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null | null | null | null | false | 926 | false | quora | 2022-11-03T16:31:49.000Z | null | false | 1307d955079a8c398f31bc000fe59a85bd6f11f8 | [] | [
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"task_categories:text-classification",
"task_ids:semantic-similarity-classification"
] | https://huggingface.co/datasets/quora/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- unknown
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- monolingual
pretty_name: Quora Question Pairs
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
papers... |
null | null | @article{allenai:quoref,
author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner},
title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
journal = {arXiv:1908.05803v2 },
year = {2019},
} | Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
coreferences before selecting the appropriate span(s) in the paragraphs for answering ques... | false | 28,838 | false | quoref | 2022-11-03T16:47:33.000Z | quoref | false | 404aa8c70fca4ee56052c8dcd0184d5378183521 | [] | [
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"task_categories:question-answering",
"tags:coreference-resolution"
] | https://huggingface.co/datasets/quoref/resolve/main/README.md | ---
annotations_creators:
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language:
- en
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- cc-by-4.0
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pretty_name: Quoref
size_categories:
- 10K<n<100K
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- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quoref
tags:
- coreference-resolution... |
null | null | @article{lai2017large,
title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},
author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},
journal={arXiv preprint arXiv:1704.04683},
year={2017}
} | Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The
dataset is collected from English examinations in China, which are designed for middle school and high school students.
The dataset can be served as the training and test sets for machine comprehension. | false | 40,391 | false | race | 2022-11-03T16:47:44.000Z | race | false | adb54bd3c4ba05646dda98d71dceb66b84c7386e | [] | [
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"task_categories:multiple-choice",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/race/resolve/main/README.md | ---
annotations_creators:
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pretty_name: RACE
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- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: race
dataset_info:
- con... |
null | null | @inproceedings{li2018conversational,
title={Towards Deep Conversational Recommendations},
author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris},
booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)},
year={2018}
} | ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users
recommend movies to each other. The dataset was collected by a team of researchers working at
Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI.
The dataset allows research at... | false | 333 | false | re_dial | 2022-11-03T16:15:35.000Z | redial | false | 3ff5c691e0ea850741849a79a3b7df7d9f628db4 | [] | [
"annotations_creators:crowdsourced",
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"task_categories:other",
"task_categories:text-classification",
"task_ids:sentiment-classification",
... | https://huggingface.co/datasets/re_dial/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
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source_datasets:
- original
task_categories:
- other
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: redial
pretty_nam... |
null | null | @article{hardalov2019beyond,
title={Beyond english-only reading comprehension: Experiments in zero-shot multilingual transfer for bulgarian},
author={Hardalov, Momchil and Koychev, Ivan and Nakov, Preslav},
journal={arXiv preprint arXiv:1908.01519},
year={2019}
} | This new dataset is designed to do reading comprehension in Bulgarian language. | false | 952 | false | reasoning_bg | 2022-11-03T16:31:39.000Z | null | false | 255c1d7a993c50c729bf1293e1a236c629d63cd2 | [] | [
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"source_datasets:original",
"task_categories:question-answering",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/reasoning_bg/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
license:
- apache-2.0
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- monolingual
size_categories:
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- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: null
pretty_name: ReasoningBg
dataset_info:
- confi... |
null | null | @inproceedings{bien-etal-2020-recipenlg,
title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation",
author = "Bie{'n}, Micha{l} and
Gilski, Micha{l} and
Maciejewska, Martyna and
Taisner, Wojciech and
Wisniewski, Dawid and
Lawrynowicz, Agnieszka",
booktitle = "Proceedings of t... | The dataset contains 2231142 cooking recipes (>2 millions). It's processed in more careful way and provides more samples than any other dataset in the area. | false | 403 | false | recipe_nlg | 2022-11-03T16:16:22.000Z | recipenlg | false | 7088df593941f4aca5283e5848964dcd6e3280cf | [] | [
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"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categori... | https://huggingface.co/datasets/recipe_nlg/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
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size_categories:
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task_categories:
- text2text-generation
- text-generation
- fill-mask
- text-retrieval
- summarization
task_ids:
- document-retrieval
- en... |
null | null | @inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year ... | Logical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary
language as the definition from LSAC. ReClor is a dataset extracted from logical reasoning questions of standardized graduate
admission examinations. Empirical results show that the state-of-the-ar... | false | 543 | false | reclor | 2022-11-03T16:31:11.000Z | reclor | false | 62ffbfe3890569fa46e966ddbb4d9d5f04eaea82 | [] | [] | https://huggingface.co/datasets/reclor/resolve/main/README.md | ---
paperswithcode_id: reclor
pretty_name: ReClor
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features:
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dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: label
dtype: string
- name: id_string
dtype: string
splits:
- name: test
num_bytes: 1017354
num_exam... |
null | null | @misc{desai2021redcaps,
title={RedCaps: web-curated image-text data created by the people, for the people},
author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
year={2021},
eprint={2111.11431},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
Images and captions from Reddit depict and describe a wide variety of objects and scenes.
The data is collected from a manually curated set of subreddits (350 total),
which give coarse image labels and allow steering of the dataset composit... | false | 272,837 | false | red_caps | 2022-11-03T16:47:48.000Z | redcaps | false | c1abc294b2f8776df76539127c11653db238912d | [] | [
"arxiv:2111.11431",
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"language_creators:found",
"language:en",
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"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:image-to-text",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/red_caps/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
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- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: redcaps
pretty_name: RedCaps
dataset_info:
features... |
null | null | @inproceedings{volske-etal-2017-tl,
title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},
author = {V{\"o}lske, Michael and Potthast, Martin and Syed, Shahbaz and Stein, Benno},
booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
month = {sep},
year = {2017},... | This corpus contains preprocessed posts from the Reddit dataset.
The dataset consists of 3,848,330 posts with an average length of 270 words for content,
and 28 words for the summary.
Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
Content is used as document and sum... | false | 1,175 | false | reddit | 2022-11-03T16:32:02.000Z | null | false | 75ec0e2f0788b6e9aaf8118104a905d2f30057ac | [] | [
"annotations_creators:no-annotation",
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"language:en",
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"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:summarization",
"tags:reddit-posts-summarization"
] | https://huggingface.co/datasets/reddit/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Reddit Webis-TLDR-17
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
train-eval-index:... |
null | null | @misc{kim2018abstractive,
title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
year={2018},
eprint={1811.00783},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
As defined in the publication, styel "short" uses title as summary and
"long" uses tldr as summary.
Features includes:
- document: post text without tldr.
- tldr: tldr line.
- title: trimmed title without tldr.
- ups: upvotes.
- score: score.... | false | 672 | false | reddit_tifu | 2022-11-03T16:31:19.000Z | reddit-tifu | false | 452cb6cdd2b404524835ad1df68ad5433ea6ea23 | [] | [
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"multilinguality:monolingual",
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"source_datasets:original",
"task_categories:summarization",
"tags:reddit-posts-summarization"
] | https://huggingface.co/datasets/reddit_tifu/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Reddit TIFU
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: reddit-tifu
tags:
- reddit-posts-summ... |
null | null | @inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing ... | The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039
English-French sentence-pairs annotated with sentence-level divergence judgments and token-level
rationales. For any questions, write to ebriakou@cs.umd.edu. | false | 323 | false | refresd | 2022-11-03T16:07:52.000Z | refresd | false | 8f277915961b47d16c7b1fa8b1a6106d6853ef55 | [] | [
"arxiv:1907.05791",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"language:en",
"language:fr",
"license:mit",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:ext... | https://huggingface.co/datasets/refresd/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- fr
license:
- mit
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-wikimatrix
task_categories:
- text-classification
task_ids:
- semantic-simila... |
null | null | @article{APTE94,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{\'{e}} and Fred Damera... | The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987. | false | 1,145 | false | reuters21578 | 2022-11-03T16:31:29.000Z | reuters-21578 | false | deb1f6c2050dd068df89e6153062ce7035a0c781 | [] | [
"language:en"
] | https://huggingface.co/datasets/reuters21578/resolve/main/README.md | ---
pretty_name: Reuters-21578 Text Categorization Collection
language:
- en
paperswithcode_id: reuters-21578
dataset_info:
- config_name: ModHayes
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: c... |
null | null | @InProceedings{lin-etal-2021-riddlesense,
title={RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge},
author={Lin, Bill Yuchen and Wu, Ziyi and Yang, Yichi and Lee, Dong-Ho and Ren, Xiang},
journal={Proceedings of the 59th Annual Meeting of the Association for Comput... | Answering such a riddle-style question is a challenging cognitive process, in that it requires
complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning
skills, which are all important abilities for advanced natural language understanding (NLU). However,
there is cur... | false | 575 | false | riddle_sense | 2022-11-03T16:30:49.000Z | null | false | 6f66492b6e000a27653524621795669d64d2e4dd | [] | [
"annotations_creators:crowdsourced",
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"task_categories:question-answering",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/riddle_sense/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: RiddleSense
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
dataset_info:
features:
- name: ans... |
null | null | @article{dumitrescu2020birth,
title={The birth of Romanian BERT},
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius and Pyysalo, Sampo},
journal={arXiv preprint arXiv:2009.08712},
year={2020}
} | This dataset is a Romanian Sentiment Analysis dataset.
It is present in a processed form, as used by the authors of `Romanian Transformers`
in their examples and based on the original data present in
`https://github.com/katakonst/sentiment-analysis-tensorflow`. The original dataset is collected
from product and movie r... | false | 324 | false | ro_sent | 2022-11-03T16:08:01.000Z | null | false | bc919126b6d549fbcad8e6ea2b06d5e33f94a6ac | [] | [
"arxiv:2009.08712",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/ro_sent/resolve/main/README.md | ---
annotations_creators:
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language_creators:
- found
language:
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license:
- unknown
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- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: RoSent
dataset_info:
... |
null | null | @inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and oth... | The RO-STS (Romanian Semantic Textual Similarity) dataset contains 8628 pairs of sentences with their similarity score. It is a high-quality translation of the STS benchmark dataset. | false | 318 | false | ro_sts | 2022-11-03T16:07:47.000Z | null | false | 4fbedb035660b25c2eac185f2140e9d524942101 | [] | [
"annotations_creators:crowdsourced",
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"language:ro",
"license:cc-by-4.0",
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"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarit... | https://huggingface.co/datasets/ro_sts/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ro
license:
- cc-by-4.0
multilinguality:
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size_categories:
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source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_i... |
null | null | @inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and oth... | The RO-STS-Parallel (a Parallel Romanian English dataset - translation of the Semantic Textual Similarity) contains 17256 sentences in Romanian and English. It is a high-quality translation of the English STS benchmark dataset into Romanian. | false | 320 | false | ro_sts_parallel | 2022-11-03T16:07:48.000Z | null | false | 4d2806a87046ac13d13603310a88cc9aec6e2e50 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"language:ro",
"license:cc-by-4.0",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-sts-b",
"task_categories:translation"
] | https://huggingface.co/datasets/ro_sts_parallel/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
- ro
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-sts-b
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: RO-STS-Parallel
datas... |
null | null | @InProceedings{Sharf:2018,
title = "Performing Natural Language Processing on Roman Urdu Datasets",
authors = "Zareen Sharf and Saif Ur Rahman",
booktitle = "International Journal of Computer Science and Network Security",
volume = "18",
number = "1",
pages = "141-148",
year = "2018"
}
@misc{Dua:2019,
author = "Dua, D... | This is an extensive compilation of Roman Urdu Dataset (Urdu written in Latin/Roman script) tagged for sentiment analysis. | false | 322 | false | roman_urdu | 2022-11-03T16:07:52.000Z | roman-urdu-data-set | false | 9aa897982eef3b6a20f99718aedf5fe6684afea5 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ur",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/roman_urdu/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ur
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: roman-urdu-data-set
pretty_name: R... |
null | null | @article{dumitrescu2019introducing,
title={Introducing RONEC--the Romanian Named Entity Corpus},
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius},
journal={arXiv preprint arXiv:1909.01247},
year={2019}
} | RONEC - the Romanian Named Entity Corpus, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. It is used for named entity recognition and represents the largest Romanian NER corpus to date. | false | 336 | false | ronec | 2022-11-03T16:16:18.000Z | ronec | false | 7f4068f5f0ca6f04ef614e2455e530d09a112031 | [] | [
"arxiv:1909.01247",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"language:ro",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:nam... | https://huggingface.co/datasets/ronec/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- found
language:
- ro
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ronec
pretty_nam... |
null | null | @inproceedings{Lin2019ReasoningOP,
title={Reasoning Over Paragraph Effects in Situations},
author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner},
booktitle={MRQA@EMNLP},
year={2019}
} | ROPES (Reasoning Over Paragraph Effects in Situations) is a QA dataset
which tests a system's ability to apply knowledge from a passage
of text to a new situation. A system is presented a background
passage containing a causal or qualitative relation(s) (e.g.,
"animal pollinators increase efficiency of fertilization in... | false | 30,999 | false | ropes | 2022-11-03T16:47:35.000Z | ropes | false | 2fdc5ed1aa6e87c49802fa75e0bca254286cb67b | [] | [
"arxiv:1908.05852",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"source_datasets:original",
"task_categorie... | https://huggingface.co/datasets/ropes/resolve/main/README.md | ---
pretty_name: ROPES
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswi... |
null | null | @InProceedings{Pang+Lee:05a,
author = {Bo Pang and Lillian Lee},
title = {Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales},
booktitle = {Proceedings of the ACL},
year = 2005
} | Movie Review Dataset.
This is a dataset of containing 5,331 positive and 5,331 negative processed
sentences from Rotten Tomatoes movie reviews. This data was first used in Bo
Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for
sentiment categorization with respect to rating scales.'', Proceedings o... | false | 74,524 | false | rotten_tomatoes | 2022-11-03T16:47:40.000Z | mr | false | eabf37641264e277b2b220d730fd9b1726360ff7 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"task_categories:text-classification",
"task_ids:sentiment-classification",
"size_categories:1K<n<10K",
"source_datasets:original"
] | https://huggingface.co/datasets/rotten_tomatoes/resolve/main/README.md | ---
pretty_name: RottenTomatoes - MR Movie Review Data
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: mr
size_categories:
- 1K<n<10K
sou... |
null | null | @article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova,
Ekaterina and Malykh, Valentin and Mikhailo... | Recent advances in the field of universal language models and transformers require the development of a methodology for
their broad diagnostics and testing for general intellectual skills - detection of natural language inference,
commonsense reasoning, ability to perform simple logical operations regardless of text su... | false | 1,914 | false | russian_super_glue | 2022-11-03T16:32:16.000Z | null | false | 2a0e2a045bcd57c23cf1bfe7ee2e34f19a1e690e | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:ru",
"language_bcp47:ru-RU",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"siz... | https://huggingface.co/datasets/russian_super_glue/resolve/main/README.md | ---
pretty_name: Russian SuperGLUE
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
language_bcp47:
- ru-RU
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
source_datasets:
- origina... |
null | null | @misc{lo2020s2orc,
title={S2ORC: The Semantic Scholar Open Research Corpus},
author={Kyle Lo and Lucy Lu Wang and Mark Neumann and Rodney Kinney and Dan S. Weld},
year={2020},
eprint={1911.02782},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | A large corpus of 81.1M English-language academic papers spanning many academic disciplines.
Rich metadata, paper abstracts, resolved bibliographic references, as well as structured full
text for 8.1M open access papers. Full text annotated with automatically-detected inline mentions of
citations, figures, and tables, ... | false | 472 | false | s2orc | 2022-11-03T16:16:20.000Z | s2orc | false | bc40149c457607a20e58292de100c16e41872f5a | [] | [
"arxiv:1911.02782",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-2.0",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"task_categories:other",
"task_categories:text-generation",
"task_categories... | https://huggingface.co/datasets/s2orc/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-... |
null | null | @article{gliwa2019samsum,
title={SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization},
author={Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander},
journal={arXiv preprint arXiv:1911.12237},
year={2019}
} | SAMSum Corpus contains over 16k chat dialogues with manually annotated
summaries.
There are two features:
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: id of a example. | false | 25,954 | false | samsum | 2022-11-03T16:47:29.000Z | samsum-corpus | false | c86cd37d075567f051cfb0b2cc75c36279a4627b | [] | [
"arxiv:1911.12237",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-nc-nd-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"tags:conversations-summarization"
... | https://huggingface.co/datasets/samsum/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: samsum-corpus
pretty_name: SAMSum Corpus
... |
null | null | @Misc{johnsonetal2014,
author = {Johnson, Kyle P. and Patrick Burns and John Stewart and Todd Cook},
title = {CLTK: The Classical Language Toolkit},
url = {https://github.com/cltk/cltk},
year = {2014--2020},
} | This dataset combines some of the classical Sanskrit texts. | false | 322 | false | sanskrit_classic | 2022-11-03T16:07:56.000Z | null | false | b1d601f145c84d035ec1a67e78c4cddee1fa98f4 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:sa",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-... | https://huggingface.co/datasets/sanskrit_classic/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- sa
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pre... |
null | null | @misc{hagrima2015,
author = "M. Alhagri",
title = "Saudi Newspapers Arabic Corpus (SaudiNewsNet)",
year = 2015,
url = "http://github.com/ParallelMazen/SaudiNewsNet"
} | The dataset contains a set of 31,030 Arabic newspaper articles alongwith metadata, extracted from various online Saudi newspapers and written in MSA. | false | 333 | false | saudinewsnet | 2022-11-03T16:15:49.000Z | null | false | c2625dd8aefeeb4c61395889e711f76e4e2cba86 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:mask... | https://huggingface.co/datasets/saudinewsnet/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
... |
null | null | @article{Efimov_2020,
title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
ISBN={9783030582197},
ISSN={1611-3349},
url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
DOI={10.1007/978-3-030-58219-7_1},
journal={Experimental IR Meets Multilinguality, Multimodality, and I... | Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original a... | false | 1,150 | false | sberquad | 2022-11-03T16:31:28.000Z | sberquad | false | 5dcac8ca44399ba6bd5a3faf2511a78358cbf4fd | [] | [
"arxiv:1912.09723",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:crowdsourced",
"language:ru",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extract... | https://huggingface.co/datasets/sberquad/resolve/main/README.md | ---
pretty_name: SberQuAD
annotations_creators:
- crowdsourced
language_creators:
- found
- crowdsourced
language:
- ru
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: sberquad
... |
null | null | @inproceedings{Lake2018GeneralizationWS,
title={Generalization without Systematicity: On the Compositional Skills of
Sequence-to-Sequence Recurrent Networks},
author={Brenden M. Lake and Marco Baroni},
booktitle={ICML},
year={2018},
url={https://arxiv.org/pdf/1711.00350.pdf},
} | SCAN tasks with various splits.
SCAN is a set of simple language-driven navigation tasks for studying
compositional learning and zero-shot generalization.
See https://github.com/brendenlake/SCAN for a description of the splits.
Example usage:
data = datasets.load_dataset('scan/length') | false | 4,352 | false | scan | 2022-11-03T16:46:45.000Z | scan | false | 4334976aacd56b5049781431eafd438f031ace9b | [] | [
"arxiv:1711.00350",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:en",
"license:bsd",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"configs:addprim_jump",
"configs:addprim_... | https://huggingface.co/datasets/scan/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- bsd
multilinguality:
- monolingual
pretty_name: SCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: scan
configs:
- addprim_jump
- ... |
null | null | @article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
} | scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS... | false | 488 | false | scb_mt_enth_2020 | 2022-11-03T16:16:39.000Z | scb-mt-en-th-2020 | false | f92ce12c0cc6d32d74c086d0d83353ebdd672342 | [] | [
"arxiv:2007.03541",
"arxiv:1909.05858",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"lan... | https://huggingface.co/datasets/scb_mt_enth_2020/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
- found
- machine-generated
language_creators:
- expert-generated
- found
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task... |
null | null | @inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
@article... | Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed.
MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing.
The data for this benchmark comes fro... | false | 1,447 | false | scene_parse_150 | 2022-11-03T16:31:54.000Z | ade20k | false | c911f00326fd6d2f2db19d3f9bd2eab84c7326f4 | [] | [
"arxiv:1608.05442",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:bsd-3-clause",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|ade20k",
"task_categories:image-segmentation",
... | https://huggingface.co/datasets/scene_parse_150/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|ade20k
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
paperswithcode_id: ade20k
... |
null | null | @inproceedings{aaai/RastogiZSGK20,
author = {Abhinav Rastogi and
Xiaoxue Zang and
Srinivas Sunkara and
Raghav Gupta and
Pranav Khaitan},
title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided
Dialogue Dataset}... | The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8).
The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant.
These conversations involve interacti... | false | 805 | false | schema_guided_dstc8 | 2022-11-03T16:32:02.000Z | sgd | false | 9063740eeffbfaf1a47aacdec02d06769bc517d1 | [] | [
"arxiv:1909.05855",
"arxiv:2002.01359",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categor... | https://huggingface.co/datasets/schema_guided_dstc8/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
... |
null | null | @InProceedings{Cohan2019Structural,
author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},
title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},
booktitle={NAACL},
year={2019}
} | This is a dataset for classifying citation intents in academic papers.
The main citation intent label for each Json object is specified with the label
key while the citation context is specified in with a context key. Example:
{
'string': 'In chacma baboons, male-infant relationships can be linked to both
formatio... | false | 808 | false | scicite | 2022-11-03T16:31:16.000Z | scicite | false | fa325daaff55f42ede7dbc59cf0f28e05a510841 | [] | [
"arxiv:1904.01608",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids... | https://huggingface.co/datasets/scicite/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: SciCite
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-cl... |
null | null | @inproceedings{soares2018large,
title={A Large Parallel Corpus of Full-Text Scientific Articles},
author={Soares, Felipe and Moreira, Viviane and Becker, Karin},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)},
year={2018}
} | A parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm. | false | 633 | false | scielo | 2022-11-03T16:30:59.000Z | null | false | 36186a3aab9cb2f89a1044a34d5aca2ae1f67a87 | [] | [
"arxiv:1905.01852",
"annotations_creators:found",
"language_creators:found",
"language:en",
"language:es",
"language:pt",
"license:unknown",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:translation",
"configs:en-es",
"configs:en-pt... | https://huggingface.co/datasets/scielo/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- es
- pt
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SciELO
configs:
- en-es
- en-pt
- en-pt-es
da... |
null | null | @article{Cohan_2018,
title={A Discourse-Aware Attention Model for Abstractive Summarization of
Long Documents},
url={http://dx.doi.org/10.18653/v1/n18-2097},
DOI={10.18653/v1/n18-2097},
journal={Proceedings of the 2018 Conference of the North American Chapter of
the Association for Com... | Scientific papers datasets contains two sets of long and structured documents.
The datasets are obtained from ArXiv and PubMed OpenAccess repositories.
Both "arxiv" and "pubmed" have two features:
- article: the body of the document, pagragraphs seperated by "/n".
- abstract: the abstract of the document, pagragra... | false | 3,122 | false | scientific_papers | 2022-11-03T16:32:34.000Z | null | false | 5f7e65b03a676d7ec77b73295603457572ee2223 | [] | [
"arxiv:1804.05685",
"annotations_creators:found",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:summarization",
"tags:abstractive-summarization"
] | https://huggingface.co/datasets/scientific_papers/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: ScientificPapers
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: null
tags:
- abstractive-summarization
dat... |
null | null | @inproceedings{Wadden2020FactOF,
title={Fact or Fiction: Verifying Scientific Claims},
author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
booktitle={EMNLP},
year={2020},
} | SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales. | false | 655 | false | scifact | 2022-11-03T16:31:04.000Z | scifact | false | 4db710170ef6536e11005419eb4a71833ba0d73d | [] | [
"annotations_creators:expert-generated",
"language:en",
"language_creators:found",
"license:cc-by-nc-2.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:fact-checking"
] | https://huggingface.co/datasets/scifact/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-2.0
multilinguality:
- monolingual
pretty_name: SciFact
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: scifact
dataset_i... |
null | null | @inproceedings{SciQ,
title={Crowdsourcing Multiple Choice Science Questions},
author={Johannes Welbl, Nelson F. Liu, Matt Gardner},
year={2017},
journal={arXiv:1707.06209v1}
} | The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided. | false | 28,662 | false | sciq | 2022-11-03T16:47:29.000Z | sciq | false | dfc9851ef301df0f6129cd07f71a3840ef1074e6 | [] | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-nc-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:closed-domain-qa"
] | https://huggingface.co/datasets/sciq/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: sciq
pretty_name: SciQ
dataset... |
null | null | inproceedings{scitail,
Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
Booktitle = {AAAI},
Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
Year = {2018}
} | The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
retrieval to obtain relevant text from a large text corpus of web sentences, and use... | false | 1,913 | false | scitail | 2022-11-03T16:32:19.000Z | scitail | false | 3a5489bbc320e62bbbd50f4c49adfc83a009db3a | [] | [
"language:en"
] | https://huggingface.co/datasets/scitail/resolve/main/README.md | ---
language:
- en
paperswithcode_id: scitail
pretty_name: SciTail
dataset_info:
- config_name: snli_format
features:
- name: sentence1_binary_parse
dtype: string
- name: sentence1_parse
dtype: string
- name: sentence1
dtype: string
- name: sentence2_parse
dtype: string
- name: sentence2
... |
null | null | @article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
} | A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden. | false | 2,704 | false | scitldr | 2022-11-03T16:32:25.000Z | scitldr | false | 58bada754582537d0e52da027f06c66b6b77e2e1 | [] | [
"arxiv:2004.15011",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:summarization",
"tags:scientific-documents-summarization"
] | https://huggingface.co/datasets/scitldr/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: scitldr
pretty_name: SciTLDR
tags:
- scientific-documents-summari... |
null | null | null | We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind
CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an exi... | false | 680 | false | search_qa | 2022-11-03T16:31:11.000Z | searchqa | false | 6717a11eee4160949fe728dfe16099b68956db0a | [] | [
"arxiv:1704.05179",
"annotations_creators:found",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/search_qa/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: SearchQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: searchqa
dataset_info:
- config_... |
null | null | @misc{hazoom2021texttosql,
title={Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data},
author={Moshe Hazoom and Vibhor Malik and Ben Bogin},
year={2021},
eprint={2106.05006},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | SEDE (Stack Exchange Data Explorer) is new dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their
natural language description. It's based on a real usage of users from the Stack Exchange Data Explorer platform,
which brings complexities and challenges never seen before in any other semantic parsing ... | false | 323 | false | sede | 2022-11-03T16:16:11.000Z | sede | false | acd754648e9e3ce67d503e65ec2dc77563878509 | [] | [
"arxiv:2106.05006",
"arxiv:2005.02539",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:parsing"
] | https://huggingface.co/datasets/sede/resolve/main/README.md | ---
pretty_name: SEDE (Stack Exchange Data Explorer)
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
paperswithcode_id: sede
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- ... |
null | null | @InProceedings{7814688,
author={T. {Jurczyk} and M. {Zhai} and J. D. {Choi}},
booktitle={2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)},
title={SelQA: A New Benchmark for Selection-Based Question Answering},
year={2016},
volume={},
number={},
pages={820-827},
doi=... | The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks,
answer sentence selection and answer triggering. | false | 1,235 | false | selqa | 2022-11-03T16:32:01.000Z | selqa | false | 6012e21ef046ab0431cf780a8b2e46c7c0bcf38b | [] | [
"arxiv:1606.00851",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/selqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: selqa
pretty_name: SelQA
dataset_info:
- con... |
null | null | @inproceedings{hendrickx-etal-2010-semeval,
title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals",
author = "Hendrickx, Iris and
Kim, Su Nam and
Kozareva, Zornitsa and
Nakov, Preslav and
{\'O} S{\'e}aghdha, Diarmuid and
Pad... | The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals.
The task was designed to compare different approaches to semantic relation classification
and to provide a standard testbed for future research. | false | 1,072 | false | sem_eval_2010_task_8 | 2022-11-03T16:31:44.000Z | semeval-2010-task-8 | false | 2beef85cfa61a4a60ae3ab9f1b5cf2d03be0bf34 | [] | [
"language:en"
] | https://huggingface.co/datasets/sem_eval_2010_task_8/resolve/main/README.md | ---
language:
- en
paperswithcode_id: semeval-2010-task-8
pretty_name: SemEval-2010 Task 8
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
sentence: text
relation: target
metrics:
- typ... |
null | null | @inproceedings{inproceedings,
author = {Marelli, Marco and Bentivogli, Luisa and Baroni, Marco and Bernardi, Raffaella and Menini, Stefano and Zamparelli, Roberto},
year = {2014},
month = {08},
pages = {},
title = {SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through... | The SemEval-2014 Task 1 focuses on Evaluation of Compositional Distributional Semantic Models
on Full Sentences through Semantic Relatedness and Entailment. The task was designed to
predict the degree of relatedness between two sentences and to detect the entailment
relation holding between them. | false | 623 | false | sem_eval_2014_task_1 | 2022-11-03T16:31:08.000Z | null | false | db6412458e15029b5de52cf5f78a332729c2fe8d | [] | [
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions",
"task_categories:text-classification",
"... | https://huggingface.co/datasets/sem_eval_2014_task_1/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions
task_categories:
- text-classification
task_ids:
- text-... |
null | null | @InProceedings{SemEval2018Task1,
author = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
title = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
address = {New Orleans, LA, USA},
... | SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification.
This is a dataset for multilabel emotion classification for tweets.
'Given a tweet, classify it as 'neutral or no emotion' or as one, or more, of eleven given emotions that best represent the mental state of the tweeter.'
It contains 22467 tw... | false | 2,740 | false | sem_eval_2018_task_1 | 2022-11-03T16:32:29.000Z | null | false | d21f5e2c572f2854277c3f02279fb006d9f309fe | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ar",
"language:en",
"language:es",
"license:unknown",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification",
... | https://huggingface.co/datasets/sem_eval_2018_task_1/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- en
- es
license:
- unknown
multilinguality:
- multilingual
pretty_name: 'SemEval-2018 Task 1: Affect in Tweets'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-clas... |
null | null | @misc{martino2020semeval2020,
title={SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles},
author={G. Da San Martino and A. Barrón-Cedeño and H. Wachsmuth and R. Petrov and P. Nakov},
year={2020},
eprint={2009.02696},
archivePrefix={arXiv},
primaryClass={cs.CL}
... | Propagandistic news articles use specific techniques to convey their message,
such as whataboutism, red Herring, and name calling, among many others.
The Propaganda Techniques Corpus (PTC) allows to study automatic algorithms to
detect them. We provide a permanent leaderboard to allow researchers both to
advertise thei... | false | 342 | false | sem_eval_2020_task_11 | 2022-11-03T16:15:46.000Z | null | false | bb424115b0577a79ce9762ac8b76e1085ad621f3 | [] | [
"arxiv:2009.02696",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_categories:token-classification",
"tags:propag... | https://huggingface.co/datasets/sem_eval_2020_task_11/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids: []
paperswithcode_id: null
pretty_name: SemEval-2020 Task 1... |
null | null | @inproceedings{filippova-altun-2013-overcoming,
title = "Overcoming the Lack of Parallel Data in Sentence Compression",
author = "Filippova, Katja and
Altun, Yasemin",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "20... | Large corpus of uncompressed and compressed sentences from news articles. | false | 568 | false | sent_comp | 2022-11-03T16:31:01.000Z | sentence-compression | false | b5534f6912c284817a658d5d2f05403b2aa74c57 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:other",
"tags:sentence-compression"
] | https://huggingface.co/datasets/sent_comp/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: sentence-compression
pretty_name: Google Sentence Compression
tags:
... |
null | null | @inproceedings{inproceedings,
author = {Chen, Yanqing and Skiena, Steven},
year = {2014},
month = {06},
pages = {383-389},
title = {Building Sentiment Lexicons for All Major Languages},
volume = {2},
journal = {52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conferenc... | This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them. | false | 13,266 | false | senti_lex | 2022-11-03T16:47:14.000Z | null | false | 1383f10019aa9796c29695637875e53a5ea4714d | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:af",
"language:an",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"language:ca",
"language:cs",
"language:cy",
"language:da",
"language:de... | https://huggingface.co/datasets/senti_lex/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
- an
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- ia
- id
- io
- is
- it
- ja
- ka
- km
- kn
- ko
- ku
- ... |
null | null | @INPROCEEDINGS{remquahey2010,
title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis},
booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)},
author = {Remus, R. and Quasthoff, U. and Heyer, G.},
year = {2010}
} | SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, and pos-tagging. The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1]. | false | 481 | false | senti_ws | 2022-11-03T16:16:33.000Z | null | false | e0840048356fe07f2494b331257f4389f3fab5bc | [] | [
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"language:de",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_categories:text-... | https://huggingface.co/datasets/senti_ws/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- de
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
- text-classification
task_ids:
- text-scoring
- sentiment-sco... |
null | null | @article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
} | Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
sentiment classification. For more detailed information please refer to the paper. | false | 1,574 | false | sentiment140 | 2022-11-03T16:31:26.000Z | sentiment140 | false | 4a7bb88d70ca3245c965c9a9c129c393ff5df5f8 | [] | [
"language:en"
] | https://huggingface.co/datasets/sentiment140/resolve/main/README.md | ---
language:
- en
paperswithcode_id: sentiment140
pretty_name: Sentiment140
train-eval-index:
- config: sentiment140
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
sentiment: target
metrics:
- type: accuracy
... |
null | null | @inproceedings{sepedi_ner,
author = {D.J. Prinsloo and
Roald Eiselen},
title = {NCHLT Sepedi Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Con... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 322 | false | sepedi_ner | 2022-11-03T16:15:32.000Z | null | false | 8f78af26f0d2de79a8fb5315e9299467dd628f0f | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:nso",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/sepedi_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- nso
license:
- other
license_details: Creative Commons Attribution 2.5 South Africa License
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named... |
null | null | @inproceedings{sesotho_ner_corpus,
author = {M. Setaka and
Roald Eiselen},
title = {NCHLT Sesotho Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluat... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 365 | false | sesotho_ner_corpus | 2022-11-03T16:16:15.000Z | null | false | 27992506d209fece9f98c338ec7d11d94d71c4d5 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:st",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/sesotho_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- st
license:
- other
license_details: Creative Commons Attribution 2.5 South Africa License
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-... |
null | null | null | SETimes – A Parallel Corpus of English and South-East European Languages
The corpus is based on the content published on the SETimes.com news portal. The news portal publishes “news and views from Southeast Europe” in ten languages: Bulgarian, Bosnian, Greek, English, Croatian, Macedonian, Romanian, Albanian and Serbia... | false | 7,240 | false | setimes | 2022-11-03T16:47:00.000Z | null | false | 30a12206f5d30fa87fc692acd62c0f17de11a060 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:bg",
"language:bs",
"language:el",
"language:en",
"language:hr",
"language:mk",
"language:ro",
"language:sq",
"language:sr",
"language:tr",
"license:cc-by-sa-4.0",
"multilinguality:multilingual",
"size_categories:100K<n<1... | https://huggingface.co/datasets/setimes/resolve/main/README.md | ---
pretty_name: SETimes – A Parallel Corpus of English and South-East European Languages
annotations_creators:
- found
language_creators:
- found
language:
- bg
- bs
- el
- en
- hr
- mk
- ro
- sq
- sr
- tr
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
... |
null | null | @inproceedings{sepedi_ner_corpus,
author = {S.S.B.M. Phakedi and
Roald Eiselen},
title = {NCHLT Setswana Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Ev... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 322 | false | setswana_ner_corpus | 2022-11-03T16:08:02.000Z | null | false | 9477bd580158ba371dca7bfff3b58666ccc6578c | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:tn",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/setswana_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- tn
license:
- other
license_details: Creative Commons Attribution 2.5 South Africa License
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-... |
null | null | @misc{saeidi2018interpretation,
title={Interpretation of Natural Language Rules in Conversational Machine Reading},
author={Marzieh Saeidi and Max Bartolo and Patrick Lewis and Sameer Singh and Tim Rocktäschel and Mike Sheldon and Guillaume Bouchard and Sebastian Riedel},
year={2018},
eprint={18... | ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules. The goal is to answer questions by possibly asking follow-up questions first. It is assumed assume that the question is often underspecified, in the sense that the question does not provide enough informati... | false | 867 | false | sharc | 2022-11-03T16:16:40.000Z | sharc | false | b5656cbc3b55e35e831ac9d14a05c1e939aca1c3 | [] | [
"arxiv:1809.01494",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"... | https://huggingface.co/datasets/sharc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: sharc
pretty_na... |
null | null | @inproceedings{verma-etal-2020-neural,
title = "Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns",
author = "Verma, Nikhil and
Sharma, Abhishek and
Madan, Dhiraj and
Contractor, Danish and
Kumar, Harshit and
Joshi, Sachindra",
booktitle = "Proceedings ... | ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models. SharcModified is a new dataset which reduces the patterns identifie... | false | 802 | false | sharc_modified | 2022-11-03T16:31:23.000Z | null | false | 3cd2386ee875c038d8d40b6a665d1e9ad6ece6fc | [] | [
"arxiv:1909.03759",
"arxiv:2009.06354",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|sharc",
"task_categories:q... | https://huggingface.co/datasets/sharc_modified/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|sharc
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_na... |
null | null | @inproceedings{marelli-etal-2014-sick,
title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
author = "Marelli, Marco and
Menini, Stefano and
Baroni, Marco and
Bentivogli, Luisa and
Bernardi, Raffaella and
Zamparelli, Roberto",
booktit... | Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailore... | false | 4,882 | false | sick | 2022-11-03T16:46:41.000Z | sick | false | 51923ceecc0665135ade7c6c3340183c593cc914 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-nc-sa-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|image-flickr-8k",
"source_datasets:extended|semeval2012-sts-msr-video",
"task_categories:text-classifi... | https://huggingface.co/datasets/sick/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|image-flickr-8k
- extended|semeval2012-sts-msr-video
task_categories:
- text-classification
task_ids:
- natural-lang... |
null | null | @inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Findings of the Association for Co... | The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection
of resources for training, evaluating, and analyzing natural language understanding systems
specifically designed for spoken language. All datasets are in the English language and cover a
variety of domains including... | false | 1,829 | false | silicone | 2022-11-03T16:32:17.000Z | null | false | db1f9af80d31c3591c1f0c3fc0983754af058f80 | [] | [
"arxiv:2009.11152",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_catego... | https://huggingface.co/datasets/silicone/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialo... |
null | null | @misc{bordes2015largescale,
title={Large-scale Simple Question Answering with Memory Networks},
author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},
year={2015},
eprint={1506.02075},
archivePrefix={arXiv},
primaryClass={cs.LG}
} | SimpleQuestions is a dataset for simple QA, which consists
of a total of 108,442 questions written in natural language by human
English-speaking annotators each paired with a corresponding fact,
formatted as (subject, relationship, object), that provides the answer
but also a complete explanation. Fast have been extra... | false | 646 | false | simple_questions_v2 | 2022-11-03T16:31:06.000Z | simplequestions | false | 905e50d1c11af3605e92c473b60ba84fa8899963 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/simple_questions_v2/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: simplequestions
pretty_name: SimpleQues... |
null | null | @inproceedings{siswati_ner_corpus,
author = {B.B. Malangwane and
M.N. Kekana and
S.S. Sedibe and
B.C. Ndhlovu and
Roald Eiselen},
title = {NCHLT Siswati Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity r... | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | false | 324 | false | siswati_ner_corpus | 2022-11-03T16:08:13.000Z | null | false | 5127f1fa545aecfdadfb2b3ae8c552b5065d2b4c | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:ss",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/siswati_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ss
license:
- other
license_details: Creative Commons Attribution 2.5 South Africa License
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- name... |
null | null | @InProceedings{SCHIERSCH18.85,
author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
booktitle = {Proce... | DFKI SmartData Corpus is a dataset of 2598 German-language documents
which has been annotated with fine-grained geo-entities, such as streets,
stops and routes, as well as standard named entity types. It has also
been annotated with a set of 15 traffic- and industry-related n-ary
relations and events, such as Accidents... | false | 321 | false | smartdata | 2022-11-03T16:15:29.000Z | null | false | 5c0cdafc846a957c471bedeea23db57bc41777f0 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:de",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/smartdata/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: SmartData
d... |
null | null | @inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
} | The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam. | false | 4,754 | false | sms_spam | 2022-11-03T16:46:51.000Z | sms-spam-collection-data-set | false | b17098019af0c7c918f752c6b4e767cdc64c85bf | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-nus-sms-corpus",
"task_categories:text-classi... | https://huggingface.co/datasets/sms_spam/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-nus-sms-corpus
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode... |
null | null | @article{DBLP:journals/corr/abs-1805-10190,
author = {Alice Coucke and
Alaa Saade and
Adrien Ball and
Th{\'{e}}odore Bluche and
Alexandre Caulier and
David Leroy and
Cl{\'{e}}ment Doumouro and
Thibault Gisselbr... | Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at
https://github.com/sonos/nlu-benchmark 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The
related paper mentioned on the github page is https://arxiv... | false | 4,086 | false | snips_built_in_intents | 2022-11-03T16:32:38.000Z | snips | false | 3728fd82854da2a768885c15d8aac8196382524a | [] | [
"arxiv:1805.10190",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/snips_built_in_intents/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: snips
pretty_name: SNIPS Nat... |
null | null | @inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title ... | The SNLI corpus (version 1.0) is a collection of 570k human-written English
sentence pairs manually labeled for balanced classification with the labels
entailment, contradiction, and neutral, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE). | false | 51,085 | false | snli | 2022-11-03T16:47:33.000Z | snli | false | 8686edffbb34aaf2635e5d549c35c5049ba62aea | [] | [
"arxiv:1909.02209",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-flicker-30k",
"source_datasets:extended|other-visual-genome",
"task_categories:... | https://huggingface.co/datasets/snli/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-flicker-30k
- extended|other-visual-genome
task_categories:
- text-classification
task_ids:
- natural-language-infe... |
null | null | @inproceedings{maruyama-yamamoto-2018-simplified,
title = "Simplified Corpus with Core Vocabulary",
author = "Maruyama, Takumi and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2... | About SNOW T15: The simplified corpus for the Japanese language. The corpus has 50,000 manually simplified and aligned sentences. This corpus contains the original sentences, simplified sentences and English translation of the original sentences. It can be used for automatic text simplification as well as translating s... | false | 726 | false | snow_simplified_japanese_corpus | 2022-11-03T16:31:17.000Z | null | false | 4a127f87d5781443678ec44b694cbaf9a205a3a1 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:other",
"language_creators:found",
"language:en",
"language:ja",
"license:cc-by-4.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/snow_simplified_japanese_corpus/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- other
language_creators:
- found
language:
- en
- ja
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SNOW T15 and T23 (simplified Japa... |
null | null | null | Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answe... | false | 655 | false | so_stacksample | 2022-11-03T16:30:57.000Z | null | false | dfdf39f9b7fbc3afbd31591a18c232067918cd91 | [] | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive... | https://huggingface.co/datasets/so_stacksample/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: nul... |
null | null | @inproceedings{sap2020socialbiasframes,
title={Social Bias Frames: Reasoning about Social and Power Implications of Language},
author={Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A and Choi, Yejin},
year={2020},
booktitle={ACL},
} | Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language.
For example, these frames are meant to distill the implication that "women (candidates) are less qualified"
behind the statement "we shouldn’t lower our standards to hire more women." | false | 362 | false | social_bias_frames | 2022-11-03T16:15:48.000Z | null | false | 6d2b45a4406b3273d8e5c5b4672507b142f21a9c | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:hate-speech-detection... | https://huggingface.co/datasets/social_bias_frames/resolve/main/README.md | ---
pretty_name: Social Bias Frames
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- hate-speech-detection
... |
null | null | We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an act... | false | 22,875 | false | social_i_qa | 2022-11-03T16:47:25.000Z | social-iqa | false | 7c7101c4243a2a759a5f95a135106977b10ad606 | [] | [
"language:en"
] | https://huggingface.co/datasets/social_i_qa/resolve/main/README.md | ---
language:
- en
paperswithcode_id: social-iqa
pretty_name: Social Interaction QA
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answerA
dtype: string
- name: answerB
dtype: string
- name: answerC
dtype: string
- name: label
dtype: st... | |
null | null | @misc{friedrich2020sofcexp,
title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},
author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},
year={2020},
eprint... | The SOFC-Exp corpus consists of 45 open-access scholarly articles annotated by domain experts.
A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested
named entity recognition and slot filling tasks as well as high annotation quality is presented
in the accompanying paper. | false | 351 | false | sofc_materials_articles | 2022-11-03T16:08:02.000Z | null | false | 05fa34a77be750e19c2f4d3f54424223349cd917 | [] | [
"arxiv:2006.03039",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-cl... | https://huggingface.co/datasets/sofc_materials_articles/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
task_ids:
- named-entity-recognition
... |
null | null | @misc{zhang2015characterlevel,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Zhao and Yann LeCun},
year={2015},
eprint={1509.01626},
archivePrefix={arXiv},
primaryClass={cs.LG}
} | The Sogou News dataset is a mixture of 2,909,551 news articles from the SogouCA and SogouCS news corpora, in 5 categories.
The number of training samples selected for each class is 90,000 and testing 12,000. Note that the Chinese characters have been converted to Pinyin.
classification labels of the news are determined... | false | 320 | false | sogou_news | 2022-11-03T16:15:37.000Z | null | false | df9e0671699f2a5a2ecf52c26e2f987e7140db6a | [] | [
"arxiv:1509.01626"
] | https://huggingface.co/datasets/sogou_news/resolve/main/README.md | ---
pretty_name: Sogou News
paperswithcode_id: null
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: label
dtype:
class_label:
names:
0: sports
1: finance
2: entertainment
3: automobile
4: techno... |
null | null | @misc{cardellinoSBWCE,
author = {Cardellino, Cristian},
title = {Spanish {B}illion {W}ords {C}orpus and {E}mbeddings},
url = {https://crscardellino.github.io/SBWCE/},
month = {August},
year = {2019}
} | An unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web.
This resources include the spanish portions of SenSem, the Ancora Corpus, some OPUS Project Corpora and the Europarl,
the Tibidabo Treebank, the IULA Spanish LSP Treebank, and dumps from the Spanish Wikipedia, Wik... | false | 373 | false | spanish_billion_words | 2022-11-03T16:16:07.000Z | sbwce | false | b8387424f6cf2923e110df19b1e1934124c63f58 | [] | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:es",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"ta... | https://huggingface.co/datasets/spanish_billion_words/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
... |
null | null | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
... | This is a collection of parallel corpora collected by Hercules Dalianis and his research group for bilingual dictionary construction.
More information in: Hercules Dalianis, Hao-chun Xing, Xin Zhang: Creating a Reusable English-Chinese Parallel Corpus for Bilingual Dictionary Construction, In Proceedings of LREC2010 (s... | false | 633 | false | spc | 2022-11-03T16:31:02.000Z | null | false | 1ad141c666dc86051e3c982d10a74b86f3ca0e6d | [] | [
"annotations_creators:found",
"language_creators:found",
"language:af",
"language:el",
"language:en",
"language:zh",
"license:unknown",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation",
"configs:af-en",
"configs:el-en",
... | https://huggingface.co/datasets/spc/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- el
- en
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: spc
configs:
- af-en
- el-en
- en-zh
da... |
null | null | @article{pafilis2013species,
title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christo... | We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition,
which we here use to identifynames of species and other taxa in text. The tool, SPECIES, is more than an order of
magnitude faster and as accurate as existing tools. The precision and recall was asses... | false | 639 | false | species_800 | 2022-11-03T16:31:19.000Z | null | false | 5255528e445e4d1f420cb4466e880a7e6c924822 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/species_800/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: sp... |
null | null | @article{speechcommandsv2,
author = { {Warden}, P.},
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1804.03209},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language, Computer Sc... | This is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. This dataset is covered in more detail ... | false | 857 | false | speech_commands | 2022-11-03T16:31:30.000Z | null | false | ffe14e1f24f6051d501afa8a6dfdc3edee0bed82 | [] | [
"arxiv:1804.03209",
"annotations_creators:other",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"source_datasets:original",
"task_categories:audio-classification",
"task_ids:keyword-spotting",
"size_categories:100K<n<1M",
"size_categories:10... | https://huggingface.co/datasets/speech_commands/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SpeechCommands
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- keyword-spotting
size_categories:
- 100K<n<1M
- 10K<n<100K
configs:
- v0.01
-... |
null | null | @article{yu2018spider,
title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and oth... | Spider is a large-scale complex and cross-domain semantic parsing and text-toSQL dataset annotated by 11 college students | false | 694 | false | spider | 2022-11-03T16:31:49.000Z | spider-1 | false | 6232cc3fad6d54c62b3ba23a364083a98ff36a17 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:text-to-sql... | https://huggingface.co/datasets/spider/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: spider-1
pretty_name: ... |
null | null | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250}... | Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. | false | 153,358 | false | squad | 2022-11-03T16:47:45.000Z | squad | false | 33c0018411a987fa8d219bc1d40adf7dbcc0f920 | [] | [
"arxiv:1606.05250",
"annotations_creators:crowdsourced",
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"language:en",
"license:cc-by-4.0",
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"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"task_categories:question-answering",
"task... | https://huggingface.co/datasets/squad/resolve/main/README.md | ---
pretty_name: SQuAD
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: ... |
null | null | @inproceedings{jia-liang-2017-adversarial,
title = "Adversarial Examples for Evaluating Reading Comprehension Systems",
author = "Jia, Robin and
Liang, Percy",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
... | Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph:
AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model.
AddOn... | false | 2,045 | false | squad_adversarial | 2022-11-03T16:32:07.000Z | null | false | 452f9fadad8eba91eec849ebd015e9382d0051b5 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|squad",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/squad_adversarial/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: '''Adversarial Examples for ... |
null | null | @article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.... | automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish | false | 1,009 | false | squad_es | 2022-11-03T16:31:17.000Z | squad-es | false | 97a56095715f9ed83585e66ffd155ba7717bb239 | [] | [
"arxiv:1912.05200",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:es",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/squad_es/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad-es
pretty_name:... |
null | null | @InProceedings{10.1007/978-3-030-03840-3_29,
author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title={Neural Learning for Question Answering in Italian},
booktitle={AI*IA 2018 -- Advances in Art... | SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the ori... | false | 492 | false | squad_it | 2022-11-03T16:30:43.000Z | squad-it | false | a32d5b28c048e9398808d9e0a884af413a6b4a2e | [] | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:it",
"language_bcp47:it-IT",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"task_categories:question-answering",
"task_ids:open-domain-qa",
... | https://huggingface.co/datasets/squad_it/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- it
language_bcp47:
- it-IT
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
pape... |
null | null | @article{lim2019korquad1,
title={Korquad1. 0: Korean qa dataset for machine reading comprehension},
author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul},
journal={arXiv preprint arXiv:1909.07005},
year={2019}
} | KorQuAD 1.0 is a large-scale Korean dataset for machine reading comprehension task consisting of human generated questions for Wikipedia articles. We benchmark the data collecting process of SQuADv1.0 and crowdsourced 70,000+ question-answer pairs. 1,637 articles and 70,079 pairs of question answers were collected. 1,4... | false | 2,181 | false | squad_kor_v1 | 2022-11-03T16:32:33.000Z | korquad | false | eaf72d7ca1043022fb06fd9d6cb1711a4e8d0a1b | [] | [
"arxiv:1909.07005",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ko",
"license:cc-by-nd-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/squad_kor_v1/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: korquad
pretty_name: The Korean Question ... |
null | null | @article{NODE09353166,
author={Youngmin Kim,Seungyoung Lim;Hyunjeong Lee;Soyoon Park;Myungji Kim},
title={{KorQuAD 2.0: Korean QA Dataset for Web Document Machine Comprehension}},
booltitle={{Journal of KIISE 제47권 제6호}},
journal={{Journal of KIISE}},
volume={{47}},
issue={{6}},
publisher={Th... | KorQuAD 2.0 is a Korean question and answering dataset consisting of a total of 100,000+ pairs. There are three major differences from KorQuAD 1.0, which is the standard Korean Q & A data. The first is that a given document is a whole Wikipedia page, not just one or two paragraphs. Second, because the document also con... | false | 573 | false | squad_kor_v2 | 2022-11-03T16:16:39.000Z | null | false | a5d0d357087784036afcff838c6287050ed2813b | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ko",
"license:cc-by-nd-4.0",
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"source_datasets:extended|squad_kor_v1",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/squad_kor_v2/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad_kor_v1
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name:... |
null | null | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250}... | Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API. | false | 320 | false | squad_v1_pt | 2022-11-03T16:16:16.000Z | null | false | a6a94a9128c66758d7e815c5b9bf8ec65d8f80ba | [] | [
"arxiv:1606.05250",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:pt",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-... | https://huggingface.co/datasets/squad_v1_pt/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- pt
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: SquadV1P... |
null | null | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250}... | combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and abstain from an... | false | 27,630 | false | squad_v2 | 2022-11-03T16:47:36.000Z | squad | false | 8de6c30169deba79c1aff62478ff207cc9aded4f | [] | [
"arxiv:1606.05250",
"annotations_creators:crowdsourced",
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"language:en",
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"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:ext... | https://huggingface.co/datasets/squad_v2/resolve/main/README.md | ---
pretty_name: SQuAD2.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_... |
null | null | @InProceedings{pmlr-v119-miller20a,
title = {The Effect of Natural Distribution Shift on Question Answering Models},
author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {6905--6916},
year ... | null | false | 3,151 | false | squadshifts | 2022-11-03T16:46:47.000Z | squad-shifts | false | 6ba1e9da14c07ff6aef2235fb26e308c32bf018c | [] | [
"annotations_creators:crowdsourced",
"language:en",
"language_creators:crowdsourced",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/squadshifts/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
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pretty_name: SQuAD-shifts
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squ... |
null | null | @misc{11356/1063,
title = {Serbian web corpus {srWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1063},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{... | The Serbian web corpus srWaC was built by crawling the .rs 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 | 324 | false | srwac | 2022-11-03T16:08:14.000Z | null | false | 6393d9bd04354dfad543163b9f592e2ed993baa6 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:sr",
"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/srwac/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- sr
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 | @inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and Perelygin, Alex and Wu, Jean and
Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher",
booktitle = "Proceedings ... | The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a
complete analysis of the compositional effects of sentiment in language. | false | 16,304 | false | sst | 2022-11-03T16:47:16.000Z | sst | false | 6bf18edbdbdc83c01be599e83149b06916a4f307 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentim... | https://huggingface.co/datasets/sst/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
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task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-classification
- sentiment-scoring
papers... |
null | null | @article{nadeem2020Stereoset,
title={Stereoset: Measuring stereotypical bias in pretrained language models},
author={Nadeem, Moin and Bethke, Anna and Reddy, Siva},
journal={arXiv preprint arXiv:2004.09456},
year={2020}
} | Stereoset is a dataset that measures stereotype bias in language models. Stereoset consists of 17,000 sentences that
measures model preferences across gender, race, religion, and profession. | false | 1,516 | false | stereoset | 2022-11-03T16:31:56.000Z | stereoset | false | 0e7d3caf840091432cde6c85f859ce3d77780ed9 | [] | [
"arxiv:2004.09456",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"tags:stereotype-detection"
] | https://huggingface.co/datasets/stereoset/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: stereoset
pretty_name: StereoSet
tags:
- stereot... |
null | null | @inproceedings{mostafazadeh2017lsdsem,
title={Lsdsem 2017 shared task: The story cloze test},
author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James},
booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics... | Story Cloze Test' is a commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story. | false | 18,666 | false | story_cloze | 2022-11-03T16:47:31.000Z | null | false | 9a4642521774769caf7c2cf7525bde0924875b33 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:other"
] | https://huggingface.co/datasets/story_cloze/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name: Story Cloze Test
dataset_info:
- config_name: '2016'
features... |
null | null | @article{isbister2020not,
title={Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity},
author={Isbister, Tim and Sahlgren, Magnus},
journal={arXiv preprint arXiv:2009.03116},
year={2020}
} | null | false | 325 | false | stsb_mt_sv | 2022-11-03T16:08:14.000Z | null | false | 497bd5f5beb50fef718d323a4ed8ced27db1c3bd | [] | [
"arxiv:2009.03116",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"language:sv",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"task_categories:text-classificatio... | https://huggingface.co/datasets/stsb_mt_sv/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- machine-generated
language:
- sv
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scorin... |
null | null | @InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
} | These are different multilingual translations and the English original of the STSbenchmark dataset. Translation has been done with deepl.com. | false | 6,095 | false | stsb_multi_mt | 2022-11-03T16:47:02.000Z | null | false | 59b9b436ef4f75e35c638533c7914ea5359add50 | [] | [
"arxiv:1708.00055",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language_creators:machine-generated",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru"... | https://huggingface.co/datasets/stsb_multi_mt/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification... |
null | null | @inproceedings{bevendorff2020shared,
title={Shared Tasks on Authorship Analysis at PAN 2020},
author={Bevendorff, Janek and Ghanem, Bilal and Giachanou, Anastasia and Kestemont, Mike and Manjavacas, Enrique and Potthast, Martin and Rangel, Francisco and Rosso, Paolo and Specht, G{\"u}nther and Stamatatos, Efstathio... | The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Detecting these positions is a crucial part of the authorship identification process, and for multi-author document analysis in general.
Access to the dataset needs to be request... | false | 481 | false | style_change_detection | 2022-11-03T16:16:34.000Z | null | false | 5e184f4fa91cc8b63389c85ebc44f7754d0d6ae7 | [] | [] | https://huggingface.co/datasets/style_change_detection/resolve/main/README.md | ---
paperswithcode_id: null
pretty_name: StyleChangeDetection
dataset_info:
- config_name: narrow
features:
- name: id
dtype: string
- name: text
dtype: string
- name: authors
dtype: int32
- name: structure
sequence: string
- name: site
dtype: string
- name: multi-author
dtype: boo... |
null | null | @inproceedings{bjerva20subjqa,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
author = "Bjerva, Johannes and
Bhutani, Nikita and
Golahn, Behzad and
Tan, Wang-Chiew and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Meth... | SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants. | false | 3,249 | false | subjqa | 2022-11-03T16:32:41.000Z | subjqa | false | 6a3ebf48d965b30f0192c1bfb1ef02ba33bbd54d | [] | [
"arxiv:2004.14283",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|yelp_review_full",
"source_datasets:extended|other-amazon_revie... | https://huggingface.co/datasets/subjqa/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|yelp_review_full
- extended|other-amazon_reviews_ucsd
- extended|other-tripadvisor_reviews
task_categories:
- questi... |
null | null | @article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00... | SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard. | false | 927,436 | false | super_glue | 2022-11-03T16:47:49.000Z | superglue | false | a4ac6a25476907f9b173604ca3f9ee49e2f2c072 | [] | [
"annotations_creators:expert-generated",
"language:en",
"language_creators:other",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"tags:superglue",
"tags:NLU",
"tags:natural language understanding",
"task_categories:text-classifi... | https://huggingface.co/datasets/super_glue/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- other
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: superglue
pretty_name: SuperGLUE
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
tags:
- superglue
- NLU
- natural language understanding
task_categ... |
null | null | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | false | 4,063 | false | superb | 2022-11-03T16:46:41.000Z | null | false | 17f7122f4b99fe5644376b8a5a97514e6e6ba6af | [] | [
"arxiv:2105.01051",
"annotations_creators:other",
"language_creators:other",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:extended|librispeech_asr",
"source_datasets:extended|other-librimix",
"source_data... | https://huggingface.co/datasets/superb/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: SUPERB
size_categories:
- unknown
source_datasets:
- original
- extended|librispeech_asr
- extended|other-librimix
- extended|other-speech_commands
task_categories:
- automatic-spee... |
null | null | @article{netzer2011reading,
title={Reading digits in natural images with unsupervised feature learning},
author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
year={2011}
} | SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting.
It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over ... | false | 826 | false | svhn | 2022-11-03T16:07:43.000Z | svhn | false | 30e1edf633e8b713df0e9288efe09600eb642b58 | [] | [
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:image-classification",
"task_categories... | https://huggingface.co/datasets/svhn/resolve/main/README.md | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
- object-detection
task_ids: []
paperswithcode_id: svhn
... |
null | null | @inproceedings{zellers2018swagaf,
title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
... | Given a partial description like "she opened the hood of the car,"
humans can reason about the situation and anticipate what might come
next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations)
is a large-scale dataset for this task of grounded commonsense
inference, unifying natural langua... | false | 16,717 | false | swag | 2022-11-03T16:47:07.000Z | swag | false | 207d26b77e60b0496b02d17aa586a397f0b39a57 | [] | [
"arxiv:1808.05326",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids... | https://huggingface.co/datasets/swag/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: swag
pretty_n... |
null | null | @InProceedings{huggingface:dataset,
title = Language modeling data for Swahili (Version 1),
authors={Shivachi Casper Shikali, & Mokhosi Refuoe.
},
year={2019},
link = http://doi.org/10.5281/zenodo.3553423
} | The Swahili dataset developed specifically for language modeling task.
The dataset contains 28,000 unique words with 6.84M, 970k, and 2M words for the train,
valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and,
the start and end of se... | false | 326 | false | swahili | 2022-11-03T16:08:03.000Z | null | false | 33676a53381bb76ca00f67f02636434afa6d8df2 | [] | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:sw",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"... | https://huggingface.co/datasets/swahili/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- sw
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithc... |
null | null | @dataset{davis_david_2020_5514203,
author = {Davis David},
title = {Swahili : News Classification Dataset},
month = dec,
year = 2020,
note = {{The news version contains both train and test sets.}},
publisher = {Zenodo},
version = {0.2},
doi = {10.5281... | Swahili is spoken by 100-150 million people across East Africa. In Tanzania, it is one of two national languages (the other is English) and it is the official language of instruction in all schools. News in Swahili is an important part of the media sphere in Tanzania.
News contributes to education, technology, and the... | false | 972 | false | swahili_news | 2022-11-03T16:15:19.000Z | null | false | 397f6c6a3823a9b51abdd6fe0f34e64a98bb9584 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:sw",
"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/swahili_news/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- sw
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: null
pretty_name: 'Swahili... |
null | null | @techreport{Jurafsky-etal:1997,
Address = {Boulder, CO},
Author = {Jurafsky, Daniel and Shriberg, Elizabeth and Biasca, Debra},
Institution = {University of Colorado, Boulder Institute of Cognitive Science},
Number = {97-02},
Title = {Switchboard {SWBD}-{DAMSL} Shallow-Discourse-Function Annotation ... | The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2 with
turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the
associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s.
The SwDA is not i... | false | 799 | false | swda | 2022-11-03T16:16:36.000Z | null | false | 6c2a2c3acc7d978d86ec19da1ffe0ef277883ff3 | [] | [
"arxiv:1811.05021",
"arxiv:1711.05568",
"arxiv:1709.04250",
"arxiv:1805.06280",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-Switchboard-1 Telephone S... | https://huggingface.co/datasets/swda/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-Switchboard-1 Telephone Speech Corpus, Release 2
task_categories:
- text-classification
task_ids:
- multi-label-classificat... |
null | null | @inproceedings{almgrenpavlovmogren2016bioner,
title={Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
author={Simon Almgren, Sean Pavlov, Olof Mogren},
booktitle={Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (... | SwedMedNER is a dataset for training and evaluating Named Entity Recognition systems on medical texts in Swedish.
It is derived from medical articles on the Swedish Wikipedia, Läkartidningen, and 1177 Vårdguiden. | false | 639 | false | swedish_medical_ner | 2022-11-03T16:30:59.000Z | null | false | 1004a7739c1e4cef25fff36ac51eeca0c5e12e6b | [] | [
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"language:sv",
"language_bcp47:sv-SE",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:token-classification... | https://huggingface.co/datasets/swedish_medical_ner/resolve/main/README.md | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- sv
language_bcp47:
- sv-SE
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
... |
null | null | null | Webbnyheter 2012 from Spraakbanken, semi-manually annotated and adapted for CoreNLP Swedish NER. Semi-manually defined in this case as: Bootstrapped from Swedish Gazetters then manually correcte/reviewed by two independent native speaking swedish annotators. No annotator agreement calculated. | false | 321 | false | swedish_ner_corpus | 2022-11-03T16:15:20.000Z | null | false | 7ac96b39f45541140378f1b7c2ed407c10d76265 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:sv",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/swedish_ner_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- sv
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: Swedish NER... |
null | null | null | null | false | 321 | false | swedish_reviews | 2022-11-03T16:15:20.000Z | null | false | 079e87e7c3486c6fe16c456b1704effe5ea11551 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:sv",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/swedish_reviews/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- sv
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: Swedish Reviews
dataset_... |
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