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null | null | @inproceedings{veyseh-et-al-2020-what,
title={{What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}},
author={Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen},
year={2020},
booktitle={Proceedings of COLING},
link={http... | Acronym identification training and development sets for the acronym identification task at SDU@AAAI-21. | false | 2,710 | false | acronym_identification | 2022-11-03T16:46:46.000Z | acronym-identification | false | 85801c4e4293b5c9341d3c51c47ea27303a436ea | [] | [
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] | https://huggingface.co/datasets/acronym_identification/resolve/main/README.md | ---
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paperswithcode_id: acronym-identification
pretty_name: Acronym Identificatio... |
null | null | @article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Languag... | ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs an... | false | 4,993 | false | ade_corpus_v2 | 2022-11-03T16:46:50.000Z | null | false | 305f690ee885b0a88c43ac9ab6187337ebcfc630 | [] | [
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"task_categori... | https://huggingface.co/datasets/ade_corpus_v2/resolve/main/README.md | ---
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task_ids:
- coreference-resolution
- fact-che... |
null | null | @article{bartolo2020beat,
author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension},
journal = {Transactions of the Association for Computational Linguistics},
... | AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop an... | false | 85,122 | false | adversarial_qa | 2022-11-03T16:47:45.000Z | adversarialqa | false | 3483241a3c43bd1b8fc5c54d1ef84231e139768b | [] | [
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"task_ids:extractive-qa",
... | https://huggingface.co/datasets/adversarial_qa/resolve/main/README.md | ---
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task_ids:
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paperswithcode_id: adversarialqa
pretty_nam... |
null | null | @misc{zhang2019email,
title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
author={Rui Zhang and Joel Tetreault},
year={2019},
eprint={1906.03497},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text. | false | 1,322 | false | aeslc | 2022-11-03T16:31:59.000Z | aeslc | false | 66826a27d23a5c4e774bab648e00da396bde149f | [] | [
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"source_datasets:original",
"task_categories:summarization",
"tags:aspect-based-summarization",
"tags:conversations-s... | https://huggingface.co/datasets/aeslc/resolve/main/README.md | ---
annotations_creators:
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- en
language_creators:
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pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
size_categories:
- 10K<n<100K
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- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: aeslc
... |
null | null | @inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Governmen... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 357 | false | afrikaans_ner_corpus | 2022-11-03T16:16:12.000Z | null | false | 20cb08ae3bb1be1ca426c079ed2d78e4dfb62a3f | [] | [
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"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/afrikaans_ner_corpus/resolve/main/README.md | ---
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license_details: Creative Commons Attribution 2.5 South Africa License
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size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_id... |
null | null | @inproceedings{Zhang2015CharacterlevelCN,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
booktitle={NIPS},
year={2015}
} | AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for researc... | false | 40,998 | false | ag_news | 2022-11-03T16:47:32.000Z | ag-news | false | f24f17e843e623e78ad023b21a0012c98ed274c4 | [] | [
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"task_ids:topic-classification"
] | https://huggingface.co/datasets/ag_news/resolve/main/README.md | ---
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task_ids:
- topic-classification
paperswithcode_id: ag-news
pretty_name: AG’s News Corpus
train-ev... |
null | null | @article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05... | A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a... | false | 50,665 | false | ai2_arc | 2022-11-03T16:47:42.000Z | null | false | e610ebfc7354f5505f1cbed3ad7bf5567e5b86e2 | [] | [
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"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choic... | https://huggingface.co/datasets/ai2_arc/resolve/main/README.md | ---
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paperswithcode_id: null... |
null | null | @inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
... | AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip... | false | 634 | false | air_dialogue | 2022-11-03T16:31:11.000Z | null | false | 3ef284c2b1ca63cebd46335641fa31b09763f4e5 | [] | [
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"task_categories:conversational",
"task_categories:text-generation",
"task_categories:fill-mask... | https://huggingface.co/datasets/air_dialogue/resolve/main/README.md | ---
pretty_name: AirDialogue
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task_ids:
- dialogue-gen... |
null | null | @inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610... | Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. | false | 439 | false | ajgt_twitter_ar | 2022-11-03T16:31:51.000Z | null | false | 3aa5f0b5245612bfb799aec499c4dd512e06f492 | [] | [
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"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/ajgt_twitter_ar/resolve/main/README.md | ---
annotations_creators:
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task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: Arabic Jordanian General ... |
null | null | @inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
... | Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted
from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale
from one (negative review) to five (positive review).
We recommend using the provi... | false | 814 | false | allegro_reviews | 2022-11-03T16:30:48.000Z | allegro-reviews | false | 5616d4df47bbb59e217e7e1591f111ed293156fe | [] | [
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"task_categories:text-classification",
"task_ids:sentiment-scoring",
"task_ids:text-scoring"
] | https://huggingface.co/datasets/allegro_reviews/resolve/main/README.md | ---
annotations_creators:
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- found
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task_categories:
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task_ids:
- sentiment-scoring
- text-scoring
paperswithcode_id: allegro-reviews
pretty_name:... |
null | null | @misc{blard2019allocine,
author = {Blard, Theophile},
title = {french-sentiment-analysis-with-bert},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},
} | Allocine Dataset: A Large-Scale French Movie Reviews Dataset.
This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.
It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k). | false | 1,227 | false | allocine | 2022-11-03T16:31:33.000Z | allocine | false | 38661ba696f097e1732d90805ad7783918278c95 | [] | [
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"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/allocine/resolve/main/README.md | ---
annotations_creators:
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task_categories:
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task_ids:
- sentiment-classification
paperswithcode_id: allocine
pretty_name: Allociné
train-e... |
null | null | @inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
book... | The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was develo... | false | 2,012 | false | alt | 2022-11-03T16:32:19.000Z | alt | false | 1a16c8a9171c3ae734f0cff59f12709db90226b1 | [] | [
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"language:km",
"language:lo",
"language:ms",
"language:my",
"language:th",
"language:vi",
"language:zh",
"license:cc-by-... | https://huggingface.co/datasets/alt/resolve/main/README.md | ---
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- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
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task_categories:
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null | null | @inproceedings{mcauley2013hidden,
title={Hidden factors and hidden topics: understanding rating dimensions with review text},
author={McAuley, Julian and Leskovec, Jure},
booktitle={Proceedings of the 7th ACM conference on Recommender systems},
pages={165--172},
year={2013}
} | The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review. | false | 55,477 | false | amazon_polarity | 2022-11-03T16:47:40.000Z | null | false | 2aae2b8442bc506e07c5dda2938182c1a2995325 | [] | [
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"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/amazon_polarity/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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language:
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source_datasets:
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task_categories:
- text-classification
task_ids:
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paperswithcode_id: null
pretty_name: Amazon R... |
null | null | @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
} | We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an a... | false | 24,235 | false | amazon_reviews_multi | 2022-11-03T16:47:19.000Z | null | false | e1914822fd1c764504257731974458f00e6da3f3 | [] | [
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"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categori... | https://huggingface.co/datasets/amazon_reviews_multi/resolve/main/README.md | ---
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tas... |
null | null | \ | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website... | false | 18,031 | false | amazon_us_reviews | 2022-11-03T16:47:17.000Z | null | false | ce11f03b8e9f4641880336bd5f75461083877fda | [] | [
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null | null | @inproceedings{ min2020ambigqa,
title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
booktitle={ EMNLP },
year={2020}
} | AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBI... | false | 858 | false | ambig_qa | 2022-11-03T16:31:34.000Z | ambigqa | false | 4b3c61e4acf755a804e74bc7186e2599ecec36ad | [] | [
"arxiv:2004.10645",
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"source_datasets:extended|natural_questions",
"source_datasets:original",
"task_categories:question-answering",
... | https://huggingface.co/datasets/ambig_qa/resolve/main/README.md | ---
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task_categories:
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task_ids:
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paperswithcode_id: ambigqa
pre... |
null | null | @article{DBLP:journals/corr/abs-2104-08726,
author = {Abteen Ebrahimi and
Manuel Mager and
Arturo Oncevay and
Vishrav Chaudhary and
Luis Chiruzzo and
Angela Fan and
John Ortega and
Ricardo Ramos and
... | AmericasNLI is an extension of XNLI (Conneau et al., 2018) – a natural language inference (NLI) dataset covering 15 high-resource languages – to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI,... | false | 4,675 | false | americas_nli | 2022-11-03T16:46:47.000Z | null | false | f75748369e4640f6092e2cdeef2078292cb6e349 | [] | [
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pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.'
size_categories:
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null | null | @inproceedings{10.1007/11677482_3,
author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain ... | The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,... | false | 1,276 | false | ami | 2022-11-03T16:31:58.000Z | null | false | 7e10ece9281808a878a2bfdaea0a9df6f5612c2b | [] | [
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] | https://huggingface.co/datasets/ami/resolve/main/README.md | ---
pretty_name: AMI Corpus
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source_datasets:
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task_categories:
- automatic-speech-recognition
task_ids: []
dataset_info:... |
null | null | @inproceedings{xing2018adaptive,
title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
pages={3619--3630},
year={2018}
} | Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In t... | false | 343 | false | amttl | 2022-11-03T16:16:06.000Z | null | false | a7f25a938f453ceddfbc1f076225c1af2075c886 | [] | [
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"task_categories:token-classification",
"task_ids:parsing"
] | https://huggingface.co/datasets/amttl/resolve/main/README.md | ---
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task_categories:
- token-classification
task_ids:
- parsing
paperswithcode_id: null
pretty_name: AMTTL
dataset_info:
features:
- na... |
null | null | @InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
bookt... | The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test s... | false | 307,984 | false | anli | 2022-11-03T16:47:48.000Z | anli | false | f5b0ccf1fb53c54eb87e8ca304b4fa63227f6ea3 | [] | [
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"source_datasets:original",
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"task... | https://huggingface.co/datasets/anli/resolve/main/README.md | ---
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language:
- en
language_creators:
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pretty_name: Adversarial NLI
size_categories:
- 100K<n<1M
source_datasets:
- original
- extended|hotpot_qa
task_categories:
- text-classification
task_ids:
- natu... |
null | null | @InProceedings{Zurich Open Repository and
Archive:dataset,
title = {Software Applications User Reviews},
authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo;
Panichella, Sebastiano},
year={2017}
} | It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versio... | false | 20,286 | false | app_reviews | 2022-11-03T16:47:21.000Z | null | false | 0ca262730c1edb7abe4c500005216da26d9b7374 | [] | [
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"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring"
] | https://huggingface.co/datasets/app_reviews/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
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size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
paperswithcode_id: null
pretty_name: Ap... |
null | null | @InProceedings{ACL,
title = {Program induction by rationale generation: Learning to solve and explain algebraic word problems},
authors={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil},
year={2017}
} | A large-scale dataset consisting of approximately 100,000 algebraic word problems.
The solution to each question is explained step-by-step using natural language.
This data is used to train a program generation model that learns to generate the explanation,
while generating the program that solves the question. | false | 1,137 | false | aqua_rat | 2022-11-03T16:31:46.000Z | aqua-rat | false | ecfa729fb45c6688d091621d808cb7f072655c76 | [] | [
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"task_categories:question-answering",
"ta... | https://huggingface.co/datasets/aqua_rat/resolve/main/README.md | ---
annotations_creators:
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- en
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source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: aqua-rat
pre... |
null | null | @misc{kulkarni2020aquamuse,
title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
year={2020},
eprint={2010.12694},
archivePrefix={arXiv},
primaryClass={cs.C... | AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl) | false | 205 | false | aquamuse | 2022-11-03T16:07:43.000Z | aquamuse | false | bbe5929c9752ca7194742663b06b609c40729874 | [] | [
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"size_categories:1K<n<10K",
"source_datasets:extended|natural_q... | https://huggingface.co/datasets/aquamuse/resolve/main/README.md | ---
annotations_creators:
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source_datasets:
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- original
task_categories:
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- ... |
null | null | @article{haouari2020arcov19,
title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed},
journal={arXiv preprint arXiv:2004.05861},
year={2020} | ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others | false | 350 | false | ar_cov19 | 2022-11-03T16:16:01.000Z | arcov-19 | false | ab307c126c2d32a3ebdb934f080e6c112381b39f | [] | [
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"source_datasets:original",
"task_categories:other",
"tags:data-mining"
] | https://huggingface.co/datasets/ar_cov19/resolve/main/README.md | ---
annotations_creators:
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task_ids: []
paperswithcode_id: arcov-19
pretty_name: ArCOV19
tags:
- data-mining
dataset_info:
features:
- name: tweetI... |
null | null | @InProceedings{10.1007/978-3-319-18117-2_2,
author="ElSahar, Hady
and El-Beltagy, Samhaa R.",
editor="Gelbukh, Alexander",
title="Building Large Arabic Multi-domain Resources for Sentiment Analysis",
booktitle="Computational Linguistics and Intelligent Text Processing",
year="2015",
publisher="Springer International Pu... | Dataset of 8364 restaurant reviews scrapped from qaym.com in Arabic for sentiment analysis | false | 448 | false | ar_res_reviews | 2022-11-03T16:16:26.000Z | null | false | 18cafbaf5cc6673eecb7bf8a597b2a6f74425567 | [] | [
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] | https://huggingface.co/datasets/ar_res_reviews/resolve/main/README.md | ---
annotations_creators:
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- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: ArRestReviews
dataset_inf... |
null | null | @inproceedings{abu-farha-magdy-2020-arabic,
title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
author = "Abu Farha, Ibrahim and Magdy, Walid",
booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offe... | ArSarcasm is a new Arabic sarcasm detection dataset.
The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD)
and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. | false | 347 | false | ar_sarcasm | 2022-11-03T16:16:06.000Z | null | false | 2e5f1fbdad2c6ec072ec07548dba13158ab67812 | [] | [
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"task_categories:text-classification",
"task_ids:sentime... | https://huggingface.co/datasets/ar_sarcasm/resolve/main/README.md | ---
annotations_creators:
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source_datasets:
- extended|other-semeval_2017
- extended|other-astd
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_... |
null | null | @article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
} | Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked ... | false | 1,919 | false | arabic_billion_words | 2022-11-03T16:32:20.000Z | null | false | 62e7e98e4b4fbd174f31f79f61905234baf6d818 | [] | [
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"source_datasets:original",
"task_categories:text-generation",
"t... | https://huggingface.co/datasets/arabic_billion_words/resolve/main/README.md | ---
annotations_creators:
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task_ids:
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paperswit... |
null | null | @InProceedings{DARWISH18.562, author = {Kareem Darwish ,Hamdy Mubarak ,Ahmed Abdelali ,Mohamed Eldesouki ,Younes Samih ,Randah Alharbi ,Mohammed Attia ,Walid Magdy and Laura Kallmeyer},
title = {Multi-Dialect Arabic POS Tagging: A CRF Approach},
booktitle = {Proceedings of the Eleventh International Conference on Lang... | The Dialectal Arabic Datasets contain four dialects of Arabic, Etyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR). Each dataset consists of a set of 350 manually segmented and POS tagged tweets. | false | 854 | false | arabic_pos_dialect | 2022-11-03T16:31:33.000Z | null | false | 1c36271a1adad5a8b8feb131d44e6fadab5185c8 | [] | [
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"task_categories:token-classification",
"task_ids:part-of-speech"
] | https://huggingface.co/datasets/arabic_pos_dialect/resolve/main/README.md | ---
annotations_creators:
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- ar
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- extended
task_categories:
- token-classification
task_ids:
- part-of-speech
paperswithcode_id: null
pretty_name: Arabic POS Dialect
data... |
null | null | @phdthesis{halabi2016modern,
title={Modern standard Arabic phonetics for speech synthesis},
author={Halabi, Nawar},
year={2016},
school={University of Southampton}
} | This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton.
The corpus was recorded in south Levantine Arabic
(Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
Note tha... | false | 390 | false | arabic_speech_corpus | 2022-11-03T16:16:15.000Z | arabic-speech-corpus | false | 8d7c2fdda1aa351f98cb2794181cb03b34adf58b | [] | [
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"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/arabic_speech_corpus/resolve/main/README.md | ---
pretty_name: Arabic Speech Corpus
annotations_creators:
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language_creators:
- crowdsourced
language:
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license:
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paperswithcode_id: arabic-speech-corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- automatic-speech-recognit... |
null | null | @inproceedings{mozannar-etal-2019-neural,
title = {Neural {A}rabic Question Answering},
author = {Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem},
booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Workshop},
month = {aug},
year = {2019},
address... | Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles. | false | 701 | false | arcd | 2022-11-03T16:31:27.000Z | arcd | false | b86342a73d4350b1f0c8c2f7f56f0ee737b2d963 | [] | [
"annotations_creators:crowdsourced",
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"language:ar",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/arcd/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
language_bcp47:
- ar-SA
license:
- mit
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size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: arcd
pretty_name: ARC... |
null | null | @article{ArSenTDLev2018,
title={ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets},
author={Baly, Ramy, and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Bashir Shaban, Khaled},
journal={OSACT3},
pages={},
year={2018}} | The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria. | false | 356 | false | arsentd_lev | 2022-11-03T16:16:01.000Z | arsentd-lev | false | 4ec3d91c42d1140affad0f4bfa4a8c9b06f60adb | [] | [
"arxiv:1906.01830",
"annotations_creators:crowdsourced",
"language_creators:found",
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"multilinguality:monolingual",
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"task_categories:text-classification",
"task_ids:sentiment-classification",
... | https://huggingface.co/datasets/arsentd_lev/resolve/main/README.md | ---
annotations_creators:
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task_ids:
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paperswithcode_id: arsentd-... |
null | null | @InProceedings{anli,
author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
title = {Abductive Commonsense Reasoning},
year = {2020}
} | the Abductive Natural Language Inference Dataset from AI2 | false | 893 | false | art | 2022-11-03T16:31:35.000Z | art-dataset | false | 544eef872ed688ad15561e2148f6da7b9390534c | [] | [
"arxiv:1908.05739",
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"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_ids:natura... | https://huggingface.co/datasets/art/resolve/main/README.md | ---
annotations_creators:
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language:
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language_creators:
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license:
- unknown
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pretty_name: Abductive Reasoning in narrative Text
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- multiple-choice
- text-classification
task_ids:
- natural-la... |
null | null | @misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
} | A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. | false | 430 | false | arxiv_dataset | 2022-11-03T16:16:19.000Z | null | false | 6c17c35ae267029198d75f3ef97465528ade3437 | [] | [
"arxiv:1905.00075",
"annotations_creators:no-annotation",
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"language:en",
"license:cc0-1.0",
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"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:translation",
"task_categories:summarization",
"task_categorie... | https://huggingface.co/datasets/arxiv_dataset/resolve/main/README.md | ---
annotations_creators:
- no-annotation
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size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
- summarization
- text-retrieval
task_ids:
- document-retrieval
- entity-linking-retriev... |
null | null | @InProceedings{nguyen2021www,
title={Advanced Semantics for Commonsense Knowledge Extraction},
author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
year={2021},
booktitle={The Web Conference 2021},
} | This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/). | false | 515 | false | ascent_kb | 2022-11-03T16:30:39.000Z | ascentkb | false | 3d1fe338bc0a449c4b6ccaa0f31674ed32096231 | [] | [
"arxiv:2011.00905",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:other",
"tags:knowledge-base"
] | https://huggingface.co/datasets/ascent_kb/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: ascentkb
pretty_name: Ascent KB
tags:
- knowledge-base
dataset_info:
- config_n... |
null | null | @inproceedings{othman2012english,
title={English-asl gloss parallel corpus 2012: Aslg-pc12},
author={Othman, Achraf and Jemni, Mohamed},
booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC},
year={2012}
} | A large synthetic collection of parallel English and ASL-Gloss texts.
There are two string features: text, and gloss. | false | 424 | false | aslg_pc12 | 2022-11-03T16:16:21.000Z | aslg-pc12 | false | 5dab5709dc7b047bad19e3627137fb85dc2b06c9 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language:ase",
"language:en",
"language_creators:found",
"license:cc-by-nc-4.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/aslg_pc12/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- ase
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- translation
pretty_name: English-ASL Gloss Parallel Corpus 2012
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pap... |
null | null | @article{garg2019tanda,
title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
year={2019},
eprint={1911.04118},
} | ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence_in_long_answer and short_answe... | false | 574 | false | asnq | 2022-11-03T16:30:58.000Z | asnq | false | ebd2dc1987a95d8e3b900c1322d34319bff60ea8 | [] | [
"arxiv:1911.04118",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:cc-by-nc-sa-3.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:extended|natural_questions",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa"
... | https://huggingface.co/datasets/asnq/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
pretty_name: Answer Sentence Natural Questions (ASNQ)
size_categories:
- 10M<n<100M
source_datasets:
- extended|natural_questions
task_categories:
- multiple-choice
task_ids:
- mu... |
null | null | @inproceedings{alva-manchego-etal-2020-asset,
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Bordes, Antoine and
Scarton, Carolina and
Sagot, B... | ASSET is a dataset for evaluating Sentence Simplification systems with multiple rewriting transformations,
as described in "ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations".
The corpus is composed of 2000 validation and 359 test original sentences tha... | false | 1,176 | false | asset | 2022-11-03T16:31:59.000Z | asset | false | e78943cbcaeea9dd64e396e7a15fa07e29acaaf1 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|other-turkcorpus",
"task_categories:text-classification",
"task_categories:te... | https://huggingface.co/datasets/asset/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|other-turkcorpus
task_categories:
- text-classification
- text2text-generation
task_ids:
- text-simplification... |
null | null | @inproceedings{fonseca2016assin,
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
pages={13--15},
yea... | The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in
Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences
extracted from news articles written in Europea... | false | 1,053 | false | assin | 2022-11-03T16:31:37.000Z | assin | false | dda6b0fc45ffdb3659e04a149c3a5de2f19605f7 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:pt",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"t... | https://huggingface.co/datasets/assin/resolve/main/README.md | ---
pretty_name: ASSIN
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-si... |
null | null | @inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
} | The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5... | false | 937 | false | assin2 | 2022-11-03T16:30:57.000Z | assin2 | false | a5939a7d4251dd89b00291d89c33659cea3844b4 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:pt",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"tas... | https://huggingface.co/datasets/assin2/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
pape... |
null | null | @article{Sap2019ATOMICAA,
title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
journal={ArXiv},
year={2019},
volume={abs/... | This dataset provides the template sentences and
relationships defined in the ATOMIC common sense dataset. There are
three splits - train, test, and dev.
From the authors.
Disclaimer/Content warning: the events in atomic have been
automatically extracted from blogs, stories and books written at
various times. The eve... | false | 355 | false | atomic | 2022-11-03T16:16:02.000Z | atomic | false | c3bf57b865d1e4e22e433d95d32a6997c93ac1f4 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:common-sense-if-then-reasoning"
] | https://huggingface.co/datasets/atomic/resolve/main/README.md | ---
pretty_name: ATOMIC
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: atomic
tags:
- common-sense-i... |
null | null | @article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
} | Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine transla... | false | 1,184 | false | autshumato | 2022-11-03T16:31:57.000Z | null | false | 1510686917d020edd02a8d177aa0585ebe793010 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"language:tn",
"language:ts",
"language:zu",
"license:cc-by-2.5",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categorie... | https://huggingface.co/datasets/autshumato/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- tn
- ts
- zu
license:
- cc-by-2.5
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: aut... |
null | null | @misc{weston2015aicomplete,
title={Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks},
author={Jason Weston and Antoine Bordes and Sumit Chopra and Alexander M. Rush and Bart van Merriënboer and Armand Joulin and Tomas Mikolov},
year={2015},
eprint={1502.05698},
archi... | The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading
comprehension via question answering. Our tasks measure understanding
in several ways: whether a system is able to answer questions via chaining facts,
simple induction, deduction and many more. The tasks are designed to be prerequisites
for any syst... | false | 41,864 | false | babi_qa | 2022-11-03T16:47:14.000Z | babi-1 | false | 71eb4fbefa0b0d793d048d749c66a74a2a494503 | [] | [
"arxiv:1502.05698",
"arxiv:1511.06931",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:en",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original"... | https://huggingface.co/datasets/babi_qa/resolve/main/README.md | ---
pretty_name: BabiQa
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: ba... |
null | null | null | BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection. | false | 9,538 | false | banking77 | 2022-11-03T16:47:01.000Z | null | false | 008b447cfaa8724ae2ac84ffd4cceb3f466ce4b2 | [] | [
"arxiv:2003.04807",
"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/banking77/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 | @misc{OPUS4-2919,
title = {Teilauszug der Datenbank des Vorhabens "Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache" vom Januar 2018},
institution = {Akademienvorhaben Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache. Text- und Wissenskultur im alten {\"A}gypten}... | This dataset comprises parallel sentences of hieroglyphic encodings, transcription and translation
as used in the paper Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian
Hieroglyph. The data triples are extracted from the digital corpus of Egyptian texts compiled by
the project "Strukturen und ... | false | 343 | false | bbaw_egyptian | 2022-11-03T16:16:18.000Z | null | false | 8998de1d627db6005bcd1ed069e49a27f4884e4e | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:de",
"language:egy",
"language:en",
"license:cc-by-4.0",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"task_categories:translation"
] | https://huggingface.co/datasets/bbaw_egyptian/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
- egy
- en
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: BbawEgyptian
dataset_... |
null | null | @inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn an... | This dataset is used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi. | false | 346 | false | bbc_hindi_nli | 2022-11-03T16:16:03.000Z | null | false | 949f4f5a4a275108c434b6ada241f2be5cad7f2f | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"language:hi",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|bbc__hindi_news_classification",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/bbc_hindi_nli/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|bbc__hindi_news_classification
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: ... |
null | null | @article{smith2008overview,
title={Overview of BioCreative II gene mention recognition},
author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and other... | Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
Here ... | false | 452 | false | bc2gm_corpus | 2022-11-03T16:16:25.000Z | null | false | 2384629484401ecf4bb77cd808816719c424e57c | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"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/bc2gm_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: ... |
null | null | @ONLINE {beansdata,
author="Makerere AI Lab",
title="Bean disease dataset",
month="January",
year="2020",
url="https://github.com/AI-Lab-Makerere/ibean/"
} | Beans is a dataset of images of beans taken in the field using smartphone
cameras. It consists of 3 classes: 2 disease classes and the healthy class.
Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
by experts from the National Crops Resources Research Institute (NaCRRI) in
Uganda and colle... | false | 5,633 | false | beans | 2022-11-03T16:46:52.000Z | null | false | 776c899e3c76c7539fcb40e3a7eb434b97ec8ae8 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/beans/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Beans
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
dataset_info:
... |
null | null | @inproceedings{kosawat2009best,
title={BEST 2009: Thai word segmentation software contest},
author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas an... | `best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by
[NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for
[BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10).
The test set answers are ... | false | 360 | false | best2009 | 2022-11-03T16:16:07.000Z | null | false | b3962bee100fc5cc69b0c6ca6bb04d3982d5109d | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:th",
"license:cc-by-nc-sa-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:token-classification",
"tags:word-tokenization"
] | https://huggingface.co/datasets/best2009/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: null
pretty_name: best2009
tags:
- word-tokeni... |
null | null | @InProceedings{ATAMAN18.6,
author = {Duygu Ataman},
title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {may},
date = {7-12},
location = {Miyaza... | A parallel news corpus in Turkish, Kurdish and English.
Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.
3 languages, 3 bitexts
total number of files: 6
total number of tokens: 2.25M
total number of sentence fragments: 0.14M | false | 677 | false | bianet | 2022-11-03T16:31:11.000Z | bianet | false | d2418c6d3c5c8caf2fe17b2d96e4060472a3bd96 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"language:ku",
"language:tr",
"license:unknown",
"multilinguality:translation",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:translation",
"configs:en-to-ku",
"co... | https://huggingface.co/datasets/bianet/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- ku
- tr
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bianet
pretty_name: Bianet
configs:
- en-to-ku
- en-... |
null | null | OPUS and A massively parallel corpus: the Bible in 100 languages, Christos Christodoulopoulos and Mark Steedman, *Language Resources and Evaluation*, 49 (2) | This is a multilingual parallel corpus created from translations of the Bible compiled by Christos Christodoulopoulos and Mark Steedman.
102 languages, 5,148 bitexts
total number of files: 107
total number of tokens: 56.43M
total number of sentence fragments: 2.84M | false | 1,194 | false | bible_para | 2022-11-03T16:31:57.000Z | null | false | 2e74af6334810a0b4b06b7915b331996526ef538 | [] | [
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"language:bsn",
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"language:ceb",
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"languag... | https://huggingface.co/datasets/bible_para/resolve/main/README.md | ---
annotations_creators:
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- fi
- fr
- gbi
- gd
- gu
- gv
- he
- hi
- hr
- hu
- hy
- id
- is
- it
-... |
null | null | @misc{sharma2019bigpatent,
title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
author={Eva Sharma and Chen Li and Lu Wang},
year={2019},
eprint={1906.03741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | BIGPATENT, consisting of 1.3 million records of U.S. patent documents
along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification
(CPC) code. There are nine such classification categories:
A (Human Necessities), B (Performing Operations; Transporting),
C... | false | 2,114 | false | big_patent | 2022-11-03T16:32:20.000Z | bigpatent | false | 231b4ba958389d53cf1f06d6879e4f697912ce72 | [] | [
"arxiv:1906.03741",
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"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:summarizatio... | https://huggingface.co/datasets/big_patent/resolve/main/README.md | ---
annotations_creators:
- no-annotation
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language:
- en
license:
- cc-by-4.0
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- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent
conf... |
null | null | @misc{kornilova2019billsum,
title={BillSum: A Corpus for Automatic Summarization of US Legislation},
author={Anastassia Kornilova and Vlad Eidelman},
year={2019},
eprint={1910.00523},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | BillSum, summarization of US Congressional and California state bills.
There are several features:
- text: bill text.
- summary: summary of the bills.
- title: title of the bills.
features for us bills. ca bills does not have.
- text_len: number of chars in text.
- sum_len: number of chars in summary. | false | 1,771 | false | billsum | 2022-11-03T16:32:27.000Z | billsum | false | c7a127c8085475d26e30f6e6a26ab70144c846ae | [] | [
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"tags:bills-summarization"
] | https://huggingface.co/datasets/billsum/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc0-1.0
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size_categories:
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task_categories:
- summarization
task_ids: []
paperswithcode_id: billsum
pretty_name: BillSum
train-eval-index:
- config: default
task... |
null | null | null | This dataset was curated from the Bing search logs (desktop users only) over the period of Jan 1st, 2020 – (Current Month - 1). Only searches that were issued many times by multiple users were included. The dataset includes queries from all over the world that had an intent related to the Coronavirus or Covid-19. In so... | false | 574 | false | bing_coronavirus_query_set | 2022-11-03T16:30:54.000Z | null | false | 52dc8b2b3516ff47aaa5c833f2df8a17a1e8516d | [] | [
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/bing_coronavirus_query_set/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: null
pretty_name: BingCoronavirusQuerySet
datas... |
null | null | @inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical L... | We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform m... | false | 1,430 | false | biomrc | 2022-11-03T16:32:03.000Z | biomrc | false | 854fbc6074b9204c56944248440825329a47d66b | [] | [
"language:en"
] | https://huggingface.co/datasets/biomrc/resolve/main/README.md | ---
language:
- en
paperswithcode_id: biomrc
pretty_name: BIOMRC
dataset_info:
- config_name: plain_text
features:
- name: abstract
dtype: string
- name: title
dtype: string
- name: entities_list
sequence: string
- name: answer
dtype: string
splits:
- name: test
num_bytes: 147832373
... |
null | null | @article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\\u{g}}anc{\\i}o{\\u{g}}lu, Gizem and {\\"O}zt{\\"u}rk, Hakime and {\\"O}zg{\\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
yea... | BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset containing articles from the biomedical domain. The sentence pairs were eval... | false | 792 | false | biosses | 2022-11-03T16:31:20.000Z | biosses | false | 41a7d298fb653c08143706f4542dad04bebcd337 | [] | [
"annotations_creators:expert-generated",
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"language:en",
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring"
] | https://huggingface.co/datasets/biosses/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: biosses
pretty_nam... |
null | null | @misc{BritishLibraryBooks2021,
author = {British Library Labs},
title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)},
year = {2021},
publisher = {British Library},
howpublished={https://doi.org/10.23636/r7w6-zy15} | A dataset comprising of text created by OCR from the 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900.
The books cover a wide range of subject areas including philosophy, history, poetry and literature. | false | 848 | false | blbooks | 2022-11-03T16:31:29.000Z | null | false | 37e47767e1b557bdc6ffbb37115d7784f8694f22 | [] | [
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"license:cc0-1.0",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:... | https://huggingface.co/datasets/blbooks/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: British Library Books
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- other
t... |
null | null | @misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annel... | This dataset contains metadata for resources belonging to the British Library’s digitised printed books (18th-19th century) collection (bl.uk/collection-guides/digitised-printed-books).
This metadata has been extracted from British Library catalogue records.
The metadata held within our main catalogue is updated regula... | false | 1,072 | false | blbooksgenre | 2022-11-03T16:31:53.000Z | null | false | 46ac3fcb10deeb0e3a3ff0ee3ea10f889e3113ce | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:de",
"language:en",
"language:fr",
"language:nl",
"license:cc0-1.0",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_data... | https://huggingface.co/datasets/blbooksgenre/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- en
- fr
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: British Library Books Genre
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classif... |
null | null | @misc{smith2020evaluating,
title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
year={2020},
eprint={2004.08449},
archivePrefix={arXiv},
primaryClass={c... | A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. | false | 1,003 | false | blended_skill_talk | 2022-11-03T16:31:55.000Z | blended-skill-talk | false | fa728c706f828d01e179edd8c4f47197b02b1332 | [] | [
"arxiv:2004.08449",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:conversational",
"task_ids:dialogue-generation"
] | https://huggingface.co/datasets/blended_skill_talk/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BlendedSkillTalk
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: blended-s... |
null | null | @article{warstadt2019blimp,
title={BLiMP: A Benchmark of Linguistic Minimal Pairs for English},
author={Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei, and Wang, Sheng-Fu and Bowman, Samuel R},
journal={arXiv preprint arXiv:1912.00582},
year={2019}
} | BLiMP is a challenge set for evaluating what language models (LMs) know about
major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
containing 1000 minimal pairs isolating specific contrasts in syntax,
morphology, or semantics. The data is automatically generated according to
expert-crafted gr... | false | 404,095 | false | blimp | 2022-11-03T16:47:49.000Z | blimp | false | f2ac429e88c56e2627c74266edf04aa8af114937 | [] | [
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:acceptability-classification"
] | https://huggingface.co/datasets/blimp/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BLiMP
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
paperswithcode_id:... |
null | null | @inproceedings{schler2006effects,
title={Effects of age and gender on blogging.},
author={Schler, Jonathan and Koppel, Moshe and Argamon, Shlomo and Pennebaker, James W},
booktitle={AAAI spring symposium: Computational approaches to analyzing weblogs},
volume={6},
pages={199--205},
year={2006}
} | The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person.
Each blog is presented as a separate file, the name of which indicat... | false | 344 | false | blog_authorship_corpus | 2022-11-03T16:16:11.000Z | blog-authorship-corpus | false | 85fc5f9e7dcc39c8d3ed4eb5e1f75dcdbd72fe00 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
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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/blog_authorship_corpus/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: blog-authorship-corpus
pretty_name: Blog Authorship Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
-... |
null | null | @misc{karim2020classification,
title={Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network},
author={Md. Rezaul Karim and Bharathi Raja Chakravarthi and John P. McCrae and Michael Cochez},
year={2020},
eprint={2004.07807},
archiveP... | The Bengali Hate Speech Dataset is a collection of Bengali articles collected from Bengali news articles,
news dump of Bengali TV channels, books, blogs, and social media. Emphasis was placed on Facebook pages and
newspaper sources because they attract close to 50 million followers and is a common source of opinions
an... | false | 334 | false | bn_hate_speech | 2022-11-03T16:15:55.000Z | bengali-hate-speech | false | bb653b02de33ee4f146abed6130522a890080d57 | [] | [
"arxiv:2004.07807",
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"language_creators:found",
"language:bn",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"tags:hate-spee... | https://huggingface.co/datasets/bn_hate_speech/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- bn
license:
- mit
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: bengali-hate-speech
pretty_name: Bengali Hate... |
null | null | @misc{bnl_newspapers,
title={Historical Newspapers},
url={https://data.bnl.lu/data/historical-newspapers/},
author={ Bibliothèque nationale du Luxembourg}, | Digitised historic newspapers from the Bibliothèque nationale (BnL) - the National Library of Luxembourg. | false | 339 | false | bnl_newspapers | 2022-11-03T16:15:57.000Z | null | false | 2ca19bcb75d142eaab61fe88014022d984574c9c | [] | [
"annotations_creators:no-annotation",
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"language:nl",
"language:pt",
"license:cc0-1.0",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original... | https://huggingface.co/datasets/bnl_newspapers/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
- da
- de
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- nl
- pt
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: BnL Historical Newspapers
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_id... |
null | null | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE I... | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanation... | false | 8,296 | false | bookcorpus | 2022-11-03T16:47:03.000Z | bookcorpus | false | 0ac726b211812e3e07dc7532b7a59093daf0dd83 | [] | [
"arxiv:2105.05241",
"annotations_creators:no-annotation",
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"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-model... | https://huggingface.co/datasets/bookcorpus/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
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pretty_name: BookCorpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
... |
null | null | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE I... | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
This version of bookcorpus has 17868 dataset items (books). Each item contains two fields: title and... | false | 766 | false | bookcorpusopen | 2022-11-03T16:31:22.000Z | bookcorpus | false | 8c4117187d73d9d3134f136995337ba8e9ce92d6 | [] | [
"arxiv:2105.05241",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-model... | https://huggingface.co/datasets/bookcorpusopen/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BookCorpusOpen
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-model... |
null | null | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair... | false | 2,818 | false | boolq | 2022-11-03T16:32:27.000Z | boolq | false | d5c4fbdd14592821de9c02cd292a326269918251 | [] | [
"annotations_creators:crowdsourced",
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"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/boolq/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: boolq
pretty_name: BoolQ
da... |
null | null | @inproceedings{inproceedings,
author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
year = {2020},
month = {05},
pages = {},
title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
} | Dataset consisting of Polish language texts annotated to recognize brand-product relations. | false | 988 | false | bprec | 2022-11-03T16:31:46.000Z | null | false | a4fbd34bcd0bb4986a18318cc442d2f2f0c77cbf | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:pl",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval"
] | https://huggingface.co/datasets/bprec/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
paperswithcode_id: null
pretty_name: bprec
da... |
null | null | @article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
} | Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact... | false | 1,475 | false | break_data | 2022-11-03T16:32:08.000Z | break | false | ad43938c7525926ff4e34f8053491d9c9aa50158 | [] | [
"annotations_creators:crowdsourced",
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"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:open-domain-abstractive-qa"
] | https://huggingface.co/datasets/break_data/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- open-domain-abstractive-qa
paperswithcode_id: break
pretty_name: BREAK... |
null | null | @inproceedings{wagner2018brwac,
title={The brwac corpus: A new open resource for brazilian portuguese},
author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},... | The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework,
which was made public for research purposes. The current corpus version, released in January 2017, is composed by
3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource... | false | 343 | false | brwac | 2022-11-03T16:16:00.000Z | brwac | false | 56b613a900d088f45485bd5d9912794307686952 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:pt",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked... | https://huggingface.co/datasets/brwac/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: brwac
p... |
null | null | @inproceedings{rikters-etal-2019-designing,
title = "Designing the Business Conversation Corpus",
author = "Rikters, Matīss and
Ri, Ryokan and
Li, Tong and
Nakazawa, Toshiaki",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
ad... | This is the Business Scene Dialogue (BSD) dataset,
a Japanese-English parallel corpus containing written conversations
in various business scenarios.
The dataset was constructed in 3 steps:
1) selecting business scenes,
2) writing monolingual conversation scenarios according to the selected scenes, and
3) transl... | false | 338 | false | bsd_ja_en | 2022-11-03T16:15:57.000Z | business-scene-dialogue | false | 0e66986473595b5bbcd84b1b438597623232bd1a | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"language:ja",
"license:cc-by-nc-sa-4.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation",
"tags:business-conversations-translation"
... | https://huggingface.co/datasets/bsd_ja_en/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- ja
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: business-scene-dialogue
pretty_name: B... |
null | null | @misc{11356/1062,
title = {Bosnian web corpus {bsWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1062},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{... | The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and lang... | false | 335 | false | bswac | 2022-11-03T16:15:55.000Z | null | false | 818a0e388c429c0e54749e48fe1a8f6708809b28 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:bs",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:... | https://huggingface.co/datasets/bswac/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bs
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: n... |
null | null | @article{sun2019investigating,
title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/1904.0967... | Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their... | false | 502 | false | c3 | 2022-11-03T16:30:39.000Z | c3 | false | a7295a14dd30989d4e8654f9d62179020ae62a7d | [] | [
"arxiv:1904.09679",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:zh",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/c3/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- zh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: c3
pretty_name: C3
dataset_inf... |
null | null | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C4 dataset by AllenAI. | false | 13,524 | false | c4 | 2022-11-03T16:47:14.000Z | c4 | false | 920e15393295f51a42b0f87e1461ce128935e76f | [] | [
"arxiv:1910.10683",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:odc-by",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeli... | https://huggingface.co/datasets/c4/resolve/main/README.md | ---
pretty_name: C4
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- odc-by
multilinguality:
- multilingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswit... |
null | null | @misc{xiao2018cail2018,
title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},
author={Chaojun Xiao and Haoxi Zhong and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},
year={2018},
eprint={180... | In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),
the first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million
criminal cases published by the Supreme People's Court of China, which are several times larger than other
datasets in existing works on j... | false | 385 | false | cail2018 | 2022-11-03T16:16:15.000Z | chinese-ai-and-law-cail-2018 | false | 12b79304c67439ddbea052ffb16cc19b6c9ddc89 | [] | [
"arxiv:1807.02478",
"annotations_creators:found",
"language_creators:found",
"language:zh",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:other",
"tags:judgement-prediction"
] | https://huggingface.co/datasets/cail2018/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- zh
license:
- unknown
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- monolingual
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- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: chinese-ai-and-law-cail-2018
pretty_name: CAIL 2018
tags:
- judgement-prediction
... |
null | null | @article{article,
author = {Salah, Ramzi and Zakaria, Lailatul},
year = {2018},
month = {12},
pages = {},
title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
volume = {96},
journal = {Journal of Theoretical and Applied Information Technology}
} | Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities. | false | 368 | false | caner | 2022-11-03T16:31:22.000Z | null | false | ca4f5eea949d7254705c1c26aede38b0e7f6aa70 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/caner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: C... |
null | null | @inproceedings{soares2018parallel,
title={A Parallel Corpus of Theses and Dissertations Abstracts},
author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={345--352},
year={2018},
... | A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were collected and aligned using the Huna... | false | 336 | false | capes | 2022-11-03T16:15:53.000Z | capes | false | 2b60fcb29e0ca31883424866bce10e3d0e94f5c9 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"language:pt",
"license:unknown",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:translation",
"tags:dissertation-abstracts-translation",
"tags:theses-translation"
] | https://huggingface.co/datasets/capes/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- pt
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: capes
pretty_name: CAPES
tags:
- dissertation-abstracts-translation
-... |
null | null | @inproceedings{chawla2021casino,
title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems},
author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan},
booktitle={Proceedings of the 2021 Conference of the North American Cha... | We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistical... | false | 360 | false | casino | 2022-11-03T16:16:00.000Z | casino | false | 512596943b4c783831b5929b71316ea84682df40 | [] | [
"annotations_creators:expert-generated",
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"language:en",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:fill-mask",
... | https://huggingface.co/datasets/casino/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- conversational
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
pretty_name: Campsite Ne... |
null | null | @inproceedings{zotova-etal-2020-multilingual,
title = "Multilingual Stance Detection in Tweets: The {C}atalonia Independence Corpus",
author = "Zotova, Elena and
Agerri, Rodrigo and
Nunez, Manuel and
Rigau, German",
booktitle = "Proceedings of the 12th Language Resources and Evaluation ... | This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
... | false | 505 | false | catalonia_independence | 2022-11-03T16:30:39.000Z | cic | false | b398582d4853293f9f6902ff7a33c40c37c2240b | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:ca",
"language:es",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"configs:catalan",
"configs:spanish"... | https://huggingface.co/datasets/catalonia_independence/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ca
- es
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: cic
pretty_name: Catalonia Indepen... |
null | null | @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th A... | null | false | 945 | false | cats_vs_dogs | 2022-11-03T16:31:29.000Z | cats-vs-dogs | false | 8a8e6794dcbeb2a71280bba3c869915662607acf | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/cats_vs_dogs/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Cats Vs. Dogs
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswith... |
null | null | @inproceedings{DBLP:conf/lrec/LjubesicT14,
author = {Nikola Ljubesic and
Antonio Toral},
editor = {Nicoletta Calzolari and
Khalid Choukri and
Thierry Declerck and
Hrafn Loftsson and
Bente Maegaard and
Joseph Mariani and
... | caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013. | false | 337 | false | cawac | 2022-11-03T16:15:53.000Z | cawac | false | b8fe93682658cef962fc065ddc8c8e5ec8411bd7 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:ca",
"license:cc-by-sa-3.0",
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"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids... | https://huggingface.co/datasets/cawac/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ca
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: ... |
null | null | @misc{hill2016goldilocks,
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
year={2016},
eprint={1511.02301},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | The Children’s Book Test (CBT) is designed to measure directly
how well language models can exploit wider linguistic context.
The CBT is built from books that are freely available. | false | 2,272 | false | cbt | 2022-11-03T16:32:19.000Z | cbt | false | d5524434a16e0ee2b3d05f443fc4a24c18ae7848 | [] | [
"arxiv:1511.02301",
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:gfdl",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:other",
"task_categories:question-answering",
... | https://huggingface.co/datasets/cbt/resolve/main/README.md | ---
pretty_name: Children’s Book Test (CBT)
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gfdl
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- n<1K
source_datasets:
- original
task_categories:
- other
- question-answering
task_ids:
- multiple-choice-qa
pape... |
null | null | @inproceedings{conneau-etal-2020-unsupervised,
title = "Unsupervised Cross-lingual Representation Learning at Scale",
author = "Conneau, Alexis and
Khandelwal, Kartikay and
Goyal, Naman and
Chaudhary, Vishrav and
Wenzek, Guillaume and
Guzm{'a}n, Francisco and
Grave, Edo... | This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-D... | false | 1,247 | false | cc100 | 2022-11-03T16:31:45.000Z | cc100 | false | 0a978215471a5d4b62b3685c7dfb00283fdc231b | [] | [
"annotations_creators:no-annotation",
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"language:af",
"language:am",
"language:ar",
"language:as",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"language:ca",
"language:cs",
"language:cy",
"language:da",
"language... | https://huggingface.co/datasets/cc100/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gn
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
-... |
null | null | @InProceedings{Hamborg2017,
author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela},
title = {news-please: A Generic News Crawler and Extractor},
year = {2017},
booktitle = {Proceedings of the 15th International Symposium of Information Science},
location = {Ber... | CC-News containing news articles from news sites all over the world The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has 708241 articles. It represents a small portion of English language subset of the CC-News dataset created using news-please(Hamborg et a... | false | 2,465 | false | cc_news | 2022-11-03T16:46:51.000Z | cc-news | false | 20891d6bb7f44212fcbd5a963e1b029965704c21 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:maske... | https://huggingface.co/datasets/cc_news/resolve/main/README.md | ---
pretty_name: CC-News
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pape... |
null | null | @inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection o... | CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching ap... | false | 1,164 | false | ccaligned_multilingual | 2022-11-03T16:31:56.000Z | ccaligned | false | 0de0120bbfd3c364007448f60f1d27133b45f4e5 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:af",
"language:ak",
"language:am",
"language:ar",
"language:as",
"language:ay",
"language:az",
"language:be",
"language:bg",
"language:bm",
"language:bn",
"language:br",
"language:bs",
"language:ca",
"language... | https://huggingface.co/datasets/ccaligned_multilingual/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- ak
- am
- ar
- as
- ay
- az
- be
- bg
- bm
- bn
- br
- bs
- ca
- ceb
- ckb
- cs
- cy
- de
- dv
- el
- eo
- es
- fa
- ff
- fi
- fo
- fr
- fy
- ga
- gl
- gn
- gu
- he
- hi
- hr
- hu
- id
- ig
- is
- it
- iu
- ja
- ka
- kac
- kg
- kk
- k... |
null | null | @inproceedings{wroblewska2017polish,
title={Polish evaluation dataset for compositional distributional semantics models},
author={Wr{\'o}blewska, Alina and Krasnowska-Kiera{\'s}, Katarzyna},
booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
page... | Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (20... | false | 498 | false | cdsc | 2022-11-03T16:30:39.000Z | polish-cdscorpus | false | 4b307f9b7d580dde85f352638f6dc799673037df | [] | [
"annotations_creators:expert-generated",
"language_creators:other",
"language:pl",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:other",
"tags:sentences entailment and relatedness"
] | https://huggingface.co/datasets/cdsc/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: polish-cdscorpus
pretty_name: Polish CDSCorpus
tags:
- sente... |
null | null | @article{ptaszynski2019results,
title={Results of the PolEval 2019 Shared Task 6: First Dataset and Open Shared Task for Automatic Cyberbullying Detection in Polish Twitter},
author={Ptaszynski, Michal and Pieciukiewicz, Agata and Dybala, Pawel},
journal={Proceedings of the PolEval 2019 Workshop},
publisher={Institute ... | The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content. | false | 337 | false | cdt | 2022-11-03T16:15:50.000Z | null | false | b8b3bc4a8c6ffd7b8ff3ab3580d1b72036fa9566 | [] | [
"annotations_creators:expert-generated",
"language_creators:other",
"language:pl",
"license:bsd-3-clause",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/cdt/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: cdt
dat... |
null | null | @article{sboev2021data,
title={Data-Driven Model for Emotion Detection in Russian Texts},
author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
journal={Procedia Computer Science},
volume={190},
pages={637--642},
year={2021},
publisher={Elsevier}
} | This new dataset is designed to solve emotion recognition task for text data in Russian. The Corpus for Emotions Detecting in
Russian-language text sentences of different social sources (CEDR) contains 9410 sentences in Russian labeled for 5 emotion
categories. The data collected from different sources: posts of the Li... | false | 556 | false | cedr | 2022-11-03T16:30:42.000Z | null | false | 60f260a9bcec2ea075887973c14c7f62babaae06 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ru",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classificati... | https://huggingface.co/datasets/cedr/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: The Corpus for Emotions Detecting in Russian-language text sentences
(CEDR)
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classificatio... |
null | null | @inproceedings{Keysers2020,
title={Measuring Compositional Generalization: A Comprehensive Method on
Realistic Data},
author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and
Hylke Buisman and Daniel Furrer and Sergii Kashubin and
Nikola Momchev and Danila Sinopalnikov and... | The CFQ dataset (and it's splits) for measuring compositional generalization.
See https://arxiv.org/abs/1912.09713.pdf for background.
Example usage:
data = datasets.load_dataset('cfq/mcd1') | false | 1,581 | false | cfq | 2022-11-03T16:32:12.000Z | cfq | false | 7f19935e0d16acb9e047bedc34e978033196189c | [] | [
"arxiv:1912.09713",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:other",
"task_ids:ope... | https://huggingface.co/datasets/cfq/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Compositional Freebase Questions
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
- other
task_ids:
- open-domain-... |
null | null | @inproceedings{zhang2020chren,
title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
booktitle={EMNLP2020},
year={2020}
} | ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
ChrEn also contains 5k Cherokee monolingual data to enabl... | false | 839 | false | chr_en | 2022-10-28T16:30:27.000Z | chren | false | 44d971c06ee38cc61bab9a2376c6f6fe0c4c8aad | [] | [
"arxiv:2010.04791",
"annotations_creators:expert-generated",
"annotations_creators:found",
"annotations_creators:no-annotation",
"language_creators:found",
"language:chr",
"language:en",
"license:other",
"multilinguality:monolingual",
"multilinguality:multilingual",
"multilinguality:translation"... | https://huggingface.co/datasets/chr_en/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- found
- no-annotation
language_creators:
- found
language:
- chr
- en
license:
- other
multilinguality:
- monolingual
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- fill-mask
- text-generatio... |
null | null | @TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
} | The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
per class. There are 50000 training images and 10000 test images. | false | 15,958 | false | cifar10 | 2022-11-03T16:47:03.000Z | cifar-10 | false | 1d021ec65081bd084eb526c3dc5fc5934ec816be | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-80-Million-Tiny-Images",
"task_categories:image-classification"
] | https://huggingface.co/datasets/cifar10/resolve/main/README.md | ---
pretty_name: Cifar10
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-80-Million-Tiny-Images
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-1... |
null | null | @TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
} | The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (act... | false | 3,634 | false | cifar100 | 2022-11-03T16:46:41.000Z | cifar-100 | false | f90290ff746108cbf7c51241dc854ba9a118d999 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-80-Million-Tiny-Images",
"task_categories:image-classification"
] | https://huggingface.co/datasets/cifar100/resolve/main/README.md | ---
pretty_name: Cifar100
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-80-Million-Tiny-Images
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-... |
null | null | @InProceedings{louis_emnlp2020,
author = "Annie Louis and Dan Roth and Filip Radlinski",
title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing",
year = "2020",
} | The Circa (meaning ‘approximately’) dataset aims to help machine learning systems
to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with
annotations for the interpretation of the answer. The data is collected in 10
di... | false | 1,007 | false | circa | 2022-11-03T16:31:45.000Z | circa | false | 932ad9cb99eb3220642e76dfc5b42de8b1dbcc66 | [] | [
"arxiv:2010.03450",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
... | https://huggingface.co/datasets/circa/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: circa
pretty_name: ... |
null | null | @article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classificati... | The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make t... | false | 431 | false | civil_comments | 2022-11-03T16:16:28.000Z | null | false | 3d2ec16f0370b85ad95edc154b7abd58112ae233 | [] | [
"arxiv:1903.04561",
"language:en"
] | https://huggingface.co/datasets/civil_comments/resolve/main/README.md | ---
language:
- en
paperswithcode_id: null
pretty_name: CivilComments
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float32
- name: severe_toxicity
dtype: float32
- name: obscene
dtype: float32
- name: threat
dtype: float32
- name: insult
dtype: float32... |
null | null | @InProceedings{clickbait_news_bg,
title = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.},
authors={Data Science Society},
year={2017},
url={https://gitlab.com/datasciencesociety/case_fake_news/}
} | Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017. | false | 335 | false | clickbait_news_bg | 2022-11-03T16:15:37.000Z | null | false | fdd92fa897f0868cd9f4d2d4d858635e0cca92cf | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:bg",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:fact-checking"
] | https://huggingface.co/datasets/clickbait_news_bg/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- bg
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: Clickbait/Fake... |
null | null | @misc{diggelmann2020climatefever,
title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims},
author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold},
year={2020},
eprint={2012.00614},
archivePrefix={arXiv},
... | A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim ... | false | 1,418 | false | climate_fever | 2022-11-03T16:32:17.000Z | climate-fever | false | 3c1e08bc209d9590ce4e02636fa094d742ed40b3 | [] | [
"arxiv:2012.00614",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia",
"source_datasets:original",
"task_catego... | https://huggingface.co/datasets/climate_fever/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- text-scoring
- fact-che... |
null | null | @inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerf... | This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in... | false | 4,402 | false | clinc_oos | 2022-11-03T16:46:50.000Z | clinc150 | false | 62854bcf4e7a60e62d7d91c61bc8f2158e92a94b | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/clinc_oos/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: clinc150
pretty_name: CL... |
null | null | @misc{xu2020clue,
title={CLUE: A Chinese Language Understanding Evaluation Benchmark},
author={Liang Xu and Xuanwei Zhang and Lu Li and Hai Hu and Chenjie Cao and Weitang Liu and Junyi Li and Yudong Li and Kai Sun and Yechen Xu and Yiming Cui and Cong Yu and Qianqian Dong and Yin Tian and Dian Yu and Bo Shi and... | CLUE, A Chinese Language Understanding Evaluation Benchmark
(https://www.cluebenchmarks.com/) is a collection of resources for training,
evaluating, and analyzing Chinese language understanding systems. | false | 3,088 | false | clue | 2022-11-03T16:32:39.000Z | clue | false | 5f1ba05ee11e560d1f8cacad12b518eac88a5d62 | [] | [
"annotations_creators:other",
"language:zh",
"language_creators:other",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_ids:topic-classification",
"task_ids:... | https://huggingface.co/datasets/clue/resolve/main/README.md | ---
annotations_creators:
- other
language:
- zh
language_creators:
- other
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'CLUE: Chinese Language Understanding Evaluation benchmark'
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- multiple-choice
task_id... |
null | null | @inproceedings{cui-emnlp2019-cmrc2018,
title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},
author = {Cui, Yiming and
Liu, Ting and
Che, Wanxiang and
Xiao, Li and
Chen, Zhipeng and
Ma, Wentao and
Wang, Shijin and
Hu, Guoping},
book... | A Span-Extraction dataset for Chinese machine reading comprehension to add language
diversities in this area. The dataset is composed by near 20,000 real questions annotated
on Wikipedia paragraphs by human experts. We also annotated a challenge set which
contains the questions that need comprehensive understanding and... | false | 1,043 | false | cmrc2018 | 2022-11-03T16:31:17.000Z | cmrc-2018 | false | 3bf0f5d49a79a4d50ff5b5c4cb3764aa74c4f3c2 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:zh",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/cmrc2018/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- zh
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: cmrc-2018
pretty_name: Chinese Mac... |
null | null | @inproceedings{cmu_dog_emnlp18,
title={A Dataset for Document Grounded Conversations},
author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
}
@inproceedings{khanuja-etal-2020-glu... | This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English only versions. Can be used for Translating between the two. | false | 343 | false | cmu_hinglish_dog | 2022-11-03T16:15:46.000Z | null | false | 9207e087c42d3770494c3fd56371d5fcd628509f | [] | [
"arxiv:1809.07358",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:en",
"language:hi",
"license:cc-by-sa-3.0",
"license:gfdl",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_cat... | https://huggingface.co/datasets/cmu_hinglish_dog/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
- hi
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
- translation
pretty_name: CMU Document Grounded Conversations
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_id... |
null | null | @article{DBLP:journals/corr/SeeLM17,
author = {Abigail See and
Peter J. Liu and
Christopher D. Manning},
title = {Get To The Point: Summarization with Pointer-Generator Networks},
journal = {CoRR},
volume = {abs/1704.04368},
year = {2017},
url = {http://a... | CNN/DailyMail non-anonymized summarization dataset.
There are two features:
- article: text of news article, used as the document to be summarized
- highlights: joined text of highlights with <s> and </s> around each
highlight, which is the target summary | false | 50,758 | false | cnn_dailymail | 2022-11-03T16:47:40.000Z | cnn-daily-mail-1 | false | 58cb4181686371689c46c9e9610f03a451e466e4 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/cnn_dailymail/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN ... |
null | null | @inproceedings{48414,
title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},
author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},
year = {2019},
booktitle = {Proceedings of the Annual SIGdial Meeting on Discourse and Dialogue}
} | A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing
movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers,
where one worker plays the role of an 'assistant', while the other plays the ro... | false | 337 | false | coached_conv_pref | 2022-11-03T16:15:35.000Z | coached-conversational-preference-elicitation | false | 94f3e06935df7c6f5db5af8fbb1fb11a97e596e3 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:... | https://huggingface.co/datasets/coached_conv_pref/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
- token-classification
task_ids:
- dialogue-modeling
- parsing
paperswi... |
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