author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1
class | downloads float64 1 1M ⌀ | gated bool 2
classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2
classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | Dataset containing 216,930 Jeopardy questions, answers and other data.
The json file is an unordered list of questions where each question has
'category' : the question category, e.g. "HISTORY"
'value' : integer $ value of the question as string, e.g. "200"
Note: This is "None" for Final Jeopardy! and Tiebreaker quest... | false | 344 | false | jeopardy | 2022-11-03T16:16:10.000Z | null | false | 586ad80bba7562cb6eb98756c5e00ba97e2688f3 | [] | [
"language:en"
] | https://huggingface.co/datasets/jeopardy/resolve/main/README.md | ---
language:
- en
paperswithcode_id: null
pretty_name: jeopardy
dataset_info:
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: int32
- name: answer
dtype: string
- name: round
dtype: string
- name: show_n... | |
null | null | @InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
author = {Napoles, Courtney
and Sakaguchi, Keisuke
and Tetreault, Joel},
title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
booktitle = {Proceedings of the 15th Conference of the Europe... | JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus.
It is a gold standard benchmark for developing and evaluating GEC systems with respect to
fluency (extent to which a text is native-sounding) as well as grammaticality.
For each source document, there are four human-written corre... | false | 1,058 | false | jfleg | 2022-11-03T16:31:37.000Z | jfleg | false | 02323d8b27ba2f759863d248065e720a49c18937 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"multilinguality:other-language-learner",
"size_categories:1K<n<10K",
"source_datasets:extended|other-GUG-grammaticality-judgements",
"task_categories:text2tex... | https://huggingface.co/datasets/jfleg/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
- other-language-learner
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-GUG-grammaticality-judgements
task_categories:
- text2text-generation
task_ids: []
paper... |
null | null | null | This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. | false | 546 | false | jigsaw_toxicity_pred | 2022-11-03T16:30:54.000Z | null | false | bde739555b9654264d4d9a6cf987e824ae394ad9 | [] | [
"annotations_creators:crowdsourced",
"language_creators:other",
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification"
] | https://huggingface.co/datasets/jigsaw_toxicity_pred/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: null
pretty_name: JigsawToxicityP... |
null | null | null | A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity. | false | 536 | false | jigsaw_unintended_bias | 2022-11-03T16:30:46.000Z | null | false | 5d3b2b77d54a6234f131006ed84b61918d38e6fd | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-scoring",
"tags:toxicity-prediction"
] | https://huggingface.co/datasets/jigsaw_unintended_bias/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Jigsaw Unintended Bias in Toxicity Classification
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scori... |
null | null | @inproceedings{kim2004introduction,
title={Introduction to the bio-entity recognition task at JNLPBA},
author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
booktitle={Proceedings of the international joint workshop on natural ... | The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemi... | false | 3,387 | false | jnlpba | 2022-11-03T16:30:43.000Z | null | false | 612661797f3a29d1c5f4e1189c08e2904ab38a0d | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-genia-v3.02",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/jnlpba/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-genia-v3.02
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ... |
null | null | \
@inproceedings{hasanain2016questions,
title={What Questions Do Journalists Ask on Twitter?},
author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
booktitle={Tenth International AAAI Conference on Web and Social Media},
year={2016}
} | \
The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
tweets manually labeled for question identification over Arabic tweets posted by journalists. | false | 327 | false | journalists_questions | 2022-11-03T16:15:31.000Z | null | false | 5f62db347415aa406e1a4db8c6e86ae4e516eb37 | [] | [
"annotations_creators:crowdsourced",
"language_creators:other",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"tags:question-identification"
] | https://huggingface.co/datasets/journalists_questions/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: null
pretty_name: JournalistsQuestions
tags:
- question-id... |
null | null | @misc{hande2021hope,
title={Hope Speech detection in under-resourced Kannada language},
author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},
year={2021},
eprint={2108.04616},
archivePrefix={a... | Numerous methods have been developed to monitor the spread of negativity in modern years by
eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively
lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in onlin... | false | 325 | false | kan_hope | 2022-11-03T16:07:47.000Z | null | false | 6428b4ea681fa50c1365783509859ef76309710d | [] | [
"arxiv:2108.04616",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:en",
"language:kn",
"language_bcp47:en-IN",
"language_bcp47:kn-IN",
"license:cc-by-4.0",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_catego... | https://huggingface.co/datasets/kan_hope/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- kn
language_bcp47:
- en-IN
- kn-IN
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: KanHope
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-la... |
null | null | null | The Kannada news dataset contains only the headlines of news article in three categories:
Entertainment, Tech, and Sports.
The data set contains around 6300 news article headlines which collected from Kannada news websites.
The data set has been cleaned and contains train and test set using which can be used to benchm... | false | 324 | false | kannada_news | 2022-11-03T16:07:50.000Z | null | false | 2fa91cb24dc05aaf29935445ba5ef0250bbf492d | [] | [
"annotations_creators:other",
"language_creators:other",
"language:kn",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/kannada_news/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- other
language:
- kn
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: null
pretty_name: KannadaNews Dataset
data... |
null | null | @inproceedings{zhou-etal-2020-kdconv,
title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
author = "Zhou, Hao and
Zheng, Chujie and
Huang, Kaili and
Huang, Minlie and
Zhu, Xiaoyan",
booktitle = "Proceedings of the 58th... | KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth ... | false | 1,414 | false | kd_conv | 2022-11-03T16:32:04.000Z | kdconv | false | aa57cc1d0921b74a84169ebf7a2845523d7c90e7 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language:zh",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask... | https://huggingface.co/datasets/kd_conv/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- zh
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: kdcon... |
null | null | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
... | A parallel corpus of KDE4 localization files (v.2).
92 languages, 4,099 bitexts
total number of files: 75,535
total number of tokens: 60.75M
total number of sentence fragments: 8.89M | false | 2,072 | false | kde4 | 2022-11-03T16:32:20.000Z | null | false | 12cd06d961fae220f6ef1ab533321b8e9ddc3533 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:af",
"language:ar",
"language:as",
"language:ast",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:crh",
"language:cs",
"language:csb",
"language:cy",
"language:da",
"language:de",... | https://huggingface.co/datasets/kde4/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- as
- ast
- be
- bg
- bn
- br
- ca
- crh
- cs
- csb
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gl
- gu
- ha
- he
- hi
- hne
- hr
- hsb
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- lb
- lt
- lv... |
null | null | @misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primary... | Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 dis... | false | 549 | false | kelm | 2022-11-03T16:30:47.000Z | kelm | false | 98fb32e81029bfe032a5dce7ffa1a5e1c28dd1ea | [] | [
"arxiv:2010.12688",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:other",
"tags:data-to-text-generation"
] | https://huggingface.co/datasets/kelm/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training ... |
null | null | @inproceedings{fb_kilt,
author = {Fabio Petroni and
Aleksandra Piktus and
Angela Fan and
Patrick Lewis and
Majid Yazdani and
Nicola De Cao and
James Thorne and
Yacine Jernite and
... | KILT tasks training and evaluation data.
- [FEVER](https://fever.ai) | Fact Checking | fever
- [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | aidayago2
- [WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking ... | false | 45,773 | false | kilt_tasks | 2022-11-03T16:47:44.000Z | kilt | false | b59459efb13083e46fd6149d9f900a7bba22572d | [] | [
"arxiv:2009.02252",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories... | https://huggingface.co/datasets/kilt_tasks/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- found
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
source_datasets:
- extended|natural_questions
- extended|other-aidayago
- extended|o... |
null | null | @inproceedings{fb_kilt,
author = {Fabio Petroni and
Aleksandra Piktus and
Angela Fan and
Patrick Lewis and
Majid Yazdani and
Nicola De Cao and
James Thorne and
Yacine Jernite and
... | KILT-Wikipedia: Wikipedia pre-processed for KILT. | false | 363 | false | kilt_wikipedia | 2022-11-03T16:16:09.000Z | null | false | 36d48adae0af9cc60bb0484e46675e3496da7259 | [] | [] | https://huggingface.co/datasets/kilt_wikipedia/resolve/main/README.md | ---
paperswithcode_id: null
pretty_name: KiltWikipedia
dataset_info:
features:
- name: kilt_id
dtype: string
- name: wikipedia_id
dtype: string
- name: wikipedia_title
dtype: string
- name: text
sequence:
- name: paragraph
dtype: string
- name: anchors
sequence:
- name: par... |
null | null | @article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
} | Kinyarwanda and Kirundi news classification datasets | false | 799 | false | kinnews_kirnews | 2022-11-03T16:31:23.000Z | kinnews-and-kirnews | false | a1b9d6fa1d1a3222e374cc5b25b0fc61356335a7 | [] | [
"arxiv:2010.12174",
"annotations_creators:expert-generated",
"language_creators:found",
"language:rn",
"language:rw",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task... | https://huggingface.co/datasets/kinnews_kirnews/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- rn
- rw
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
paperswithco... |
null | null | @misc{park2021klue,
title={KLUE: Korean Language Understanding Evaluation},
author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Je... | KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions.... | false | 9,799 | false | klue | 2022-11-03T16:47:11.000Z | klue | false | 1562b55ca52eaa42f348df306fc9f20071459a3c | [] | [
"arxiv:2105.09680",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ko",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:fill-mask",
"task_categories:question-answering",
"t... | https://huggingface.co/datasets/klue/resolve/main/README.md | ---
pretty_name: KLUE
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- fill-mask
- question-answering
- text-classification
- text-generation
-... |
null | null | @article{cho2018speech,
title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency},
author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1811.04231},
year={2018}
} | This dataset is designed to identify speaker intention based on real-life spoken utterance in Korean into one of
7 categories: fragment, description, question, command, rhetorical question, rhetorical command, utterances. | false | 334 | false | kor_3i4k | 2022-11-03T16:16:00.000Z | null | false | ef51bf6bb3c01039b1bf3d095bf31289a9435d6c | [] | [
"arxiv:1811.04231",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ko",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/kor_3i4k/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: null
pretty_name: 3i... |
null | null | @inproceedings{moon-etal-2020-beep,
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
... | Human-annotated Korean corpus collected from a popular domestic entertainment news aggregation platform
for toxic speech detection. Comments are annotated for gender bias, social bias and hate speech. | false | 828 | false | kor_hate | 2022-11-03T16:31:04.000Z | korean-hatespeech-dataset | false | f0d5eb3debd9de138fa527f0d67f29150b60f495 | [] | [
"arxiv:2005.12503",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language:ko",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_... | https://huggingface.co/datasets/kor_hate/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: korean-hat... |
null | null | @InProceedings{Kim:2016,
title = "Korean Named Entity Recognition Dataset",
authors = "Jae-Hoon Kim",
publisher = "GitHub",
year = "2016"
} | Korean named entity recognition dataset | false | 324 | false | kor_ner | 2022-11-03T16:08:19.000Z | null | false | 1c2110f3a0f50ec0e6d2a80c20a6ace86bb5381e | [] | [
"annotations_creators:expert-generated",
"language_creators:other",
"language:ko",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/kor_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: KorNER
dataset_in... |
null | null | @article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
} | Korean Natural Language Inference datasets | false | 652 | false | kor_nli | 2022-11-03T16:31:06.000Z | kornli | false | c695f3f448a768e894a42e00ebd620d5b4829aa8 | [] | [
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language_creators:expert-generated",
"language:ko",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|multi_nli",
"source_datasets:extended|snli",
"source_datase... | https://huggingface.co/datasets/kor_nli/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|multi_nli
- extended|snli
- extended|xnli
task_categories:
- text-classification
task_ids:
- n... |
null | null | null | The dataset contains data for bechmarking korean models on NLI and STS | false | 572 | false | kor_nlu | 2022-11-03T16:30:54.000Z | null | false | d08dc8e3888f25e14c9004e91ff451bc1099f24c | [] | [
"arxiv:2004.03289",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"language:ko",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|snli",
"task_cate... | https://huggingface.co/datasets/kor_nlu/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
- machine-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic... |
null | null | @misc{Song:2018,
title = "Paired Question v.2",
authors = "Youngsook Song",
publisher = "GitHub",
year = "2018"
} | This is a Korean paired question dataset containing labels indicating whether two questions in a given pair are semantically identical. This dataset was used to evaluate the performance of [KoGPT2](https://github.com/SKT-AI/KoGPT2#subtask-evaluations) on a phrase detection downstream task. | false | 325 | false | kor_qpair | 2022-11-03T16:15:30.000Z | null | false | 84b0f7fec5cb3ad1e9a09ce95de53eda6b298f78 | [] | [
"annotations_creators:expert-generated",
"language_creators:other",
"language:ko",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:semantic-similarity-classification"
] | https://huggingface.co/datasets/kor_qpair/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
paperswithcode_id: null
pretty_name: KorQpair... |
null | null | @article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
} | This new dataset is designed to extract intent from non-canonical directives which will help dialog managers
extract intent from user dialog that may have no clear objective or are paraphrased forms of utterances. | false | 327 | false | kor_sae | 2022-11-03T16:08:10.000Z | null | false | e38ea26334bb5e1e224b88897f85d4dadf78a150 | [] | [
"arxiv:1912.00342",
"arxiv:1811.04231",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ko",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:... | https://huggingface.co/datasets/kor_sae/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: null
pretty_name:... |
null | null | null | This is a dataset designed to detect sarcasm in Korean because it distorts the literal meaning of a sentence
and is highly related to sentiment classification. | false | 326 | false | kor_sarcasm | 2022-11-03T16:15:46.000Z | null | false | 4b06188bc0749be5c86235745fa292abf9186a41 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:ko",
"license:mit",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"tags:sarcasm-detection"
] | https://huggingface.co/datasets/kor_sarcasm/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: null
pretty_name: Korean Sarcasm Detection
tags:
- sarcasm-d... |
null | null | @inproceedings{aly2013labr,
title={Labr: A large scale arabic book reviews dataset},
author={Aly, Mohamed and Atiya, Amir},
booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={494--498},
year={2013}
} | This dataset contains over 63,000 book reviews in Arabic.It is the largest sentiment analysis dataset for Arabic to-date.The book reviews were harvested from the website Goodreads during the month or March 2013.Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the te... | false | 331 | false | labr | 2022-11-03T16:15:15.000Z | labr | false | 73c566896e539c239fb411fc31fa571a68accccc | [] | [
"annotations_creators:found",
"language_creators:found",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/labr/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: labr
pretty_name: LABR
dataset_info:
... |
null | null | @inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={201... | LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA. | false | 2,533 | false | lama | 2022-11-03T16:32:36.000Z | lama | false | b93708b047c7057b5f1eab0a97e70975b8bbc806 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
... | https://huggingface.co/datasets/lama/resolve/main/README.md | ---
pretty_name: 'LAMA: LAnguage Model Analysis'
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n... |
null | null | @InProceedings{paperno-EtAl:2016:P16-1,
author = {Paperno, Denis and Kruszewski, Germ\'{a}n and Lazaridou,
Angeliki and Pham, Ngoc Quan and Bernardi, Raffaella and Pezzelle,
Sandro and Baroni, Marco and Boleda, Gemma and Fernandez, Raquel},
title = {The {LAMBADA} dataset: Word prediction requ... | The LAMBADA evaluates the capabilities of computational models
for text understanding by means of a word prediction task.
LAMBADA is a collection of narrative passages sharing the characteristic
that human subjects are able to guess their last word if
they are exposed to the whole passage, but not if they
only see the ... | false | 2,265 | false | lambada | 2022-11-03T16:32:24.000Z | lambada | false | 17a854471e417243f5027373ac6e0010aa5db239 | [] | [
"task_categories:text2text-generation",
"multilinguality:monolingual",
"language:en",
"language_creators:found",
"annotations_creators:expert-generated",
"source_datasets:extended|bookcorpus",
"size_categories:10K<n<100K",
"license:cc-by-4.0",
"tags:long-range-dependency"
] | https://huggingface.co/datasets/lambada/resolve/main/README.md | ---
task_categories:
- text2text-generation
task_ids: []
multilinguality:
- monolingual
language:
- en
language_creators:
- found
annotations_creators:
- expert-generated
source_datasets:
- extended|bookcorpus
size_categories:
- 10K<n<100K
license:
- cc-by-4.0
paperswithcode_id: lambada
pretty_name: LAMBADA
tags:
- lon... |
null | null | @dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
} | The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, "all_wiki" only includes examples from Spanish Wikipedia. By default, the config is set to "combined" which loads al... | false | 2,723 | false | large_spanish_corpus | 2022-11-03T16:32:33.000Z | null | false | 3f78450b49d890ad6b4a90a5e7273ecc411e9e94 | [] | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:es",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:100M<n<1B",
"size_categories:10K<n<100K",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_datas... | https://huggingface.co/datasets/large_spanish_corpus/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 100M<n<1B
- 10K<n<100K
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name... |
null | null | @article{
tache2101clustering,
title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
journal={ArXiv},
year = {2021}
} | LaRoSeDa (A Large Romanian Sentiment Data Set) contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative.
Star ratings of 1 and 2 and of 4 and 5 are provided for negative and positive reviews respectively.
The current dataset uses star rating as the label for mu... | false | 324 | false | laroseda | 2022-11-03T16:07:54.000Z | null | false | 616ebaf2ccb912e55a65ca136321c2828346f20f | [] | [
"arxiv:2101.04197",
"arxiv:1901.06543",
"annotations_creators:found",
"language_creators:found",
"language:ro",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"... | https://huggingface.co/datasets/laroseda/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: LaRoSeDa
dataset_info... |
null | null | @inproceedings{dubey2017lc2,
title={LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia},
author={Dubey, Mohnish and Banerjee, Debayan and Abdelkawi, Abdelrahman and Lehmann, Jens},
booktitle={Proceedings of the 18th International Semantic Web Conference (ISWC)},
year={2019},
organizat... | LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework. | false | 423 | false | lc_quad | 2022-11-03T16:15:27.000Z | lc-quad-2-0 | false | ad2030ab0ddd582b6b838494cd51bc7f6aa169a0 | [] | [
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"tags:knowledge-base-qa"
] | https://huggingface.co/datasets/lc_quad/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-3.0
multilinguality:
- monolingual
pretty_name: 'LC-QuAD 2.0: Large-scale Complex Question Answering Dataset'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
p... |
null | null | @inproceedings{luz_etal_propor2018,
author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
Renato R. R. {de Oliveira} and Matheus Stauffer and
Samuel Couto and Paulo Bermejo},
title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
booktitle = {Internat... | LeNER-Br is a Portuguese language dataset for named entity recognition
applied to legal documents. LeNER-Br consists entirely of manually annotated
legislation and legal cases texts and contains tags for persons, locations,
time entities, organizations, legislation and legal cases.
To compose the dataset, 66 legal docu... | false | 1,441 | false | lener_br | 2022-11-03T16:32:09.000Z | lener-br | false | 0295750df0908f2aa443329f41a7fd67da6f1f9c | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:pt",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/lener_br/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: lener-br
pretty_na... |
null | null | @article{chalkidis-etal-2021-lexglue,
title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},
author={Chalkidis, Ilias and
Jana, Abhik and
Hartung, Dirk and
Bommarito, Michael and
Androutsopoulos, Ion and
Katz, Daniel Martin and
Aletras, Nikola... | Legal General Language Understanding Evaluation (LexGLUE) benchmark is
a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks | false | 6,816 | false | lex_glue | 2022-11-03T16:47:09.000Z | null | false | 66371dbe17556beef62ea2a12503958361cd4d3c | [] | [
"arxiv:2110.00976",
"arxiv:2109.00904",
"arxiv:1805.01217",
"arxiv:2104.08671",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended",
"task_categories:question-answer... | https://huggingface.co/datasets/lex_glue/resolve/main/README.md | ---
pretty_name: LexGLUE
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended
task_categories:
- question-answering
- text-classification
task_ids:
- multi-class-classification
- multi-label-... |
null | null | @inproceedings{wang-2017-liar,
title = "{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection",
author = "Wang, William Yang",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address =... | LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for... | false | 3,545 | false | liar | 2022-11-03T16:32:29.000Z | liar | false | e56867541d557df763f378ba7be4687aa3d1922c | [] | [
"arxiv:1705.00648",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"tags:fake-news-detection"
] | https://huggingface.co/datasets/liar/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: liar
pretty_name: LIAR
train-eval-index:
- config: def... |
null | null | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | false | 12,702 | false | librispeech_asr | 2022-11-03T16:47:12.000Z | librispeech-1 | false | 926743fcdf58a0345dd8e76fc5fcc857bd505a07 | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:automatic-speech-recognition",
"task_categor... | https://huggingface.co/datasets/librispeech_asr/resolve/main/README.md | ---
pretty_name: LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-reco... |
null | null | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus. | false | 378 | false | librispeech_lm | 2022-11-03T16:08:10.000Z | null | false | 5e40536e1dce5b3aa1fd9aa1e2fda349bc74a5b5 | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:found",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:text-generation",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/librispeech_lm/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: LibrispeechLm
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: null
dataset_info... |
null | null | @inproceedings{manotas-etal-2020-limit,
title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
author = "Manotas, Irene and
Vo, Ngoc Phuoc An and
Sheinin, Vadim",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
ad... | Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physica... | false | 624 | false | limit | 2022-11-03T16:31:07.000Z | limit | false | 50eda4897c82348c582b8058c95ec4ae4f3ccf82 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|net-activities-captions",
"source_datasets:original",
"task_categories:token-classification",
"task_categori... | https://huggingface.co/datasets/limit/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|net-activities-captions
- original
task_categories:
- token-classification
- text-classification
task_ids:
- multi-class-cla... |
null | null | @inproceedings{aguilar-etal-2020-lince,
title = "{L}in{CE}: A Centralized Benchmark for Linguistic Code-switching Evaluation",
author = "Aguilar, Gustavo and
Kar, Sudipta and
Solorio, Thamar",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
... | LinCE is a centralized Linguistic Code-switching Evaluation benchmark
(https://ritual.uh.edu/lince/) that contains data for training and evaluating
NLP systems on code-switching tasks. | false | 2,249 | false | lince | 2022-11-03T16:32:22.000Z | lince | false | 679e962055b3618d775394f9e91231d7bf4a0f5d | [] | [] | https://huggingface.co/datasets/lince/resolve/main/README.md | ---
paperswithcode_id: lince
pretty_name: Linguistic Code-switching Evaluation Dataset
dataset_info:
- config_name: lid_spaeng
features:
- name: idx
dtype: int32
- name: words
sequence: string
- name: lid
sequence: string
splits:
- name: test
num_bytes: 1337727
num_examples: 8289
- nam... |
null | null | @article{gerner2010linnaeus,
title={LINNAEUS: a species name identification system for biomedical literature},
author={Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
journal={BMC bioinformatics},
volume={11},
number={1},
pages={85},
year={2010},
... | A novel corpus of full-text documents manually annotated for species mentions. | false | 377 | false | linnaeus | 2022-11-03T16:15:29.000Z | linnaeus | false | 2db303efd7a0efcd7b428aa4087a2c14a4f21f44 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/linnaeus/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: linnaeus
pretty_na... |
null | null | @inproceedings{qianying-etal-2020-liveqa,
title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
author = "Qianying, Liu and
Sicong, Jiang and
Yizhong, Wang and
Sujian, Li",
booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
... | This is LiveQA, a Chinese dataset constructed from play-by-play live broadcast.
It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games,
which are collected from the Chinese Hupu website. | false | 328 | false | liveqa | 2022-11-03T16:15:28.000Z | liveqa | false | 0a266e868920e5043c16b24eb1a1e996cc2244b8 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:zh",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/liveqa/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: liveqa
pretty_name: LiveQA
dataset_info:
features:
... |
null | null | @misc{ljspeech17,
author = {Keith Ito and Linda Johnson},
title = {The LJ Speech Dataset},
howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
} | This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading
passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length
from 1 to 10 seconds and have a total length of approximately 24 hours.
Note that in order to limit the... | false | 561 | false | lj_speech | 2022-11-03T16:16:34.000Z | ljspeech | false | 1f4efe7e65b06de1f89e6b3f27b569ea2066e1fb | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unlicense",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/lj_speech/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unlicense
multilinguality:
- monolingual
paperswithcode_id: ljspeech
pretty_name: LJ Speech
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
train-eval-... |
null | null | @article{DBLP:journals/corr/ChelbaMSGBK13,
author = {Ciprian Chelba and
Tomas Mikolov and
Mike Schuster and
Qi Ge and
Thorsten Brants and
Phillipp Koehn},
title = {One Billion Word Benchmark for Measuring Progress in Statistical Langu... | A benchmark corpus to be used for measuring progress in statistical language modeling. This has almost one billion words in the training data. | false | 1,319 | false | lm1b | 2022-11-03T16:31:54.000Z | billion-word-benchmark | false | 718afef54ea897877cce6ad1308b634f21de22f6 | [] | [] | https://huggingface.co/datasets/lm1b/resolve/main/README.md | ---
pretty_name: Lm1b
paperswithcode_id: billion-word-benchmark
dataset_info:
features:
- name: text
dtype: string
config_name: plain_text
splits:
- name: test
num_bytes: 42942045
num_examples: 306688
- name: train
num_bytes: 4238206516
num_examples: 30301028
download_size: 1792209805
... |
null | null | @article{boonkwan2020annotation,
title={The Annotation Guideline of LST20 Corpus},
author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai},
journal={arXiv prepri... | LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002... | false | 351 | false | lst20 | 2022-11-03T16:15:31.000Z | null | false | 37d3286d07ec65339fa10aa0f17f656dfe832d88 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:th",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"ta... | https://huggingface.co/datasets/lst20/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: null
pretty_na... |
null | null | @article{kassner2021multilingual,
author = {Nora Kassner and
Philipp Dufter and
Hinrich Sch{\"{u}}tze},
title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
Language Models},
journal = {CoRR},
volume = {abs/2102.00894},
year ... | mLAMA: a multilingual version of the LAMA benchmark (T-REx and GoogleRE) covering 53 languages. | false | 330 | false | m_lama | 2022-11-03T16:15:15.000Z | null | false | a55b2300263c90f58ef28ba31c771979574b8c60 | [] | [
"arxiv:2102.00894",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:af",
"language:ar",
"language:az",
... | https://huggingface.co/datasets/m_lama/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- ga
- gl
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
-... |
null | null | @article{fonseca2015evaluating,
title={Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese},
author={Fonseca, Erick R and Rosa, Joao Luis G and Aluisio, Sandra Maria},
journal={Journal of the Brazilian Computer Society},
volume={21},
number={1},
pages={2},
year={2015},... | Mac-Morpho is a corpus of Brazilian Portuguese texts annotated with part-of-speech tags.
Its first version was released in 2003 [1], and since then, two revisions have been made in order
to improve the quality of the resource [2, 3].
The corpus is available for download split into train, development and test sections.
... | false | 327 | false | mac_morpho | 2022-11-03T16:07:59.000Z | null | false | b7a492de7c443929983ff1a5a48cd4e51dfce742 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:pt",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:part-of-speech"
] | https://huggingface.co/datasets/mac_morpho/resolve/main/README.md | ---
pretty_name: Mac-Morpho
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- part-of-speech
paperswithcode_id: null
dataset_... |
null | null | null | An Urdu text corpus for machine learning, natural language processing and linguistic analysis. | false | 323 | false | makhzan | 2022-11-03T16:07:47.000Z | null | false | 768c5a742b597d6c5d41f69e4302acb9544148aa | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ur",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"tas... | https://huggingface.co/datasets/makhzan/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode... |
null | null | @article{Adelani2021MasakhaNERNE,
title={MasakhaNER: Named Entity Recognition for African Languages},
author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos
and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen May... | MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages.
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 wit... | false | 3,696 | false | masakhaner | 2022-11-03T16:46:55.000Z | null | false | ca9011a3c6a80a10978f173e4e19cb370c4dac38 | [] | [
"arxiv:2103.11811",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:am",
"language:ha",
"language:ig",
"language:lg",
"language:luo",
"language:pcm",
"language:rw",
"language:sw",
"language:wo",
"language:yo",
"license:unknown",
"multilinguality:m... | https://huggingface.co/datasets/masakhaner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- am
- ha
- ig
- lg
- luo
- pcm
- rw
- sw
- wo
- yo
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-r... |
null | null | @article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
} | Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities ... | false | 13,394 | false | math_dataset | 2022-11-03T16:47:15.000Z | mathematics | false | 12d6b832e06d05b5de7cb4ee73801081a356e478 | [] | [
"language:en"
] | https://huggingface.co/datasets/math_dataset/resolve/main/README.md | ---
pretty_name: Mathematics Dataset
language:
- en
paperswithcode_id: mathematics
dataset_info:
- config_name: algebra__linear_1d
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: test
num_bytes: 516405
num_examples: 10000
- name: train
num_bytes: 920... |
null | null | Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options. | false | 11,096 | false | math_qa | 2022-11-03T16:47:07.000Z | mathqa | false | e95117a7407f96b46d4138bc25498fd897c777cd | [] | [
"annotations_creators:crowdsourced",
"language:en",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|aqua_rat",
"task_categories:question-answering",
"task_ids:multip... | https://huggingface.co/datasets/math_qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: MathQA
size_categories:
- 10K<n<100K
source_datasets:
- extended|aqua_rat
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
pa... | |
null | null | @inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and
Pei, Jiaxin and
Wu, Hongtao and
Liu, Yiyu and
Li, Chenliang",
booktitle = "Proceedings of the 58th Annu... | MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.
MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question
descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, i... | false | 816 | false | matinf | 2022-11-03T16:31:26.000Z | matinf | false | d349f66009d816c7a71cf9335d90bd7e0323390b | [] | [] | https://huggingface.co/datasets/matinf/resolve/main/README.md | ---
paperswithcode_id: matinf
pretty_name: Maternal and Infant Dataset
dataset_info:
- config_name: age_classification
features:
- name: question
dtype: string
- name: description
dtype: string
- name: label
dtype:
class_label:
names:
0: 0-1岁
1: 1-2岁
2: 2-... |
null | null | @article{austin2021program,
title={Program Synthesis with Large Language Models},
author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
journal={arXiv preprint arXiv:2108.... | The MBPP (Mostly Basic Python Problems) dataset consists of around 1,000 crowd-sourced Python
programming problems, designed to be solvable by entry level programmers, covering programming
fundamentals, standard library functionality, and so on. Each problem consists of a task
description, code solution and 3 automated... | false | 3,083 | false | mbpp | 2022-11-03T16:32:39.000Z | null | false | eca9357a29a516f4fb477e2b856630033f165fd8 | [] | [
"arxiv:2108.07732",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_c... | https://huggingface.co/datasets/mbpp/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Mostly Basic Python Problems
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
task_i... |
null | null | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI. | false | 19,155 | false | mc4 | 2022-10-28T16:36:33.000Z | mc4 | false | 7a59adaeb35b9f744da81f2e56b727d8d5eeb935 | [] | [
"arxiv:1910.10683",
"annotations_creators:no-annotation",
"language_creators:found",
"language:af",
"language:am",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:ca",
"language:ceb",
"language:co",
"language:cs",
"language:cy",
"language:da",
"la... | https://huggingface.co/datasets/mc4/resolve/main/README.md | ---
pretty_name: mC4
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- he
- hi
- hmn
- ht
- hu
- hy
- id
- ig
- is
- it
- iw
- ja
- jv
... |
null | null | @inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
} | MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer
pairs that require temporal commonsense comprehension. A system receives a sentence
providing context information, a question designed to require temporal commonsense
knowledge, and multiple candidate answers. More than one candidate ans... | false | 1,680 | false | mc_taco | 2022-11-03T16:32:11.000Z | mc-taco | false | 7150e593c21994f04eb3b6e1883bf84a0fcc0aa4 | [] | [
"arxiv:1909.03065",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categor... | https://huggingface.co/datasets/mc_taco/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mc-tac... |
null | null | @inproceedings{md_gender_bias,
author = {Emily Dinan and
Angela Fan and
Ledell Wu and
Jason Weston and
Douwe Kiela and
Adina Williams},
editor = {Bonnie Webber and
Trevor Cohn and
Yulan He and
... | Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of th... | false | 2,302 | false | md_gender_bias | 2022-11-03T16:32:24.000Z | md-gender | false | 6a796d49786db8a1dd9d887c3953f321ee66560a | [] | [
"arxiv:1811.00552",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories... | https://huggingface.co/datasets/md_gender_bias/resolve/main/README.md | ---
pretty_name: Multi-Dimensional Gender Bias Classification
annotations_creators:
- crowdsourced
- found
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n<1K
source_datasets:
- ... |
null | null | @misc{dodge2016evaluating,
title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
year={2016},
eprint={1511.06931},
a... | The Movie Dialog dataset (MDD) is designed to measure how well
models can perform at goal and non-goal orientated dialog
centered around the topic of movies (question answering,
recommendation and discussion). | false | 1,414 | false | mdd | 2022-11-03T16:32:07.000Z | mdd | false | a6879af66c3a237d426516edbfcdf5c16d53c8dc | [] | [
"arxiv:1511.06931",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-ma... | https://huggingface.co/datasets/mdd/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: mdd
pretty_name: Mov... |
null | null | @misc{welbl2018constructing,
title={Constructing Datasets for Multi-hop Reading Comprehension Across Documents},
author={Johannes Welbl and Pontus Stenetorp and Sebastian Riedel},
year={2018},
eprint={1710.06481},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | MedHop is based on research paper abstracts from PubMed, and the queries are about interactions between pairs of drugs. The correct answer has to be inferred by combining information from a chain of reactions of drugs and proteins. | false | 482 | false | med_hop | 2022-11-03T16:16:32.000Z | medhop | false | b1e85d4a355a6c021a84ac5ee21a75d7d83c277f | [] | [
"arxiv:1710.06481",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"tags:multi... | https://huggingface.co/datasets/med_hop/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: medhop
pretty_name: MedHop
tags:... |
null | null | @inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
mon... | A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate | false | 557 | false | medal | 2022-11-03T16:30:50.000Z | medal | false | 2db88df736ef55c1e5443fd662af7ee08076ae6c | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:other",
"tags:disambiguation"
] | https://huggingface.co/datasets/medal/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: medal
pretty_name: MeDAL
tags:
- disambiguation
dataset_i... |
null | null | @article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint ar... | The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.
All copyrights of the data belong to healthcaremagic.com and ... | false | 530 | false | medical_dialog | 2022-11-03T16:30:43.000Z | null | false | fc744005474eb516c018bd1467b08b172bf6dfe1 | [] | [
"arxiv:2004.03329",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language:en",
"language:zh",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"task_categories:question-answering",
"task... | https://huggingface.co/datasets/medical_dialog/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: null
pretty_name: MedDialog
... |
null | null | @misc{mccreery2020effective,
title={Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
author={Clara H. McCreery and Namit Katariya and Anitha Kannan and Manish Chablani and Xavier Amatriain},
year={2020},
eprint={2008.13546},
archiveP... | This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. | false | 3,432 | false | medical_questions_pairs | 2022-11-03T16:32:36.000Z | null | false | b16bc9c2679407e5cfe261ca994ea8f050bc3abe | [] | [
"arxiv:2008.13546",
"annotations_creators:expert-generated",
"language_creators:other",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:semantic-similarity-classification"
] | https://huggingface.co/datasets/medical_questions_pairs/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
paperswithcode_id: null
pretty_name: Medi... |
null | null | @dataset{david_ifeoluwa_adelani_2020_4297448,
author = {David Ifeoluwa Adelani and
Jesujoba O. Alabi and
Damilola Adebonojo and
Adesina Ayeni and
Mofe Adeyemi and
Ayodele Awokoya},
title = {MENYO-20k: A Multi-doma... | MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, ... | false | 324 | false | menyo20k_mt | 2022-11-03T16:15:29.000Z | null | false | eb09ed7e1a905ff28e223a29bfa1c3a75c9413f8 | [] | [
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:found",
"language:en",
"language:yo",
"license:cc-by-4.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/menyo20k_mt/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- found
language_creators:
- found
language:
- en
- yo
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: MENYO-20k
dataset_info:
fea... |
null | null | @InProceedings{shalyminov2020fast,
author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Proce... | MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop meth... | false | 1,035 | false | meta_woz | 2022-11-03T16:31:48.000Z | metalwoz | false | 52094fbbeeed4f9dd80988431f99896bfbcca49d | [] | [
"arxiv:2003.01680",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-m... | https://huggingface.co/datasets/meta_woz/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
license_details: Microsoft Research Data License Agreement
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialog... |
null | null | @inproceedings{gautam2020metooma,
title={# MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement},
author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn},
booktitle={Proceedings of the International AAAI Conference on We... | The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories.
Due to Twitter's development policies, we only provide the tweet ID's and corresponding labels,
other data can be fetched via Twitter API.
The data has been labelled by experts, with the majority taken into the a... | false | 328 | false | metooma | 2022-11-03T16:15:15.000Z | metooma | false | 81874362812f273bd4566776b7ba3ed524bc5782 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:multi-class-classification... | https://huggingface.co/datasets/metooma/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
pap... |
null | null | @article{metrec2020,
title={MetRec: A dataset for meter classification of arabic poetry},
author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan},
journal={Data in Brief},
year={2020},
publisher={Elsevier}
} | Arabic Poetry Metric Classification.
The dataset contains the verses and their corresponding meter classes.Meter classes are represented as numbers from 0 to 13. The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.The train dataset contains 47,124 re... | false | 385 | false | metrec | 2022-11-03T16:16:11.000Z | metrec | false | c77812011dc3db425d3318662179b360ca2de451 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:ar",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"tags:poetry-classification"
] | https://huggingface.co/datasets/metrec/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: metrec
pretty_name: MetRec
tags:
- poetry-classification
... |
null | null | @unpublished{
anonymous2021cross-lingual,
title={Cross-Lingual Pretraining Methods for Spoken Dialog},
author={Anonymous},
journal={OpenReview Preprint},
year={2021},
url{https://openreview.net/forum?id=c1oDhu_hagR},
note={anonymous preprint under review}
} | Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and
analyzing natural language understanding systems specifically designed for spoken language. Datasets
are in English, French, German, Italian and Spanish. They cover a variety of domains including
spontaneous speech, scripted sce... | false | 1,021 | false | miam | 2022-11-03T16:31:51.000Z | null | false | da159314e67841f69e545dbe42f83fde5443d568 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-gene... | https://huggingface.co/datasets/miam/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- de
- en
- es
- fr
- it
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialogu... |
null | null | @misc{siripragada2020multilingual,
title={A Multilingual Parallel Corpora Collection Effort for Indian Languages},
author={Shashank Siripragada and Jerin Philip and Vinay P. Namboodiri and C V Jawahar},
year={2020},
eprint={2007.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | The Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages. | false | 7,277 | false | mkb | 2022-11-03T16:46:59.000Z | null | false | ceb110b075206f185960888158583a7abb9fdcce | [] | [
"arxiv:2007.07691",
"task_categories:text-generation",
"task_categories:fill-mask",
"multilinguality:translation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language:bn",
"language:en",
"language:gu",
"language:hi",
"language:ml",
"language:mr",
"language:or",
"la... | https://huggingface.co/datasets/mkb/resolve/main/README.md | ---
task_categories:
- text-generation
- fill-mask
multilinguality:
- translation
task_ids:
- language-modeling
- masked-language-modeling
language:
- bn
- en
- gu
- hi
- ml
- mr
- or
- pa
- ta
- te
- ur
annotations_creators:
- no-annotation
source_datasets:
- original
size_categories:
- 1K<n<10K
- n<1K
license:
- cc-b... |
null | null | @misc{mkqa,
title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering},
author = {Shayne Longpre and Yi Lu and Joachim Daiber},
year = {2020},
URL = {https://arxiv.org/pdf/2007.15207.pdf}
} | We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries ... | false | 433 | false | mkqa | 2022-11-03T16:16:22.000Z | mkqa | false | 688aa479d5f4a4427e93e39b510a2e676e227c5f | [] | [
"arxiv:2007.15207",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ar",
"language:da",
"language:de",
"language:en",
"language:es",
"language:fi",
"language:fr",
"language:he",
"language:hu",
"language:it",
"language:ja",
"language:km",
"language:ko",
"lang... | https://huggingface.co/datasets/mkqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- da
- de
- en
- es
- fi
- fr
- he
- hu
- it
- ja
- km
- ko
- ms
- nl
- 'no'
- pl
- pt
- ru
- sv
- th
- tr
- vi
- zh
license:
- cc-by-3.0
multilinguality:
- multilingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- exte... |
null | null | @article{lewis2019mlqa,
title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
journal={arXiv preprint arXiv:1910.07475},
year={2019}
} | MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA i... | false | 11,295 | false | mlqa | 2022-11-03T16:47:08.000Z | mlqa | false | 53d4fb125fa31049845e038595c3281fdb500aa2 | [] | [
"language:en",
"language:de",
"language:es",
"language:ar",
"language:zh",
"language:vi",
"language:hi",
"license:cc-by-sa-3.0",
"source_datasets:original",
"size_categories:10K<n<100K",
"language_creators:crowdsourced",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
... | https://huggingface.co/datasets/mlqa/resolve/main/README.md | ---
pretty_name: MLQA (MultiLingual Question Answering)
language:
- en
- de
- es
- ar
- zh
- vi
- hi
license:
- cc-by-sa-3.0
source_datasets:
- original
size_categories:
- 10K<n<100K
language_creators:
- crowdsourced
annotations_creators:
- crowdsourced
multilinguality:
- multilingual
task_categories:
- question-answer... |
null | null | @article{scialom2020mlsum,
title={MLSUM: The Multilingual Summarization Corpus},
author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
journal={arXiv preprint arXiv:2004.14900},
year={2020}
} | We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the collected d... | false | 4,113 | false | mlsum | 2022-11-03T16:46:41.000Z | mlsum | false | 3993b67b7030ff3aac9887312001358d7dadaf90 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:de",
"language:es",
"language:fr",
"language:ru",
"language:tr",
"license:other",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:extended|cnn_dailymail",
"source_da... | https://huggingface.co/datasets/mlsum/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- es
- fr
- ru
- tr
license:
- other
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- extended|cnn_dailymail
- original
task_categories:
- summarization
- translation
- text-classification
task_ids:
-... |
null | null | @article{lecun2010mnist,
title={MNIST handwritten digit database},
author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
volume={2},
year={2010}
} | The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000
images per class. There are 60,000 training images and 10,000 test images. | false | 7,014 | false | mnist | 2022-11-03T16:46:54.000Z | mnist | false | 6c5fed17b4a853735e7d56709d184e50374af4a6 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-nist",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/mnist/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-nist
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: mnist
pretty_n... |
null | null | @inproceedings{Chen2020MOCHAAD,
author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
booktitle={EMNLP},
year={2020}
} | Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To ad... | false | 592 | false | mocha | 2022-11-03T16:30:52.000Z | mocha | false | 703aef4f0336ac0d13c6071b22434b670edd7a8c | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"tags:generative-reading-comprehension-metric"
] | https://huggingface.co/datasets/mocha/resolve/main/README.md | ---
pretty_name: MOCHA
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: mocha
tags:
- generative-reading-co... |
null | null | @inproceedings{ Butnaru-ACL-2019,
author = {Andrei M. Butnaru and Radu Tudor Ionescu},
title = "{MOROCO: The Moldavian and Romanian Dialectal Corpus}",
booktitle = {Proceedings of ACL},
year = {2019},
pages={688--698},
} | The MOROCO (Moldavian and Romanian Dialectal Corpus) dataset contains 33564 samples of text collected from the news domain.
The samples belong to one of the following six topics:
- culture
- finance
- politics
- science
- sports
- tech | false | 326 | false | moroco | 2022-11-03T16:07:54.000Z | moroco | false | 52df16c46be939e95e5b223ba84d616c4c646adc | [] | [
"arxiv:1901.06543",
"annotations_creators:found",
"language_creators:found",
"language:ro",
"language_bcp47:ro-MD",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"... | https://huggingface.co/datasets/moroco/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
language_bcp47:
- ro-MD
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: moroco
pretty_name:... |
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{zaidan-eisner-piatko-2008:nips,
author = {Omar F. Zaidan ... | The movie rationale dataset contains human annotated rationales for movie
reviews. | false | 1,621 | false | movie_rationales | 2022-11-03T16:31:51.000Z | null | false | fabf4e7a2138d7bfcd7886942e28e571d551884b | [] | [
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/movie_rationales/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: MovieRationales
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
da... |
null | null | @inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNL... | The MRQA 2019 Shared Task focuses on generalization in question answering.
An effective question answering system should do more than merely
interpolate from the training set to answer test examples drawn
from the same distribution: it should also be able to extrapolate
to out-of-distribution examples — a significantly... | false | 1,364 | false | mrqa | 2022-11-03T16:46:41.000Z | mrqa-2019 | false | 2bf1271f70a7eaeb547122b48cb5ac0f5165cb3f | [] | [
"arxiv:1910.09753",
"arxiv:1606.05250",
"arxiv:1611.09830",
"arxiv:1705.03551",
"arxiv:1704.05179",
"arxiv:1809.09600",
"arxiv:1903.00161",
"arxiv:1804.07927",
"arxiv:1704.04683",
"arxiv:1706.04115",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",... | https://huggingface.co/datasets/mrqa/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|drop
- extended|hotpot_qa
- extended|natural_questions
- extended|race
- extended|search_qa
- extended|squad
- extended|trivia_qa
task_ca... |
null | null | @article{DBLP:journals/corr/NguyenRSGTMD16,
author = {Tri Nguyen and
Mir Rosenberg and
Xia Song and
Jianfeng Gao and
Saurabh Tiwary and
Rangan Majumder and
Li Deng},
title = {{MS} {MARCO:} {A} Human Generated MAchine Re... | Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
Since then we released a 1,000,000 question dataset, a natural langauge generation... | false | 2,182 | false | ms_marco | 2022-11-03T16:32:29.000Z | ms-marco | false | 98fbdb13460e8857359314becd5e6633d1e6bc5a | [] | [
"arxiv:1611.09268",
"language:en"
] | https://huggingface.co/datasets/ms_marco/resolve/main/README.md | ---
language:
- en
paperswithcode_id: ms-marco
pretty_name: Microsoft Machine Reading Comprehension Dataset
dataset_info:
- config_name: v1.1
features:
- name: answers
sequence: string
- name: passages
sequence:
- name: is_selected
dtype: int32
- name: passage_text
dtype: string
- ... |
null | null | null | The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products.
It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary
for language development in the nearly 100 languages available. ... | false | 327 | false | ms_terms | 2022-11-03T16:08:00.000Z | null | false | 959e003c42ba6eb7d93be482ce39c603e917888f | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:bs",
"language:ca",
"language:chr",
"language:cs",
"language:cy",
"language:d... | https://huggingface.co/datasets/ms_terms/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- bs
- ca
- chr
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fil
- fr
- ga
- gd
- gl
- gu
- guc
- ha
- he
- hi
- hr
- hu
- hy
- id
- ig
- is
- it
- iu
- ja
- ka
- kk
- km
- kn... |
null | null | @inproceedings{toutanova-etal-2016-compositional,
title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
author = "Toutanova, Kristina and
Lin, Victoria and
Yih, Wen-tau and
Poon, Hoifung and
Quirk, Chris",
booktitle = "Proceedings of the 54... | The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowle... | false | 327 | false | msr_genomics_kbcomp | 2022-11-03T16:08:00.000Z | null | false | 32cd5a69fef52227f77d6a81a8ab5ca5537e18d3 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:other",
"tags:genomics-knowledge-base-bompletion"
] | https://huggingface.co/datasets/msr_genomics_kbcomp/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name: MsrGenomicsKbcomp
tags:
- genomics-knowle... |
null | null | @inproceedings{iyyer2017search,
title={Search-based neural structured learning for sequential question answering},
author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},
booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={1821-... | Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-re... | false | 335 | false | msr_sqa | 2022-11-03T16:15:34.000Z | null | false | df99ab4db03aebdf7463bea31f6d7b092af89d10 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:ms-pl",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/msr_sqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- ms-pl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: Microsoft Research Sequential ... |
null | null | @inproceedings{Toutanova2016ADA,
title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs},
author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi},
booktitle={EMNLP},
year={2016}
} | This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpu... | false | 327 | false | msr_text_compression | 2022-11-03T16:15:15.000Z | null | false | 3c099cbd7acd7fac67fe8f309711e3de75fb1dbc | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-Open-American-National-Corpus-(OANC1)",
"task_categories:summarization"
] | https://huggingface.co/datasets/msr_text_compression/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
license_details: Microsoft Research Data License Agreement
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-Open-American-National-Corpus-(OANC1)
task_categories:
- summarizati... |
null | null | @misc{hassan2018achieving,
title={Achieving Human Parity on Automatic Chinese to English News Translation},
author={ Hany Hassan and Anthony Aue and Chang Chen and Vishal Chowdhary and Jonathan Clark
and Christian Federmann and Xuedong Huang and Marcin Junczys-Dowmunt and William Lewis
... | Translator Human Parity Data
Human evaluation results and translation output for the Translator Human Parity Data release,
as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/.
The Translator Human Parity Data release contains all human evaluation results and translations
rela... | false | 327 | false | msr_zhen_translation_parity | 2022-11-03T16:08:10.000Z | null | false | 956f42d1a6d570f86ca92802f064fe9b2f61a98d | [] | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:ms-pl",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|other-newstest2017",
"task_categori... | https://huggingface.co/datasets/msr_zhen_translation_parity/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- ms-pl
multilinguality:
- monolingual
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-newstest2017
task_categories:
- translation
task_ids: []
paperswithcode_id: null
... |
null | null | @inproceedings{levow2006third,
author = {Gina{-}Anne Levow},
title = {The Third International Chinese Language Processing Bakeoff: Word
Segmentation and Named Entity Recognition},
booktitle = {SIGHAN@COLING/ACL},
pages = {108--117},
publisher = {Association for Computational Linguist... | The Third International Chinese Language
Processing Bakeoff was held in Spring
2006 to assess the state of the art in two
important tasks: word segmentation and
named entity recognition. Twenty-nine
groups submitted result sets in the two
tasks across two tracks and a total of five
corpora. We found strong results in b... | false | 907 | false | msra_ner | 2022-11-03T16:31:18.000Z | null | false | ed52cbce4a2b00405e49d3259f5501075487fc98 | [] | [
"annotations_creators:crowdsourced",
"language_creators:found",
"language:zh",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/msra_ner/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: MSRA NER
train-... |
null | null | @inproceedings{Luong-Manning:iwslt15,
Address = {Da Nang, Vietnam}
Author = {Luong, Minh-Thang and Manning, Christopher D.},
Booktitle = {International Workshop on Spoken Language Translation},
Title = {Stanford Neural Machine Translation Systems for Spoken Language Domain},
Yea... | Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese. | false | 574 | false | mt_eng_vietnamese | 2022-11-03T16:30:44.000Z | null | false | 75220c89dcc7962e603cd9f44331fc33dc6b8ee1 | [] | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"language:en",
"language:vi",
"license:unknown",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/mt_eng_vietnamese/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
multilinguality:
- multilingual
language:
- en
- vi
license:
- unknown
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: MtEngVietnamese
dataset_info:
- config_name: iwslt... |
null | null | null | The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language,
each with a shorter summary review, and a rating on a 1-5 scale. | false | 371 | false | muchocine | 2022-11-03T16:15:39.000Z | null | false | 0704b59c07f98be7f58ae30f573e36b2cb08bc8c | [] | [
"annotations_creators:found",
"language_creators:found",
"language:es",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/muchocine/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- es
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: Muchocine
dataset_info:
... |
null | null | @inproceedings{Barnes2018multibooked,
author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources an... | MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each revie... | false | 485 | false | multi_booked | 2022-11-03T16:16:31.000Z | multibooked | false | 10b91b2497fd120857689b0239f80585b6fb2e60 | [] | [
"arxiv:1803.08614",
"annotations_creators:expert-generated",
"language_creators:found",
"language:ca",
"language:eu",
"license:cc-by-3.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-classification"... | https://huggingface.co/datasets/multi_booked/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
- eu
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: multibooked
pretty_name: Mult... |
null | null | @InProceedings{chalkidis-etal-2021-multieurlex,
author = {Chalkidis, Ilias
and Fergadiotis, Manos
and Androutsopoulos, Ion},
title = {MultiEURLEX -- A multi-lingual and multi-label legal document
classification dataset for zero-shot cross-lingual transfer},
booktitle... | MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given ... | false | 5,668 | false | multi_eurlex | 2022-11-03T16:46:54.000Z | null | false | 0f037dc78ce0a1f9811328423fef36cdcc025cde | [] | [
"arxiv:2109.00904",
"annotations_creators:found",
"language_creators:found",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:hr",
"language:hu",
"language:it",
"language:lt... | https://huggingface.co/datasets/multi_eurlex/resolve/main/README.md | ---
pretty_name: MultiEURLEX
annotations_creators:
- found
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- orig... |
null | null | @misc{alex2019multinews,
title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},
author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},
year={2019},
eprint={1906.01749},
archivePrefix={arXiv},
primaryClass={... | Multi-News, consists of news articles and human-written summaries
of these articles from the site newser.com.
Each summary is professionally written by editors and
includes links to the original articles cited.
There are two features:
- document: text of news articles seperated by special token "|||||".
- summary:... | false | 24,552 | false | multi_news | 2022-11-03T16:47:26.000Z | multi-news | false | 0f7cd97cbbdd8375a1d98e60cefadb907a426cf2 | [] | [
"arxiv:1906.01749",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/multi_news/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: ... |
null | null | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of th... | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre gener... | false | 11,261 | false | multi_nli | 2022-11-03T16:47:08.000Z | multinli | false | 64732e5a263e7ad75ee81a3a61ee101ae80b0595 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-3.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories... | https://huggingface.co/datasets/multi_nli/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- tex... |
null | null | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of th... | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre gener... | false | 331 | false | multi_nli_mismatch | 2022-11-03T16:15:15.000Z | multinli | false | 228f40f05dfdc1fa1fe6cc6b9762a7e88052918e | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-3.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories... | https://huggingface.co/datasets/multi_nli_mismatch/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- tex... |
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... | Parallel corpora from Web Crawls collected in the ParaCrawl project and further processed for making it a multi-parallel corpus by pivoting via English. Here we only provide the additional language pairs that came out of pivoting. The bitexts for English are available from the ParaCrawl release.
40 languages, 669 bitex... | false | 957 | false | multi_para_crawl | 2022-11-03T16:31:38.000Z | null | false | c3f0973b084e333f13c346f948722c8709039254 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:es",
"language:et",
"language:eu",
"language:fi",
"language:fr",
"language:ga",
"language:gl",
"language:ha",
"language:hr",
... | https://huggingface.co/datasets/multi_para_crawl/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- ca
- cs
- da
- de
- el
- es
- et
- eu
- fi
- fr
- ga
- gl
- ha
- hr
- hu
- ig
- is
- it
- km
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- ps
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
license:
- cc0-1.0
multilinguality:
- multilingua... |
null | null | @misc{m2020multireqa,
title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models},
author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant},
year={2020},
eprint={2005.02507},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and te... | false | 1,609 | false | multi_re_qa | 2022-11-03T16:32:10.000Z | multireqa | false | 3bbe2967cc196395df88dafb0fc56db3c6b52f45 | [] | [
"arxiv:2005.02507",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories... | https://huggingface.co/datasets/multi_re_qa/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
source_datasets:
- extended|other-BioASQ
- extended|other-DuoRC
- extended|other-HotpotQA
- ... |
null | null | @article{corr/abs-2007-12720,
author = {Xiaoxue Zang and
Abhinav Rastogi and
Srinivas Sunkara and
Raghav Gupta and
Jianguo Zhang and
Jindong Chen},
title = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections
... | Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
improved version of the d... | false | 8,437 | false | multi_woz_v22 | 2022-11-03T16:47:00.000Z | multiwoz | false | b615ca9f891784ead61fe7eaf6ce003b04908359 | [] | [
"arxiv:1810.00278",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
... | https://huggingface.co/datasets/multi_woz_v22/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
ta... |
null | null | @article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
year={2020}
} | Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. | false | 1,865 | false | multi_x_science_sum | 2022-11-03T16:32:29.000Z | multi-xscience | false | 62e4ed6a3a89f076ea3824d2ae190b32d125a76e | [] | [
"arxiv:2010.14235",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"tags:paper-abstract-generation"
] | https://huggingface.co/datasets/multi_x_science_sum/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: multi-xscience
pretty_name: Multi-XScience
tags:
- paper-abstract-gener... |
null | null | @inproceedings{feng2021multidoc2dial,
title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},
author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},
booktitle={EMNLP},
year={2021}
} | MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented ... | false | 669 | false | multidoc2dial | 2022-11-03T16:31:07.000Z | multidoc2dial | false | b08b30643187bd6bca6c776679fb6ff6bb9e5a61 | [] | [
"arxiv:2109.12595",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:... | https://huggingface.co/datasets/multidoc2dial/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: MultiDoc2Dial
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- extended|doc2dial
task_categories:
- question-answering
task_ids... |
null | null | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. | false | 1,406 | false | multilingual_librispeech | 2022-11-03T16:32:00.000Z | librispeech-1 | false | 55f8a694d50714c28bcd26491338f5f0b55bc2e6 | [] | [
"arxiv:2012.03411",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:de",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"license:cc-by-4.0",
"multilinguality:multilingual",
... | https://huggingface.co/datasets/multilingual_librispeech/resolve/main/README.md | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- es
- fr
- it
- nl
- pl
- pt
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- origi... |
null | null | @inproceedings{he-etal-2017-learning,
title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings",
author = "He, He and
Balakrishnan, Anusha and
Eric, Mihail and
Liang, Percy",
booktitle = "Proceedings of the 55th Annual Meeting of the Association ... | Our goal is to build systems that collaborate with people by exchanging
information through natural language and reasoning over structured knowledge
base. In the MutualFriend task, two agents, A and B, each have a private
knowledge base, which contains a list of friends with multiple attributes
(e.g., name, school, maj... | false | 327 | false | mutual_friends | 2022-11-03T16:08:10.000Z | mutualfriends | false | 226a071921e4b38a6861492b51e67ed0983386d9 | [] | [
"arxiv:1704.07130",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue... | https://huggingface.co/datasets/mutual_friends/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: mutualfriends
pretty_name:... |
null | null | @article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
} | Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. | false | 577 | false | mwsc | 2022-11-03T16:30:52.000Z | null | false | 6ff2ef45808d5a8d8525b4105cee480bc888fc2f | [] | [
"arxiv:1806.08730",
"annotations_creators:expert-generated",
"language:en",
"language_creators:expert-generated",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|winograd_wsc",
"task_categories:multiple-choice",
"task_ids:multiple-choice-corefe... | https://huggingface.co/datasets/mwsc/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Modified Winograd Schema Challenge (MWSC)
size_categories:
- n<1K
source_datasets:
- extended|winograd_wsc
task_categories:
- multiple-choice
task_ids:
- mul... |
null | null | null | The Myanmar news dataset contains article snippets in four categories:
Business, Entertainment, Politics, and Sport.
These were collected in October 2017 by Aye Hninn Khine | false | 327 | false | myanmar_news | 2022-11-03T16:08:01.000Z | null | false | b9ec6c542a984b09d9127cb0f54351eac209252c | [] | [
"annotations_creators:found",
"language_creators:found",
"language:my",
"license:gpl-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/myanmar_news/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- my
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: null
pretty_name: MyanmarNews
dataset_info:
f... |
null | null | @article{narrativeqa,
author = {Tom\\'a\\v s Ko\\v cisk\\'y and Jonathan Schwarz and Phil Blunsom and
Chris Dyer and Karl Moritz Hermann and G\\'abor Melis and
Edward Grefenstette},
title = {The {NarrativeQA} Reading Comprehension Challenge},
journal = {Transactions of the Association for Computatio... | The NarrativeQA dataset for question answering on long documents (movie scripts, books). It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers. | false | 747 | false | narrativeqa | 2022-11-03T16:31:18.000Z | narrativeqa | false | f4e69246ebc8e35d81435b3c5fe93b6cfc4d9ba5 | [] | [
"arxiv:1712.07040",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:abstractive-qa"
] | https://huggingface.co/datasets/narrativeqa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
paperswithcode_id: narrativeqa
pretty_name: NarrativeQA
dat... |
null | null | @article{kovcisky2018narrativeqa,
title={The narrativeqa reading comprehension challenge},
author={Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, G{\'a}bor and Grefenstette, Edward},
journal={Transactions of the Association for Comput... | The Narrative QA Manual dataset is a reading comprehension dataset, in which the reader must answer questions about stories by reading entire books or movie scripts. The QA tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pa... | false | 366 | false | narrativeqa_manual | 2022-11-03T16:16:11.000Z | narrativeqa | false | ad8919a9cd63faa9c1f6170a6e5aed4bee78b2db | [] | [
"arxiv:1712.07040",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:abstractive-qa"
] | https://huggingface.co/datasets/narrativeqa_manual/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
paperswithcode_id: narrativeqa
pretty_name: NarrativeQA
dat... |
null | null | @article{47761,
title = {Natural Questions: a Benchmark for Question Answering Research},
author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina... | The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a... | false | 1,074 | false | natural_questions | 2022-11-03T16:31:11.000Z | natural-questions | false | 3d1d4a0b43cd5a994838f68ffec260e59603579d | [] | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/natural_questions/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: Natural Questions
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: na... |
null | null | @article{dougan2014ncbi,
title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
journal={Journal of biomedical informatics},
volume={47},
pages={1--10},
year... | This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed
abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural
language processing community. Each PubMed abstract was manually annotated by two anno... | false | 1,594 | false | ncbi_disease | 2022-11-03T16:32:18.000Z | ncbi-disease-1 | false | 54e171a3650f342dc84bb57d57d3be369181e885 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/ncbi_disease/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ncbi-disease-1
prett... |
null | null | @inproceedings{eiselen2014developing,
title={Developing Text Resources for Ten South African Languages.},
author={Eiselen, Roald and Puttkammer, Martin J},
booktitle={LREC},
pages={3698--3703},
year={2014}
} | The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources ... | false | 1,758 | false | nchlt | 2022-11-03T16:32:13.000Z | null | false | a24b6ff363927295ec439aa4de7489c8b313f216 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:af",
"language:nr",
"language:nso",
"language:ss",
"language:tn",
"language:ts",
"language:ve",
"language:xh",
"language:zu",
"license:cc-by-2.5",
"multilinguality:multilingual",
"size_categories:1K<n<... | https://huggingface.co/datasets/nchlt/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
- nr
- nso
- ss
- tn
- ts
- ve
- xh
- zu
license:
- cc-by-2.5
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recogni... |
null | null | @misc{dataset:databases2007volumes,
title={Volumes 2--7},
author={Databases, NCSLGR},
year={2007},
publisher={American Sign Language Linguistic Research Project (Distributed on CD-ROM~…}
} | A small corpus of American Sign Language (ASL) video data from native signers, annotated with non-manual features. | false | 492 | false | ncslgr | 2022-11-03T16:16:28.000Z | null | false | 12eef2bcf793e96b3e1ca490c4fd976f97f6f1f4 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ase",
"language:en",
"license:mit",
"multilinguality:translation",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/ncslgr/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ase
- en
license:
- mit
multilinguality:
- translation
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: NCSLGR
dataset_info:
- config_name: e... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.