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null | null | @inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montr... | dataset contains discourse annotation and relation on threads from reddit during 2016 | false | 346 | false | coarse_discourse | 2022-11-03T16:15:50.000Z | coarse-discourse | false | dea20017eafd719e8a28dc78f969410fe804a303 | [] | [
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] | https://huggingface.co/datasets/coarse_discourse/resolve/main/README.md | ---
annotations_creators:
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pretty_name: Coarse Discourse
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task_categories:
- text-classification
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paperswithcode_id: c... |
null | null | @inproceedings{chen2019codah,
title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
pages=... | The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use... | false | 2,705 | false | codah | 2022-11-03T16:32:31.000Z | codah | false | 971e4459a5cd4f1cda1e4f1da03ad651e1da9b31 | [] | [
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"task_categories:question-answering",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/codah/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
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- crowdsourced
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- en
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- unknown
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task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: codah
pretty_name: COmmonsense Datas... |
null | null | @article{husain2019codesearchnet,
title={{CodeSearchNet} challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | CodeSearchNet corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and prep... | false | 3,424 | false | code_search_net | 2022-11-03T16:46:45.000Z | codesearchnet | false | 235fcf2420e1ad1a4816e5f4ec9c08198bd20a2e | [] | [
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"task_categories:t... | https://huggingface.co/datasets/code_search_net/resolve/main/README.md | ---
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task_ids:
- language-modeling
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null | null | @inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={47... | Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax... | false | 340 | false | code_x_glue_cc_clone_detection_big_clone_bench | 2022-11-03T16:31:19.000Z | null | false | 7173b13dee3932fae0ea4d05a5e404c42945c539 | [] | [
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"task_categories:text-classification",
"task_ids:semantic-similarity-classification"
] | https://huggingface.co/datasets/code_x_glue_cc_clone_detection_big_clone_bench/resolve/main/README.md | ---
annotations_creators:
- found
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- found
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task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: CodeXGlueCcCloneDetectionBigCloneBench
... |
null | null | @inproceedings{mou2016convolutional,
title={Convolutional neural networks over tree structures for programming language processing},
author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages={1287--1293},
year={2016... | Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task. | false | 380 | false | code_x_glue_cc_clone_detection_poj104 | 2022-11-03T16:30:54.000Z | null | false | ec35d50dad50797c42f9bbe8befe2e69767dabaf | [] | [
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"task_categories:text-retrieval",
"task_ids:document-retrieval"
] | https://huggingface.co/datasets/code_x_glue_cc_clone_detection_poj104/resolve/main/README.md | ---
pretty_name: CodeXGlueCcCloneDetectionPoj104
annotations_creators:
- found
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- found
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- code
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- c-uda
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source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
dataset_info:
features:
... |
null | null | @article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and... | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing dataset... | false | 1,163 | false | code_x_glue_cc_cloze_testing_all | 2022-11-03T16:31:55.000Z | null | false | c2c03a20a49135a556270caa8fe36e175409ed0a | [] | [
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"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",... | https://huggingface.co/datasets/code_x_glue_cc_cloze_testing_all/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
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- c-uda
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- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcClozeTestingAll
configs:
- go... |
null | null | @article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and... | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing dataset... | false | 1,174 | false | code_x_glue_cc_cloze_testing_maxmin | 2022-11-03T16:31:56.000Z | null | false | c10c3e94c36e06aa1745144527f9464b8699c4eb | [] | [
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"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",... | https://huggingface.co/datasets/code_x_glue_cc_cloze_testing_maxmin/resolve/main/README.md | ---
annotations_creators:
- found
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task_ids:
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pretty_name: CodeXGlueCcClozeTestingMaxmin
configs:
-... |
null | null | @article{raychev2016probabilistic,
title={Probabilistic Model for Code with Decision Trees},
author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
journal={ACM SIGPLAN Notices},
pages={731--747},
year={2016},
publisher={ACM New York, NY, USA}
}
@inproceedings{allamanis2013mining,
title={Mining Source Code Rep... | Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity.
We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method ca... | false | 521 | false | code_x_glue_cc_code_completion_line | 2022-11-03T16:30:39.000Z | null | false | 6b215759d9bca559031b04d5772145afc79b96ce | [] | [
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"task_categories:fill-mask",
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"co... | https://huggingface.co/datasets/code_x_glue_cc_code_completion_line/resolve/main/README.md | ---
annotations_creators:
- found
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task_categories:
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task_ids:
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pretty_name: CodeXGlueCcCodeCompletionLine
configs:
- go
- ... |
null | null | @article{raychev2016probabilistic,
title={Probabilistic Model for Code with Decision Trees},
author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
journal={ACM SIGPLAN Notices},
pages={731--747},
year={2016},
publisher={ACM New York, NY, USA}
}
@inproceedings{allamanis2013mining,
t... | Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation... | false | 501 | false | code_x_glue_cc_code_completion_token | 2022-11-03T16:16:39.000Z | null | false | 6022f8680a1542ed7525809539ad4ddebaa4009b | [] | [
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"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-langu... | https://huggingface.co/datasets/code_x_glue_cc_code_completion_token/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
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task_categories:
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task_ids:
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pretty_name: CodeXGlueCcCodeComp... |
null | null | @article{10.1145/3340544,
author = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
title = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},
year = {2019},
issue_date = {October 2019},
publisher = {... | We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. | false | 586 | false | code_x_glue_cc_code_refinement | 2022-11-03T16:30:55.000Z | null | false | 8843b9c12d0a441c561e963faa3bcd501dd05ff5 | [] | [
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"task_categories:text2text-generation",
"tags:debugging"
] | https://huggingface.co/datasets/code_x_glue_cc_code_refinement/resolve/main/README.md | ---
annotations_creators:
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source_datasets:
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task_categories:
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task_ids: []
pretty_name: CodeXGlueCcCodeRefinement
tags:
- debugging... |
null | null | @article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin... | The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplica... | false | 562 | false | code_x_glue_cc_code_to_code_trans | 2022-11-03T16:30:56.000Z | null | false | 15a14032f7fd782d524b9d03eef7f4507b326beb | [] | [
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"task_categories:translation",
"tags:code-to-code"
] | https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/resolve/main/README.md | ---
annotations_creators:
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source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueCcCodeToCodeTrans
tags:
- code-to-code
data... |
null | null | @inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={101... | Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
The dataset we use comes from the... | false | 386 | false | code_x_glue_cc_defect_detection | 2022-11-03T16:16:10.000Z | null | false | ed47cb7ccc43e622908f0cd0b268bea8a8a497ba | [] | [
"annotations_creators:found",
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/code_x_glue_cc_defect_detection/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: CodeXGlueCcDefectDetection
da... |
null | null | @article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | false | 1,434 | false | code_x_glue_ct_code_to_text | 2022-11-03T16:32:07.000Z | null | false | 19540e0d82e0c1ae1b5ca57efbd76bb3f0aa46a1 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:code",
"language:en",
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"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation",
"configs:go",
"configs:... | https://huggingface.co/datasets/code_x_glue_ct_code_to_text/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueCtCodeToText
configs:
- go
- java
- j... |
null | null | @article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | false | 339 | false | code_x_glue_tc_nl_code_search_adv | 2022-11-03T16:15:33.000Z | null | false | b05d1207340200841b6cbab72e0f1ab38d9a7291 | [] | [
"annotations_creators:found",
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"language:code",
"language:en",
"license:c-uda",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-retrieval",
"task_ids:document-retrieval"
] | https://huggingface.co/datasets/code_x_glue_tc_nl_code_search_adv/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
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multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
pretty_name: CodeXGlueTcNlCodeSearchAdv
dataset_inf... |
null | null | @article{iyer2018mapping,
title={Mapping language to code in programmatic context},
author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.09588},
year={2018}
} | We use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details. | false | 613 | false | code_x_glue_tc_text_to_code | 2022-11-03T16:30:49.000Z | null | false | 95fe0cf9957c311885ae79c0ecc82d65f95190d3 | [] | [
"annotations_creators:found",
"language_creators:found",
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"language:en",
"license:c-uda",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:translation",
"tags:text-to-code"
] | https://huggingface.co/datasets/code_x_glue_tc_text_to_code/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueTcTextToCode
tags:
- text-to-code
dataset_info:
... |
null | null | @article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin... | The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/. | false | 835 | false | code_x_glue_tt_text_to_text | 2022-11-03T16:31:27.000Z | null | false | 1d3999e01a7e1f7fa2e8195cd64bb433ef368ccd | [] | [
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"tags:code-documentati... | https://huggingface.co/datasets/code_x_glue_tt_text_to_text/resolve/main/README.md | ---
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pretty_name: CodeXGlueTtTextToText
tags:
- code-documentation-translation... |
null | null | @inproceedings{abujabal-etal-2019-comqa,
title = "{ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters",
author = {Abujabal, Abdalghani and
Saha Roy, Rishiraj and
Yahya, Mohamed and
Weikum, Gerhard},
booktitle = {Proceedings of the 2019 Con... | ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website.
By collecting questions from such a site we ensure that the information needs are ones of interest to actual users.
Moreover, questions posed there are often cannot be answered by commercial search eng... | false | 342 | false | com_qa | 2022-11-03T16:15:38.000Z | comqa | false | 95d8a61aedefa19fce584da013f732286191d015 | [] | [
"language:en"
] | https://huggingface.co/datasets/com_qa/resolve/main/README.md | ---
language:
- en
paperswithcode_id: comqa
pretty_name: ComQA
dataset_info:
features:
- name: cluster_id
dtype: string
- name: questions
sequence: string
- name: answers
sequence: string
splits:
- name: test
num_bytes: 273384
num_examples: 2243
- name: train
num_bytes: 696645
... |
null | null | @inproceedings{lin-etal-2020-commongen,
title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
author = "Lin, Bill Yuchen and
Zhou, Wangchunshu and
Shen, Ming and
Zhou, Pei and
Bhagavatula, Chandra and
Choi, Yejin and
Ren,... | CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.
CommonGen is challengi... | false | 42,653 | false | common_gen | 2022-11-03T16:47:34.000Z | commongen | false | 6c78beef85d845adf596effdd3b256b5b3cf893a | [] | [
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"task_categories:text2text-generation",
"tags:concepts-to-t... | https://huggingface.co/datasets/common_gen/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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license:
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pretty_name: CommonGen
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: commongen
tags:
- conce... |
null | null | @dataset{ganesh_sinisetty_2021_5036977,
author = {Ganesh Sinisetty and
Pavlo Ruban and
Oleksandr Dymov and
Mirco Ravanelli},
title = {CommonLanguage},
month = jun,
year = 2021,
publisher = {Zenodo},
version = {0.1},
... | This dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database.
The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language).
The dataset has been extracted from CommonVoice to train language-id systems. | false | 1,926 | false | common_language | 2022-11-03T16:32:11.000Z | null | false | 7f134dc23084617a609051f13142552165597805 | [] | [
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"language:cy",
"language:de",
"language:dv",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"l... | https://huggingface.co/datasets/common_language/resolve/main/README.md | ---
pretty_name: Common Language
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
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- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy
- ia
- id
- it
- ja
- ka
- kab
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- lv
- mn
- mt
- nl
- pl
- pt
- rm
- ro
- ru
- rw
- sah
- sl
- sv
- ta
- tr
- t... |
null | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | Common Voice is Mozilla's initiative to help teach machines how real people speak.
The dataset currently consists of 7,335 validated hours of speech in 60 languages, but we’re always adding more voices and languages. | false | 22,835 | false | common_voice | 2022-11-03T16:47:22.000Z | common-voice | false | 25e9cba507bee2860d7c6e39f817584493e43419 | [] | [
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"language:dv",
"language:el",
"language:en",
"language:eo",
"l... | https://huggingface.co/datasets/common_voice/resolve/main/README.md | ---
pretty_name: Common Voice
annotations_creators:
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language_creators:
- crowdsourced
language:
- ab
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- ja
- ka
- kab
- ky
- lg
- lt
- lv
- mn
- mt
- nl
- or
- pa
- pl
-... |
null | null | @inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the Nort... | CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random... | false | 3,644 | false | commonsense_qa | 2022-11-03T16:46:41.000Z | commonsenseqa | false | 745d1ef4676f282d239f7bad8551803525afba95 | [] | [
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"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/commonsense_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
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license:
- mit
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pretty_name: CommonsenseQA
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: commonsenseqa
dat... |
null | null | @article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
... | The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations. | false | 3,251 | false | competition_math | 2022-11-03T16:46:41.000Z | null | false | 5a45cb0fd1d39bf3fc16b2a73ff8a586f8ea6f14 | [] | [
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:explanation-generation"
] | https://huggingface.co/datasets/competition_math/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
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pretty_name: Mathematics Aptitude Test of Heuristics (MATH)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:... |
null | null | @inproceedings{suglia2020compguesswhat,
title={CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning},
author={Suglia, Alessandro, Konstas, Ioannis, Vanzo, Andrea, Bastianelli, Emanuele, Desmond Elliott, Stella Frank and Oliver Lemon},
booktitle={Proceed... | CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
scene is an image in VisualGenome. Visit the website for more details:... | false | 501 | false | compguesswhat | 2022-11-03T16:30:39.000Z | compguesswhat | false | 0d6df9f412355346bc09ceb592ba3722060350e7 | [] | [
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"task_categories:visual-question-answering",
"task_ids:visual-question-answering"
] | https://huggingface.co/datasets/compguesswhat/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
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pretty_name: CompGuessWhat?!
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-guesswhat
task_categories:
- visual-question-answering
task_ids:
- visual-question-answeri... |
null | null | \
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31.
} | This dataset is designed to provide training data
for common sense relationships pulls together from various sources.
The dataset is multi-lingual. See langauge codes and language info
here: https://github.com/commonsense/conceptnet5/wiki/Languages
This dataset provides an interface for the conceptnet5 csv fi... | false | 717 | false | conceptnet5 | 2022-11-03T16:31:12.000Z | conceptnet | false | 394eea5ff3889cea902d4c17da7b2ffb9b066b67 | [] | [
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"language:nl",
"language:pt",
"language:ru",
"language:zh",
"license:cc-by-4.0",
"multilinguality:mo... | https://huggingface.co/datasets/conceptnet5/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
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- fr
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size_categories:
- 100K<n<1M
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_... |
null | null | @inproceedings{tksbuchholz2000conll,
author = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
title = "Introduction to the CoNLL-2000 Shared Task: Chunking",
editor = "Claire Cardie and Walter Daelemans and Claire
Nedellec and Tjong Kim Sang, Erik",
booktitle = "Proceedings of ... | Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP onl... | false | 407 | false | conll2000 | 2022-10-28T16:32:08.000Z | conll-2000-1 | false | 84a73b141eb3a2b1176d7f4802d0205a41518b37 | [] | [
"language:en"
] | https://huggingface.co/datasets/conll2000/resolve/main/README.md | ---
language:
- en
paperswithcode_id: conll-2000-1
pretty_name: CoNLL-2000
dataset_info:
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dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
0: ''''''
1: '#'
2: $
3: (
4: )
... |
null | null | @inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
... | Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-indep... | false | 878 | false | conll2002 | 2022-11-03T16:31:31.000Z | conll-2002 | false | a5723e802a8e371be85af5c1605fcc86b766bc1f | [] | [
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"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-o... | https://huggingface.co/datasets/conll2002/resolve/main/README.md | ---
annotations_creators:
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language_creators:
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- es
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license:
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- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2002... |
null | null | @inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and
De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning... | The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns se... | false | 26,380 | false | conll2003 | 2022-11-03T16:47:22.000Z | conll-2003 | false | 202f160377918ecd4ee85875bef317a62398bc0c | [] | [
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"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-... | https://huggingface.co/datasets/conll2003/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
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- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_i... |
null | null | @inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing ... | CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set are included for completeness.
For more details see https://www.aclweb.org/anthology/D19-1519/ and https://github.com/ZihanWangKi/CrossWei... | false | 3,362 | false | conllpp | 2022-11-03T16:32:21.000Z | conll | false | bd6e6b132637c326e4ee7b1b711f39749e18ae64 | [] | [
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"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/conllpp/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
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source_datasets:
- extended|conll2003
task_categories:
- token-classification
task_ids:
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paperswithcode_id: conll
pretty_name: ... |
null | null | \ | null | false | 352 | false | consumer-finance-complaints | 2022-11-03T16:16:07.000Z | null | false | 0b02c9ad622d489201a9f39e08aa579c2455ee3c | [] | [
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"task_ids:topic-classification"
] | https://huggingface.co/datasets/consumer-finance-complaints/resolve/main/README.md | ---
annotations_creators:
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- crowdsourced
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- cc0-1.0
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pretty_name: consumer-finance-complaints
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
dataset_inf... |
null | null | null | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue sy... | false | 612 | false | conv_ai | 2022-11-03T16:30:55.000Z | null | false | 6ea3599d4683e70680ad01e171df860b9e5c2361 | [] | [
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"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"tags:... | https://huggingface.co/datasets/conv_ai/resolve/main/README.md | ---
annotations_creators:
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task_categories:
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task_ids:
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paperswithcode_id: null
pretty_name: ConvAi... |
null | null | @misc{dinan2019second,
title={The Second Conversational Intelligence Challenge (ConvAI2)},
author={Emily Dinan and Varvara Logacheva and Valentin Malykh and Alexander Miller and Kurt Shuster and Jack Urbanek and Douwe Kiela and Arthur Szlam and Iulian Serban and Ryan Lowe and Shrimai Prabhumoye and Alan W B... | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue sy... | false | 701 | false | conv_ai_2 | 2022-11-03T16:31:09.000Z | convai2 | false | 47919ed60b2e320bb84e1e377dff3417abe0020a | [] | [
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"task_ids:t... | https://huggingface.co/datasets/conv_ai_2/resolve/main/README.md | ---
annotations_creators:
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task_categories:
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task_ids:
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paperswithcode_id: convai2
pretty_name: Con... |
null | null | @misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
... | The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such... | false | 610 | false | conv_ai_3 | 2022-11-03T16:30:50.000Z | null | false | eb0fce2361d4a28f810424d7c042af08213c8a42 | [] | [
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"source_datasets:original",
"task_categories:conversational",
"task_categories:text-classification",
"task_ids... | https://huggingface.co/datasets/conv_ai_3/resolve/main/README.md | ---
annotations_creators:
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- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: More... |
null | null | @InProceedings{christmann2019look,
title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},
author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},
booktitle={Proceedings of the 28th ACM Int... | ConvQuestions is the first realistic benchmark for conversational question answering over knowledge graphs.
It contains 11,200 conversations which can be evaluated over Wikidata. The questions feature a variety of complex
question phenomena like comparisons, aggregations, compositionality, and temporal reasoning. | false | 343 | false | conv_questions | 2022-11-03T16:15:51.000Z | null | false | 80b569d1a5fd93798143caa51729e260af8805b4 | [] | [
"arxiv:1910.03262",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"language_bcp47:en-US",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:t... | https://huggingface.co/datasets/conv_questions/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
language_bcp47:
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license:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text-generation
- fill-mask
task_ids:
- open-domain-qa
-... |
null | null | @article{reddy-etal-2019-coqa,
title = "{C}o{QA}: A Conversational Question Answering Challenge",
author = "Reddy, Siva and
Chen, Danqi and
Manning, Christopher D.",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "C... | CoQA: A Conversational Question Answering Challenge | false | 1,420 | false | coqa | 2022-11-03T16:31:50.000Z | coqa | false | aab93b0200f595ba0eafe074bd59ba14eb59fcdd | [] | [
"arxiv:1808.07042",
"arxiv:1704.04683",
"arxiv:1506.03340",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|race",
"source_datasets:extended|cnn_dailymail",
"sou... | https://huggingface.co/datasets/coqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: 'CoQA: Conversational Question Answering Challenge'
size_categories:
- 1K<n<10K
source_datasets:
- extended|race
- extended|cnn_dailymail
- extended|wikipedia
- extended|other
... |
null | null | @article{Wang2020CORD19TC,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and
K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and
... | The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related
historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information
retrieval systems over its rich collection of metadata and structured full text papers. Since ... | false | 1,044 | false | cord19 | 2022-11-03T16:31:53.000Z | cord-19 | false | 50508938c74b7faee130f9b164b1d4d55d4e77e0 | [] | [
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"annotations_creators:no-annotation",
"language_creators:found",
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"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:other"
] | https://huggingface.co/datasets/cord19/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nd-4.0
- cc-by-sa-4.0
- other
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size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: cord-19
pretty_name: CORD-19
dataset_info:
-... |
null | null | @InProceedings{Danescu-Niculescu-Mizil+Lee:11a,
author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
title={Chameleons in imagined conversations:
A new approach to understanding coordination of linguistic style in dialogs.},
booktitle={Proceedings of the
Workshop on Cognitive Modeling and Co... | This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release y... | false | 364 | false | cornell_movie_dialog | 2022-11-03T16:16:06.000Z | cornell-movie-dialogs-corpus | false | d586028a3f3134d9973fc7aa38cc94b70d4a7033 | [] | [
"language:en"
] | https://huggingface.co/datasets/cornell_movie_dialog/resolve/main/README.md | ---
language:
- en
paperswithcode_id: cornell-movie-dialogs-corpus
pretty_name: Cornell Movie-Dialogs Corpus
dataset_info:
features:
- name: movieID
dtype: string
- name: movieTitle
dtype: string
- name: movieYear
dtype: string
- name: movieIMDBRating
dtype: string
- name: movieNoIMDBVotes
... |
null | null | @inproceedings{rajani2019explain,
title = {Explain Yourself! Leveraging Language models for Commonsense Reasoning},
author = {Rajani, Nazneen Fatema and
McCann, Bryan and
Xiong, Caiming and
Socher, Richard}
year={2019}
booktitle = {Proceedings of the 2019 Conference of the Associ... | Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. | false | 34,739 | false | cos_e | 2022-11-03T16:47:40.000Z | cos-e | false | e1b623172c49b55083224feb7d7453493f7d2da7 | [] | [
"arxiv:1906.02361",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:crowdsourced",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|commonsense_qa",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/cos_e/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: Commonsense Explanations
size_categories:
- 10K<n<100K
source_datasets:
- extended|commonsense_qa
task_categories:
- question-answering
task_ids:
- open-domain-qa
pape... |
null | null | @inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in ... | Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events tha... | false | 37,317 | false | cosmos_qa | 2022-11-03T16:47:40.000Z | cosmosqa | false | f386421c4a8085dc4adca3ca22c0e8c81c6b75d2 | [] | [
"arxiv:1909.00277",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/cosmos_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CosmosQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: cosmosqa
dataset_inf... |
null | null | @Article{Sharjeel2016,
author="Sharjeel, Muhammad
and Nawab, Rao Muhammad Adeel
and Rayson, Paul",
title="COUNTER: corpus of Urdu news text reuse",
journal="Language Resources and Evaluation",
year="2016",
pages="1--27",
issn="1574-0218",
doi="10.1007/s10579-016-9367-2",
url="http://dx.doi.org/10.1007/s10579-016-9367-2... | The COrpus of Urdu News TExt Reuse (COUNTER) corpus contains 1200 documents with real examples of text reuse from the field of journalism. It has been manually annotated at document level with three levels of reuse: wholly derived, partially derived and non derived. | false | 333 | false | counter | 2022-11-03T16:15:33.000Z | counter | false | 6330e3d241f426b913e02b5af98e17432d4948a8 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ur",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-... | https://huggingface.co/datasets/counter/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
- topic-classifica... |
null | null | @article{tang2020rapidly,
title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
journal={arXiv preprint arXiv:2004.11339},
year={2020}
} | CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. | false | 595 | false | covid_qa_castorini | 2022-11-03T16:30:54.000Z | covidqa | false | 830827bcdd1304c084c05d6f3511878ba39b1cf3 | [] | [
"arxiv:2004.11339",
"annotations_creators:expert-generated",
"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",
"task_ids:extractiv... | https://huggingface.co/datasets/covid_qa_castorini/resolve/main/README.md | ---
annotations_creators:
- expert-generated
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
- extractive-qa
paperswithcode_id: covidqa
pretty_name: Cov... |
null | null | @inproceedings{moller2020covid,
title={COVID-QA: A Question Answering Dataset for COVID-19},
author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
year={2020}
} | COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. | false | 655 | false | covid_qa_deepset | 2022-11-03T16:31:16.000Z | null | false | 1aaf679526aae36f371c5f3c5a304d8358e509b7 | [] | [
"annotations_creators:expert-generated",
"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:closed-domain-qa",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/covid_qa_deepset/resolve/main/README.md | ---
annotations_creators:
- expert-generated
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:
- closed-domain-qa
- extractive-qa
paperswithcode_id: null
pretty_name: COVI... |
null | null | @article{ju2020CovidDialog,
title={CovidDialog: Medical Dialogue Datasets about COVID-19},
author={Ju, Zeqian and Chakravorty, Subrato and He, Xuehai and Chen, Shu and Yang, Xingyi and Xie, Pengtao},
journal={ https://github.com/UCSD-AI4H/COVID-Dialogue},
year={2020}
} | null | false | 497 | false | covid_qa_ucsd | 2022-11-03T16:16:40.000Z | null | false | 1ca0ae2424dbb8268be323404f2e5a3d1c539f21 | [] | [
"arxiv:2005.05442",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language:en",
"language:zh",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:qu... | https://huggingface.co/datasets/covid_qa_ucsd/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: null
pretty_name: Cov... |
null | null | No paper about this dataset is published yet. Please cite this dataset as "鈴木 優: COVID-19 日本語 Twitter データセット （http://www.db.info.gifu-u.ac.jp/covid-19-twitter-dataset/）" | 53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. The annotation is by majority decision by 5 - 10 crowd workers. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020. The original tweets are not contained. Please use Twitter API... | false | 332 | false | covid_tweets_japanese | 2022-11-03T16:15:43.000Z | null | false | 084b46fad5e1feef65c7d087875b2839fe5f9efb | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ja",
"license:cc-by-nd-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:fact-checking"
] | https://huggingface.co/datasets/covid_tweets_japanese/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ja
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: COVID-19 日本語Twitterデータ... |
null | null | @misc{wang2020covost,
title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus},
author={Changhan Wang and Anne Wu and Juan Pino},
year={2020},
eprint={2007.10310},
archivePrefix={arXiv},
primaryClass={cs.CL} | CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of crowdsourced voice recordings.
Note that in order to limit the required storage for prepari... | false | 6,014 | false | covost2 | 2022-11-03T16:46:56.000Z | null | false | ced5284682122b1d8fd698e457130968c1500436 | [] | [
"arxiv:2007.10310",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:ar",
"language:ca",
"language:cy",
"language:de",
"language:es",
"language:et",
"language:fa",
"language:fr",
"language:id",
"language:it",
"lang... | https://huggingface.co/datasets/covost2/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ar
- ca
- cy
- de
- es
- et
- fa
- fr
- id
- it
- ja
- lv
- mn
- nl
- pt
- ru
- sl
- sv
- ta
- tr
- zh
language_bcp47:
- sv-SE
- zh-CN
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- ... |
null | null | @misc{dagli2021cppe5,
title={CPPE-5: Medical Personal Protective Equipment Dataset},
author={Rishit Dagli and Ali Mustufa Shaikh},
year={2021},
eprint={2112.09569},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
to allow the study of subordinate categorization of medical personal protective equipments,
which is not possible with other popular data sets that focus on broad level categories. | false | 503 | false | cppe-5 | 2022-11-03T16:30:39.000Z | cppe-5 | false | fdbcfc6dbc432ce9163240d56cb6f4704dedf201 | [] | [
"arxiv:2112.09569",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:object-detection",
"tags:medical-personal-protective-equipment-detection"
] | https://huggingface.co/datasets/cppe-5/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
paperswithcode_id: cppe-5
pretty_name: CPPE - 5
tags:
- medical-personal-protectiv... |
null | null | @misc{he2018decoupling,
title={Decoupling Strategy and Generation in Negotiation Dialogues},
author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang},
year={2018},
eprint={1808.09637},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | We study negotiation dialogues where two agents, a buyer and a seller,
negotiate over the price of an time for sale. We collected a dataset of more
than 6K negotiation dialogues over multiple categories of products scraped from Craigslist.
Our goal is to develop an agent that negotiates with humans through such convers... | false | 664 | false | craigslist_bargains | 2022-11-03T16:30:54.000Z | craigslistbargains | false | 4bb3c7f885687cafb45b3f5fd6e8a57d2896fa0c | [] | [
"arxiv:1808.09637",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialo... | https://huggingface.co/datasets/craigslist_bargains/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: craigslistbargains
pret... |
null | null | @inproceedings{zrs2020urlsegmentation,
title={Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities},
author={Hao Zhang and Jae Ro and Richard William Sproat},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
... | Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl"). Breaking domain names such as "openresearch" into component words "open" and "research" is important for applications such as Text-to-Speech synthesis and web search. Common Crawl is an... | false | 387 | false | crawl_domain | 2022-11-03T16:16:09.000Z | common-crawl-domain-names | false | a0812940ef1cdde868f0969d20262bd833c7dd28 | [] | [
"arxiv:2011.03138",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-Common-Crawl",
... | https://huggingface.co/datasets/crawl_domain/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-Common-Crawl
- original
task_categories:
- other
task_ids: []
paperswithcode_id: common-c... |
null | null | @inproceedings{
title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
author = {Rameshkumar, Revanth and Bailey, Peter},
year = {2020},
publisher = {Association for Computational Linguistics},
conference = {ACL}
} | Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398... | false | 383 | false | crd3 | 2022-11-03T16:16:19.000Z | crd3 | false | f9a79753d9d574bfad4b74374ca08db2433e68ce | [] | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"source_datasets:original",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
... | https://huggingface.co/datasets/crd3/resolve/main/README.md | ---
pretty_name: CRD3 (Critical Role Dungeons and Dragons Dataset)
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
task_ids:
- dialogue... |
null | null | null | \ | false | 1,532 | false | crime_and_punish | 2022-11-03T16:32:06.000Z | null | false | 09e36e4e95bdb023decd2693ec35ed58ae567079 | [] | [
"language:en"
] | https://huggingface.co/datasets/crime_and_punish/resolve/main/README.md | ---
language:
- en
paperswithcode_id: null
pretty_name: CrimeAndPunish
dataset_info:
features:
- name: line
dtype: string
splits:
- name: train
num_bytes: 1270540
num_examples: 21969
download_size: 1201735
dataset_size: 1270540
---
# Dataset Card for "crime_and_punish"
## Table of Contents
- [... |
null | null | @inproceedings{nangia2020crows,
title = "{CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models}",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods... | CrowS-Pairs, a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models (MLMs). | false | 8,267 | false | crows_pairs | 2022-11-03T16:47:25.000Z | crows-pairs | false | c4e0d5a2ed9f7937ac5ef88509ac0f0b4950bdf6 | [] | [
"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:text-scoring",
"tags:bias-evaluation"
] | https://huggingface.co/datasets/crows_pairs/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:
- text-scoring
paperswithcode_id: crows-pairs
pretty_name: CrowS-Pairs... |
null | null | @misc{efrat2021cryptonite,
title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language},
author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},
year={2021},
eprint={2103.01242},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite,
a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each
example in... | false | 332 | false | cryptonite | 2022-11-03T16:15:49.000Z | null | false | f592b8023df593ef913922e2f8a2d0d830c9c525 | [] | [
"arxiv:2103.01242",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"tas... | https://huggingface.co/datasets/cryptonite/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: null
pretty_nam... |
null | null | @article{DBLP:journals/corr/abs-1910-05298,
author = {Ondrej Dusek and
Filip Jurcicek},
title = {Neural Generation for Czech: Data and Baselines},
journal = {CoRR},
volume = {abs/1910.05298},
year = {2019},
url = {http://arxiv.org/abs/1910.05298},
archivePrefix = {arX... | This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as
a translation of the English San Francisco Restaurants dataset by Wen et al. (2015). | false | 333 | false | cs_restaurants | 2022-11-03T16:15:36.000Z | czech-restaurant-information | false | 5113fda5198f35c924df27173ca63a8eac594659 | [] | [
"arxiv:1910.05298",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:cs",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-san-francisco-restaurants",
"task_categories... | https://huggingface.co/datasets/cs_restaurants/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- expert-generated
- machine-generated
language:
- cs
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-san-francisco-restaurants
task_categories:
- text2text-generation
- text-generation
- fill-mask
tas... |
null | null | @article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
} | Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions. | false | 822 | false | cuad | 2022-11-03T16:31:13.000Z | cuad | false | 6f09662825bb1f9cc07591c065524029e6ed894b | [] | [
"arxiv:2103.06268",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extrac... | https://huggingface.co/datasets/cuad/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: cuad
pretty_name: CUA... |
null | null | @inproceedings{rodriguez2020curiosity,
title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
year = 2020,
booktitle = {Empirical Methods in Natural Language Processing}
} | This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like
geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog
acts, grounding to Wikipedia, and user reactions to messages. | false | 596 | false | curiosity_dialogs | 2022-11-03T16:31:00.000Z | curiosity | false | c3ae8fecfe5b225d978804dc055795828b300840 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"ta... | https://huggingface.co/datasets/curiosity_dialogs/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: curiosity
pretty_name... |
null | null | @InProceedings{li2017dailydialog,
author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},
booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCN... | We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way
and cover various topics about our daily life. We also manually label the developed dataset with commun... | false | 3,820 | false | daily_dialog | 2022-11-03T16:32:31.000Z | dailydialog | false | faa7da5265093c5eb4a93ceb9ba88a8b6e9e96c5 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification",
"tags:emotion-classif... | https://huggingface.co/datasets/daily_dialog/resolve/main/README.md | ---
paperswithcode_id: dailydialog
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_n... |
null | null | @inproceedings{hvingelby-etal-2020-dane,
title = "{D}a{NE}: A Named Entity Resource for {D}anish",
author = "Hvingelby, Rasmus and
Pauli, Amalie Brogaard and
Barrett, Maria and
Rosted, Christina and
Lidegaard, Lasse Malm and
Søgaard, Anders",
booktitle = "Proceedings of th... | The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC
by the Alexandra Institute.
It is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank)
which has annotations for dependency parsing and part-of-speech (POS) tagging.
The Danish UD treebank (Johannsen et al., 2015, U... | false | 1,825 | false | dane | 2022-11-03T16:32:07.000Z | dane | false | cec09c380e5ff74da0e61245781d6fe4cf020dc0 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:da",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-Danish-Universal-Dependencies-treebank",
"task_categories:token-classification",
"task_ids:named-entit... | https://huggingface.co/datasets/dane/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-Danish-Universal-Dependencies-treebank
task_categories:
- token-classification
task_ids:
- named-entity-recognition
... |
null | null | null | The dataset consists of 9008 sentences that are labelled with fine-grained polarity in the range from -2 to 2 (negative to postive). The quality of the fine-grained is not cross validated and is therefore subject to uncertainties; however, the simple polarity has been cross validated and therefore is considered to be m... | false | 332 | false | danish_political_comments | 2022-11-03T16:16:00.000Z | null | false | 261ce48f1a71ef323b735b7dcec391f96c50e668 | [] | [
"annotations_creators:expert-generated",
"language_creators:other",
"language:da",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/danish_political_comments/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- da
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: null
pretty_name: DanishPoliti... |
null | null | @article{radev2020dart,
title={DART: Open-Domain Structured Data Record to Text Generation},
author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwant... | DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality
sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.
It consists of 82191 examples across different domains with each input being a semantic RDF triple set deri... | false | 351 | false | dart | 2022-10-28T16:32:19.000Z | dart | false | 8440fc0cf105d4ff79c78eb9c147e827b5c13427 | [] | [
"arxiv:2007.02871",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikitable... | https://huggingface.co/datasets/dart/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikitable_questions
- extended|wikisql
- extended|web_nlg
- extended|cleaned_e2e
task_... |
null | null | @InProceedings{huggingface:dataset,
title = {Data Commons 2019 Fact Checks},
authors={datacommons.org},
year={2019}
} | A dataset of fact checked claims by news media maintained by datacommons.org | false | 508 | false | datacommons_factcheck | 2022-11-03T16:16:40.000Z | null | false | 6c0d702232ae970556f3f4213e4c680ca1582c2e | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:fact-checking",
"configs:fctc... | https://huggingface.co/datasets/datacommons_factcheck/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: DataCommons F... |
null | null | @article{lehmann2015dbpedia,
title={DBpedia--a large-scale, multilingual knowledge base extracted from Wikipedia},
author={Lehmann, Jens and Isele, Robert and Jakob, Max and Jentzsch, Anja and Kontokostas,
Dimitris and Mendes, Pablo N and Hellmann, Sebastian and Morsey, Mohamed and Van Kleef,
Patrick and Auer, ... | The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes
from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we
randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size
of the training dataset is 560,000 an... | false | 26,170 | false | dbpedia_14 | 2022-11-03T16:47:23.000Z | dbpedia | false | 0a6dd973e1067ccb60539bf21b21619bb256029e | [] | [
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/dbpedia_14/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: dbpedia
pretty_name: D... |
null | null | @article{DBLP:journals/corr/abs-1910-00896,
author = {Benjamin van der Burgh and
Suzan Verberne},
title = {The merits of Universal Language Model Fine-tuning for Small Datasets
- a case with Dutch book reviews},
journal = {CoRR},
volume = {abs/1910.00896},
year =... | The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and created due to a lack of annotated datasets in Dutch that are suitable for this task. | false | 338 | false | dbrd | 2022-11-03T16:15:20.000Z | dbrd | false | f2a16538498b62c6662e1f4fcafc9c5d72d14c0c | [] | [
"arxiv:1910.00896",
"annotations_creators:found",
"language_creators:found",
"language:nl",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-cla... | https://huggingface.co/datasets/dbrd/resolve/main/README.md | ---
pretty_name: DBRD
annotations_creators:
- found
language_creators:
- found
language:
- nl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-lan... |
null | null | @article{lewis2017deal,
title={Deal or no deal? end-to-end learning for negotiation dialogues},
author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
journal={arXiv preprint arXiv:1706.05125},
year={2017}
} | A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach anagreement (o a deal) via natural language dialogue. | false | 507 | false | deal_or_no_dialog | 2022-11-03T16:16:39.000Z | negotiation-dialogues-dataset | false | f8b094f1bb125c7c8fd0b68ae89586b1bb6e3b0c | [] | [
"arxiv:1706.05125",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:conversational"
] | https://huggingface.co/datasets/deal_or_no_dialog/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
task_ids: []
paperswithcode_id: negotiation-dialogues-dataset
pretty_name: Deal or No ... |
null | null | @inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
p... | Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived... | false | 1,372 | false | definite_pronoun_resolution | 2022-11-03T16:31:33.000Z | definite-pronoun-resolution-dataset | false | f3a8519ac72c41c85c5d0bfb1da666a272b872b7 | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:word-sense-disambiguation"
] | https://huggingface.co/datasets/definite_pronoun_resolution/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- word-sense-disambiguation
paperswithcode_id: definite-pronoun-resolu... |
null | null | @INPROCEEDINGS{8459963,
author={E. D. {Livelo} and C. {Cheng}},
booktitle={2018 IEEE International Conference on Agents (ICA)},
title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
year={2018},
volume={},
number={},
pag... | Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets. | false | 332 | false | dengue_filipino | 2022-11-03T16:07:59.000Z | dengue | false | 0bf5ee1ab86b482521ad5ed603e7b90afdd61e82 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:tl",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-cla... | https://huggingface.co/datasets/dengue_filipino/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: dengue
... |
null | null | @inproceedings{yu2020dialogue,
title={Dialogue-Based Relation Extraction},
author={Yu, Dian and Sun, Kai and Cardie, Claire and Yu, Dong},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/2004.08056v1}
} | DialogRE is the first human-annotated dialogue based relation extraction (RE) dataset aiming
to support the prediction of relation(s) between two arguments that appear in a dialogue.
The dataset annotates all occurrences of 36 possible relation types that exist between pairs
of arguments in the 1,788 dialogues originat... | false | 355 | false | dialog_re | 2022-11-03T16:16:01.000Z | dialogre | false | c73e4194380361257ed6f44ddf31e375d7d11546 | [] | [
"arxiv:2004.08056",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:other",
"task_categories:text-generation",
"task_categories:f... | https://huggingface.co/datasets/dialog_re/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: dialogre
prett... |
null | null | @inproceedings{peskov-etal-2020-takes,
title = "It Takes Two to Lie: One to Lie, and One to Listen",
author = "Peskov, Denis and
Cheng, Benny and
Elgohary, Ahmed and
Barrow, Joe and
Danescu-Niculescu-Mizil, Cristian and
Boyd-Graber, Jordan",
booktitle = "Proceedings of the... | null | false | 427 | false | diplomacy_detection | 2022-11-03T16:07:59.000Z | null | false | fa2a7188566638034e48f02aa2c4459fd7cf60c9 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/diplomacy_detection/resolve/main/README.md | ---
annotations_creators:
- found
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- found
language:
- en
license:
- unknown
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- monolingual
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task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: null
pretty_name: HateOffensive
dataset_info:
fe... |
null | null | @inproceedings{title={Multilingual Disaster Response Messages}
} | This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters.
The data has been encoded with 36 dif... | false | 353 | false | disaster_response_messages | 2022-11-03T16:15:44.000Z | null | false | 5cf6708e2eeb63eb21b1420ff27e58b85fb30482 | [] | [
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"task_categories:text2text-generati... | https://huggingface.co/datasets/disaster_response_messages/resolve/main/README.md | ---
annotations_creators:
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- crowdsourced
language:
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- es
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license:
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size_categories:
- 10K<n<100K
source_datasets:
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task_categories:
- text2text-generation
- text-classification
task_ids:
- intent-classification
-... |
null | null | @InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Lingu... | DISCOFUSE is a large scale dataset for discourse-based sentence fusion. | false | 916 | false | discofuse | 2022-11-03T16:31:37.000Z | discofuse | false | 665587c7cb9b0e22ff7a632ee9d5f3d528ccd04e | [] | [
"arxiv:1902.10526",
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"task_categories:text2text-generation",
"tags:sentence-fusion"
] | https://huggingface.co/datasets/discofuse/resolve/main/README.md | ---
annotations_creators:
- machine-generated
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- en
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- cc-by-sa-3.0
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pretty_name: DiscoFuse
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source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: discofuse
tags:
- senten... |
null | null | @inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican C... | null | false | 3,551 | false | discovery | 2022-11-03T16:32:38.000Z | discovery | false | 75ff1b1f317515bd697e68524353f62cda06a3a6 | [] | [
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"source_datasets:original",
"task_categories:text-classification",
"configs:discovery",
"configs:discoverysmall",
... | https://huggingface.co/datasets/discovery/resolve/main/README.md | ---
annotations_creators:
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task_categories:
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task_ids: []
paperswithcode_id: discovery
pretty_name: Discovery
configs:
- discovery
... |
null | null | @inproceedings{gupta-etal-2021-disflqa,
title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
booktitle = "Findings of ACL",
year = "2021"
} | Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
a sou... | false | 335 | false | disfl_qa | 2022-11-03T16:15:20.000Z | null | false | c17b98f71bbe22e8601551b9e3c002ebf31654a3 | [] | [
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"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-doma... | https://huggingface.co/datasets/disfl_qa/resolve/main/README.md | ---
annotations_creators:
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language:
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license:
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pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question
Answering'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- ques... |
null | null | @inproceedings{feng-etal-2020-doc2dial,
title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
author = "Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natu... | Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. It includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets this dataset covers a variety of di... | false | 672 | false | doc2dial | 2022-11-03T16:31:16.000Z | doc2dial | false | ee5f78cb0e74862f8458b8902bbd7a23077a39ed | [] | [
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"task_categories:question-answering",
"task_ids:closed-domain-qa"
] | https://huggingface.co/datasets/doc2dial/resolve/main/README.md | ---
annotations_creators:
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- found
language:
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- cc-by-3.0
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task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: doc2dial
pretty_name: doc2dial
dataset_... |
null | null | @inproceedings{yao2019DocRED,
title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},
author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},
booktitle={Proceedings of ACL 2019},
year={20... | Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, ... | false | 954 | false | docred | 2022-11-03T16:31:37.000Z | docred | false | c49e9ca9ffca6e67b2bc3ec6235c1f7cec1375fc | [] | [
"arxiv:1906.06127",
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"language:en",
"license:mit",
"multilinguality:monolingual",
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"source_datasets:original",
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval"
] | https://huggingface.co/datasets/docred/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
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license:
- mit
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paperswithcode_id: docred
pretty_name: DocRED
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
datase... |
null | null | @misc{campos2020doqa,
title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
year={2020},
eprint={2005.01328},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as... | false | 687 | false | doqa | 2022-11-03T16:31:11.000Z | doqa | false | c1cf61f376381da890f739a521995f787fbb58b0 | [] | [
"arxiv:2005.01328",
"language:en"
] | https://huggingface.co/datasets/doqa/resolve/main/README.md | ---
language:
- en
paperswithcode_id: doqa
pretty_name: DoQA
dataset_info:
- config_name: cooking
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
... |
null | null | @article{sundream2018,
title={{DREAM}: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension},
author={Sun, Kai and Yu, Dian and Chen, Jianshu and Yu, Dong and Choi, Yejin and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2019},
url={https://... | DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. | false | 29,742 | false | dream | 2022-11-03T16:47:24.000Z | dream | false | 572ff737aa667b24c7ab269e2d6ed0b2ba51d5d7 | [] | [
"annotations_creators:expert-generated",
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"language:en",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/dream/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
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- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: dream
pretty_name: DREAM
d... |
null | null | @inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
} | DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counti... | false | 2,245 | false | drop | 2022-11-03T16:32:14.000Z | drop | false | 98340bb6cfb9f7a73dbd1524daa8e56cd56c6522 | [] | [
"annotations_creators:crowdsourced",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:extractive-... | https://huggingface.co/datasets/drop/resolve/main/README.md | ---
pretty_name: DROP
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- extractive-qa
- abstractiv... |
null | null | @inproceedings{DuoRC,
author = { Amrita Saha and Rahul Aralikatte and Mitesh M. Khapra and Karthik Sankaranarayanan},title = {{DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension}},
booktitle = {Meeting of the Association for Computational Linguistics (ACL)},
year = {2018}
} | DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. | false | 56,358 | false | duorc | 2022-11-03T16:47:44.000Z | duorc | false | 6b4c49944a86c24848063c6fc244ae3d75226525 | [] | [
"arxiv:1804.07927",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:te... | https://huggingface.co/datasets/duorc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
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- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- abstractive-qa
- extractive-qa
paperswith... |
null | null | @data{FK2/MTPTL7_2020,
author = {Gupta, Aakash},
publisher = {COVID-19 Data Hub},
title = {{Dutch social media collection}},
year = {2020},
version = {DRAFT VERSION},
doi = {10.5072/FK2/MTPTL7},
url = {https://doi.org/10.5072/FK2/MTPTL7}
} | The dataset contains around 271,342 tweets. The tweets are filtered via the official Twitter API to
contain tweets in Dutch language or by users who have specified their location information within Netherlands
geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes... | false | 406 | false | dutch_social | 2022-11-03T16:15:45.000Z | null | false | 37cff9d2b7d3a2caf58ade214c8642db92eb5846 | [] | [
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"multilinguality:multilingual",
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"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification",
"... | https://huggingface.co/datasets/dutch_social/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
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- nl
license:
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size_categories:
- 100K<n<1M
source_datasets:
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task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-label-classification
pa... |
null | null | @inproceedings{marcinczuk2013open,
title={Open dataset for development of Polish Question Answering systems},
author={Marcinczuk, Michal and Ptak, Marcin and Radziszewski, Adam and Piasecki, Maciej},
booktitle={Proceedings of the 6th Language & Technology Conference: Human Language Technologies as a Challenge for Compu... | The Did You Know (pol. Czy wiesz?) dataset consists of human-annotated question-answer pairs. The task is to predict if the answer is correct. We chose the negatives which have the largest token overlap with a question. | false | 332 | false | dyk | 2022-11-03T16:08:16.000Z | null | false | 57bab16607110f765e5a1b6ea233c2908c61e32f | [] | [
"annotations_creators:expert-generated",
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"language:pl",
"license:bsd-3-clause",
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"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/dyk/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
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source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: null
pretty_name: dyk
dataset_info:
... |
null | null | @article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = ... | The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variatio... | false | 1,281 | false | e2e_nlg | 2022-11-03T16:32:11.000Z | e2e | false | 6a3c3d4072138adf9d30602303135049edecc565 | [] | [
"arxiv:1706.09254",
"arxiv:1901.11528",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:meaning-rep... | https://huggingface.co/datasets/e2e_nlg/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tag... |
null | null | @inproceedings{dusek-etal-2019-semantic,
title = "Semantic Noise Matters for Neural Natural Language Generation",
author = "Du{\v{s}}ek, Ond{\v{r}}ej and
Howcroft, David M. and
Rieser, Verena",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
m... | An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:
Ondřej Dušek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan. | false | 3,680 | false | e2e_nlg_cleaned | 2022-11-03T16:32:00.000Z | null | false | 3560a7316194c58bc3393d1dd167722b55d8abd2 | [] | [
"arxiv:1706.09254",
"arxiv:1901.11528",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:meaning-rep... | https://huggingface.co/datasets/e2e_nlg_cleaned/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: null
pretty_name: the Cleaned Version of the ... |
null | null | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | Original source: Website and documentatuion from the European Central Bank, compiled and made available by Alberto Simoes (thank you very much!)
19 languages, 170 bitexts
total number of files: 340
total number of tokens: 757.37M
total number of sentence fragments: 30.55M | false | 975 | false | ecb | 2022-11-03T16:31:41.000Z | ecb | false | 72a0cf3a1cfd704f678156302d1013ab2dc74584 | [] | [
"annotations_creators:found",
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"language:fi",
"language:fr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
... | https://huggingface.co/datasets/ecb/resolve/main/README.md | ---
annotations_creators:
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language_creators:
- found
language:
- cs
- da
- de
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- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- sk
- sl
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
pa... |
null | null | @InProceedings{chalkidis-et-al-2021-ecthr,
title = "Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Tsarapatsanis, Dimitrios and Aletras, Nikolaos and Androutsopoulos, Ion and Malakasiotis, ... | The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases. | false | 955 | false | ecthr_cases | 2022-11-03T16:31:32.000Z | ecthr | false | aee14246aa6d4c47b0e1f11de267f87a2f286804 | [] | [
"arxiv:2103.13084",
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"annotations_creators:found",
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"language:en",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_id... | https://huggingface.co/datasets/ecthr_cases/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: ecthr
pretty... |
null | null | null | Eduge news classification dataset is provided by Bolorsoft LLC. It is used for training the Eduge.mn production news classifier
75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин | false | 332 | false | eduge | 2022-11-03T16:08:06.000Z | null | false | 1e7c47f1339bb6d98cbeed201c06fd2b3d70ee43 | [] | [
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] | https://huggingface.co/datasets/eduge/resolve/main/README.md | ---
pretty_name: Eduge
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task_ids:
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dataset_info:
f... |
null | null | @inproceedings{overview_ehealthkd2020,
author = {Piad{-}Morffis, Alejandro and
Guti{\'{e}}rrez, Yoan and
Cañizares-Diaz, Hian and
Estevez{-}Velarde, Suilan and
Almeida{-}Cruz, Yudivi{\'{a}}n and
Muñoz, Rafael and
Montoyo, And... | Dataset of the eHealth Knowledge Discovery Challenge at IberLEF 2020. It is designed for
the identification of semantic entities and relations in Spanish health documents. | false | 331 | false | ehealth_kd | 2022-11-03T16:08:17.000Z | null | false | 8b8d2251faad6d81f2082a4e134a748dc87dd97b | [] | [
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"task_categories:token-classification",
"task_ids:named-entity-recognition",
"tags:relatio... | https://huggingface.co/datasets/ehealth_kd/resolve/main/README.md | ---
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task_ids:
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paperswithcode_id: null
pretty_... |
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},
... | EiTB-ParCC: Parallel Corpus of Comparable News. A Basque-Spanish parallel corpus provided by Vicomtech (https://www.vicomtech.org), extracted from comparable news produced by the Basque public broadcasting group Euskal Irrati Telebista. | false | 330 | false | eitb_parcc | 2022-11-03T16:15:31.000Z | eitb-parcc | false | a3df155544c9b00ae6a6978638cbf390beabdfbf | [] | [
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"task_categories:translation"
] | https://huggingface.co/datasets/eitb_parcc/resolve/main/README.md | ---
annotations_creators:
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task_ids: []
paperswithcode_id: eitb-parcc
pretty_name: EiTB-ParCC
dataset_info:
features:
- nam... |
null | null | @inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
ad... | This new dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014. | false | 508 | false | electricity_load_diagrams | 2022-11-03T16:18:46.000Z | null | false | dacd4d6b0db079c4741bd625a79d322c3f1152c9 | [] | [
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"task_ids:univariate-time-series-forecasting"
] | https://huggingface.co/datasets/electricity_load_diagrams/resolve/main/README.md | ---
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language: []
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pretty_name: Electricity Load Diagrams
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source_datasets:
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task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
dat... |
null | null | @inproceedings{DBLP:conf/acl/FanJPGWA19,
author = {Angela Fan and
Yacine Jernite and
Ethan Perez and
David Grangier and
Jason Weston and
Michael Auli},
editor = {Anna Korhonen and
David R. Traum and
Lluis ... | Explain Like I'm 5 long form QA dataset | false | 1,870 | false | eli5 | 2022-11-03T16:32:19.000Z | eli5 | false | 94f581d054cdefd41b722a33e81c50474a24eee3 | [] | [
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"task_ids:abstractive-qa",
... | https://huggingface.co/datasets/eli5/resolve/main/README.md | ---
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task_categories:
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task_ids:
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- open-domain-abstractive-qa
paperswithcode_id: eli5
pretty_na... |
null | null | @inproceedings{eli5-category,
author = {Jingsong Gao and
Qingren Zhou and
Rui Qiu},
title = {{ELI5-Category:} A categorized open-domain QA dataset},
year = {2021}
} | The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized into different topics according to their tags. Since the training and validation set is built by questions in diff... | false | 335 | false | eli5_category | 2022-11-03T16:08:17.000Z | null | false | 2167171d0189b5f540fcd1c77fb102af1cc47519 | [] | [
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"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa"
] | https://huggingface.co/datasets/eli5_category/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
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- unknown
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paperswithcode_id: null
pretty_name: ELI5-Category
size_categories:
- 100K<n<1M
source_datasets:
- extended|eli5
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-... |
null | null | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in ... | false | 985 | false | emea | 2022-11-03T16:31:39.000Z | null | false | 1c7188ad85d76c4272ee4935546d1c8a08764740 | [] | [
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... | https://huggingface.co/datasets/emea/resolve/main/README.md | ---
annotations_creators:
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- ro
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- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
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t... |
null | null | @inproceedings{chatterjee-etal-2019-semeval,
title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
year={20... | In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. | false | 871 | false | emo | 2022-11-03T16:31:34.000Z | emocontext | false | 4519729767e779229bdf978e2284114d79147c28 | [] | [
"annotations_creators:expert-generated",
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"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/emo/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
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language:
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license:
- unknown
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source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: emocontext
pretty_name:... |
null | null | @inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empi... | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | false | 18,297 | false | emotion | 2022-11-03T16:47:16.000Z | emotion | false | b7dfe4482299c487641788dd6d81797842665744 | [] | [
"annotations_creators:machine-generated",
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"size_categories:10K<n<100K",
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"task_categories:text-classification",
"task_ids:multi-class-classification",
"tags:emotion-cl... | https://huggingface.co/datasets/emotion/resolve/main/README.md | ---
pretty_name: Emotion
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
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- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcod... |
null | null | @inbook{inbook,
author = {Al-Khatib, Amr and El-Beltagy, Samhaa},
year = {2018},
month = {01},
pages = {105-114},
title = {Emotional Tone Detection in Arabic Tweets: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part II},
isbn = {978-3-319-77115-1},
doi = {1... | Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text | false | 406 | false | emotone_ar | 2022-11-03T16:16:19.000Z | null | false | 874d9b444a2a8d297e393dbe2ffcc9288fe8f33a | [] | [
"annotations_creators:found",
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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/emotone_ar/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
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size_categories:
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- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: Emotional Tone in Arabi... |
null | null | @inproceedings{rashkin2019towards,
title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},
author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},
booktitle = {ACL},
year = {2019},
} | PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset | false | 1,010 | false | empathetic_dialogues | 2022-11-03T16:31:42.000Z | empatheticdialogues | false | 720ee2fa7acde0e84ef7ee6c3906453e3ecd1a09 | [] | [
"arxiv:1811.00207",
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"language:en",
"language_creators:crowdsourced",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
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"source_datasets:original",
"task_categories:conversational",
"task_categories:question-answering",
"task... | https://huggingface.co/datasets/empathetic_dialogues/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
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pretty_name: EmpatheticDialogues
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
- question-answering
task_ids:
- dialogue-generati... |
null | null | @InProceedings{ferreiraetal2018,
author = "Castro Ferreira, Thiago and Moussallem, Diego and Wubben, Sander and Krahmer, Emiel",
title = "Enriching the WebNLG corpus",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
year = "2018",
series = {INLG'18},
publis... | WebNLG is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the de... | false | 1,031 | false | enriched_web_nlg | 2022-11-03T16:31:35.000Z | null | false | ae65d969d7206e07298a988f237734983cc6cad8 | [] | [
"annotations_creators:found",
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"source_datasets:extended|other-web-nlg",
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"configs:de",
"c... | https://huggingface.co/datasets/enriched_web_nlg/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- de
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license:
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size_categories:
- 1K<n<10K
source_datasets:
- extended|other-web-nlg
task_categories:
- tabular-to-text
task_ids:
- rdf-to-text
paperswithcode_id: null
pretty_name: Enriched We... |
null | null | @unpublished{eraser2019,
title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@inproceedings{MultiRC2018,
author = {Daniel Khashabi and Snigdha Chaturve... | Eraser Multi RC is a dataset for queries over multi-line passages, along with
answers and a rationalte. Each example in this dataset has the following 5 parts
1. A Mutli-line Passage
2. A Query about the passage
3. An Answer to the query
4. A Classification as to whether the answer is right or wrong
5. An Explanation j... | false | 401 | false | eraser_multi_rc | 2022-11-03T16:16:06.000Z | null | false | d8cab99c11e6026daff73e115220a3a5123e3711 | [] | [
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa"
] | https://huggingface.co/datasets/eraser_multi_rc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- other
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pretty_name: Eraser MultiRC (Multi-Sentence Reading Comprehension)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-q... |
null | null | @incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. L... | The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. | false | 18,252 | false | esnli | 2022-11-03T16:47:11.000Z | e-snli | false | 6b70ca1b4ade3aba9fa44c21f550364659656fa2 | [] | [
"language:en"
] | https://huggingface.co/datasets/esnli/resolve/main/README.md | ---
language:
- en
paperswithcode_id: e-snli
pretty_name: e-SNLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
0: entailment
1: neutral
2: contradiction
- name: explanation... |
null | null | @inproceedings{kanade2020learning,
title={Learning and Evaluating Contextual Embedding of Source Code},
author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen},
booktitle={International Conference on Machine Learning},
pages={5110--5121},
year={2020},
organization={PMLR}
} | A redistributable subset of the ETH Py150 corpus, introduced in the ICML 2020 paper 'Learning and Evaluating Contextual Embedding of Source Code' | false | 358 | false | eth_py150_open | 2022-11-03T16:15:54.000Z | eth-py150-open | false | 3d6c32c9535e81d0946255b323fea79701187ad8 | [] | [
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"language:en",
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"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:other",
"tags:contextual-embeddings"
] | https://huggingface.co/datasets/eth_py150_open/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
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license:
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source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: eth-py150-open
pretty_name: ethpy150open
tags:
- contextu... |
null | null | @misc{mollas2020ethos,
title={ETHOS: an Online Hate Speech Detection Dataset},
author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas},
year={2020},
eprint={2006.08328},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech
detection on social media platforms, called Ethos. There are two variations of the dataset:
Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label
about hate speech presence or absence. 565 of the... | false | 3,393 | false | ethos | 2022-11-03T16:32:39.000Z | ethos | false | 06e068d1db1e56f721cbe5a0bf5cd82d2475a7e6 | [] | [
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"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-clas... | https://huggingface.co/datasets/ethos/resolve/main/README.md | ---
annotations_creators:
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size_categories:
- n<1K
source_datasets:
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task_categories:
- text-classification
task_ids:
- multi-label-classification
- sentiment-classification
pa... |
null | null | @inproceedings{chalkidis-etal-2021-regir,
title = "Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Manginas, Nikos and Katakalou, Eva, and Malakasiotis, Prodromos",
... | EURegIR: Regulatory Compliance IR (EU/UK) | false | 503 | false | eu_regulatory_ir | 2022-11-03T16:16:32.000Z | null | false | d9fd23e591102c9db50efba5ad58374867530999 | [] | [
"arxiv:2101.10726",
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"language_creators:found",
"language:en",
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"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"tags:document-to-docum... | https://huggingface.co/datasets/eu_regulatory_ir/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
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license:
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multilinguality:
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size_categories:
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- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
paperswithcode_id: null
pretty_name: the RegIR datasets
tags:
-... |
null | null | @inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Comp... | EURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts. | false | 524 | false | eurlex | 2022-11-03T16:16:27.000Z | eurlex57k | false | c6a936acc11daed4c127c2e967eada7c02d7273e | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification",
"tags:legal-topic-classification"
] | https://huggingface.co/datasets/eurlex/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: eurlex57k
pretty_name: the EUR-Lex... |
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