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null | null | @InProceedings{NEUDECKER16.110,
author = {Clemens Neudecker},
title = {An Open Corpus for Named Entity Recognition in Historic Newspapers},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
year = {2016},
month = {may},
date = {23-28},
locati... | The corpora comprise of files per data provider that are encoded in the IOB format (Ramshaw & Marcus, 1995). The IOB format is a simple text chunking format that divides texts into single tokens per line, and, separated by a whitespace, tags to mark named entities. The most commonly used categories for tags are PER (pe... | false | 986 | false | euronews | 2022-11-03T16:31:42.000Z | europeana-newspapers | false | 826b54f798b352046ac33155034008f3fa3d8388 | [] | [
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"task_ids:named-entity-recognitio... | https://huggingface.co/datasets/euronews/resolve/main/README.md | ---
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paperswithcode_id: europeana-newspap... |
null | null | @Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An ov... | In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM.
EAC-TM covers... | false | 658 | false | europa_eac_tm | 2022-11-03T16:31:06.000Z | null | false | 443f431c9de0b92b44e325fa2cdca4b323e3fb5d | [] | [
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"language:it... | https://huggingface.co/datasets/europa_eac_tm/resolve/main/README.md | ---
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null | null | @Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An ov... | In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. This resource bears the name EAC Translation Memory, short EAC-TM.
ECDC... | false | 771 | false | europa_ecdc_tm | 2022-11-03T16:31:26.000Z | null | false | bb669710f5e686044ffed0ebf8d680a79c26df19 | [] | [
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"language:it... | https://huggingface.co/datasets/europa_ecdc_tm/resolve/main/README.md | ---
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... |
null | null | null | A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinburgh). The main intended use is to aid statistical machine translation research. | false | 1,131 | false | europarl_bilingual | 2022-11-03T16:31:58.000Z | null | false | d53ac07927a7d3bece24ea465bbeac4cbe51d681 | [] | [
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... | https://huggingface.co/datasets/europarl_bilingual/resolve/main/README.md | ---
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task_i... |
null | null | @inproceedings{event2Mind,
title={Event2Mind: Commonsense Inference on Events, Intents, and Reactions},
author={Hannah Rashkin and Maarten Sap and Emily Allaway and Noah A. Smith† Yejin Choi},
year={2018}
} | In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants. | false | 342 | false | event2Mind | 2022-11-03T16:15:33.000Z | event2mind | false | 96d2206d23dadba172f1cb9a476be1c7e23268bf | [] | [
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"tags:common-sense-inference"
] | https://huggingface.co/datasets/event2Mind/resolve/main/README.md | ---
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pretty_name: Event2Mind
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task_ids: []
paperswithcode_id: event2mind
tags:
- common-sense-i... |
null | null | @inproceedings{lehman-etal-2019-inferring,
title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
author = "Lehman, Eric and
DeYoung, Jay and
Barzilay, Regina and
Wallace, Byron C.",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chap... | Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.
The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of... | false | 959 | false | evidence_infer_treatment | 2022-11-03T16:31:38.000Z | null | false | 1a6795278d0696f778623ae69f24c69307b0ceae | [] | [
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] | https://huggingface.co/datasets/evidence_infer_treatment/resolve/main/README.md | ---
pretty_name: Evidence Infer Treatment
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paperswithco... |
null | null | @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},
... | 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. | false | 4,540 | false | exams | 2022-11-03T16:46:47.000Z | exams | false | 26a8f4636b6c5b4bec3ab9af29452303061f2189 | [] | [
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"language:sr... | https://huggingface.co/datasets/exams/resolve/main/README.md | ---
pretty_name: EXAMS
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task... |
null | null | @inproceedings{10.1145/3323503.3361698,
author = {Moreno, Jo\\~{a}o and Bressan, Gra\\c{c}a},
title = {FACTCK.BR: A New Dataset to Study Fake News},
year = {2019},
isbn = {9781450367639},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org... | A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.
The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.
The FAC... | false | 344 | false | factckbr | 2022-11-03T16:15:20.000Z | null | false | c0516e7ffdbf5cea8bb72b450ebe8b8cdf2a874a | [] | [
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"task_ids:fact-checking"
] | https://huggingface.co/datasets/factckbr/resolve/main/README.md | ---
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paperswithcode_id: null
pretty_name: FACTCK BR
dataset_info:
fea... |
null | null | @inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussen... | Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addre... | false | 452 | false | fake_news_english | 2022-11-03T16:16:24.000Z | null | false | 18b2fd085fcdbb21e792bf4232e9c43f50b7826e | [] | [
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"task_ids:multi-label-classification"
] | https://huggingface.co/datasets/fake_news_english/resolve/main/README.md | ---
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task_ids:
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paperswithcode_id: null
pretty_name: Fake ... |
null | null | @inproceedings{cruz2020localization,
title={Localization of Fake News Detection via Multitask Transfer Learning},
author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={2596--... | Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake. | false | 331 | false | fake_news_filipino | 2022-11-03T16:15:20.000Z | fake-news-filipino-dataset | false | b4b30abe8c7211b1a8e3a2181880f01ce9249589 | [] | [
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"task_ids:fact-checking"
] | https://huggingface.co/datasets/fake_news_filipino/resolve/main/README.md | ---
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paperswithcode_id: fake-news-filipino-dataset
pretty_na... |
null | null | \ | Contains Farsi (Persian) datasets for Machine Learning tasks, particularly NLP.
These datasets have been extracted from the RSS feed of two Farsi news agency websites:
- Hamshahri
- RadioFarda | false | 332 | false | farsi_news | 2022-11-03T16:15:15.000Z | null | false | 4d21f69c5b3259d7da738f7d5ac641f88d3eddf3 | [] | [
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"task_ids:masked-languag... | https://huggingface.co/datasets/farsi_news/resolve/main/README.md | ---
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paperswithcode_id: null
pretty_nam... |
null | null | @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 = {... | 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 ... | false | 68,638 | false | fashion_mnist | 2022-11-03T16:47:37.000Z | fashion-mnist | false | 70a9993bf9e2b9d1fd301bfb8fb7f4930c0448fb | [] | [
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"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/fashion_mnist/resolve/main/README.md | ---
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paperswithcode_id: fashion-mnist
pretty_name... |
null | null | null | null | false | 3,945 | false | fever | 2022-11-03T16:46:45.000Z | fever | false | e7eab2a98c973d0f34112d546549ca2d1868725e | [] | [
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"source_datasets:extended|wikipedia",
"task_categories:text-classification",
"tags:knowledge-verification"
] | https://huggingface.co/datasets/fever/resolve/main/README.md | ---
language:
- en
paperswithcode_id: fever
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pretty_name: FEVER
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
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task_ids: []
tags:
- k... |
null | null | @inproceedings{han-etal-2018-fewrel,
title = "{F}ew{R}el: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation",
author = "Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong",
booktitle = "Proceedings of the 2018... | FewRel is a large-scale few-shot relation extraction dataset, which contains more than one hundred relations and tens of thousands of annotated instances cross different domains. | false | 754 | false | few_rel | 2022-11-03T16:31:22.000Z | fewrel | false | fd24e7c373611d5eb1db9f14ee9af81c95c217d5 | [] | [
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"size_categories:n<1K",
"source_datasets:original",
"task_categories:other",
"configs:default",
"... | https://huggingface.co/datasets/few_rel/resolve/main/README.md | ---
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: fewrel
pretty_name: Few-Shot Relation Classificat... |
null | null | @article{Malo2014GoodDO,
title={Good debt or bad debt: Detecting semantic orientations in economic texts},
author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
journal={Journal of the Association for Information Science and Technology},
year={2014},
volume={65}
} | The key arguments for the low utilization of statistical techniques in
financial sentiment analysis have been the difficulty of implementation for
practical applications and the lack of high quality training data for building
such models. Especially in the case of finance and economic texts, annotated
collections are a... | false | 16,248 | false | financial_phrasebank | 2022-11-03T16:47:12.000Z | null | false | 3196d78ac2536c10ab7c1a3af7320e96a85bf654 | [] | [
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-cla... | https://huggingface.co/datasets/financial_phrasebank/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
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size_categories:
- 1K<n<10K
source_datasets:
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task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
paperswithcode... |
null | null | @article{ruokolainen2019finnish,
title={A finnish news corpus for named entity recognition},
author={Ruokolainen, Teemu and Kauppinen, Pekka and Silfverberg, Miikka and Lind{\'e}n, Krister},
journal={Language Resources and Evaluation},
pages={1--26},
year={2019},
publisher={Springer}
} | The directory data contains a corpus of Finnish technology related news articles with a manually prepared
named entity annotation (digitoday.2014.csv). The text material was extracted from the archives of Digitoday,
a Finnish online technology news source (www.digitoday.fi). The corpus consists of 953 articles
(193,742... | false | 335 | false | finer | 2022-11-03T16:15:34.000Z | finer | false | eb01f2d9195c79df3414937671649dab1b5fb8ff | [] | [
"arxiv:1908.04212",
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"language:fi",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/finer/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
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- fi
license:
- mit
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source_datasets:
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task_categories:
- token-classification
task_ids:
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paperswithcode_id: finer
pretty_name: Finnish News C... |
null | null | @misc{guzmn2019new,
title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato},
year={2019},
epr... | Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English. | false | 503 | false | flores | 2022-11-03T16:16:38.000Z | flores | false | bac16cd1b1d8505132e60b5450d4f25955196ed5 | [] | [
"arxiv:1902.01382",
"annotations_creators:found",
"language_creators:found",
"language:en",
"language:ne",
"language:si",
"license:cc-by-4.0",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia",
"source_datasets:extended|opus_gnome",
"source_dataset... | https://huggingface.co/datasets/flores/resolve/main/README.md | ---
pretty_name: Flores
annotations_creators:
- found
language_creators:
- found
language:
- en
- ne
- si
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
- extended|opus_gnome
- extended|opus_ubuntu
- extended|open_subtitles
- extended|paracrawl
- ex... |
null | null | @misc{le2019flaubert,
title={FlauBERT: Unsupervised Language Model Pre-training for French},
author={Hang Le and Loïc Vial and Jibril Frej and Vincent Segonne and Maximin Coavoux and Benjamin Lecouteux and Alexandre Allauzen and Benoît Crabbé and Laurent Besacier and Didier Schwab},
year={2019},
eprint=... | FLUE is an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language. | false | 219 | false | flue | 2022-11-03T16:07:43.000Z | null | false | 42289fefae384cdef5e2b6453504d414d71ac262 | [] | [
"arxiv:1912.05372",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:fr",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"... | https://huggingface.co/datasets/flue/resolve/main/README.md | ---
pretty_name: FLUE
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- fr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- semantic-... |
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}
} | null | false | 9,517 | false | food101 | 2022-11-03T16:47:04.000Z | food-101 | false | bbaf95de292391c807fd2bd7e1c5dd7d1d268002 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-foodspotting",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/food101/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Food-101
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-foodspotting
task_categories:
- image-classification
task_ids:
- multi-class-image-classificat... |
null | null | @ARTICLE{2020arXiv200206071
author = {Martin, d'Hoffschmidt and Maxime, Vidal and
Wacim, Belblidia and Tom, Brendlé},
title = "{FQuAD: French Question Answering Dataset}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = "2020",
... | FQuAD: French Question Answering Dataset
We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%. | false | 346 | false | fquad | 2022-11-03T16:15:39.000Z | fquad | false | 23c403c622536b2a65a77140f78eca582244702c | [] | [
"arxiv:2002.06071",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language:fr",
"license:cc-by-nc-sa-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_categ... | https://huggingface.co/datasets/fquad/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- fr
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
paperswi... |
null | null | @article{jiang2019freebaseqa,
title={FreebaseQA: A New Factoid QA Dataset Matching Trivia-Style Question-Answer Pairs with Freebase},
author={Jiang, Kelvin and Wu, Dekun and Jiang, Hui},
journal={north american chapter of the association for computational linguistics},
year={2019}
} | FreebaseQA is for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase. | false | 850 | false | freebase_qa | 2022-11-03T16:31:52.000Z | freebaseqa | false | 626c19ba65346674682ca5ba3711afe25a61c995 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|trivia_qa",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/freebase_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|trivia_qa
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: freebaseqa
pretty_name: FreebaseQA
... |
null | null | @article{DBLP:journals/corr/abs-1810-05201,
author = {Kellie Webster and
Marta Recasens and
Vera Axelrod and
Jason Baldridge},
title = {Mind the {GAP:} {A} Balanced Corpus of Gendered Ambiguous Pronouns},
journal = {CoRR},
volume = {abs/1810.05201},
yea... | GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of
(ambiguous pronoun, antecedent name), sampled from Wikipedia and released by
Google AI Language for the evaluation of coreference resolution in practical
applications. | false | 501 | false | gap | 2022-11-03T16:16:15.000Z | gap | false | 55403e38ab0b799e742a4d93fb1d886afda4b88a | [] | [
"arxiv:1810.05201",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:coreference-resolution"
] | https://huggingface.co/datasets/gap/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: GAP Benchmark Suite
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- coreference-resolution
paperswithcode_id: gap
... |
null | null | @article{gem_benchmark,
author = {Sebastian Gehrmann and
Tosin P. Adewumi and
Karmanya Aggarwal and
Pawan Sasanka Ammanamanchi and
Aremu Anuoluwapo and
Antoine Bosselut and
Khyathi Raghavi Chandu and
Miruna{-}A... | GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation,
both through human annotations and automated Metrics.
GEM aims to:
- measure NLG progress across 13 datasets spanning many NLG tasks and languages.
- provide an in-depth analysis of data and models presented via data stateme... | false | 10,597 | false | gem | 2022-11-03T16:47:05.000Z | gem | false | 5577931a94afd984c3e666175aeba436b9a1f624 | [] | [
"arxiv:2102.01672",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:found",
"language_creators:machine-generated",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:ru",
"language:tr",
"language:vi",
... | https://huggingface.co/datasets/gem/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
- machine-generated
language:
- cs
- de
- en
- es
- ru
- tr
- vi
license:
- other
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- extended|other-vision-dataset... |
null | null | @article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
} | `generated_reviews_enth`
Generated product reviews dataset for machine translation quality prediction, part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)
`generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task.
This dataset... | false | 667 | false | generated_reviews_enth | 2022-11-03T16:31:22.000Z | null | false | 12e9e18b3c7ffe8d65b3076c6987ee3a983f6697 | [] | [
"arxiv:2007.03541",
"arxiv:1909.05858",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:en",
"language:th",
"license:cc-by-sa-4.0",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:ori... | https://huggingface.co/datasets/generated_reviews_enth/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
- text-classification
task_ids:
- multi-class-classif... |
null | null | @InProceedings{huggingface:dataset,
title = {GenericsKB: A Knowledge Base of Generic Statements},
authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},
year={2020},
publisher = {Allen Institute for AI},
} | The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large... | false | 838 | false | generics_kb | 2022-11-03T16:31:25.000Z | genericskb | false | 4b387a06c372a9c99b0ddcd986ad9a79ee5a7636 | [] | [
"arxiv:2005.00660",
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:other",
"configs:generics_kb",
"con... | https://huggingface.co/datasets/generics_kb/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: genericskb
pretty_name: GenericsKB
configs:
- generics... |
null | null | @inproceedings{leitner2019fine,
author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
title = {{Fine-grained Named Entity Recognition in Legal Documents}},
booktitle = {Semantic Systems. The Power of AI and Knowledge
Graphs. Proceedings of the 15th International Conference
... | \ | false | 1,467 | false | german_legal_entity_recognition | 2022-11-03T16:32:05.000Z | legal-documents-entity-recognition | false | 43967a33425df3c90e44bf0ecece0518e5262970 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:de",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/german_legal_entity_recognition/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: legal-documents-entity-recognitio... |
null | null | @inproceedings{Benikova2015GermaNERFO,
title={GermaNER: Free Open German Named Entity Recognition Tool},
author={Darina Benikova and S. Yimam and Prabhakaran Santhanam and Chris Biemann},
booktitle={GSCL},
year={2015}
} | GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) sy... | false | 481 | false | germaner | 2022-11-03T16:30:39.000Z | null | false | 6d8c4f437939e697be55b6be3d35b1a809ecd870 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:de",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/germaner/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: GermaNER
dat... |
null | null | @inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'... | The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER ann... | false | 3,860 | false | germeval_14 | 2022-11-03T16:46:41.000Z | null | false | 668ca86d7a57ae68ef9d5a69f33868d428d89a5e | [] | [] | https://huggingface.co/datasets/germeval_14/resolve/main/README.md | ---
paperswithcode_id: null
pretty_name: GermEval14
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
0: O
1: B-LOC
2: I-LOC
3: B-LOC... |
null | null | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}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},
... | Giga-word corpus for French-English from WMT2010 collected by Chris Callison-Burch
2 languages, total number of files: 452
total number of tokens: 1.43G
total number of sentence fragments: 47.55M | false | 333 | false | giga_fren | 2022-11-03T16:15:21.000Z | null | false | f3928e9b0cfc2a1c571dd4c9765a2b14fcc259a5 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"language:fr",
"license:unknown",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/giga_fren/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- fr
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: GigaFren
dataset_info:
features:
- name: id
... |
null | null | @article{graff2003english,
title={English gigaword},
author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
journal={Linguistic Data Consortium, Philadelphia},
volume={4},
number={1},
pages={34},
year={2003}
}
@article{Rush_2015,
title={A Neural Attention Model for Abstractive Sentence... | Headline-generation on a corpus of article pairs from Gigaword consisting of
around 4 million articles. Use the 'org_data' provided by
https://github.com/microsoft/unilm/ which is identical to
https://github.com/harvardnlp/sent-summary but with better format.
There are two features:
- document: article.
- summary:... | false | 25,234 | false | gigaword | 2022-11-03T16:47:31.000Z | null | false | 340305f9be71a68f1312faf76aa5d650bbbab181 | [] | [
"arxiv:1509.00685",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|gigaword_2003",
"task_categories:summarization",
"tags:headline-generation"
] | https://huggingface.co/datasets/gigaword/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|gigaword_2003
task_categories:
- summarization
task_ids: []
paperswithcode_id: null
pretty_name: Gigaword
train-eval-index:
- config: default... |
null | null | @inproceedings{mostafazadeh2020glucose,
title={GLUCOSE: GeneraLized and COntextualized Story Explanations},
author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll},
year={2020},
booktitle={The Conference on Emp... | When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the... | false | 342 | false | glucose | 2022-11-03T16:15:45.000Z | glucose | false | 7599948212d3532d82abb43e17b59fb361aff5c3 | [] | [
"arxiv:2009.07758",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-ROC-stories",
"task_categories:fill-mask",
"task_categories:text-generation",
... | https://huggingface.co/datasets/glucose/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-ROC-stories
task_categories:
- fill-mask
- text-generation
paperswithcode_id: glucose
pretty_name: GLUCOSE
tags:
-... |
null | null | @inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
} | GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems. | false | 1,004,077 | false | glue | 2022-11-03T16:47:49.000Z | glue | false | b4b3e3965b74b673e8a6528d1bc059d7a77b53ff | [] | [
"annotations_creators:other",
"language_creators:other",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference... | https://huggingface.co/datasets/glue/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-sco... |
null | null | null | This dataset is intended to advance topic classification for German texts. A classifier that is efffective in
English may not be effective in German dataset because it has a higher inflection and longer compound words.
The 10kGNAD dataset contains 10273 German news articles from an Austrian online newspaper categorized... | false | 419 | false | gnad10 | 2022-11-03T16:16:19.000Z | null | false | f1309a8cd08b5df73b3bc460ecaba8c8a4c96391 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:de",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-from-One-Million-Posts-Corpus",
"task_categories:text-classification",
"task_ids:topic-classification... | https://huggingface.co/datasets/gnad10/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-from-One-Million-Posts-Corpus
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcod... |
null | null | @inproceedings{demszky2020goemotions,
author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)},
title = {{GoEmotions: A Dataset of Fine-Grained Emotions}},
y... | The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire,
disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief,... | false | 3,457 | false | go_emotions | 2022-11-03T16:46:45.000Z | goemotions | false | 3e84f9dc4ef6ce8a26d8fb673faa8dfef267eeb9 | [] | [
"arxiv:2005.00547",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-cl... | https://huggingface.co/datasets/go_emotions/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
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size_categories:
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- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
papersw... |
null | null | @article{gooaq2021,
title={GooAQ: Open Question Answering with Diverse Answer Types},
author={Khashabi, Daniel and Ng, Amos and Khot, Tushar and Sabharwal, Ashish and Hajishirzi, Hannaneh and Callison-Burch, Chris},
journal={arXiv preprint},
year={2021}
} | GooAQ is a large-scale dataset with a variety of answer types. This dataset contains over
5 million questions and 3 million answers collected from Google. GooAQ questions are collected
semi-automatically from the Google search engine using its autocomplete feature. This results in
naturalistic questions of practical in... | false | 386 | false | gooaq | 2022-11-03T16:16:00.000Z | gooaq | false | f1f9d4e733a258960c0b51492ad3549688999795 | [] | [
"arxiv:2104.08727",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/gooaq/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: gooaq
pretty_name: 'GooAQ: O... |
null | null | @misc{faruqui2018identifying,
title={Identifying Well-formed Natural Language Questions},
author={Manaal Faruqui and Dipanjan Das},
year={2018},
eprint={1808.09419},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. | false | 1,010 | false | google_wellformed_query | 2022-11-03T16:31:47.000Z | null | false | cdb93f5a0a177641bbbe0eb5cdda1b3bad764387 | [] | [
"arxiv:1808.09419",
"task_categories:text-classification",
"multilinguality:monolingual",
"task_ids:text-scoring",
"language:en",
"annotations_creators:crowdsourced",
"source_datasets:extended",
"size_categories:10K<n<100K",
"license:cc-by-sa-4.0",
"language_creators:found"
] | https://huggingface.co/datasets/google_wellformed_query/resolve/main/README.md | ---
task_categories:
- text-classification
multilinguality:
- monolingual
task_ids:
- text-scoring
language:
- en
annotations_creators:
- crowdsourced
source_datasets:
- extended
size_categories:
- 10K<n<100K
license:
- cc-by-sa-4.0
paperswithcode_id: null
pretty_name: GoogleWellformedQuery
language_creators:
- found
d... |
null | null | @misc{gu2020iid,
title={Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases},
author={Yu Gu and Sue Kase and Michelle Vanni and Brian Sadler and Percy Liang and Xifeng Yan and Yu Su},
year={2020},
eprint={2011.07743},
archivePrefix={arXiv},
primaryClass={cs.CL... | Strongly Generalizable Question Answering (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test thr... | false | 423 | false | grail_qa | 2022-11-03T16:15:55.000Z | null | false | 2384346f3d678f5d1c21d24d55d852343c8b2327 | [] | [
"arxiv:2011.07743",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"tags:knowledge-base-qa"
] | https://huggingface.co/datasets/grail_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: null
pretty_name: Grail QA
tags:
- knowledge-base-qa
datase... |
null | null | @inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
author = {Vincent J. Hellendoorn and
Charles Sutton and
Rishabh Singh and
Petros Maniatis and
David Bieber},
title = {Global Relational Models of Source Code},
booktitle = {8th International Confere... | The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensi... | false | 578 | false | great_code | 2022-11-03T16:30:56.000Z | null | false | f89204fd2cb2c53560f249b6d0976bb0cc86a6c8 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:table-to-text"
] | https://huggingface.co/datasets/great_code/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: null
pretty_name: GREAT
dataset_info:
features:
- nam... |
null | null | @inproceedings{papaloukas-etal-2021-glc,
title = "Multi-granular Legal Topic Classification on Greek Legislation",
author = "Papaloukas, Christos and Chalkidis, Ilias and Athinaios, Konstantinos and Pantazi, Despina-Athanasia and Koubarakis, Manolis",
booktitle = "Proceedings of the 3rd Natural Legal Langua... | Greek_Legal_Code contains 47k classified legal resources from Greek Legislation. Its origin is “Permanent Greek Legislation Code - Raptarchis”,
a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories. | false | 3,653 | false | greek_legal_code | 2022-10-28T16:35:07.000Z | null | false | a8c688a5edc93c44ce32b6f78eb90ad4e9a97cd1 | [] | [
"arxiv:2109.15298",
"annotations_creators:found",
"language_creators:found",
"language:el",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-... | https://huggingface.co/datasets/greek_legal_code/resolve/main/README.md | ---
pretty_name: Greek Legal Code
annotations_creators:
- found
language_creators:
- found
language:
- el
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
dat... |
null | null | @article{article,
author = {Stamatatos, Efstathios},
year = {2013},
month = {01},
pages = {421-439},
title = {On the robustness of authorship attribution based on character n-gram features},
volume = {21},
journal = {Journal of Law and Policy}
}
@inproceedings{stamatatos2017authorship,
... | A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
3- The same-... | false | 4,402 | false | guardian_authorship | 2022-11-03T16:46:49.000Z | null | false | 0bbc5b4c51a8be0abede632244a5a906685a2438 | [] | [
"annotations_creators:found",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/guardian_authorship/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: GuardianAuthorship
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
paper... |
null | null | @misc{kim2020time,
title={What time is it? Temporal Analysis of Novels},
author={Allen Kim and Charuta Pethe and Steven Skiena},
year={2020},
eprint={2011.04124},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg. | false | 3,264 | false | gutenberg_time | 2022-11-03T16:32:34.000Z | gutenberg-time-dataset | false | 07f088bc2c0708a006adbb14e84355a95b4da27b | [] | [
"arxiv:2011.04124",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/gutenberg_time/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: gutenberg-time-dataset
pretty_nam... |
null | null | @article{DBLP:journals/corr/abs-1902-01007,
author = {R. Thomas McCoy and
Ellie Pavlick and
Tal Linzen},
title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
Language Inference},
journal = {CoRR},
volume = {abs/1902.01007},
y... | The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn. | false | 5,582 | false | hans | 2022-11-03T16:32:32.000Z | hans | false | 2b1f3977b3336b0a840ae695be69cc93a705fb9e | [] | [
"arxiv:1902.01007",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:natural-language-inference"... | https://huggingface.co/datasets/hans/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: hans
pretty_name:... |
null | null | This release contains 1.3 million pairs of aligned text chunks (sentences or smaller fragments)
from the official records (Hansards) of the 36th Canadian Parliament.
The complete Hansards of the debates in the House and Senate of the 36th Canadian Parliament,
as far as available, were aligned. The corpus was then spli... | false | 487 | false | hansards | 2022-11-03T16:16:29.000Z | null | false | c3d56624d2a86b80746264a6f1ecbd5fc87dff29 | [] | [] | https://huggingface.co/datasets/hansards/resolve/main/README.md | ---
paperswithcode_id: null
pretty_name: hansards
dataset_info:
- config_name: senate
features:
- name: fr
dtype: string
- name: en
dtype: string
splits:
- name: test
num_bytes: 5711686
num_examples: 25553
- name: train
num_bytes: 40324278
num_examples: 182135
download_size: 152473... | |
null | null | @incollection{elnagar2018hotel,
title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},
author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
booktitle={Intelligent Natural Language Processing: Trends and Applications},
pages={35--52},
year={2018},
publisher={Springe... | This dataset contains 93700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset. | false | 336 | false | hard | 2022-11-03T16:15:25.000Z | hard | false | 8c5082bbab6193f5b69d283275139ad6bcb70b93 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/hard/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hard
pretty_name: Hotel Arabic-Reviews D... |
null | null | @inproceedings{santos2006harem,
title={Harem: An advanced ner evaluation contest for portuguese},
author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceeding... | The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,
from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM
documents are the validation set and the mini... | false | 625 | false | harem | 2022-11-03T16:30:43.000Z | null | false | 76a3c88ad49a10797cc7f09bbb69a7628afe7577 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:pt",
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"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/harem/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: HAREM
dataset_inf... |
null | null | @misc{bhakthavatsalam2020dogs,
title={Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations},
author={Sumithra Bhakthavatsalam and Kyle Richardson and Niket Tandon and Peter Clark},
year={2020},
eprint={2006.07510},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms... | false | 332 | false | has_part | 2022-11-03T16:15:21.000Z | haspart-kb | false | 29650bca049f78caa438e72a95d65480ea556840 | [] | [
"arxiv:2006.07510",
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-Generics-KB",
"task_categories:text-classification",
"task_ids:text-scoring",
"t... | https://huggingface.co/datasets/has_part/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-Generics-KB
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: haspart-kb
pretty_name:... |
null | null | @article{article,
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
year = {2017},
month = {03},
pages = {},
title = {Automated Hate Speech Detection and the Problem of Offensive Language}
} | null | false | 406 | false | hate_offensive | 2022-11-03T16:16:18.000Z | hate-speech-and-offensive-language | false | 412b747835b60c97625cf8920ad89119e7e55aa0 | [] | [
"arxiv:1905.12516",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"ta... | https://huggingface.co/datasets/hate_offensive/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- mit
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- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hate-speech-and-offensiv... |
null | null | @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,
... | 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. | false | 6,724 | false | hate_speech18 | 2022-11-03T16:47:01.000Z | hate-speech | false | 7d0e6db95d7c23f30eafaf437bf940f3b8b37744 | [] | [
"annotations_creators:found",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/hate_speech18/resolve/main/README.md | ---
annotations_creators:
- found
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:
- intent-classification
paperswithcode_id: hate-speech
pretty_name: Hate Speech
tr... |
null | null | @article{Cabasag-2019-hate-speech,
title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.},
author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng},
journal={Philippine Computing... | Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections. | false | 341 | false | hate_speech_filipino | 2022-11-03T16:15:58.000Z | null | false | 96a893fc75c47e592f6c68d8a89e41b22fbd31e8 | [] | [
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:tl",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-twitter-data-philippine-election",
"task_categories:text-classification",
"task_ids:sentiment-ana... | https://huggingface.co/datasets/hate_speech_filipino/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-twitter-data-philippine-election
task_categories:
- text-classification
task_ids:
- sentiment-analysis
paperswi... |
null | null | @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... | An annotated dataset for hate speech and offensive language detection on tweets. | false | 6,512 | false | hate_speech_offensive | 2022-11-03T16:47:01.000Z | hate-speech-and-offensive-language | false | 699419222b39b63165b552fa3db27af8cfa76507 | [] | [
"arxiv:1703.04009",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"tags:hat... | https://huggingface.co/datasets/hate_speech_offensive/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
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task_categories:
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task_ids: []
paperswithcode_id: hate-speech-and-offensive-language
pret... |
null | null | null | HateSpeech corpus in the current version contains over 2000 posts crawled from public Polish web. They represent various types and degrees of offensive language, expressed toward minorities (eg. ethnical, racial). The data were annotated manually. | false | 332 | false | hate_speech_pl | 2022-11-03T16:15:27.000Z | null | false | ae975330419c551c244ea03dc5c9dd591577d999 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:pl",
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"size_categories:10K<n<100K",
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"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:multi-class-classificatio... | https://huggingface.co/datasets/hate_speech_pl/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
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- pl
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- cc-by-nc-sa-3.0
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- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- multi-class-classification
- multi-label-classifica... |
null | null | @inproceedings{fortuna-etal-2019-hierarchically,
title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
author = "Fortuna, Paula and
Rocha da Silva, Jo{\\~a}o and
Soler-Company, Juan and
Wanner, Leo and
Nunes, S{\'e}rgio",
booktitle = "Proceedings of the Third Workshop on Abusive Langu... | Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate'). | false | 332 | false | hate_speech_portuguese | 2022-11-03T16:15:15.000Z | null | false | cf110c4bf1bdbb46545237632f85de3da027f0fc | [] | [
"annotations_creators:expert-generated",
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"task_categories:text-classification",
"tags:hate-speech-detection"
] | https://huggingface.co/datasets/hate_speech_portuguese/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
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- unknown
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- monolingual
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- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: null
pretty_name: HateSpeechPortuguese
tags:
- hate-spee... |
null | null | @misc{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
year={2020},
eprint={2012.10289},
archivePrefix={arXiv},
pr... | Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of ... | false | 1,225 | false | hatexplain | 2022-11-03T16:31:33.000Z | hatexplain | false | b785b2d519b98d33f76e09149cad5afee74aaed6 | [] | [
"arxiv:2012.10289",
"arxiv:1703.04009",
"arxiv:1908.11049",
"arxiv:1812.01693",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:t... | https://huggingface.co/datasets/hatexplain/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
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- cc-by-4.0
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- 10K<n<100K
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- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hatexplain
pretty_name: hatexplain
tags:
- hate-s... |
null | null | @inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klak... | The Hausa VOA NER dataset is a labeled dataset for named entity recognition in Hausa. The texts were obtained from
Hausa Voice of America News articles https://www.voahausa.com/ . We concentrate on
four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].
The Hausa VOA... | false | 329 | false | hausa_voa_ner | 2022-11-03T16:08:07.000Z | null | false | 15b9e865e0ffbc31422a6960b27b419c7319bfdd | [] | [
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"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/hausa_voa_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ha
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- cc-by-4.0
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- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: ... |
null | null | @inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow... | A collection of news article headlines in Hausa from VOA Hausa.
Each headline is labeled with one of the following classes: Nigeria,
Africa, World, Health or Politics.
The dataset was presented in the paper:
Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
Distant Supervision for Multilingual Tran... | false | 333 | false | hausa_voa_topics | 2022-11-03T16:08:18.000Z | null | false | a60daceb5c0883ac327d56b8c83816877c5bdcf0 | [] | [
"annotations_creators:expert-generated",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/hausa_voa_topics/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ha
license:
- unknown
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- monolingual
size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: null
pretty_name: Hausa Voa News Top... |
null | null | @inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn an... | This dataset is a recasted version of the Hindi Discourse Analysis Dataset used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi. | false | 334 | false | hda_nli_hindi | 2022-11-03T16:15:25.000Z | null | false | f4ff490f872f235dcbf285094f984aec87fe4d95 | [] | [
"annotations_creators:machine-generated",
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"source_datasets:extended|hindi_discourse",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/hda_nli_hindi/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|hindi_discourse
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: null
pretty_nam... |
null | null | @inproceedings{vilares-gomez-rodriguez-2019-head,
title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
author = "Vilares, David and
G{\'o}mez-Rodr{\'i}guez, Carlos",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
... | HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
de Sanidad, Consumo y Bienestar Social.
The dataset contains questions about the fol... | false | 29,858 | false | head_qa | 2022-11-03T16:47:27.000Z | headqa | false | d258a8a6f7513dd840982d3a452c23d08a8c4ce0 | [] | [
"annotations_creators:no-annotation",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"configs:en",
"co... | https://huggingface.co/datasets/head_qa/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
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- es
license:
- mit
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- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: headqa
pretty_name: HEAD-QA
co... |
null | null | @inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing (EMNLP)",
month = nov,
year = "... | PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of
public health claims. Each instance in the PUBHEALTH dataset has an associated
veracity label (true, false, unproven, mixture). Furthermore each instance in the
dataset has an explanation text field. The explanation is a justification for w... | false | 3,418 | false | health_fact | 2022-11-03T16:32:41.000Z | pubhealth | false | 8017a385851bc480df367a3c59603df04f3a7edc | [] | [
"arxiv:2010.09926",
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"language:en",
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"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:multi-class-cl... | https://huggingface.co/datasets/health_fact/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
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- original
task_categories:
- text-classification
task_ids:
- fact-checking
- multi-class-classification
paperswithcode_id: pubhealth
pretty... |
null | null | @article{,
author = {},
title = {Public domain texts from Project Ben-Yehuda},
journal = {},
url = {https://github.com/projectbenyehuda/public_domain_dump},
year = {2020},
} | This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud). The metadata (pseudocatalogue.csv) file is a list of titles, authors, genres, and file paths, to help you process the dump.
All these works are in the p... | false | 329 | false | hebrew_projectbenyehuda | 2022-11-03T16:15:45.000Z | null | false | dbd611ebf182d1a197decc309936cd7d5e253666 | [] | [
"annotations_creators:expert-generated",
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"language:he",
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"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:maske... | https://huggingface.co/datasets/hebrew_projectbenyehuda/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- he
license:
- mit
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source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
p... |
null | null | @inproceedings{amram-etal-2018-representations,
title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew",
author = "Amram, Adam and
Ben David, Anat and
Tsarfaty, Reut",
booktitle = "Proceedings of the 27th ... | HebrewSentiment is a data set consists of 12,804 user comments to posts on the official Facebook page of Israel’s
president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder,
2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014,
th... | false | 500 | false | hebrew_sentiment | 2022-11-03T16:31:15.000Z | modern-hebrew-sentiment-dataset | false | 4b7a9011b52b67ede42792e697aee44575fe4c46 | [] | [
"annotations_creators:expert-generated",
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"language:he",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/hebrew_sentiment/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- he
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: modern-hebrew-sentiment-dataset
pr... |
null | null | null | HebrewThisWorld is a data set consists of 2028 issues of the newspaper 'This World' edited by Uri Avnery and were published between 1950 and 1989. Released under the AGPLv3 license. | false | 330 | false | hebrew_this_world | 2022-11-03T16:08:08.000Z | null | false | 81385c1382abc334d097e5050ac82b2d727caaf7 | [] | [
"annotations_creators:expert-generated",
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"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:ma... | https://huggingface.co/datasets/hebrew_this_world/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
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license:
- agpl-3.0
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size_categories:
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source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: nul... |
null | null | @inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={20... | HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. | false | 98,161 | false | hellaswag | 2022-11-03T16:47:45.000Z | hellaswag | false | a15f9f8ddcbb9858ac8e20b4ea6482570d8dbde2 | [] | [
"language:en"
] | https://huggingface.co/datasets/hellaswag/resolve/main/README.md | ---
language:
- en
paperswithcode_id: hellaswag
pretty_name: HellaSwag
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: ... |
null | null | @article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)}... | This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more. | false | 14,181 | false | hendrycks_test | 2022-11-03T16:47:14.000Z | null | false | dc9e50511b454c7679d43f04e9e014a83a8a872d | [] | [
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:en",
"language_bcp47:en-US",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:origin... | https://huggingface.co/datasets/hendrycks_test/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
pretty_name: HendrycksTest
d... |
null | null | @InProceedings{hindencorp05:lrec:2014,
author = {Ond{\v{r}}ej Bojar and Vojt{\v{e}}ch Diatka
and Pavel Rychl{\'{y}} and Pavel Stra{\v{n}}{\'{a}}k
and V{\'{}}t Suchomel and Ale{\v{s}} Tamchyna and Daniel Zeman},
title = "{HindEnCorp - Hindi-English and Hindi-only Corpus for Machine
... | HindEnCorp parallel texts (sentence-aligned) come from the following sources:
Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest ... | false | 331 | false | hind_encorp | 2022-11-03T16:15:40.000Z | hindencorp | false | eeae2e522f7ae6019f3c3335f13e4a5bfd5b7314 | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
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"license:cc-by-nc-sa-3.0",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/hind_encorp/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- hi
license:
- cc-by-nc-sa-3.0
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- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: hindencorp
pretty_name:... |
null | null | @inproceedings{swapnil2020,
title={An Annotated Dataset of Discourse Modes in Hindi Stories},
author={Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent},
booktitle={Proceedings of the 1... | The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
It contains sentences from stories written by 11 famous authors from the 20th Century.
4-5 stories by each author have been selected which were available in the public domain resulting
in a collection of 53 stories.... | false | 330 | false | hindi_discourse | 2022-11-03T16:15:15.000Z | null | false | 421d77042c315481fa3aad6488d20233f55fe928 | [] | [
"annotations_creators:other",
"language_creators:found",
"language:hi",
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"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification",
"tags:discourse-analysis"
] | https://huggingface.co/datasets/hindi_discourse/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- found
language:
- hi
license:
- other
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- monolingual
size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: null
pretty_name: Discourse Analysis datase... |
null | null | @inproceedings{sap-etal-2020-recollection,
title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
author = "Sap, Maarten and
Horvitz, Eric and
Choi, Yejin and
Smith, Noah A. and
Pennebaker, James",
booktitle = "Proceedings of t... | To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide thes... | false | 334 | false | hippocorpus | 2022-11-03T16:15:25.000Z | null | false | 0fceaffb57487c2b825971a95ac19abf4ad74269 | [] | [
"annotations_creators:expert-generated",
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"language:en",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"tags:narrative-flow"
] | https://huggingface.co/datasets/hippocorpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: hippocorpus
tags:... |
null | null | @article{luke2015hong,
author={Luke, Kang-Kwong and Wong, May LY},
title={The Hong Kong Cantonese corpus: design and uses},
journal={Journal of Chinese Linguistics},
year={2015},
pages={309-330},
month={12}
}
@misc{lee2020,
author = {Lee, Jackson},
title = {PyCantonese: Cantonese Linguistics and NLP in ... | The Hong Kong Cantonese Corpus (HKCanCor) comprise transcribed conversations
recorded between March 1997 and August 1998. It contains recordings of
spontaneous speech (51 texts) and radio programmes (42 texts),
which involve 2 to 4 speakers, with 1 text of monologue.
In total, the corpus contains around 230,000 Chines... | false | 336 | false | hkcancor | 2022-11-03T16:15:26.000Z | hong-kong-cantonese-corpus | false | 77b832bdfc26a02db1c43b5b6aaead45bc39497c | [] | [
"annotations_creators:expert-generated",
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"task_categories:translation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_i... | https://huggingface.co/datasets/hkcancor/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
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- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: hong-kong-ca... |
null | null | @inproceedings{Laban2021NewsHG,
title={News Headline Grouping as a Challenging NLU Task},
author={Philippe Laban and Lucas Bandarkar},
booktitle={NAACL 2021},
publisher = {Association for Computational Linguistics},
year={2021}
} | HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating
whether the two headlines describe the same underlying world event or not. | false | 3,047 | false | hlgd | 2022-11-03T16:32:25.000Z | null | false | b8c74b17b1d77ee57b4f168d93e974f4df023f82 | [] | [
"annotations_creators:crowdsourced",
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"language:en",
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"multilinguality:monolingual",
"source_datasets:original",
"task_categories:text-classification",
"size_categories:10K<n<100K",
"tags:headline-grouping"
] | https://huggingface.co/datasets/hlgd/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
source_datasets:
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task_categories:
- text-classification
task_ids: []
size_categories:
- 10K<n<100K
pretty_name: Headline Grouping (HLGD)
tags:
- headline-grouping... |
null | null | @inproceedings{chakravarthi-2020-hopeedi,
title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
author = "Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Socia... | A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. | false | 664 | false | hope_edi | 2022-11-03T16:31:06.000Z | hopeedi | false | 087f81e92c2c57ed591e53fc221a2e222f028d0a | [] | [
"annotations_creators:expert-generated",
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"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"tas... | https://huggingface.co/datasets/hope_edi/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
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license:
- cc-by-4.0
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size_categories:
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- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hopeedi
p... |
null | null | @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... | 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... | false | 2,520 | false | hotpot_qa | 2022-11-03T16:32:21.000Z | hotpotqa | false | 3355c5052f2a9c7cc8ed52a3fca60393ca6063db | [] | [
"arxiv:1809.09600",
"annotations_creators:crowdsourced",
"language:en",
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"source_datasets:original",
"task_categories:question-answering",
"tags:multi-hop"
] | https://huggingface.co/datasets/hotpot_qa/resolve/main/README.md | ---
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... |
null | null | @inproceedings{jiang2020hover,
title={{HoVer}: A Dataset for Many-Hop Fact Extraction And Claim Verification},
author={Yichen Jiang and Shikha Bordia and Zheng Zhong and Charles Dognin and Maneesh Singh and Mohit Bansal.},
booktitle={Findings of the Conference on Empirical Methods in Natural Language Processing (... | HoVer is an open-domain, many-hop fact extraction and claim verification dataset built upon the Wikipedia corpus. The original 2-hop claims are adapted from question-answer pairs from HotpotQA. It is collected by a team of NLP researchers at UNC Chapel Hill and Verisk Analytics. | false | 526 | false | hover | 2022-11-03T16:30:44.000Z | hover | false | eda20cb6c0555ed3374e42aebd6d0f77db23c440 | [] | [
"arxiv:2011.03088",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
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"language:en",
"license:cc-by-sa-4.0",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-retrieval",
"task_id... | https://huggingface.co/datasets/hover/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- found
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- en
license:
- cc-by-sa-4.0
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- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- fact-checking-retrieval
paperswithcode_id: hover
pretty... |
null | null | @misc{11356/1058,
title = {Croatian-English parallel corpus {hrenWaC} 2.0},
author = {Ljube{\v s}i{\'c}, Nikola and Espl{\'a}-Gomis, Miquel and Ortiz Rojas, Sergio and Klubi{\v c}ka, Filip and Toral, Antonio},
url = {http://hdl.handle.net/11356/1058},
note = {Slovenian language resource repository {CLARIN}.{SI}},
... | The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia.
The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The a... | false | 326 | false | hrenwac_para | 2022-11-03T16:07:49.000Z | null | false | b47abd9d1f683ff8e5db3df1fca90ebd906c184f | [] | [
"annotations_creators:no-annotation",
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"license:cc-by-sa-3.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/hrenwac_para/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- hr
license:
- cc-by-sa-3.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: HrenwacPara
dataset_info:
features:... |
null | null | @misc{11356/1064,
title = {Croatian web corpus {hrWaC} 2.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1064},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-... | The Croatian web corpus hrWaC was built by crawling the .hr top-level domain in 2011 and again in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the U... | false | 330 | false | hrwac | 2022-11-03T16:15:15.000Z | null | false | 555e03f2291780a71a3c6ff1f8fd64d3a11a2eac | [] | [
"annotations_creators:no-annotation",
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"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:m... | https://huggingface.co/datasets/hrwac/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- hr
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1B<n<10B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: nu... |
null | null | @article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
journal={arXiv preprint arXiv:1906.00274},
year={2019}
} | This new dataset is designed to assess the funniness of edited news headlines. | false | 1,535 | false | humicroedit | 2022-11-03T16:32:11.000Z | humicroedit | false | c1b6f3959e08ec4d49c1e15c4f0cfaf7afe07b00 | [] | [
"annotations_creators:crowdsourced",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",... | https://huggingface.co/datasets/humicroedit/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
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- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: humicroedit
pretty_n... |
null | null | @article{chen2020hybridqa,
title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
journal={Findings of EMNLP 2020},
year={2020}
} | Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present Hyb... | false | 387 | false | hybrid_qa | 2022-11-03T16:16:40.000Z | hybridqa | false | 2a67722f3075d6ecf346aa816b965418fa8b7358 | [] | [
"arxiv:1909.05358",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"tags:multihop-tabular-text-qa"
] | https://huggingface.co/datasets/hybrid_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: hybridqa
pretty_name: HybridQA
tags:
- multihop-tabu... |
null | null | @article{kiesel2019data,
title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},
author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},
year={2019}
} | Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
There are 2 parts:
- byarticle: Labeled t... | false | 1,341 | false | hyperpartisan_news_detection | 2022-11-03T16:32:01.000Z | null | false | 84ccd254dc8a899697184a21fa29fea08d3e2846 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language:en",
"language_creators:found",
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"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text-classification",
"tags:bias-classification"
] | https://huggingface.co/datasets/hyperpartisan_news_detection/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: HyperpartisanNewsDetection
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id:... |
null | null | @dataset{kobkrit_viriyayudhakorn_2021_4539916,
author = {Kobkrit Viriyayudhakorn and
Charin Polpanumas},
title = {iapp_wiki_qa_squad},
month = feb,
year = 2021,
publisher = {Zenodo},
version = 1,
doi = {10.5281/zenodo.4539916},
url ... | `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
5761/742/739 questions from 1529/191... | false | 417 | false | iapp_wiki_qa_squad | 2022-11-03T16:16:23.000Z | null | false | 5c428b78e6ecca9573db030549e53d0db0c5f1b5 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:th",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-iapp-wiki-qa-dataset",
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-... | https://huggingface.co/datasets/iapp_wiki_qa_squad/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-iapp-wiki-qa-dataset
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null... |
null | null | @inproceedings{id_clickbait,
author = {Andika William, Yunita Sari},
title = {CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines},
year = {2020},
url = {http://dx.doi.org/10.17632/k42j7x2kpn.1},
} | The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article... | false | 512 | false | id_clickbait | 2022-11-03T16:16:32.000Z | null | false | 2d41bd9691e8b2c01a8b30729e53ceb977d67a6f | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:id",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:fact-checking"
] | https://huggingface.co/datasets/id_clickbait/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: Indonesian... |
null | null | @inproceedings{id_liputan6,
author = {Fajri Koto, Jey Han Lau, Timothy Baldwin},
title = {Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
year = {2020},
url = {https://arxiv.org/abs/2011.00679},
} | In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multil... | false | 793 | false | id_liputan6 | 2022-11-03T16:31:21.000Z | null | false | 6679d568460ce6e89f1eb822a40a279a4e9df5ae | [] | [
"arxiv:2011.00679",
"annotations_creators:no-annotation",
"language_creators:found",
"language:id",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization",
"tags:extractive... | https://huggingface.co/datasets/id_liputan6/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Large-scale Indones... |
null | null | @inproceedings{id_nergrit_corpus,
author = {Gria Inovasi Teknologi},
title = {NERGRIT CORPUS},
year = {2019},
url = {https://github.com/grit-id/nergrit-corpus},
} | Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment
Analysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as
follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'G... | false | 718 | false | id_nergrit_corpus | 2022-11-03T16:31:17.000Z | nergrit-corpus | false | 7e9828cf0ab1bf82db924eba7d31127734c3daea | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:id",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/id_nergrit_corpus/resolve/main/README.md | ---
pretty_name: Nergrit Corpus
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithc... |
null | null | @inproceedings{id_newspapers_2018,
author = {},
title = {Indonesian Newspapers 2018},
year = {2019},
url = {https://github.com/feryandi/Dataset-Artikel},
} | The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo,
CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018
(with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.t... | false | 446 | false | id_newspapers_2018 | 2022-11-03T16:16:15.000Z | null | false | 5461ad850d416d6a2c22312b4ff5bde05ef19575 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:id",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:mas... | https://huggingface.co/datasets/id_newspapers_2018/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null... |
null | null | @inproceedings{id_panl_bppt,
author = {PAN Localization - BPPT},
title = {Parallel Text Corpora, English Indonesian},
year = {2009},
url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf},
} | Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing
Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topi... | false | 329 | false | id_panl_bppt | 2022-11-03T16:08:08.000Z | null | false | 354c79459938584bba7ba1edb60e490245d60d9f | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"language:id",
"license:unknown",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/id_panl_bppt/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- id
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: IdPanlBppt
dataset_info:
f... |
null | null | null | Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author. | false | 329 | false | id_puisi | 2022-11-03T16:08:09.000Z | null | false | 634e8616c1b484e65ca225897c1038ffe2b4e519 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:id",
"license:mit",
"multilinguality:monolingual",
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"source_datasets:original",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"tags:poem... | https://huggingface.co/datasets/id_puisi/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
task_ids: []
paperswithcode_id: null
pretty_name: Indonesian Pui... |
null | null | @misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://a... | Parallel Igbo-English Dataset | false | 334 | false | igbo_english_machine_translation | 2022-11-03T16:15:23.000Z | igbonlp-datasets | false | db3d187f0ccddc12a2f3ab0a805cc2f5e122e288 | [] | [
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"language:ig",
"license:unknown",
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"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/igbo_english_machine_translation/resolve/main/README.md | ---
annotations_creators:
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- found
language:
- en
- ig
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- unknown
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- multilingual
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- 10K<n<100K
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- original
task_categories:
- translation
task_ids: []
paperswithcode_id: igbonlp-datasets
pretty_name: IgboNLP Datasets
dataset_info:
feat... |
null | null | @misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | A dataset is a collection of Monolingual Igbo sentences. | false | 1,609 | false | igbo_monolingual | 2022-11-03T16:32:10.000Z | null | false | 20fd75546b704e2e00f8f2dc7fb54fdd7ab3d8c0 | [] | [
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"task_categories:text-generation",
"task_categories:fill-mask",
"task_id... | https://huggingface.co/datasets/igbo_monolingual/resolve/main/README.md | ---
annotations_creators:
- found
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- found
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- ig
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- unknown
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- original
task_categories:
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task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pre... |
null | null | @misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Igbo Named Entity Recognition Dataset | false | 488 | false | igbo_ner | 2022-11-03T16:16:30.000Z | null | false | c8513fa5dd0c00fc606214925fd23c147b275fdb | [] | [
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"multilinguality:monolingual",
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"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/igbo_ner/resolve/main/README.md | ---
annotations_creators:
- found
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- found
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- ig
license:
- unknown
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- monolingual
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- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: Igbo NER dataset
datas... |
null | null | null | This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family –
Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri, and Magahi. | false | 330 | false | ilist | 2022-11-03T16:08:10.000Z | null | false | a7c2bdeaecb52c47089c34dfb922475d61fda643 | [] | [
"task_categories:text-classification",
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"language:awa",
"language:bho",
"language:bra",
"language:hi",
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"language_creators:found",
"annotations_creators:no-annotation",
"source_datasets:original",
"size_categories:10K<n<100K",
"license:cc-by-4.0",
... | https://huggingface.co/datasets/ilist/resolve/main/README.md | ---
task_categories:
- text-classification
multilinguality:
- multilingual
task_ids: []
language:
- awa
- bho
- bra
- hi
- mag
language_creators:
- found
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- no-annotation
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- original
size_categories:
- 10K<n<100K
license:
- cc-by-4.0
paperswithcode_id: null
pretty_name: ilist
tags:
... |
null | null | @InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for... | Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\ | false | 179,992 | false | imdb | 2022-11-03T16:47:48.000Z | imdb-movie-reviews | false | 3be66bfb24e2346afdeed795cf363d31f404b6d9 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/imdb/resolve/main/README.md | ---
pretty_name: IMDB
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
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- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: imd... |
null | null | @InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly,nRaymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y...},
title = {Learning Word Vectors for Sentiment Analysis},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Associati... | Large Movie translated Urdu Reviews Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous
benchmark datasets. We provide a set of 40,000 highly polar movie reviews for training, and 10,000 for testing.
To increase the availability of sentiment analysis dataset f... | false | 326 | false | imdb_urdu_reviews | 2022-11-03T16:08:19.000Z | null | false | d37d186bddd1322ec4b401d208d819f80fff7dc7 | [] | [
"annotations_creators:found",
"language_creators:machine-generated",
"language:ur",
"license:odbl",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/imdb_urdu_reviews/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- ur
license:
- odbl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: ImDB Urdu Revi... |
null | null | @inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
booktitle = "Proceedings of the 58th Annual... | Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize se... | false | 2,706 | false | imppres | 2022-11-03T16:32:32.000Z | imppres | false | 73fb024013f3d8351df473dcbf0704f1edd5d51f | [] | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/imppres/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: imppres
pret... |
null | null | @inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pra... | IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. | false | 9,762 | false | indic_glue | 2022-11-03T16:47:05.000Z | null | false | 23668782041ab3ac86b8fd0f98cf026191858111 | [] | [
"annotations_creators:other",
"language:as",
"language:bn",
"language:en",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"language_creators:found",
"license:other",
"multilinguality:multilingual",
... | https://huggingface.co/datasets/indic_glue/resolve/main/README.md | ---
annotations_creators:
- other
language:
- as
- bn
- en
- gu
- hi
- kn
- ml
- mr
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- pa
- ta
- te
language_creators:
- found
license:
- other
multilinguality:
- multilingual
pretty_name: IndicGLUE
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
task_categories:
- text-classification
- token-classif... |
null | null | @inproceedings{mahendra-etal-2021-indonli,
title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natu... | IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.
It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various ... | false | 428 | false | indonli | 2022-11-03T16:15:45.000Z | indonli | false | 587b43614f79e2d9b09381e792e9bf727feb59e4 | [] | [
"annotations_creators:expert-generated",
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"language_creators:expert-generated",
"language:id",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:natur... | https://huggingface.co/datasets/indonli/resolve/main/README.md | ---
pretty_name: IndoNLI
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- id
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-infer... |
null | null | @inproceedings{wilie2020indonlu,
title = {{IndoNLU}: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
authors={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and... | The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. | false | 2,160 | false | indonlu | 2022-11-03T16:32:26.000Z | indonlu-benchmark | false | 83a21987f88331cbcfddc862c57e4b69ec54259d | [] | [
"arxiv:1809.03391",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:id",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:question-... | https://huggingface.co/datasets/indonlu/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
- text-classification
- token-classification
task_ids:
- close... |
null | null | @InProceedings{ko2020inquisitive,
author = {Ko, Wei-Jen and Chen, Te-Yuan and Huang, Yiyan and Durrett, Greg and Li, Junyi Jessy},
title = {Inquisitive Question Generation for High Level Text Comprehension},
booktitle = {Proceedings of EMNLP},
year = {2020},
} | A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. Because these questions are generated while the readers are ... | false | 327 | false | inquisitive_qg | 2022-11-03T16:15:24.000Z | inquisitive | false | c7655d3e1e440cfb1956c5cddc8f383bde2b5f69 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:question-generation"
] | https://huggingface.co/datasets/inquisitive_qg/resolve/main/README.md | ---
pretty_name: InquisitiveQg
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: inquisitive
tags:
- que... |
null | null | null | It is a Turkish news data set consisting of 273601 news in 17 categories, compiled from print media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. | false | 346 | false | interpress_news_category_tr | 2022-11-03T16:15:49.000Z | null | false | 1ce83f6d2e10a239815011d8a5fc648976db056e | [] | [
"annotations_creators:found",
"language_creators:found",
"language:tr",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"tags:news-category-classification"
] | https://huggingface.co/datasets/interpress_news_category_tr/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: null
pretty_name: Interpress Turkish News Category Dataset (270K)
... |
null | null | null | It is a Turkish news data set consisting of 273601 news in 10 categories, compiled from print media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. It has been rearranged as easily separable and with fewer classes. | false | 365 | false | interpress_news_category_tr_lite | 2022-11-03T16:16:11.000Z | null | false | d768a89249fd4958ca5ea27747138f5ee9b18c90 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:tr",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|interpress_news_category_tr",
"task_categories:text-classification",
"tags:news-category-classification"
] | https://huggingface.co/datasets/interpress_news_category_tr_lite/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|interpress_news_category_tr
task_categories:
- text-classification
task_ids: []
paperswithcode_id: null
pretty_name: Interpress Turkish N... |
null | null | @inproceedings{kummerfeld-etal-2019-large,
title = "A Large-Scale Corpus for Conversation Disentanglement",
author = "Kummerfeld, Jonathan K. and
Gouravajhala, Sai R. and
Peper, Joseph J. and
Athreya, Vignesh and
Gunasekara, Chulaka and
Ganhotra, Jatin and
Patel, Siva S... | Disentangling conversations mixed together in a single stream of messages is
a difficult task, made harder by the lack of large manually annotated
datasets. This new dataset of 77,563 messages manually annotated with
reply-structure graphs that both disentangle conversations and define
internal conversation structure. ... | false | 490 | false | irc_disentangle | 2022-11-03T16:16:33.000Z | irc-disentanglement | false | 82f5b2bd754425cfaeda9355e9c9d1860fcea90c | [] | [
"arxiv:1810.11118",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"tags:conversation-disentanglement"
] | https://huggingface.co/datasets/irc_disentangle/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: irc-disentanglement
pretty_name: IRC Disentanglemen... |
null | null | @inproceedings{isixhosa_ner_corpus,
author = {K. Podile and
Roald Eiselen},
title = {NCHLT isiXhosa Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluat... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 337 | false | isixhosa_ner_corpus | 2022-11-03T16:15:41.000Z | null | false | 2f21fc1d3f22cc177a830feaa7a8a0658898d486 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:xh",
"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/isixhosa_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- xh
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{isizulu_ner_corpus,
author = {A.N. Manzini and
Roald Eiselen},
title = {NCHLT isiZulu Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evalua... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 337 | false | isizulu_ner_corpus | 2022-11-03T16:15:41.000Z | null | false | 845ce330f0a96c9af3945b2715a3a1de216a1732 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:zu",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/isizulu_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- zu
license:
- other
license_details: Creative Commons Attribution 2.5 South Africa
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- n... |
null | null | @inproceedings{cettolo-etal-2017-overview,
title = "Overview of the {IWSLT} 2017 Evaluation Campaign",
author = {Cettolo, Mauro and
Federico, Marcello and
Bentivogli, Luisa and
Niehues, Jan and
St{\\"u}ker, Sebastian and
Sudoh, Katsuhito and
Yoshino, Koichiro and
... | The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. As unofficial task, conventional bilingual text translation is offered between English and Arabic, French, Japanese, Chinese... | false | 6,415 | false | iwslt2017 | 2022-10-28T16:35:28.000Z | iwslt-2017 | false | fc451e3790dc9caf5cbfac08a42346a7454d2c6f | [] | [
"annotations_creators:crowdsourced",
"language:ar",
"language:de",
"language:en",
"language:fr",
"language:it",
"language:ja",
"language:ko",
"language:nl",
"language:ro",
"language:zh",
"language_creators:expert-generated",
"license:cc-by-nc-nd-4.0",
"multilinguality:translation",
"size... | https://huggingface.co/datasets/iwslt2017/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- ar
- de
- en
- fr
- it
- ja
- ko
- nl
- ro
- zh
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: IWSLT 2017
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []... |
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