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null | null | @inproceedings{mitchell2015,
added-at = {2015-01-27T15:35:24.000+0100},
author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Plat... | This dataset provides version 1115 of the belief
extracted by CMU's Never Ending Language Learner (NELL) and version
1110 of the candidate belief extracted by NELL. See
http://rtw.ml.cmu.edu/rtw/overview. NELL is an open information
extraction system that attempts to read the Clueweb09 of 500 million
web pages (http:/... | false | 816 | false | nell | 2022-11-03T16:31:26.000Z | nell | false | 543d71527212e349696655027ec98a1b78cbea9f | [] | [
"annotations_creators:machine-generated",
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"language:en",
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"size_categories:10M<n<100M",
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"source_datasets:original",
"task_categories:text-retrieval",
"task_... | https://huggingface.co/datasets/nell/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
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size_categories:
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- 1M<n<10M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
- fact-checking-retriev... |
null | null | @InProceedings{huggingface:dataset,
title = {Neural Code Search Evaluation Dataset},
authors = {Hongyu Li, Seohyun Kim and Satish Chandra},
journal = {arXiv e-prints},
year = 2018,
eid = {arXiv:1908.09804 [cs.SE]},
pages = {arXiv:1908.09804 [cs.SE]},
archivePrefix = {arXiv... | Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs and a search corpus consisting of code snippets collected from the most popular Android repositories on GitHub. | false | 807 | false | neural_code_search | 2022-11-03T16:31:29.000Z | neural-code-search-evaluation-dataset | false | 6cecc166f956e49fdd8e7605175a16a0dfed9db3 | [] | [
"arxiv:1908.09804",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:ext... | https://huggingface.co/datasets/neural_code_search/resolve/main/README.md | ---
pretty_name: Neural Code Search
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
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size_categories:
- 1M<n<10M
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithco... |
null | null | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: http://www.casmacat.eu/corpus/news-commentary.html
12 languages, 63 bitexts
total number of files: 61,928
total number of tokens: 49.66M
total number of sentence fragments: 1.93M | false | 19,831 | false | news_commentary | 2022-11-03T16:47:41.000Z | null | false | 4a29d7fd5e025b55e9f32ff9e36a1db1640f5565 | [] | [
"annotations_creators:found",
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"language:cs",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:ja",
"language:nl",
"language:pt",
"language:ru",
"language:zh",
"license:unknown",
"multilinguality:multilingual"... | https://huggingface.co/datasets/news_commentary/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- cs
- de
- en
- es
- fr
- it
- ja
- nl
- pt
- ru
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name:... |
null | null | @inproceedings{Lang95,
author = {Ken Lang},
title = {Newsweeder: Learning to filter netnews}
year = {1995}
booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}
pages = {331-339}
} | The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across
20 different newsgroups. The 20 newsgroups collection has become a popular data set for experiments in text applications of
machine learning techniques, such as text classification and text cluster... | false | 15,638 | false | newsgroup | 2022-11-03T16:47:15.000Z | 20-newsgroups | false | 34e8356b46daf7a09434b13610cdc33f0f071577 | [] | [
"annotations_creators:found",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/newsgroup/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 20 Newsgroups
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: 20-newsgroup... |
null | null | @article{cruz2020investigating,
title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
journal={arXiv preprint arXiv:2010.1... | Large-scale dataset of Filipino news articles. Sourced for the NewsPH-NLI Project (Cruz et al., 2020). | false | 335 | false | newsph | 2022-11-03T16:07:51.000Z | newsph-nli | false | 6455ee930cebb321a3b5a307708f09a55a2663f2 | [] | [
"arxiv:2010.11574",
"annotations_creators:no-annotation",
"language_creators:found",
"language:fil",
"language:tl",
"license:gpl-3.0",
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"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_id... | https://huggingface.co/datasets/newsph/resolve/main/README.md | ---
annotations_creators:
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language_creators:
- found
language:
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license:
- gpl-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: n... |
null | null | @article{cruz2020investigating,
title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
journal={arXiv preprint arXiv:... | First benchmark dataset for sentence entailment in the low-resource Filipino language.
Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs,
in 70-15-15 split for training, validation, and testing. | false | 318 | false | newsph_nli | 2022-11-03T16:07:47.000Z | newsph-nli | false | 05d606e134d178aaeae070a313c26662f7ddfb23 | [] | [
"arxiv:2010.11574",
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"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/newsph_nli/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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language:
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license:
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source_datasets:
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task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: newsph-nli
pretty_name: News... |
null | null | @article{Moniz2018MultiSourceSF,
title={Multi-Source Social Feedback of Online News Feeds},
author={N. Moniz and L. Torgo},
journal={ArXiv},
year={2018},
volume={abs/1801.07055}
} | This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn.
The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and pale... | false | 1,048 | false | newspop | 2022-11-03T16:31:06.000Z | null | false | 9b735505b3eba4b67aa8c9d0c370e8e7211c822d | [] | [
"arxiv:1801.07055",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"tags:social-media-shar... | https://huggingface.co/datasets/newspop/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
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size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: News Popularity in Multipl... |
null | null | @inproceedings{trischler2017newsqa,
title={NewsQA: A Machine Comprehension Dataset},
author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},
booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},
pages=... | NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. | false | 684 | false | newsqa | 2022-11-03T16:31:08.000Z | newsqa | false | badf790bd40e61c348db1c80c035f2aaad76be5b | [] | [
"annotations_creators:crowdsourced",
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"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"configs:comb... | https://huggingface.co/datasets/newsqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
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license:
- mit
multilinguality:
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source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: newsqa
pretty_name: NewsQA
conf... |
null | null | @inproceedings{N18-1065,
author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
title = {NEWSROOM: A Dataset of 1.3 Million Summaries
with Diverse Extractive Strategies},
booktitle = {Proceedings of the 2018 Conference of the
North American Chapter of the Association for
... | NEWSROOM is a large dataset for training and evaluating summarization systems.
It contains 1.3 million articles and summaries written by authors and
editors in the newsrooms of 38 major publications.
Dataset features includes:
- text: Input news text.
- summary: Summary for the news.
And additional features:
- t... | false | 374 | false | newsroom | 2022-11-03T16:16:05.000Z | newsroom | false | 40f556ae3f2f1bd68151acd186442e734fea7bbf | [] | [
"annotations_creators:expert-generated",
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"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/newsroom/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: CORNELL NEWSROOM
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_i... |
null | null | @book{przepiorkowski2012narodowy,
title={Narodowy korpus jezyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
} | The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity. | false | 323 | false | nkjp-ner | 2022-11-03T16:07:52.000Z | null | false | 079ca7fb499d078200bc1cb49e584be0d3bf9339 | [] | [
"annotations_creators:expert-generated",
"language_creators:other",
"language:pl",
"license:gpl-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/nkjp-ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: NJKP NER
da... |
null | null | \
@inproceedings{budur-etal-2020-data,
title = "Data and Representation for Turkish Natural Language Inference",
author = "Budur, Emrah and
\"{O}zçelik, Rıza and
G\"{u}ng\"{o}r, Tunga",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMN... | \
The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate. | false | 534 | false | nli_tr | 2022-11-03T16:16:39.000Z | nli-tr | false | 03b6eff79cf6b364c74fae0a08ec490b0159024b | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:tr",
"license:cc-by-3.0",
"license:cc-by-4.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|snli",
"source_... | https://huggingface.co/datasets/nli_tr/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- tr
license:
- cc-by-3.0
- cc-by-4.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
- ex... |
null | null | @InProceedings{XLiu.etal:IWSDS2019,
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technolo... | Raw part of NLU Evaluation Data. It contains 25 715 non-empty examples (original dataset has 25716 examples) from 68 unique intents belonging to 18 scenarios. | false | 579 | false | nlu_evaluation_data | 2022-11-03T16:31:11.000Z | null | false | 8a38fd77a6e5c076d9dcd8c496eb72fba8eaafd0 | [] | [
"arxiv:1903.05566",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification",
... | https://huggingface.co/datasets/nlu_evaluation_data/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
paperswith... |
null | null | @InProceedings{VelOvrBer18,
author = {Erik Velldal and Lilja Ovrelid and
Eivind Alexander Bergem and Cathrine Stadsnes and
Samia Touileb and Fredrik Jorgensen},
title = {{NoReC}: The {N}orwegian {R}eview {C}orpus},
booktitle = {Proceedings of the 11th edition of the
Language... | NoReC was created as part of the SANT project (Sentiment Analysis for Norwegian Text), a collaboration between the Language Technology Group (LTG) at the Department of Informatics at the University of Oslo, the Norwegian Broadcasting Corporation (NRK), Schibsted Media Group and Aller Media. This first release of the co... | false | 170 | false | norec | 2022-11-03T16:07:43.000Z | norec | false | 070fd41fe502b164ebfd6bd11951b7a156b388c2 | [] | [
"annotations_creators:expert-generated",
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"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognit... | https://huggingface.co/datasets/norec/resolve/main/README.md | ---
pretty_name: NoReC
annotations_creators:
- expert-generated
language_creators:
- found
language:
- nb
- nn
- 'no'
license:
- cc-by-nc-4.0
multilinguality:
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size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcod... |
null | null | @inproceedings{johansen2019ner,
title={NorNE: Annotating Named Entities for Norwegian},
author={Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg,
Lilja Øvrelid, and Erik Velldal},
booktitle={LREC 2020},
year={2020},
url={https://arxiv.org/abs/1911.12146}
} | NorNE is a manually annotated
corpus of named entities which extends the annotation of the existing
Norwegian Dependency Treebank. Comprising both of the official standards of
written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000
tokens and annotates a rich set of entity types including persons,
or... | false | 644 | false | norne | 2022-11-03T16:16:26.000Z | null | false | ce386f3c9e6a76c29f06a6703999fff2c2812c24 | [] | [
"arxiv:1911.12146",
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"multilinguality:monolingual",
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"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/norne/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- 'no'
license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: 'Nor... |
null | null | @inproceedings{johansen2019ner,
title={Named-Entity Recognition for Norwegian},
author={Johansen, Bjarte},
booktitle={Proceedings of the 22nd Nordic Conference on Computational Linguistics, NoDaLiDa},
year={2019}
} | Named entities Recognition dataset for Norwegian. It is
a version of the Universal Dependency (UD) Treebank for both Bokmål and Nynorsk (UDN) where
all proper nouns have been tagged with their type according to the NER tagging scheme. UDN is a converted
version of the Norwegian Dependency Treebank into the UD scheme. | false | 165 | false | norwegian_ner | 2022-11-03T16:07:43.000Z | null | false | c63dbaf31a969f14e3ea3d076f2894c79bd4eb34 | [] | [
"annotations_creators:expert-generated",
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"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/norwegian_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- 'no'
license:
- unknown
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- token-classification
task_ids:
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paperswithcode_id: null
pretty_name: No... |
null | null | @article{doi:10.1162/tacl_a_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, ... | The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia. | false | 1,716 | false | nq_open | 2022-11-03T16:32:11.000Z | null | false | d678e706856531a9f7ecf5861a54b9726ff8d837 | [] | [
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"source_datasets:extended|natural_questions",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/nq_open/resolve/main/README.md | ---
annotations_creators:
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language_creators:
- other
language:
- en
license:
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multilinguality:
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pretty_name: NQ-Open
size_categories:
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task_ids:
- open-domain-qa
paperswithcode_i... |
null | null | @InProceedings{Park:2016,
title = "Naver Sentiment Movie Corpus",
author = "Lucy Park",
year = "2016",
howpublished = {\\url{https://github.com/e9t/nsmc}}
} | This is a movie review dataset in the Korean language. Reviews were scraped from Naver movies. The dataset construction is based on the method noted in Large movie review dataset from Maas et al., 2011. | false | 2,140 | false | nsmc | 2022-11-03T16:32:14.000Z | nsmc | false | c61e21c88321f59d31ad36f9fefdf09b32642613 | [] | [
"annotations_creators:crowdsourced",
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/nsmc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
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- ko
license:
- cc-by-2.0
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source_datasets:
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task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: nsmc
pretty_name: Naver Sentiment... |
null | null | @inproceedings{lin2020numersense,
title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren},
booktitle={Proceedings of EMNLP},
year={2020},
note={to appear}
} | NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes.
We propose to study whether numerical commonsense knowledge can be induced from pre-trained language models like BERT, and to what extent this access to knowledge robust agains... | false | 834 | false | numer_sense | 2022-11-03T16:31:30.000Z | numersense | false | 2b75aac5511f49c54dbcca7386cf381e39b634d5 | [] | [
"arxiv:2005.00683",
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"task_categories:fill-mask",
"task_ids:sl... | https://huggingface.co/datasets/numer_sense/resolve/main/README.md | ---
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task_categories:
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- fill-mask
task_ids:
- slot-filling
paperswithcode_id: numersense
pretty_name: N... |
null | null | @article{elazar_head,
author = {Elazar, Yanai and Goldberg, Yoav},
title = {Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {519-... | Fused Head constructions are noun phrases in which the head noun is missing and is said to be "fused" with its dependent modifier. This missing information is implicit and is important for sentence understanding.The missing heads are easily filled in by humans, but pose a challenge for computational models.
For examp... | false | 483 | false | numeric_fused_head | 2022-11-03T16:16:35.000Z | numeric-fused-head | false | ac59013c2b2efc1f0dd15909cab973c6d5002836 | [] | [
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task_categories:
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task_ids: []
paperswithcode_id: numeric-fuse... |
null | null | @misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }
@InProceedings{AlOmari2019oclar,
title = {Sentiment Classifier: Lo... | The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato
website (https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops,
etc.The corpus finally contains 3916 reviews in 5-rating scale. For this research ... | false | 331 | false | oclar | 2022-11-03T16:15:26.000Z | null | false | f5fdbc046fe78ba24b9fcbf479393c7c8fcf07e1 | [] | [
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"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-classification",
"ta... | https://huggingface.co/datasets/oclar/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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language:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-classification
- sentiment-scoring
paperswithcod... |
null | null | @article{Pelle2017,
title={Offensive Comments in the Brazilian Web: a dataset and baseline results},
author={Rogers P. de Pelle and Viviane P. Moreira},
booktitle={6th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)},
year={2017},
} | OffComBR: an annotated dataset containing for hate speech detection in Portuguese composed of news comments on the Brazilian Web. | false | 482 | false | offcombr | 2022-11-03T16:16:32.000Z | offcombr | false | 34c21f4a6990dcf1270164929db43e55f10ac7f2 | [] | [
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"task_categories:text-classification",
"tags:hate-speech-detection"
] | https://huggingface.co/datasets/offcombr/resolve/main/README.md | ---
annotations_creators:
- expert-generated
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language:
- pt
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- unknown
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task_categories:
- text-classification
task_ids: []
paperswithcode_id: offcombr
pretty_name: Offensive Comments in the Brazilia... |
null | null | @InProceedings{coltekin2020lrec,
author = {Cagri Coltekin},
year = {2020},
title = {A Corpus of Turkish Offensive Language on Social Media},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
pages = {6174--6184},
address = {Marseille, France},
url = {https://www.aclweb.or... | OffensEval-TR 2020 is a Turkish offensive language corpus. The corpus consist of randomly sampled tweets and annotated in a similar way to OffensEval and GermEval. | false | 936 | false | offenseval2020_tr | 2022-11-03T16:31:35.000Z | null | false | 830e6e9857bd68d1698a6c23fad5ec52cc7e6dce | [] | [
"annotations_creators:found",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"tags:offensive-language-classification"
] | https://huggingface.co/datasets/offenseval2020_tr/resolve/main/README.md | ---
annotations_creators:
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- tr
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- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: null
pretty_name: OffensEval-TR 2020
tags:
- offensive-language... |
null | null | @inproceedings{dravidianoffensive-eacl,
title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada},
author={Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Jose, Navya and
M, Anand Kumar and
Mandl, Thomas and
Kumaresan, Prasanna Kumar and
Ponnsamy, Rah... | Offensive language identification in dravidian lanaguages dataset. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. | false | 643 | false | offenseval_dravidian | 2022-11-03T16:30:59.000Z | null | false | f2e66a62f3a02ea3d5fa3dfd7204f4211199e08d | [] | [
"annotations_creators:expert-generated",
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"source_datasets:original",
"task_categories:tex... | https://huggingface.co/datasets/offenseval_dravidian/resolve/main/README.md | ---
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task_ids: []
paperswithcode_id: null
pretty_name: ... |
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},
... | Texts from the Ofis Publik ar Brezhoneg (Breton Language Board) provided by Francis Tyers
2 languages, total number of files: 278
total number of tokens: 2.12M
total number of sentence fragments: 0.13M | false | 325 | false | ofis_publik | 2022-11-03T16:15:15.000Z | null | false | 690dd439c8fdc5d0939f3f6db6ebfcf5e577f455 | [] | [
"annotations_creators:found",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/ofis_publik/resolve/main/README.md | ---
annotations_creators:
- found
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- br
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- unknown
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size_categories:
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- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OfisPublik
dataset_info:
features:
- name: id... |
null | null | @InProceedings{10.1007/978-1-4471-2099-5_20,
author="Hersh, William
and Buckley, Chris
and Leone, T. J.
and Hickam, David",
editor="Croft, Bruce W.
and van Rijsbergen, C. J.",
title="OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research",
booktitle="SIGIR '94",
year="1994",
publisher="... | The OHSUMED test collection is a set of 348,566 references from
MEDLINE, the on-line medical information database, consisting of
titles and/or abstracts from 270 medical journals over a five-year
period (1987-1991). The available fields are title, abstract, MeSH
indexing terms, author, source, and publication type. | false | 372 | false | ohsumed | 2022-11-03T16:16:03.000Z | null | false | ec47bb8ec0772b2aad31aa1fdb24a7f1086ff103 | [] | [
"annotations_creators:expert-generated",
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification"
] | https://huggingface.co/datasets/ohsumed/resolve/main/README.md | ---
pretty_name: Ohsumed
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
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size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_... |
null | null | @inproceedings{ollie-emnlp12,
author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},
title = {Open Language Learning for Information Extraction},
booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Lea... | The Ollie dataset includes two configs for the data
used to train the Ollie informatation extraction algorithm, for 18M
sentences and 3M sentences respectively.
This data is for academic use only. From the authors:
Ollie is a program that automatically identifies and extracts binary
relationships from English sentenc... | false | 481 | false | ollie | 2022-11-03T16:16:30.000Z | null | false | 203df07e6eddfe600b91d0432379ffe0340ce123 | [] | [
"annotations_creators:machine-generated",
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"source_datasets:original",
"configs:ollie_lemmagrep",
"configs:ollie_patterned",
"tags:relation-ex... | https://huggingface.co/datasets/ollie/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
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license:
- other
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task_categories: []
task_ids: []
pretty_name: Ollie
configs:
- ollie_lemmagrep
- ollie_patterned
tags:
- rel... |
null | null | @InProceedings{Schabus2017,
Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp},
Title = {One Million Posts: A Data Set of German Online Discussions},
Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},
Pages ... | The “One Million Posts” corpus is an annotated data set consisting of
user comments posted to an Austrian newspaper website (in German language).
DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website,
there is a discussion section below each news article where readers engage in
online disc... | false | 641 | false | omp | 2022-11-03T16:31:04.000Z | one-million-posts-corpus | false | e7e37fef0449009178bec22f6ea43694ac70d13f | [] | [
"annotations_creators:expert-generated",
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/omp/resolve/main/README.md | ---
pretty_name: One Million Posts
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- de
license:
- cc-by-nc-sa-4.0
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- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pap... |
null | null | @inproceedings{vajjala-lucic-2018-onestopenglish,
title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification},
author = {Sowmya Vajjala and Ivana Lučić},
booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Appli... | This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file.
Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance).
This dataset demonstrates its usefulness for through two applica-tions - automatic readabili... | false | 3,408 | false | onestop_english | 2022-11-03T16:32:38.000Z | onestopenglish | false | 9e7ff83811e0a9de2e40a565bae845b1b8f909ec | [] | [
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"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
... | https://huggingface.co/datasets/onestop_english/resolve/main/README.md | ---
annotations_creators:
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- found
language:
- en
license:
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source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- multi-class-classification
- text-simplification
paperswithcode... |
null | null | @inproceedings{starc2020,
author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},
title = {STARC: Structured Annotations for Reading Comprehension},
booktitle = {ACL},
year = {2020},
publisher = {Association for Computational Linguistics}
} | OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in thr... | false | 389 | false | onestop_qa | 2022-11-03T16:15:42.000Z | onestopqa | false | 3f9a4fb7718a02e3f30b7f75d1fb5441669ea842 | [] | [
"arxiv:2004.14797",
"annotations_creators:expert-generated",
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"language:en",
"language_bcp47:en-US",
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"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|onestop_english",
"ta... | https://huggingface.co/datasets/onestop_qa/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
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language_bcp47:
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license:
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multilinguality:
- monolingual
paperswithcode_id: onestopqa
pretty_name: OneStopQA
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|onestop_english
task_cat... |
null | null | P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | This is a new collection of translated movie subtitles from http://www.opensubtitles.org/.
IMPORTANT: If you use the OpenSubtitle corpus: Please, add a link to http://www.opensubtitles.org/ to your website and to your reports and publications produced with the data!
This is a slightly cleaner version of the subtitle ... | false | 1,194 | false | open_subtitles | 2022-11-03T16:31:50.000Z | opensubtitles | false | 050393bbd8dda3935ed0e58150e8cacad684aab9 | [] | [
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"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
... | https://huggingface.co/datasets/open_subtitles/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
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- eo
- es
- et
- eu
- fa
- fi
- fr
- gl
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- ko
- lt
- lv
- mk
- ml
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- si
- sk
- sl
- sq
- sr
- sv
- ... |
null | null | @misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gr... | The HumanEval dataset released by OpenAI contains 164 handcrafted programming challenges together with unittests to very the viability of a proposed solution. | false | 2,935 | false | openai_humaneval | 2022-11-03T16:32:28.000Z | null | false | dc590dd58d5e4fc8b9768511c2348a6d9d708909 | [] | [
"arxiv:2107.03374",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:code-generation"
] | https://huggingface.co/datasets/openai_humaneval/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: OpenAI HumanEval
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:
- code-generation
dataset_info:
f... |
null | null | @inproceedings{OpenBookQA2018,
title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
booktitle={EMNLP},
year={2018}
} | OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic
(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In
particular, it contains questions that require multi-step reasoning, use of additio... | false | 20,129 | false | openbookqa | 2022-11-03T16:47:34.000Z | openbookqa | false | 076385e41f81bdedd1307af4a631389e9fa4c519 | [] | [
"annotations_creators:crowdsourced",
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"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-q... | https://huggingface.co/datasets/openbookqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: OpenBookQA
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithco... |
null | null | SLR32:
@inproceedings{van-niekerk-etal-2017,
title = {{Rapid development of TTS corpora for four South African languages}},
author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson
and Martin Jansche and Linne Ha},
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null | null | @misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
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null | null | @inproceedings{ganesan2010opinosis,
title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
pages={340--348},
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da... |
null | null | @misc{zhang2020improving,
title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation},
author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},
year={2020},
eprint={2004.11867},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side.
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null | null | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php
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... | https://huggingface.co/datasets/opus_books/resolve/main/README.md | ---
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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},
... | A collection of translation memories provided by the JRC. Source: https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory
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total number of sentence fragments: 113.52M | false | 1,707 | false | opus_dgt | 2022-11-03T16:32:11.000Z | null | false | 22e89ef38b24eab44c7d5615da6a92244e2d7650 | [] | [
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null | null | @inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Tur... | This is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya. | false | 316 | false | opus_dogc | 2022-11-03T16:07:43.000Z | null | false | ee0b45b78d53aa646ad6430d36a9d0a742de79f1 | [] | [
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null | null | @InProceedings{opus:Elhuyar,
title = {Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)},
authors={J. Tiedemann},
year={2012}
} | Dataset provided by the foundation Elhuyar, which is having data in languages Spanish to Basque. | false | 317 | false | opus_elhuyar | 2022-11-03T16:07:47.000Z | null | false | 2b1d5ab9ab358707d078cfaefec0b4df29d39aa5 | [] | [
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null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | A parallel corpus collected from the European Constitution for 21 language. | false | 33,890 | false | opus_euconst | 2022-11-03T16:47:26.000Z | null | false | 8628eed6033702703a32a86d70e5000b21bf29bc | [] | [
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null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files. | false | 325 | false | opus_finlex | 2022-11-03T16:08:11.000Z | null | false | 32548153e33a8772dba9918b412fc22b0bb5a4a7 | [] | [
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null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | fiskmo, a massive parallel corpus for Finnish and Swedish. | false | 322 | false | opus_fiskmo | 2022-11-03T16:08:01.000Z | null | false | 8967f7ae63f063d123429e68b09f83edb545a8ce | [] | [
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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},
... | A parallel corpus of GNOME localization files. Source: https://l10n.gnome.org
187 languages, 12,822 bitexts
total number of files: 113,344
total number of tokens: 267.27M
total number of sentence fragments: 58.12M | false | 1,746 | false | opus_gnome | 2022-11-03T16:32:14.000Z | null | false | 6f0e7e05298acd163d7dd101bf53170475e03bab | [] | [
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null | null | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | A parallel corpus of 12 languages, 66 bitexts. | false | 10,592 | false | opus_infopankki | 2022-11-03T16:47:07.000Z | null | false | 9942d0e5d88cbdd08536a09c0c4f4af03b707b89 | [] | [
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pretty_name:... |
null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation between ixiXhosa and English, with a focus on the medical domain. | false | 325 | false | opus_memat | 2022-11-03T16:08:11.000Z | null | false | f330ca446da7dddfa07c8bab6aaf45f87bb79af9 | [] | [
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null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | Opus MontenegrinSubs dataset for machine translation task, for language pair en-me: english and montenegrin | false | 324 | false | opus_montenegrinsubs | 2022-11-03T16:08:11.000Z | null | false | 6348068fac1fafbbe14ba6456c079c48d15ad483 | [] | [
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null | null | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | A collection of documents from http://www.openoffice.org/. | false | 4,580 | false | opus_openoffice | 2022-11-03T16:46:49.000Z | null | false | 4fb66be1832d002780e01e24e14f8118b73866dd | [] | [
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... |
null | null | null | Parallel corpora from Web Crawls collected in the ParaCrawl project.
42 languages, 43 bitexts
total number of files: 59,996
total number of tokens: 56.11G
total number of sentence fragments: 3.13G | false | 1,780 | false | opus_paracrawl | 2022-11-03T16:32:15.000Z | null | false | 88bff84a2e481006a0a0eaaf11f607cc3f24f693 | [] | [
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- uk
- zh
license:
- cc0-1.0
multilinguality:
- multil... |
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},
... | RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations. | false | 1,764 | false | opus_rf | 2022-11-03T16:32:14.000Z | null | false | 04d609087fc21da2ae7132729a607c21a91b6c99 | [] | [
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"task_categories:translation",
"configs:de-en"... | https://huggingface.co/datasets/opus_rf/resolve/main/README.md | ---
annotations_creators:
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task_categories:
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task_ids: []
paperswithcode_id: null
pretty_name: OpusRf
configs:
- de-en
- de-... |
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},
... | This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license.
2 languages, total number of files: 2
total number of tokens: 2.81M
t... | false | 324 | false | opus_tedtalks | 2022-11-03T16:15:24.000Z | null | false | d12ce9db2419cd92455d0178af75e55313a01ee0 | [] | [
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"task_categories:translation"
] | https://huggingface.co/datasets/opus_tedtalks/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
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- hr
license:
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multilinguality:
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: null
pretty_name: OpusTedtalks
dataset_info:
features:
- name: ... |
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},
... | A parallel corpus of Ubuntu localization files. Source: https://translations.launchpad.net
244 languages, 23,988 bitexts
total number of files: 30,959
total number of tokens: 29.84M
total number of sentence fragments: 7.73M | false | 1,764 | false | opus_ubuntu | 2022-11-03T16:32:16.000Z | null | false | 35ac7b245d279ee25bf98ccff8c8e5a229b35d33 | [] | [
"annotations_creators:crowdsourced",
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"language:ang",
"language:ar",
"language:ary",
"language:as",
"language:ast",
"language:az",
"language:ba",... | https://huggingface.co/datasets/opus_ubuntu/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
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language_creators:
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- co
- crh
- cs
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- cy
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- dsb
- dv
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-... |
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},
... | This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Please cite the following publication if you use the data: Krzysztof Wołk and Krzysztof Marasek: Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs., Procedia Technology, 18,... | false | 978 | false | opus_wikipedia | 2022-11-03T16:31:44.000Z | null | false | 6f577257ac63be10c5f9304fa984f57f39d61dd8 | [] | [
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"language:he",
"language:hu",
"language:it",
"language:nl",
"language:pl",
"language:pt",
... | https://huggingface.co/datasets/opus_wikipedia/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
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- en
- es
- fa
- fr
- he
- hu
- it
- nl
- pl
- pt
- ro
- ru
- sl
- tr
- vi
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translati... |
null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | This dataset is designed for machine translation from English to Xhosa. | false | 322 | false | opus_xhosanavy | 2022-11-03T16:08:13.000Z | null | false | 70d285ab81b0e0cd87af8d98240ee0fe246180a9 | [] | [
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"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/opus_xhosanavy/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusXhosanavy
dataset_info:
features:
- name: ... |
null | null | @article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis},
journal={arXiv preprint arXiv:2010.12321},
year={2020}
} | The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to ... | false | 600 | false | orange_sum | 2022-11-03T16:30:49.000Z | orangesum | false | 3a260ca7faf0007226b013a4b78380e1abd25a8b | [] | [
"arxiv:2010.12321",
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"language:fr",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-headline-generation",
"task_ids:news-ar... | https://huggingface.co/datasets/orange_sum/resolve/main/README.md | ---
pretty_name: OrangeSum
annotations_creators:
- found
language_creators:
- found
language:
- fr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-headline-generation
- news-articles-summarization
pape... |
null | null | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Associat... | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | false | 69,233 | false | oscar | 2022-11-03T16:47:37.000Z | oscar | false | acfb8f4e095fc702af9cc53edd5a6ebae2192df5 | [] | [
"arxiv:2010.14571",
"annotations_creators:no-annotation",
"language_creators:found",
"language:af",
"language:als",
"language:am",
"language:an",
"language:ar",
"language:arz",
"language:as",
"language:ast",
"language:av",
"language:az",
"language:azb",
"language:ba",
"language:bar",
... | https://huggingface.co/datasets/oscar/resolve/main/README.md | ---
pretty_name: OSCAR
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
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- ast
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- ba
- bar
- bcl
- be
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- bpy
- br
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- bxr
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- cbk
- ce
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- cs
- cv
- cy
- da
- de
- diq
- dsb
- dv
- el
- eml
- en
- eo
- es
- e... |
null | null | @misc {paracrawl,
title = {ParaCrawl},
year = {2018},
url = {http://paracrawl.eu/download.html.}
} | null | false | 3,812 | false | para_crawl | 2022-11-03T16:46:44.000Z | paracrawl | false | 80173d568f229fbc4e8ddaa77400571d57bf24b8 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language... | https://huggingface.co/datasets/para_crawl/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc0-1.0
multilinguality:
- translation
pretty_name: ParaCrawl
size_categories:
- 10M<n<100M
source_datasets:
-... |
null | null | @inproceedings{soares-etal-2020-parapat,
title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts",
author = "Soares, Felipe and
Stevenson, Mark and
Bartolome, Diego and
Zaretskaya, Anna",
booktitle = "Proceedings of The 12th Language Resources and Evaluati... | ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algor... | false | 3,336 | false | para_pat | 2022-11-03T16:32:43.000Z | parapat | false | 9d7bbc1283e737f2b7db5ccd482d6ecac7b202d5 | [] | [
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language:cs",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hu",
"language:ja",
"language:ko",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:u... | https://huggingface.co/datasets/para_pat/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- cs
- de
- el
- en
- es
- fr
- hu
- ja
- ko
- pt
- ro
- ru
- sk
- uk
- zh
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill... |
null | null | @article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian,... | A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. | false | 324 | false | parsinlu_reading_comprehension | 2022-11-03T16:15:15.000Z | null | false | 2f9519fe5087392f62054489702cb932988dba26 | [] | [
"arxiv:2012.06154",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:fa",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia|google",
"task_categories:question-answering",
"task_ids:extra... | https://huggingface.co/datasets/parsinlu_reading_comprehension/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia|google
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pre... |
null | null | @Article{asano21pass,
author = "Yuki M. Asano and Christian Rupprecht and Andrew Zisserman and Andrea Vedaldi",
title = "PASS: An ImageNet replacement for self-supervised pretraining without humans",
journal = "NeurIPS Track on Datasets and Benchmarks",
year = "2021"
} | PASS (Pictures without humAns for Self-Supervision) is a large-scale dataset of 1,440,191 images that does not include any humans
and which can be used for high-quality pretraining while significantly reducing privacy concerns.
The PASS images are sourced from the YFCC-100M dataset. | false | 322 | false | pass | 2022-11-03T16:15:51.000Z | pass | false | d2a18cc9520a07ab8061591dc1bda8e1b17d734b | [] | [
"arxiv:2109.13228",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|yffc100M",
"task_categories:other",
"tag... | https://huggingface.co/datasets/pass/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- extended|yffc100M
task_categories:
- other
task_ids: []
paperswithcode_id: pass
pretty_name: Pictures with... |
null | null | @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
} | PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. Engl... | false | 3,732 | false | paws-x | 2022-11-03T16:46:54.000Z | paws-x | false | 18a9bcf4d8460880eaba67e96a714dc03b5bf74b | [] | [
"arxiv:1908.11828",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:ko",
"language:zh",
"licens... | https://huggingface.co/datasets/paws-x/resolve/main/README.md | ---
pretty_name: 'PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification'
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
- machine-generated
language:
- de
- en
- es
- fr
- ja
- ko
- zh
license:
- other
multilinguality:
- multilingual
size_categorie... |
null | null | @InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
} | PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature
the importance of modeling structure, context, and word order information for the problem
of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the
o... | false | 47,082 | false | paws | 2022-11-04T10:48:10.000Z | paws | false | 7c2732746fe1b1e5d0c3032a56a5cb65000c9140 | [] | [
"arxiv:1904.01130",
"annotations_creators:expert-generated",
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"language:en",
"license:other",
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"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"tas... | https://huggingface.co/datasets/paws/resolve/main/README.md | ---
pretty_name: 'PAWS: Paraphrase Adversaries from Word Scrambling'
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
-... |
null | null | \
@inproceedings{zhong2020towards,
title = "Towards Persona-Based Empathetic Conversational Models",
author = "Zhong, Peixiang and
Zhang, Chen and
Wang, Hao and
Liu, Yong and
Miao, Chunyan",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural L... | \
A dataset of around 350K persona-based empathetic conversations. Each speaker is associated with a persona, which comprises multiple persona sentences. The response of each conversation is empathetic. | false | 695 | false | pec | 2022-11-03T16:31:14.000Z | pec | false | 0a78ce2adc13868710430f1aaba01c18686cfb57 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:gpl-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_ids:dialogue-... | https://huggingface.co/datasets/pec/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-retrieval
task_ids:
- dialogue-modeling
- utterance-retrieval
paperswithcode_id: pe... |
null | null | @inproceedings{kang18naacl,
title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
booktitle = {Meeting of the North American Chapter o... | PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a sub... | false | 487 | false | peer_read | 2022-11-03T16:16:35.000Z | peerread | false | 6fb30fbcaa4b000a0966f7f7d7bba5cef8a86636 | [] | [
"arxiv:1804.09635",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"tags:acceptability-classification"
] | https://huggingface.co/datasets/peer_read/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: peerread
pretty_name: PeerRead
tags:
- acceptability-c... |
null | null | null | People's Daily NER Dataset is a commonly used dataset for Chinese NER, with
text from People's Daily (人民日报), the largest official newspaper.
The dataset is in BIO scheme. Entity types are: PER (person), ORG (organization)
and LOC (location). | false | 470 | false | peoples_daily_ner | 2022-11-03T16:16:24.000Z | null | false | 2bb8f6cd566bf69286239c620822e874df0d0d23 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:zh",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/peoples_daily_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: People's Da... |
null | null | @inproceedings{bastan2020authors,
title={Author's Sentiment Prediction},
author={Mohaddeseh Bastan and Mahnaz Koupaee and Youngseo Son and Richard Sicoli and Niranjan Balasubramanian},
year={2020},
eprint={2011.06128},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Person SenTiment (PerSenT) is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotation for 5.3k documents and 38k paragraphs covering 3.2k unique entities.
The dataset consists of sentiment annotations on news articles about people. Fo... | false | 463 | false | per_sent | 2022-11-03T16:15:58.000Z | persent | false | 4ab1b02762a1f2ebf5351bf98bc13c1a13de2063 | [] | [
"arxiv:2011.06128",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
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"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-MPQA-KBP Challenge-MediaRank",
"task_categories:text-classification",
"task_ids:sentiment... | https://huggingface.co/datasets/per_sent/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
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source_datasets:
- extended|other-MPQA-KBP Challenge-MediaRank
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: p... |
null | null | @inproceedings{poostchi-etal-2016-personer,
title = "{P}erso{NER}: {P}ersian Named-Entity Recognition",
author = "Poostchi, Hanieh and
Zare Borzeshi, Ehsan and
Abdous, Mohammad and
Piccardi, Massimo",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Comput... | The dataset includes 250,015 tokens and 7,682 Persian sentences in total. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format. | false | 639 | false | persian_ner | 2022-11-03T16:31:03.000Z | null | false | 2dc0dfbb341d3e0830380fb517e53d721eb8644a | [] | [
"annotations_creators:expert-generated",
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"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/persian_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
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- expert-generated
language:
- fa
license:
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- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Persian NER
dataset_info... |
null | null | @article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
... | This repository contains the PG-19 language modeling benchmark.
It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.
It also contains metadata of book titles and publication dates.
PG-19 is over double the size of the Billion Word benchmark and contains docume... | false | 508 | false | pg19 | 2022-11-03T16:15:39.000Z | pg-19 | false | c4bef002fd2b51d4c663f7296004a183e7faa681 | [] | [
"arxiv:1911.05507",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/pg19/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: pg-19
pretty_name: PG-19
da... |
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},
... | A parallel corpus originally extracted from http://se.php.net/download-docs.php. The original documents are written in English and have been partly translated into 21 languages. The original manuals contain about 500,000 words. The amount of actually translated texts varies for different languages between 50,000 and 38... | false | 963 | false | php | 2022-11-03T16:31:41.000Z | null | false | 76a2574e0050f2bde35b96d6fb1e4f100355113c | [] | [
"annotations_creators:found",
"language_creators:found",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:fi",
"language:fr",
"language:he",
"language:hu",
"language:it",
"language:ja",
"language:ko",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
... | https://huggingface.co/datasets/php/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- cs
- de
- en
- es
- fi
- fr
- he
- hu
- it
- ja
- ko
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- pl
- pt
- ro
- ru
- sk
- sl
- sv
- tr
- tw
- zh
language_bcp47:
- pt-BR
- zh-TW
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- origina... |
null | null | @InProceedings{keraron-EtAl:2020:LREC,
author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo},
title = {Project PIAF: Building a Native French Question-Answering Dat... | Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. | false | 708 | false | piaf | 2022-11-03T16:31:15.000Z | null | false | f7293b384902c44f6919d10851de0ea1b8eaf2f3 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:fr",
"language_bcp47:fr-FR",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domai... | https://huggingface.co/datasets/piaf/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
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- crowdsourced
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- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null... |
null | null | @inproceedings{siripragada-etal-2020-multilingual,
title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages",
author = "Siripragada, Shashank and
Philip, Jerin and
Namboodiri, Vinay P. and
Jawahar, C V",
booktitle = "Proceedings of the 12th Language Resources an... | Sentence aligned parallel corpus between 11 Indian Languages, crawled and extracted from the press information bureau
website. | false | 8,813 | false | pib | 2022-11-03T16:47:02.000Z | null | false | 045a12664d57d3790df1b43a3dedda1ddfd3b43f | [] | [
"arxiv:2008.04860",
"task_categories:translation",
"task_categories:text-generation",
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"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:translation",
"language:bn",
"language:en",
"language:gu",
"language:hi",
"language:ml",
"lan... | https://huggingface.co/datasets/pib/resolve/main/README.md | ---
task_categories:
- translation
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
multilinguality:
- translation
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- mr
- or
- pa
- ta
- te
- ur
language_creators:
- other
annotations_creators:
- no-annotation
source_datasets:
- original
size_cate... |
null | null | @inproceedings{Bisk2020,
author = {Yonatan Bisk and Rowan Zellers and
Ronan Le Bras and Jianfeng Gao
and Yejin Choi},
title = {PIQA: Reasoning about Physical Commonsense in
Natural Language},
booktitle = {Thirty-Fourth AAAI Conference on
Artificial Intelligence},
... | To apply eyeshadow without a brush, should I use a cotton swab or a toothpick?
Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art
natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning
and a corresponding benchmark dataset P... | false | 41,922 | false | piqa | 2022-11-03T16:47:39.000Z | piqa | false | a36adf3e32a28cd0f2242961b587fa7b447e51f1 | [] | [
"arxiv:1911.11641",
"arxiv:1907.10641",
"arxiv:1904.09728",
"arxiv:1808.05326",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:orig... | https://huggingface.co/datasets/piqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
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language:
- en
license:
- unknown
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size_categories:
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source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: piqa
pretty_name: 'Physica... |
null | null | @article{pnSummary, title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization},
author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri},
year={2020},
eprint={2012.11204},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
It is imperative to consider that t... | false | 438 | false | pn_summary | 2022-11-03T16:16:15.000Z | pn-summary | false | fbd956a8f028447fbf0883eb1b8afc008634af2a | [] | [
"arxiv:2012.11204",
"annotations_creators:found",
"language_creators:found",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_categories:text-classification",
"task_ids:news-articles-summ... | https://huggingface.co/datasets/pn_summary/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- fa
license:
- mit
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source_datasets:
- original
task_categories:
- summarization
- text-classification
task_ids:
- news-articles-summarization
- news-articles-headline-generation
- text-si... |
null | null | @misc{sheng2020investigating,
title={Investigating Societal Biases in a Poetry Composition System},
author={Emily Sheng and David Uthus},
year={2020},
eprint={2011.02686},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. This dataset can be used for tasks such as sentiment classification or style transfer for poems. | false | 14,559 | false | poem_sentiment | 2022-11-03T16:47:07.000Z | gutenberg-poem-dataset | false | 055c70f358520bf0071d5162dcdf149c7a63f955 | [] | [
"arxiv:2011.02686",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/poem_sentiment/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: gutenberg-poem-dataset
pretty_... |
null | null | @inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Milkowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learni... | The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation. | false | 483 | false | polemo2 | 2022-11-03T16:16:31.000Z | null | false | 00d0897f93852ad539a30acc75a3343b96ccdb60 | [] | [
"annotations_creators:expert-generated",
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"language:pl",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/polemo2/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: polemo2
d... |
null | null | @proceedings{ogr:kob:19:poleval,
editor = {Maciej Ogrodniczuk and Łukasz Kobyliński},
title = {{Proceedings of the PolEval 2019 Workshop}},
year = {2019},
address = {Warsaw, Poland},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
url = {http://2019.poleval.pl/fi... | In Task 6-1, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets
that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and
related phenomena.
In Task 6-2, the participants shall distinguish between three classes of twee... | false | 527 | false | poleval2019_cyberbullying | 2022-11-03T16:16:37.000Z | null | false | 886e4cefcb4ba7cc23ac54a0a3ef26eb70fb0a0b | [] | [
"annotations_creators:found",
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"language:pl",
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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/poleval2019_cyberbullying/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- pl
license:
- unknown
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: null
pretty_name: Poleval 2019 cyberbullying... |
null | null | null | PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according topre-established procedures. One of the tasks in PolEval-2019 was Machine Tran... | false | 798 | false | poleval2019_mt | 2022-11-03T16:31:23.000Z | null | false | 73079edea1aa70bbd556f96402cadef295811b8a | [] | [
"annotations_creators:no-annotation",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/poleval2019_mt/resolve/main/README.md | ---
annotations_creators:
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language_creators:
- expert-generated
- found
language:
- en
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license:
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multilinguality:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: Poleval2019Mt
data... |
null | null | @inproceedings{
ogro:kop:14:lrec,
author = "Ogrodniczuk, Maciej and Kopeć, Mateusz",
pdf = "http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf",
title = "The {P}olish {S}ummaries {C}orpus",
pages = "3712--3715",
crossref = "lrec:14"
}
@proceedings{
lrec:14,
editor = "Calzolari, Nicoletta ... | Polish Summaries Corpus: the corpus of Polish news summaries. | false | 325 | false | polsum | 2022-11-03T16:07:56.000Z | null | false | ccee60de431d1bbc3a2a0fb3e1fc67217d7bcaaa | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:pl",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/polsum/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-3.0
multilinguality:
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size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Polish S... |
null | null | @article{polyglotner,
author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, C... | Polyglot-NER
A training dataset automatically generated from Wikipedia and Freebase the task
of named entity recognition. The dataset contains the basic Wikipedia based
training data for 40 languages we have (with coreference resolution) for the task of
named entity recognition. The details of the procedure of generati... | false | 6,749 | false | polyglot_ner | 2022-11-03T16:46:57.000Z | polyglot-ner | false | 18d21c333db70a862488e4e59656df9ebf340f7a | [] | [
"annotations_creators:machine-generated",
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"language:ar",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"lang... | https://huggingface.co/datasets/polyglot_ner/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- ar
- bg
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- en
- es
- et
- fa
- fi
- fr
- he
- hi
- hr
- hu
- id
- it
- ja
- ko
- lt
- lv
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- sk
- sl
- sr
- sv
- th
- tl
- tr
- uk
- vi
- zh
license:
- unknown
multilinguality:... |
null | null | @misc{prachathai67k,
author = {cstorm125, lukkiddd },
title = {prachathai67k},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/PyThaiNLP/prachathai-67k}},
} | `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The prachathai-67k dataset was scraped from the news site Prachathai.
We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
It contains 67,889 articles wtih 12 curated tags fro... | false | 424 | false | prachathai67k | 2022-11-03T16:16:09.000Z | prachathai-67k | false | 7fed26b921f7ef5a34d352cb8e32d2419e1632e1 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/prachathai67k/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: prachathai-67k
pretty_name: prachathai67k
dat... |
null | null | @misc{sileo2019discoursebased,
title={Discourse-Based Evaluation of Language Understanding},
author={Damien Sileo and Tim Van-de-Cruys and Camille Pradel and Philippe Muller},
year={2019},
eprint={1907.08672},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Evaluation of language understanding with a 11 datasets benchmark focusing on discourse and pragmatics | false | 4,340 | false | pragmeval | 2022-11-03T16:46:49.000Z | null | false | a2b99fc1491e0868ffdbaa3b8d934721ec83c212 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-cla... | https://huggingface.co/datasets/pragmeval/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: null
pretty_name: pra... |
null | null | @InProceedings{huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors={Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year={2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://gi... | This dataset is for studying computational models trained to reason about prototypical situations. Using deterministic filtering a sampling from a larger set of all transcriptions was built. It contains 9789 instances where each instance represents a survey question from Family Feud game. Each instance exactly is a que... | false | 641 | false | proto_qa | 2022-11-03T16:31:01.000Z | protoqa | false | 9e2ae9fd1c28f55859e79eb8f4dbfc5a54e71245 | [] | [
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"task_ids:multipl... | https://huggingface.co/datasets/proto_qa/resolve/main/README.md | ---
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paperswithcode_id: protoqa
p... |
null | null | @inproceedings{ogro:kop:14:lrec,
title={The {P}olish {S}ummaries {C}orpus},
author={Ogrodniczuk, Maciej and Kope{\'c}, Mateusz},
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
year = "2014",
} | The Polish Summaries Corpus contains news articles and their summaries. We used summaries of the same article as positive pairs and sampled the most similar summaries of different articles as negatives. | false | 325 | false | psc | 2022-11-03T16:07:52.000Z | null | false | faad1a7cafa0f4717d22caea1bf44736ec355729 | [] | [
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"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/psc/resolve/main/README.md | ---
annotations_creators:
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task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: psc
dataset_... |
null | null | @article{marcus-etal-1993-building,
title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank",
author = "Marcus, Mitchell P. and
Santorini, Beatrice and
Marcinkiewicz, Mary Ann",
journal = "Computational Linguistics",
volume = "19",
number = "2",
year = "1993"... | This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. This corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. | false | 8,148 | false | ptb_text_only | 2022-11-03T16:46:41.000Z | null | false | f78c655337ecc44f82cc69113370ba21c917c92c | [] | [
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"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:mas... | https://huggingface.co/datasets/ptb_text_only/resolve/main/README.md | ---
annotations_creators:
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license_details: LDC User Agreement for Non-Members
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task_categories:
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task_ids:
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null | null | Courtesy of the U.S. National Library of Medicine. | NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information. | false | 509 | false | pubmed | 2022-11-03T16:30:43.000Z | pubmed | false | 6951413fed3cab7423f0772967708ff81a6d6d3f | [] | [
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"... | https://huggingface.co/datasets/pubmed/resolve/main/README.md | ---
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task_ids:
- language-modeling
- masked-language-modelin... |
null | null | @inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Intern... | PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative
statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
PubMedQA has 1k expe... | false | 6,823 | false | pubmed_qa | 2022-11-03T16:46:58.000Z | pubmedqa | false | 6f27c996a6b0d3f6b7a7c5db8ec58d516199f23e | [] | [
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"source... | https://huggingface.co/datasets/pubmed_qa/resolve/main/README.md | ---
annotations_creators:
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task_ids:
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papers... |
null | null | @InProceedings{OOPSLA ’16, ACM,
title = {Probabilistic Model for Code with Decision Trees.},
authors={Raychev, V., Bielik, P., and Vechev, M.},
year={2016}
} | Dataset consisting of parsed ASTs that were used to train and
evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository)
,keeping only programs that parse and have at most 30'000 nodes in the AST and
we ... | false | 321 | false | py_ast | 2022-11-03T16:07:47.000Z | null | false | 2882f0b169f96a740e38a14d1b66273e2c6127fe | [] | [
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"task_categories:text-generation",
"task_ca... | https://huggingface.co/datasets/py_ast/resolve/main/README.md | ---
pretty_name: PyAst
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- code
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task_categories:
- text2text-generation
- text-generation
- fill-mask
task_ids: []
paperswith... |
null | null | null | QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in
question answering and reading comprehension. The dataset contains a supporting
passage and a set of questions corresponding to the passage. Multiple options
for answers are provided for each question, of which only one is correc... | false | 3,785 | false | qa4mre | 2022-11-03T16:46:45.000Z | null | false | 31885415b608262acabe49c83bd369a579c064eb | [] | [
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"task_categories:mult... | https://huggingface.co/datasets/qa4mre/resolve/main/README.md | ---
annotations_creators:
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- ar
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pretty_name: 'QA4MRE: Question Answering for Machine Reading Evaluation'
size_categories:
- 1K<n<10K
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task_categories:
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null | null | @InProceedings{huggingface:dataset,
title = {QA-SRL: Question-Answer Driven Semantic Role Labeling},
authors={Luheng He, Mike Lewis, Luke Zettlemoyer},
year={2015}
publisher = {cs.washington.edu},
howpublished={\\url{https://dada.cs.washington.edu/qasrl/#page-top}},
} | The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
There were 2 datsets used in the paper, newswire and wikipedia. Unfortunately t... | false | 896 | false | qa_srl | 2022-11-03T16:31:36.000Z | qa-srl | false | 62ca22bdad79a90e79a16ae0f723ff5c787e6be2 | [] | [
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"source_datasets:original",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/qa_srl/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
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- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: qa-srl
pr... |
null | null | @inproceedings{levy-etal-2017-zero,
title = "Zero-Shot Relation Extraction via Reading Comprehension",
author = "Levy, Omer and
Seo, Minjoon and
Choi, Eunsol and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 201... | A dataset reducing relation extraction to simple reading comprehension questions | false | 589 | false | qa_zre | 2022-11-03T16:30:59.000Z | null | false | b40a5c12a0f472e0c8174bf5c41f832cdd6f6b65 | [] | [
"annotations_creators:no-annotation",
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"source_datasets:original",
"task_categories:question-answering",
"tags:zero-shot-relation-extraction"
] | https://huggingface.co/datasets/qa_zre/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: QaZre
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- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: null
tags:
- zero-shot-relatio... |
null | null | We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.
Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.
Our aim is to build Reading Comprehension method... | false | 795 | false | qangaroo | 2022-11-03T16:31:24.000Z | null | false | 89a60019d969002ab25deb5f4cf1e40a72c20dec | [] | [
"language:en"
] | https://huggingface.co/datasets/qangaroo/resolve/main/README.md | ---
language:
- en
paperswithcode_id: null
pretty_name: qangaroo
dataset_info:
- config_name: medhop
features:
- name: query
dtype: string
- name: supports
sequence: string
- name: candidates
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- name: answer
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- name: id
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- name: train... | |
null | null | @article{Rodriguez2019QuizbowlTC,
title={Quizbowl: The Case for Incremental Question Answering},
author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
journal={ArXiv},
year={2019},
volume={abs/1904.04792}
} | The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. | false | 7,034 | false | qanta | 2022-11-03T16:31:38.000Z | quizbowl | false | 9c955467574cc39cdbf45e3c1b6227ec29dca80a | [] | [
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"task_categories:question-answering",
"tags:quizbowl"
] | https://huggingface.co/datasets/qanta/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
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pretty_name: Quizbowl
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- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quizbowl
tags:
- quizbowl
dataset... |
null | null | @article{allenai:qasc,
author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
title = {QASC: A Dataset for Question Answering via Sentence Composition},
journal = {arXiv:1910.11473v2},
year = {2020},
} | QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences. | false | 25,210 | false | qasc | 2022-11-03T16:47:31.000Z | qasc | false | 340c95d19b66f36d315b7e59cea4175c6ae02d46 | [] | [
"arxiv:1910.11473",
"annotations_creators:crowdsourced",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:extrac... | https://huggingface.co/datasets/qasc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
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license:
- cc-by-4.0
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pretty_name: Question Answering via Sentence Composition (QASC)
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- multiple-choice
task_ids:... |
allenai | null | @inproceedings{Dasigi2021ADO,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
year={2021}
} | A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners. | false | 314 | false | allenai/qasper | 2022-10-07T22:04:11.000Z | qasper | false | fdc9d8214fbab5dd782958601db4d678e6934a54 | [] | [
"arxiv:2105.03011",
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"size_categories:10K<n<100K",
"source_datasets:extended|s2orc",
"task_categories:question-answering",
"tas... | https://huggingface.co/datasets/allenai/qasper/resolve/main/README.md | ---
pretty_name: QASPER
annotations_creators:
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language_creators:
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language:
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language_bcp47:
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license:
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size_categories:
- 10K<n<100K
source_datasets:
- extended|s2orc
task_categories:
- question-answering
task_ids:
- closed-domai... |
null | null | @misc{lamm2020qed,
title={QED: A Framework and Dataset for Explanations in Question Answering},
author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},
year={2020},
eprint={2009.06354},
archivePrefix={arXiv},
... | QED, is a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations... | false | 801 | false | qed | 2022-11-03T16:31:09.000Z | qed | false | 088afcb25437751257e04ad1e36768b1e8bedcfb | [] | [
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"source_datasets:extended|natural_questions",
"task_categories:question-answering",
"task_ids:extractive-qa",
"tags... | https://huggingface.co/datasets/qed/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
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language:
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task_categories:
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task_ids:
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paperswithcode_id: qed
pretty_name: QED
tags:... |
null | null | A. Abdelali, F. Guzman, H. Sajjad and S. Vogel, "The AMARA Corpus: Building parallel language resources for the educational domain", The Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14). Reykjavik, Iceland, 2014. Pp. 1856-1862. Isbn. 978-2-9517408-8-4. | The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform.
Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group
The QED C... | false | 965 | false | qed_amara | 2022-11-03T16:31:42.000Z | null | false | 38ca16750265b1af70fbe98b7f8b6a296c1c816f | [] | [
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"language:an",
"language:ar",
"language:arq",
"language:arz",
"language:as",
"language:ase",
"language:ast",
"language:av... | https://huggingface.co/datasets/qed_amara/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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- ch
- cho
- cku
- cnh
- co
- cr
- cs
- cu
- cv
- cy
- da
- de
- dv
- dz
- ... |
null | null | @inproceedings{choi-etal-2018-quac,
title = "QUAC: Question answering in context",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform qu... | Question Answering in Context is a dataset for modeling, understanding,
and participating in information seeking dialog. Data instances consist
of an interactive dialog between two crowd workers: (1) a student who
poses a sequence of freeform questions to learn as much as possible
about a hidden Wikipedia text, and (2)... | false | 1,046 | false | quac | 2022-11-03T16:32:07.000Z | quac | false | b30a6682c68663b20b9c2250963b4eb08af38c47 | [] | [
"arxiv:1808.07036",
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"language:en",
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"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"task_categories:question-answering",
"task_categ... | https://huggingface.co/datasets/quac/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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- found
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license:
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source_datasets:
- extended|wikipedia
task_categories:
- question-answering
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
- extracti... |
null | null | @inproceedings{DBLP:conf/aaai/RogersKDR20,
author = {Anna Rogers and
Olga Kovaleva and
Matthew Downey and
Anna Rumshisky},
title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
Real Tasks},
booktitle = {The Thirty-Fo... | QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types.\ | false | 48,876 | false | quail | 2022-11-03T16:47:36.000Z | quail | false | 70e17fae6f37b7f5f88423a4cae81508c4752695 | [] | [
"annotations_creators:crowdsourced",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/quail/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
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pretty_name: Question Answering for Artificial Intelligence (QuAIL)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multip... |
null | null | @inproceedings{quarel_v1,
title={QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships},
author={Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal},
year={2018},
journal={arXiv:1805.05377v1}
} | QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms. | false | 21,769 | false | quarel | 2022-11-03T16:47:23.000Z | quarel | false | df234c3412949e9c965b322080056346cca72a21 | [] | [
"language:en"
] | https://huggingface.co/datasets/quarel/resolve/main/README.md | ---
language:
- en
paperswithcode_id: quarel
pretty_name: QuaRel
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- name: id
dtype: string
- name: answer_index
dtype: int32
- name: logical_forms
sequence: string
- name: logical_form_pretty
dtype: string
- name: world_literals
sequence:
- name: world1
d... |
null | null | @InProceedings{quartz,
author = {Oyvind Tafjord and Matt Gardner and Kevin Lin and Peter Clark},
title = {"QUARTZ: An Open-Domain Dataset of Qualitative Relationship
Questions"},
year = {"2019"},
} | QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each
question is paired with one of 405 different background sentences (sometimes short paragraphs).
The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is... | false | 24,145 | false | quartz | 2022-11-03T16:47:31.000Z | quartz | false | 300d95633a75483e35d99af51e08bbee50395ff2 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/quartz/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: quartz
pretty_name: Qu... |
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