author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1
class | downloads float64 1 1M ⌀ | gated bool 2
classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2
classes | sha stringlengths 40 40 | siblings sequence | tags sequence | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | @inproceedings{veyseh-et-al-2020-what,
title={{What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}},
author={Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen},
year={2020},
booktitle={Proceedings of COLING},
link={http... | Acronym identification training and development sets for the acronym identification task at SDU@AAAI-21. | false | 2,710 | false | acronym_identification | 2022-11-03T16:46:46.000Z | acronym-identification | false | 85801c4e4293b5c9341d3c51c47ea27303a436ea | [] | [
"arxiv:2010.14678",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"tags:acronym-identification"
] | https://huggingface.co/datasets/acronym_identification/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: acronym-identification
pretty_name: Acronym Identificatio... |
null | null | @article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Languag... | ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs an... | false | 4,993 | false | ade_corpus_v2 | 2022-11-03T16:46:50.000Z | null | false | 305f690ee885b0a88c43ac9ab6187337ebcfc630 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_categori... | https://huggingface.co/datasets/ade_corpus_v2/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids:
- coreference-resolution
- fact-che... |
null | null | @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},
... | 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... | false | 85,122 | false | adversarial_qa | 2022-11-03T16:47:45.000Z | adversarialqa | false | 3483241a3c43bd1b8fc5c54d1ef84231e139768b | [] | [
"arxiv:2002.00293",
"arxiv:1606.05250",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
... | https://huggingface.co/datasets/adversarial_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: adversarialqa
pretty_nam... |
null | null | @misc{zhang2019email,
title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
author={Rui Zhang and Joel Tetreault},
year={2019},
eprint={1906.03497},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text. | false | 1,322 | false | aeslc | 2022-11-03T16:31:59.000Z | aeslc | false | 66826a27d23a5c4e774bab648e00da396bde149f | [] | [
"arxiv:1906.03497",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"tags:aspect-based-summarization",
"tags:conversations-s... | https://huggingface.co/datasets/aeslc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: aeslc
... |
null | null | @inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Governmen... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 357 | false | afrikaans_ner_corpus | 2022-11-03T16:16:12.000Z | null | false | 20cb08ae3bb1be1ca426c079ed2d78e4dfb62a3f | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:af",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/afrikaans_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
license:
- other
license_details: Creative Commons Attribution 2.5 South Africa License
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_id... |
null | null | @inproceedings{Zhang2015CharacterlevelCN,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
booktitle={NIPS},
year={2015}
} | AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for researc... | false | 40,998 | false | ag_news | 2022-11-03T16:47:32.000Z | ag-news | false | f24f17e843e623e78ad023b21a0012c98ed274c4 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/ag_news/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: ag-news
pretty_name: AG’s News Corpus
train-ev... |
null | null | @article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05... | A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a... | false | 50,665 | false | ai2_arc | 2022-11-03T16:47:42.000Z | null | false | e610ebfc7354f5505f1cbed3ad7bf5567e5b86e2 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"language_bcp47:en-US",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choic... | https://huggingface.co/datasets/ai2_arc/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
language_bcp47:
- en-US
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- multiple-choice-qa
paperswithcode_id: null... |
null | null | @inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
... | AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip... | false | 634 | false | air_dialogue | 2022-11-03T16:31:11.000Z | null | false | 3ef284c2b1ca63cebd46335641fa31b09763f4e5 | [] | [
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:fill-mask... | https://huggingface.co/datasets/air_dialogue/resolve/main/README.md | ---
pretty_name: AirDialogue
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- conversational
- text-generation
- fill-mask
task_ids:
- dialogue-gen... |
null | null | @inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610... | Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. | false | 439 | false | ajgt_twitter_ar | 2022-11-03T16:31:51.000Z | null | false | 3aa5f0b5245612bfb799aec499c4dd512e06f492 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/ajgt_twitter_ar/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: Arabic Jordanian General ... |
null | null | @inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
... | Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted
from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale
from one (negative review) to five (positive review).
We recommend using the provi... | false | 814 | false | allegro_reviews | 2022-11-03T16:30:48.000Z | allegro-reviews | false | 5616d4df47bbb59e217e7e1591f111ed293156fe | [] | [
"annotations_creators:found",
"language_creators:found",
"language:pl",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-scoring",
"task_ids:text-scoring"
] | https://huggingface.co/datasets/allegro_reviews/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-scoring
- text-scoring
paperswithcode_id: allegro-reviews
pretty_name:... |
null | null | @misc{blard2019allocine,
author = {Blard, Theophile},
title = {french-sentiment-analysis-with-bert},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},
} | Allocine Dataset: A Large-Scale French Movie Reviews Dataset.
This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.
It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k). | false | 1,227 | false | allocine | 2022-11-03T16:31:33.000Z | allocine | false | 38661ba696f097e1732d90805ad7783918278c95 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:fr",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/allocine/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fr
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: allocine
pretty_name: Allociné
train-e... |
null | null | @inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
book... | The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was develo... | false | 2,012 | false | alt | 2022-11-03T16:32:19.000Z | alt | false | 1a16c8a9171c3ae734f0cff59f12709db90226b1 | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:bn",
"language:en",
"language:fil",
"language:hi",
"language:id",
"language:ja",
"language:km",
"language:lo",
"language:ms",
"language:my",
"language:th",
"language:vi",
"language:zh",
"license:cc-by-... | https://huggingface.co/datasets/alt/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- bn
- en
- fil
- hi
- id
- ja
- km
- lo
- ms
- my
- th
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translati... |
null | null | @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}
} | 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. | false | 55,477 | false | amazon_polarity | 2022-11-03T16:47:40.000Z | null | false | 2aae2b8442bc506e07c5dda2938182c1a2995325 | [] | [
"arxiv:1509.01626",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/amazon_polarity/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: Amazon R... |
null | null | @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
} | We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an a... | false | 24,235 | false | amazon_reviews_multi | 2022-11-03T16:47:19.000Z | null | false | e1914822fd1c764504257731974458f00e6da3f3 | [] | [
"arxiv:2010.02573",
"annotations_creators:found",
"language_creators:found",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:zh",
"license:other",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categori... | https://huggingface.co/datasets/amazon_reviews_multi/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- en
- es
- fr
- ja
- zh
license:
- other
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
tas... |
End of preview. Expand in Data Studio
- Downloads last month
- 6