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5.93k
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google/wit | false | [
"task_categories:text-retrieval",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"language:af",
"language:ar",
"language:ast",
"language:azb",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:hr",
"language:hu",
"language:hy",
"language:id",
"language:it",
"language:iw",
"language:ja",
"language:ka",
"language:ko",
"language:la",
"language:lt",
"language:lv",
"language:mk",
"language:ml",
"language:ms",
"language:nl",
"language:nn",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:th",
"language:tr",
"language:uk",
"language:ur",
"language:vi",
"language:vo",
"language:zh",
"license:cc-by-sa-3.0",
"arxiv:2103.01913"
]
| Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models. | 15 | 8 |
hnchen/ricr_data | false | []
| null | 0 | 0 |
lanesket/r-asts-splitted | false | []
| null | 0 | 0 |
jdrl96/sv_corpora_parliament_processed | false | []
| null | 0 | 0 |
Gifted/iris | false | []
| null | 0 | 0 |
Davincilee/door_inner_with_SAE | false | [
"license:other"
]
| null | 0 | 0 |
shanya/crd3 | false | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0"
]
| Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding
abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player
collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,
and semantic ties to the previous dialogues. | 13 | 0 |
wikimedia/wit_base | false | [
"task_categories:image-to-text",
"task_categories:text-retrieval",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"language:af",
"language:an",
"language:ar",
"language:arz",
"language:ast",
"language:az",
"language:azb",
"language:ba",
"language:bar",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"language:ca",
"language:ce",
"language:ceb",
"language:ckb",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fil",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:hi",
"language:hr",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:ia",
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"language:oc",
"language:pa",
"language:pl",
"language:pt",
"language:qu",
"language:ro",
"language:ru",
"language:sco",
"language:si",
"language:sk",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:tg",
"language:th",
"language:tr",
"language:tt",
"language:uk",
"language:ur",
"language:uz",
"language:vec",
"language:vi",
"language:vo",
"language:war",
"language:xmf",
"language:yue",
"language:zh",
"license:cc-by-sa-4.0",
"text-image-retrieval",
"arxiv:2103.01913",
"arxiv:1512.03385",
"arxiv:1905.00641"
]
| null | 1,482 | 11 |
nielsr/cord-layoutlmv3 | false | []
| https://github.com/clovaai/cord/ | 348 | 2 |
pauli31/czech-subjectivity-dataset | false | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:cs-CZ",
"license:cc-by-nc-sa-4.0",
"arxiv:2204.13915"
]
| null | 3 | 1 |
ppate192/conll.eng.testa | false | []
| null | 0 | 0 |
arbml/masader | false | [
"license:mit"
]
| Masader is the largest public catalogue for Arabic NLP datasets, which consists of more than 200 datasets annotated with 25 attributes. | 3 | 3 |
wza/roc_stories | false | []
| This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | 88 | 1 |
Apo/AKE20K_sky | false | []
| null | 0 | 0 |
carlosejimenez/snli_aug | false | []
| null | 0 | 0 |
Apo/ADE20K_sky_13labels | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
Apo/AKE20K_sky_13labels | false | []
| null | 0 | 0 |
Vardan/train | false | []
| null | 0 | 0 |
Erwin/coffee_reviews_corpus | false | [
"license:mit"
]
| null | 2 | 0 |
lanesket/r-asts-splitted-tokenized | false | []
| null | 0 | 0 |
NishinoTSK/Dissertacao | false | []
| null | 0 | 0 |
tne | false | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2109.12085"
]
| TNE is an NLU task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions.
The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document. | 179 | 0 |
vencortex/TechNews | false | [
"doi:10.57967/hf/0115"
]
| null | 1 | 1 |
carlosejimenez/qqp_aug | false | []
| null | 0 | 0 |
LHF/escorpius-mr | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:multilingual",
"size_categories:100B<n<1T",
"source_datasets:original",
"language:af",
"language:ar",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:gl",
"language:hi",
"language:hr",
"language:it",
"language:ja",
"language:ko",
"language:mt",
"language:nl",
"language:no",
"language:oc",
"language:pa",
"language:pl",
"language:pt",
"language:ro",
"language:sl",
"language:sr",
"language:sv",
"language:tr",
"language:uk",
"language:ur",
"license:cc-by-nc-nd-4.0",
"arxiv:2206.15147"
]
| null | 0 | 1 |
farazeftekhar/geojson | false | [
"license:other"
]
| null | 0 | 0 |
orieg/elsevier-oa-cc-by | false | [
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:text-classification",
"task_ids:masked-language-modeling",
"task_ids:news-articles-summarization",
"task_ids:news-articles-headline-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2008.00774"
]
| Elsevier OA CC-By is a corpus of 40k (40, 091) open access (OA) CC-BY articles
from across Elsevier’s journals and include the full text of the article, the metadata,
the bibliographic information for each reference, and author highlights. | 32 | 8 |
enoriega/keyword_pubmed | false | []
| null | 0 | 0 |
allenai/drug-combo-extraction | false | [
"license:mit"
]
| null | 8 | 2 |
Ukhushn/home-depot | false | [
"task_categories:sentence-similarity",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:afl-3.0"
]
| null | 0 | 0 |
vumichien/japanese_large_dataset | false | []
| null | 0 | 0 |
ntinouldinho/el_corpora_parliament_processed | false | []
| null | 0 | 0 |
Seledorn/SwissProt-EC-leaf_upd | false | []
| null | 0 | 0 |
Seledorn/SwissProt-EC-leaf_upt_again | false | []
| null | 0 | 0 |
cestwc/lsnli | false | []
| null | 0 | 0 |
nielsr/test | false | []
| null | 0 | 0 |
nlpconnect/DocVQA | false | [
"license:apache-2.0"
]
| null | 6 | 0 |
cncoleman/CA_LEA_Manuals | false | []
| null | 0 | 0 |
IsaacRodgz/DravidianCodeMix-Dataset | false | []
| null | 0 | 0 |
Alayt/gcode | false | []
| null | 0 | 0 |
textvqa | false | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1904.08920",
"arxiv:2007.00398"
]
| TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. | 674 | 1 |
chrisvinsen/id_kenlm_language_model | false | []
| null | 0 | 0 |
00data00/data | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
ett | false | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"task_ids:multivariate-time-series-forecasting",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:cc-by-4.0",
"arxiv:2012.07436"
]
| The data of Electricity Transformers from two separated counties
in China collected for two years at hourly and 15-min frequencies.
Each data point consists of the target value "oil temperature" and
6 power load features. The train/val/test is 12/4/4 months. | 14 | 2 |
BK-V/arman | false | []
| ArmanPersoNERCorpus includes 250,015 tokens and 7,682 Persian sentences in total.The NER tags are in IOB format. | 0 | 0 |
ghomasHudson/hotpotExtendedAnoLM | false | []
| null | 0 | 0 |
truthisneverlinear/eleventh-doctor-scripts | false | [
"language:en",
"NLP",
"conservation",
"dialogue"
]
| null | 0 | 1 |
marwansalam/art_split_datasets | false | []
| null | 0 | 0 |
marwansalam/araucaria_split_datasets | false | []
| null | 0 | 0 |
marwansalam/araucaria_nolabel_split_datasets | false | []
| null | 0 | 0 |
boli-ai-admin/vishal | false | [
"license:apache-2.0"
]
| null | 0 | 0 |
PoolC/news-corpus-mini | false | []
| null | 0 | 0 |
ablam/gcode | false | []
| null | 0 | 0 |
adsabs/WIESP2022-NER | false | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0"
]
| null | 1 | 3 |
dlwh/wikitext_103_detokenized | false | []
| null | 8 | 0 |
dlwh/wikitext_2_detokenized | false | []
| null | 1 | 0 |
nateraw/gradio-guides-files | false | [
"license:mit"
]
| null | 2 | 0 |
BennoKrojer/ImageCoDe | false | [
"license:afl-3.0",
"arxiv:2203.15867"
]
| null | 40 | 1 |
ashraq/youtube-transcription | false | []
| This is YouTube video transcription dataset built from YTTTS Speech Collection for semantic search. | 10 | 1 |
nateraw/test-imagefolder-metadata | false | []
| null | 0 | 0 |
nateraw/test-imagefolder-metadata-csv | false | []
| null | 0 | 0 |
nateraw/test-imagefolder-metadata-2 | false | []
| null | 0 | 0 |
zzzzzzttt/train | false | []
| null | 0 | 0 |
ntt123/viet-tts-dataset | false | [
"license:cc-by-nc-4.0"
]
| null | 3 | 0 |
searle-j/kote | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:kor",
"license:mit"
]
| 50k Korean online comments labeled for 44 emotion categories. | 161 | 2 |
crystina-z/mmarco-passage | false | []
| null | 1 | 0 |
filwsyl/video_understanding | false | []
| null | 0 | 1 |
mdroth/github-issues | false | []
| null | 0 | 0 |
medmcqa | false | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0"
]
| MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.
The dataset contains questions about the following topics: Anesthesia, Anatomy, Biochemistry, Dental, ENT, Forensic Medicine (FM)
Obstetrics and Gynecology (O&G), Medicine, Microbiology, Ophthalmology, Orthopedics Pathology, Pediatrics, Pharmacology, Physiology,
Psychiatry, Radiology Skin, Preventive & Social Medicine (PSM) and Surgery | 1,580 | 9 |
Fhrozen/FSD50k | false | [
"task_categories:audio-classification",
"annotations_creators:unknown",
"language_creators:unknown",
"size_categories:10K<n<100K",
"source_datasets:unknown",
"license:cc-by-4.0",
"arxiv:2010.00475"
]
| null | 10 | 0 |
SetFit/amazon_massive_scenario_af-ZA | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_am-ET | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_ar-SA | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_az-AZ | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_bn-BD | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_cy-GB | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_da-DK | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_de-DE | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_el-GR | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_en-US | false | []
| null | 1 | 0 |
SetFit/amazon_massive_scenario_es-ES | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_fa-IR | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_fi-FI | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_fr-FR | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_he-IL | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_hi-IN | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_hu-HU | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_hy-AM | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_id-ID | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_is-IS | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_it-IT | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_ja-JP | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_jv-ID | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_ka-GE | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_km-KH | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_kn-IN | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_ko-KR | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_lv-LV | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_mn-MN | false | []
| null | 0 | 0 |
SetFit/amazon_massive_scenario_ms-MY | false | []
| null | 0 | 0 |
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