id
stringlengths 2
115
| private
bool 1
class | tags
list | description
stringlengths 0
5.93k
⌀ | downloads
int64 0
1.14M
| likes
int64 0
1.79k
|
|---|---|---|---|---|---|
M-CLIP/ImageCaptions-7M-Embeddings | false | []
| null | 2 | 0 |
erickdp/set-date | false | []
| null | 0 | 0 |
erickdp/dse | false | []
| null | 0 | 0 |
emilylearning/cond_ft_none_on_reddit__prcnt_na__test_run_True | false | []
| null | 0 | 0 |
erickdp/xmio | false | []
| null | 0 | 0 |
nboudad/ASCED | false | []
| null | 0 | 0 |
emilylearning/cond_ft_subreddit_on_reddit__prcnt_na__test_run_True | false | []
| null | 0 | 0 |
emilylearning/cond_ft_none_on_wiki_bio__prcnt_na__test_run_True | false | []
| null | 0 | 0 |
emilylearning/cond_ft_birth_date_on_wiki_bio__prcnt_na__test_run_True | false | []
| null | 0 | 0 |
Yingda/test | false | [
"license:apache-2.0"
]
| null | 0 | 0 |
justmywyw/datasets | false | [
"license:apache-2.0"
]
| null | 0 | 0 |
paust/ko_daily_dialog | false | []
| null | 0 | 0 |
PontifexMaximus/Persian-English | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
Gwangho/test | false | [
"license:apache-2.0"
]
| null | 0 | 0 |
PontifexMaximus/En-as | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
Daliny/ground | false | []
| null | 0 | 0 |
strombergnlp/x-stance | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:de",
"language:fr",
"license:mit",
"stance-detection",
"arxiv:2003.08385"
]
| The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. The comments are partly German, partly French and Italian. The data have been extracted from the Swiss voting advice platform Smartvote. | 24 | 1 |
veriga/dactilo | false | []
| null | 0 | 0 |
spoiled/ecqa_classify_94 | false | []
| null | 0 | 0 |
cestwc/cnn_dailymail-coref | false | []
| null | 1 | 0 |
cestwc/cnn_dailymail-coref1 | false | []
| null | 0 | 0 |
rajeshvarma/QA_on_SLA | false | [
"annotations_creators:no-annotations",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0"
]
| null | 0 | 0 |
khalidalt/tydiqa-goldp | false | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"language:en",
"language:ar",
"language:bn",
"language:fi",
"language:id",
"language:ja",
"language:sw",
"language:ko",
"language:ru",
"language:te",
"language:th",
"license:apache-2.0"
]
| TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD). | 750 | 2 |
JorenGij/inventorytest | false | []
| null | 0 | 0 |
hongcui/test | false | []
| null | 0 | 0 |
vk-mittal14/CryptoBubbles | false | []
| null | 0 | 0 |
nateraw/imagenet-sketch-data | false | [
"license:other"
]
| null | 0 | 0 |
rungalileo/20_Newsgroups_Fixed | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown"
]
| null | 71 | 0 |
aaraki/github-issues6 | false | []
| null | 0 | 0 |
Yoonseong/climatebert_factcheck | false | []
| null | 0 | 0 |
brook/fullwiki-context | false | []
| null | 0 | 0 |
namnv1906/librispeech-100h | false | []
| null | 0 | 0 |
jdd/jddtest | false | [
"license:afl-3.0"
]
| null | 2 | 0 |
statworx/haiku | false | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en"
]
| null | 41 | 1 |
strombergnlp/nlpcc-stance | false | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:cc-by-4.0",
"stance-detection"
]
| This is a stance prediction dataset in Chinese.
The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data. | 25 | 2 |
HuggingFaceM4/yttemporal180m | false | [
"license:other"
]
| YT-Temporal-180M, a large and diverse dataset of 6 million videos (spanning 180M extracted frames)
that covers diverse topics. | 3 | 0 |
Dus/tokenkorpus | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
mteb/sts17-crosslingual-sts | false | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:ko",
"language:tr"
]
| STS17 Cross-lingual dataset | 16,153 | 1 |
SoBytes/rubrix-test | false | [
"license:unlicense"
]
| null | 0 | 0 |
mteb/mtop_intent | false | [
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:th"
]
| null | 1,853 | 0 |
mteb/mtop_domain | false | [
"task_categories:text-classification",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:th"
]
| null | 1,836 | 0 |
avacaondata/modified_randomqa | false | []
| null | 0 | 0 |
GEM/FairytaleQA | false | [
"task_categories:other",
"annotations_creators:expert-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"arxiv:2203.13947"
]
| \
The FairytaleQA dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. This is for the Question Generation Task of FairytaleQA. | 151 | 1 |
s-nlp/paradetox | false | [
"task_categories:text-generation",
"language:en",
"license:afl-3.0"
]
| null | 5 | 4 |
jacklin/msmarco_passage_ranking_official_train | false | [
"arxiv:1611.09268"
]
| null | 0 | 0 |
mteb/tatoeba-bitext-mining | false | [
"language:eng",
"language:sqi",
"language:fry",
"language:kur",
"language:tur",
"language:deu",
"language:nld",
"language:ron",
"language:ang",
"language:ido",
"language:jav",
"language:isl",
"language:slv",
"language:cym",
"language:kaz",
"language:est",
"language:heb",
"language:gla",
"language:mar",
"language:lat",
"language:bel",
"language:pms",
"language:gle",
"language:pes",
"language:nob",
"language:bul",
"language:cbk",
"language:hun",
"language:uig",
"language:rus",
"language:spa",
"language:hye",
"language:tel",
"language:afr",
"language:mon",
"language:arz",
"language:hrv",
"language:nov",
"language:gsw",
"language:nds",
"language:ukr",
"language:uzb",
"language:lit",
"language:ina",
"language:lfn",
"language:zsm",
"language:ita",
"language:cmn",
"language:lvs",
"language:glg",
"language:ceb",
"language:bre",
"language:ben",
"language:swg",
"language:arq",
"language:kab",
"language:fra",
"language:por",
"language:tat",
"language:oci",
"language:pol",
"language:war",
"language:aze",
"language:vie",
"language:nno",
"language:cha",
"language:mhr",
"language:dan",
"language:ell",
"language:amh",
"language:pam",
"language:hsb",
"language:srp",
"language:epo",
"language:kzj",
"language:awa",
"language:fao",
"language:mal",
"language:ile",
"language:bos",
"language:cor",
"language:cat",
"language:eus",
"language:yue",
"language:swe",
"language:dtp",
"language:kat",
"language:jpn",
"language:csb",
"language:xho",
"language:orv",
"language:ind",
"language:tuk",
"language:max",
"language:swh",
"language:hin",
"language:dsb",
"language:ber",
"language:tam",
"language:slk",
"language:tgl",
"language:ast",
"language:mkd",
"language:khm",
"language:ces",
"language:tzl",
"language:urd",
"language:ara",
"language:kor",
"language:yid",
"language:fin",
"language:tha",
"language:wuu"
]
| Tatoeba multilingual test set | 1,641 | 1 |
jjjonathan14/mango1 | false | []
| null | 0 | 0 |
mteb/bucc-bitext-mining | false | [
"multilinguality:monolingual",
"multilinguality:multilingual",
"language:de",
"language:en",
"language:fr",
"language:ru",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2104.06893",
"arxiv:2010.02573",
"arxiv:2003.04807",
"arxiv:2204.08582",
"arxiv:2008.09335",
"arxiv:2104.07081"
]
| BUCC 2018 Shared Task test dataset | 543 | 0 |
erickdp/ndat | false | []
| null | 0 | 0 |
Ruohao/pcmr | false | [
"language:en"
]
| PCMR | 0 | 1 |
aaraki/github-issues7 | false | []
| null | 0 | 0 |
yzhou992/tiny_imageNet | false | []
| null | 0 | 0 |
readerbench/ConversationalAgent-Ro | false | [
"language:ro"
]
| null | 0 | 0 |
NLPC-UOM/Sinhala-English-Code-Mixed-Code-Switched-Dataset | false | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:hate-speech-detection",
"task_ids:language-identification",
"multilinguality:multilingual",
"language:si",
"language:en",
"license:mit"
]
| null | 7 | 0 |
HuggingFaceM4/ego4d | false | []
| EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated video and a wide range of annotations across five new benchmark tasks. It covers hundreds of scenarios (household, outdoor, workplace, leisure, etc.) of daily life activity captured in-the-wild by 926 unique camera wearers from 74 worldwide locations and 9 different countries. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. The approach to data collection was designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. | 0 | 0 |
paul21/nq_processed_v2 | false | []
| null | 0 | 0 |
hongdijk/kluetest | false | [
"license:other"
]
| null | 0 | 0 |
reallycarlaost/emobank-w-valence | false | []
| null | 0 | 0 |
markscrivo/oddson2 | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
strombergnlp/ans-stance | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:apache-2.0",
"stance-detection",
"arxiv:2005.10410"
]
| The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance. | 0 | 1 |
tomekkorbak/pile-chunk-toxicity-scored-3 | false | []
| null | 1 | 0 |
imagenet_sketch | false | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imagenet-1k",
"language:en",
"license:unknown",
"arxiv:1905.13549"
]
| ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes.
We construct the data set with Google Image queries "sketch of __", where __ is the standard class name.
We only search within the "black and white" color scheme. We initially query 100 images for every class,
and then manually clean the pulled images by deleting the irrelevant images and images that are for similar
but different classes. For some classes, there are less than 50 images after manually cleaning, and then we
augment the data set by flipping and rotating the images. | 288 | 1 |
tomekkorbak/pile-toxicity-balanced3 | false | []
| null | 0 | 0 |
spoiled/ecqa_model_generate_roberta | false | []
| null | 0 | 0 |
DigitalUmuganda/kinyarwanda-tts-dataset | false | []
| null | 0 | 0 |
DigitalUmuganda/common-voice-kinyarwanda-text-dataset | false | [
"annotations_creators:crowd-sourced",
"language_creators:Digital Umuganda",
"multilinguality:monolingual",
"size_categories:1M<n<3M",
"source_datasets:original",
"language:rw",
"license:cc-by-4.0"
]
| null | 1 | 0 |
Rexhaif/ru-med-ner | false | [
"arxiv:2201.06499"
]
| null | 0 | 1 |
scoup123/testing | false | []
| null | 0 | 0 |
scoup123/tr_movie_reviews_training | false | [
"license:other"
]
| null | 0 | 0 |
cpllab/syntaxgym_sentences | false | []
| null | 0 | 0 |
arize-ai/movie_reviews_with_context_drift | false | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imdb",
"language:en",
"license:mit"
]
| null | 0 | 0 |
s3prl/flashlight | false | []
| null | 0 | 0 |
EddieChen372/react_repos | false | []
| null | 1 | 0 |
Hongwei/CoQG | false | []
| null | 0 | 0 |
ccdv/mediasum | false | [
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation"
]
| MediaSum dataset for summarization.
From paper: "MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization" by C. Zhu et al." | 45 | 3 |
avacaondata/fullrandomq | false | []
| null | 0 | 0 |
charly/next_500 | false | []
| null | 0 | 0 |
Shuchen/codeparrot-train | false | [
"license:apache-2.0"
]
| null | 0 | 0 |
Shuchen/codeparrot-valid | false | [
"license:apache-2.0"
]
| null | 0 | 0 |
conceptofmind/pile_cc | false | [
"arxiv:2101.00027"
]
| null | 1,915 | 2 |
avacaondata/fullrandomv2 | false | []
| null | 0 | 0 |
myradeng/testing | false | []
| null | 0 | 0 |
myradeng/cs-230-news-v2 | false | []
| null | 0 | 0 |
rajistics/million-headlines | false | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0"
]
| null | 0 | 0 |
renatorangel/hedgies | false | []
| null | 0 | 0 |
feyzaakyurek/BBNLI | false | [
"task_categories:text-generation",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit"
]
| null | 14 | 1 |
hidude562/Fake-and-real-words | false | []
| null | 0 | 0 |
laion/laion2B-en-aesthetic-tags | false | []
| null | 0 | 1 |
laion/laion2B-multi-aesthetic-tags | false | []
| null | 0 | 2 |
laion/laion1B-nolang-aesthetic-tags | false | []
| null | 1 | 1 |
avacaondata/blindrandom | false | []
| null | 0 | 0 |
laion/laion2B-en-aesthetic | false | []
| null | 63 | 17 |
laion/laion2B-multi-aesthetic | false | []
| null | 2 | 3 |
laion/laion1B-nolang-aesthetic | false | []
| null | 0 | 0 |
zhangqiaobit/chinese_poetrys | false | []
| null | 0 | 4 |
mesolitica/ms-wiki | false | [
"language:ms"
]
| null | 0 | 0 |
laion/laion5B-aesthetic-tags-kv | false | [
"license:cc-by-4.0"
]
| null | 3 | 3 |
laion/laion-art | false | []
| null | 239 | 11 |
launch/gov_report | false | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0"
]
| GovReport long document summarization dataset.
There are three configs:
- plain_text: plain text document-to-summary pairs
- plain_text_with_recommendations: plain text doucment-summary pairs, with "What GAO recommends" included in the summary
- structure: data with section structure | 36 | 2 |
IngoStatworx/Py150ExceptionClassification | false | []
| null | 0 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.