | null | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website... | \ | 54 | 11,271 | 2022-03-02T23:29:22 | ---
annotations_creators:
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task_ids:
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-... | 60,400 | [
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duorc | 2023-06-01T14:59:57.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
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"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"sourc... | null | DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. | @inproceedings{DuoRC,
author = { Amrita Saha and Rahul Aralikatte and Mitesh M. Khapra and Karthik Sankaranarayanan},title = {{DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension}},
booktitle = {Meeting of the Association for Computational Linguistics (ACL)},
year = {2018}
} | 26 | 11,072 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
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language:
- en
license:
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source_datasets:
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paperswith... | 9,127 | [
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dair-ai/emotion | 2023-04-20T08:08:15.000Z | [
"task_categories:text-classification",
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"language:en",
"license:other",
"emotion-classific... | dair-ai | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | @inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empi... | 132 | 10,634 | 2022-03-02T23:29:22 | ---
annotations_creators:
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task_categories:
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paperswithcode_id: emotion
pretty_na... | 8,780 | [
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paws | 2023-06-01T14:59:56.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machi... | null | PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature
the importance of modeling structure, context, and word order information for the problem
of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the
o... | @InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
} | 17 | 10,626 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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MLCommons/peoples_speech | 2023-05-16T16:11:10.000Z | [
"task_categories:automatic-speech-recognition",
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"size_categories:1T<n",
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"language:en",
... | MLCommons | The People's Speech is a free-to-download 30,000-hour and growing supervised
conversational English speech recognition dataset licensed for academic and
commercial usage under CC-BY-SA (with a CC-BY subset). | @article{DBLP:journals/corr/abs-2111-09344,
author = {Daniel Galvez and
Greg Diamos and
Juan Ciro and
Juan Felipe Ceron and
Keith Achorn and
Anjali Gopi and
David Kanter and
Maximilian Lam and
Ma... | 27 | 10,420 | 2022-08-16T14:21:49 | ---
annotations_creators:
- crowdsourced
- machine-generated
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- crowdsourced
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language:
- en
license:
- cc-by-2.0
- cc-by-2.5
- cc-by-3.0
- cc-by-4.0
- cc-by-sa-3.0
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
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source_datasets:
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task_categories:
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wino_bias | 2023-01-25T15:02:31.000Z | [
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The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter). | @article{DBLP:journals/corr/abs-1804-06876,
author = {Jieyu Zhao and
Tianlu Wang and
Mark Yatskar and
Vicente Ordonez and
Kai{-}Wei Chang},
title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
journal = {CoRR},
vo... | 9 | 10,386 | 2022-03-02T23:29:22 | ---
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paperswithcode_id: winobias
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tydiqa | 2023-04-05T13:42:46.000Z | [
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"size_categories:unknown",
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"l... | null | TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize a... | @article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of... | 15 | 10,378 | 2022-03-02T23:29:22 | ---
pretty_name: TyDi QA
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CarperAI/openai_summarize_tldr | 2023-01-10T02:53:40.000Z | [
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] | CarperAI | null | null | 15 | 10,192 | 2023-01-10T02:53:30 | ---
dataset_info:
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gsarti/flores_101 | 2022-10-27T08:37:36.000Z | [
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"langua... | gsarti | One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the
lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource
languages, consider only restricted domains, or are low quality because they are constructed using
s... | @inproceedings{,
title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={
Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and
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zh-plus/tiny-imagenet | 2022-07-12T09:04:30.000Z | [
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annotations_creators:
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copenlu/answerable_tydiqa | 2022-09-12T11:19:54.000Z | [
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pretty_name: Answerable TyDi QA
size_categories:
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mc4 | 2022-10-28T16:36:33.000Z | [
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"size_categories:1... | null | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI. | @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 = {2... | 107 | 10,015 | 2022-03-02T23:29:22 | ---
pretty_name: mC4
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OpenAssistant/oasst1 | 2023-05-02T13:21:21.000Z | [
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license: apache-2.0
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phiyodr/coco2017 | 2023-06-26T11:40:47.000Z | [
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] | phiyodr | null | null | 1 | 9,817 | 2023-06-26T08:48:25 | ---
language:
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adversarial_qa | 2022-11-18T17:31:37.000Z | [
"task_categories:question-answering",
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"arxiv:2002.0... | null | AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop an... | @article{bartolo2020beat,
author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension},
journal = {Transactions of the Association for Computational Linguistics},
... | 27 | 9,615 | 2022-03-02T23:29:22 | ---
annotations_creators:
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multi_woz_v22 | 2023-01-25T14:41:08.000Z | [
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"task_ids:dialogue-modeling",
"task_ids:multi-class-classification",
"task_ids:parsing",
"annotations_creators:machine-generated",
"language_creators:crowdso... | null | Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
improved version of the d... | @article{corr/abs-2007-12720,
author = {Xiaoxue Zang and
Abhinav Rastogi and
Srinivas Sunkara and
Raghav Gupta and
Jianguo Zhang and
Jindong Chen},
title = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections
... | 14 | 9,572 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
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license:
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ta... | 15,312 | [
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ola13/small-the_pile | 2022-11-24T11:40:52.000Z | [
"region:us"
] | ola13 | null | null | 3 | 9,533 | 2022-11-24T11:40:27 | ---
dataset_info:
features:
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dtype: string
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splits:
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download_size: 328667964
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#... | 487 | [
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flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl | 2022-07-11T13:13:27.000Z | [
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"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | 5 | 9,488 | 2022-03-02T23:29:22 | ---
annotations_creators:
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pretty_name: stackexchange
size_categories:
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---
# Dataset Card Creation Guide
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yahma/alpaca-cleaned | 2023-04-10T20:29:06.000Z | [
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"language:en",
"license:cc-by-4.0",
"instruction-finetuning",
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] | yahma | null | null | 250 | 9,414 | 2023-03-24T18:27:58 | ---
license: cc-by-4.0
language:
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tags:
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pretty_name: Alpaca-Cleaned
task_categories:
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---
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset re... | 11,604 | [
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skt/kobest_v1 | 2022-08-22T09:00:17.000Z | [
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"license:cc-by-sa-4.0",
"arxiv:2204.04541",
"region:us"
] | skt | The dataset contains data for KoBEST dataset | null | 18 | 9,130 | 2022-04-07T13:54:23 | ---
pretty_name: KoBEST
annotations_creators:
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license:
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multilinguality:
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source_datasets:
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---
# Dataset Card for KoBEST
## Table of Contents
- [Table of Contents](#table-of-cont... | 5,556 | [
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opus_euconst | 2022-11-03T16:47:26.000Z | [
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"language:el",
"language:en",
"language:es",
"language:et",
"langua... | null | A parallel corpus collected from the European Constitution for 21 language. | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | 7 | 9,101 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- sk
- sl
- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
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source_datasets:
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task_categories:
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task... | 55,732 | [
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argilla/gutenberg_spacy-ner | 2023-06-28T06:34:37.000Z | [
"language:en",
"region:us"
] | argilla | null | null | 4 | 9,026 | 2022-10-07T13:22:03 | ---
dataset_info:
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jfleg | 2022-11-18T20:15:50.000Z | [
"task_categories:text2text-generation",
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"language_creators:found",
"multilinguality:monolingual",
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"size_categories:1K<n<10K",
"source_datasets:extended|other-GUG-grammaticality-judgements",
"language:en",
"license:cc-... | null | JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus.
It is a gold standard benchmark for developing and evaluating GEC systems with respect to
fluency (extent to which a text is native-sounding) as well as grammaticality.
For each source document, there are four human-written corre... | @InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
author = {Napoles, Courtney
and Sakaguchi, Keisuke
and Tetreault, Joel},
title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
booktitle = {Proceedings of the 15th Conference of the Europe... | 35 | 8,931 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids: []
paper... | 5,815 | [
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uonlp/CulturaX | 2023-09-25T10:43:45.000Z | [
"task_categories:text-generation",
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"task_ids:language-modeling",
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"language_creators:found",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:1... | uonlp | CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages \ | @misc{nguyen2023culturax,
title={CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages},
author={Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen},
year={2023}... | 220 | 8,825 | 2023-09-04T08:20:39 | ---
pretty_name: CulturaX
annotations_creators:
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language_creators:
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language:
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- am
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- ar
- arz
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-... | 22,372 | [
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openai/summarize_from_feedback | 2023-01-03T16:55:41.000Z | [
"arxiv:2009.01325",
"region:us"
] | openai | Summarize from Feedback contains the human feedback data released by the "Learning to summarize from human feedback" paper. | @inproceedings{stienon2020learning,
author = {Nisan Stiennon and Long Ouyang and Jeff Wu and Daniel M. Ziegler and Ryan Lowe and Chelsea Voss and Alec Radford and Dario Amodei and Paul Christiano},
title = {Learning to summarize from human feedback},
booktitle = {NeurIPS},
year = 2020,
} | 124 | 8,747 | 2022-12-28T03:42:47 | ---
pretty_name: Summarize from Feedback
---
# Dataset Card for Summarize from Feedback
## Dataset Description
In the [Learning to Summarize from Human Feedback paper](https://arxiv.org/abs/2009.01325), a reward model was trained from human feedback.
The reward model was then used to train a summarization model to al... | 1,610 | [
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nq_open | 2022-11-03T16:32:11.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia. | @article{doi:10.1162/tacl_a_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, ... | 5 | 8,534 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: NQ-Open
size_categories:
- 10K<n<100K
source_datasets:
- extended|natural_questions
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_i... | 8,632 | [
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Dahoas/rm-static | 2023-03-06T00:13:07.000Z | [
"region:us"
] | Dahoas | null | null | 87 | 8,234 | 2022-12-22T16:50:14 | ---
dataset_info:
features:
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download... | 530 | [
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vwxyzjn/summarize_from_feedback_tldr_3_filtered | 2023-09-19T20:10:04.000Z | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | vwxyzjn | null | null | 1 | 8,189 | 2023-09-19T20:07:59 | ---
license: mit
task_categories:
- summarization
language:
- en
size_categories:
- 1K<n<10K
---
This is the query dataset taken directly from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset | 261 | [
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math_dataset | 2023-04-05T10:09:32.000Z | [
"language:en",
"region:us"
] | null | Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities ... | @article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
} | 46 | 8,168 | 2022-03-02T23:29:22 | ---
pretty_name: Mathematics Dataset
language:
- en
paperswithcode_id: mathematics
dataset_info:
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food101 | 2023-01-25T14:30:37.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-foodspotting",
"language:en",
"license:unknown",
... | null | null | @inproceedings{bossard14,
title = {Food-101 -- Mining Discriminative Components with Random Forests},
author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
booktitle = {European Conference on Computer Vision},
year = {2014}
} | 24 | 8,116 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
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size_categories:
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source_datasets:
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task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id:... | 10,337 | [
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ropes | 2022-11-18T21:42:43.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"source_datasets:original",
"language... | null | ROPES (Reasoning Over Paragraph Effects in Situations) is a QA dataset
which tests a system's ability to apply knowledge from a passage
of text to a new situation. A system is presented a background
passage containing a causal or qualitative relation(s) (e.g.,
"animal pollinators increase efficiency of fertilization in... | @inproceedings{Lin2019ReasoningOP,
title={Reasoning Over Paragraph Effects in Situations},
author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner},
booktitle={MRQA@EMNLP},
year={2019}
} | 12 | 8,060 | 2022-03-02T23:29:22 | ---
pretty_name: ROPES
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswi... | 8,518 | [
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0.... |
hf-internal-testing/cats_vs_dogs_sample | 2023-04-11T17:04:37.000Z | [
"region:us"
] | hf-internal-testing | null | \\n@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14t... | 0 | 8,002 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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paws-x | 2023-01-25T14:42:16.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:exper... | null | PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. Engl... | @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
} | 17 | 7,998 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
- machine-generated
language:
- de
- en
- es
- fr
- ja
- ko
- zh
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-paws
task_categories:
- text-classifica... | 11,782 | [
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ought/raft | 2022-10-25T09:54:19.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:ext... | ought | Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants?
[RAFT](https://raft.elicit.org) is a few-shot classification benchm... | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 32 | 7,960 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|ade_corpus_v2
- extended|banking77
task_categories:
- text-classification
task_ids:
- multi-c... | 15,188 | [
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roneneldan/TinyStories | 2023-08-16T16:54:12.000Z | [
"arxiv:2305.07759",
"region:us"
] | roneneldan | null | null | 264 | 7,811 | 2023-05-12T19:04:09 | License: CDLA-Sharing-1.0
-------------
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 tinystori... | 946 | [
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multi_nli | 2023-04-05T10:10:15.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ori... | null | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre gener... | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of th... | 39 | 7,694 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
- mult... | 8,669 | [
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garage-bAInd/Open-Platypus | 2023-09-17T16:56:19.000Z | [
"size_categories:10K<n<100K",
"language:en",
"arxiv:2308.07317",
"region:us"
] | garage-bAInd | null | null | 238 | 7,681 | 2023-08-03T19:31:18 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 30776452
n... | 5,340 | [
[
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... |
hotpot_qa | 2023-04-05T10:07:23.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"multi-hop",
"arxiv:1809.09600",
"region:us"
] | null | HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
(1) the questions require finding and reasoning over multiple supporting documents to answer;
(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
(3) we provide sente... | @inproceedings{yang2018hotpotqa,
title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
booktitle={Conference on Empirical Methods in... | 19 | 7,663 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: HotpotQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: hotpotqa
tags:
- multi-hop
datase... | 9,191 | [
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stsb_multi_mt | 2022-11-18T21:48:48.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K... | null | These are different multilingual translations and the English original of the STSbenchmark dataset. Translation has been done with deepl.com. | @InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
} | 33 | 7,630 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification... | 9,974 | [
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0.019500... |
hf-internal-testing/librispeech_asr_demo | 2022-04-07T07:06:24.000Z | [
"region:us"
] | hf-internal-testing | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.
Note that in order to limit the re... | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | 2 | 7,573 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379... |
SetFit/sst2 | 2021-12-25T06:16:15.000Z | [
"region:us"
] | SetFit | null | null | 3 | 7,552 | 2022-03-02T23:29:22 | # Stanford Sentiment Treebank - Binary
[Stanford Sentiment Treebank](http://nlp.stanford.edu/sentiment/) with 2 labels: negative, positive
Splits are from:
[https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data](https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data)
... | 378 | [
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0.004589080... |
hate_speech18 | 2023-03-27T14:11:55.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | These files contain text extracted from Stormfront, a white supremacist forum. A random set of
forums posts have been sampled from several subforums and split into sentences. Those sentences
have been manually labelled as containing hate speech or not, according to certain annotation guidelines. | @inproceedings{gibert2018hate,
title = "{Hate Speech Dataset from a White Supremacy Forum}",
author = "de Gibert, Ona and
Perez, Naiara and
Garcia-Pablos, Aitor and
Cuadros, Montse",
booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
month = oct,
... | 13 | 7,521 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: hate-speech
pretty_name: Hate Speech
da... | 5,610 | [
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-0.00308036804... |
hate_speech_offensive | 2023-01-25T14:31:41.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"hate-speech-detection",
"arx... | null | An annotated dataset for hate speech and offensive language detection on tweets. | @inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {20... | 8 | 7,518 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hate-speech-and-offensive-language
pret... | 5,834 | [
[
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0.0... |
flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl | 2022-07-11T13:13:11.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | 5 | 7,476 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
... | 8,655 | [
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... |
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