id
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class | tags
list | description
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5.93k
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int64 0
1.79k
|
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xiaobendanyn/nyt10 | false | [] | null | 4 | 0 |
xiaobendanyn/tacred | false | [] | null | 1 | 3 |
xkang/github-issues | false | [] | null | 0 | 0 |
xuyeliu/notebookCDG | false | [
"arxiv:2104.01002"
] | null | 0 | 1 |
yabramuvdi/wfh-problematic | false | [] | null | 0 | 0 |
yannobla/Sunshine | false | [] | null | 0 | 0 |
yazdipour/text-to-sparql-kdwd | false | [] | null | 1 | 0 |
ydshieh/coco_dataset_script | false | [] | COCO is a large-scale object detection, segmentation, and captioning dataset. | 8,967 | 2 |
yerevann/sst2 | false | [] | null | 0 | 0 |
yharyarias/tirads_tiroides | false | [] | null | 0 | 1 |
yhavinga/mc4_nl_cleaned | false | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:en-nl",
"source_datasets:extended",
"language:nl",
"language:en",
"license:odc-by",
"arxiv:1910.10683"
] | A thoroughly cleaned version of the Dutch portion of the multilingual
colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
detailed in the repository README file. | 5 | 6 |
yluisfern/PBU | false | [] | null | 0 | 0 |
yo/devparty | false | [] | null | 0 | 1 |
yonesuke/Ising2D | false | [] | null | 0 | 0 |
yonesuke/Vicsek | false | [
"license:mit"
] | null | 2 | 0 |
yonesuke/kuramoto | false | [] | null | 0 | 0 |
ysharma/rickandmorty | false | [] | null | 6 | 0 |
yuanchuan/annotated_reference_strings | false | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0"
] | A repository of reference strings annotated using CSL processor using citations obtained from various sources. | 0 | 0 |
yuchenlin/OntoRock | false | [] | null | 0 | 0 |
yuvalkirstain/asset | false | [] | null | 0 | 0 |
yuvalkirstain/contract_nli-debug | false | [] | null | 0 | 0 |
yuvalkirstain/contract_nli_t5 | false | [] | null | 0 | 0 |
yuvalkirstain/contract_nli_t5_lm | false | [] | null | 0 | 0 |
yuvalkirstain/qasper_t5 | false | [] | null | 1 | 0 |
yuvalkirstain/qasper_t5_lm | false | [] | null | 1 | 0 |
yuvalkirstain/qmsum_t5 | false | [] | null | 0 | 0 |
yuvalkirstain/qmsum_t5_lm | false | [] | null | 114 | 0 |
yuvalkirstain/quality | false | [] | null | 0 | 0 |
yuvalkirstain/quality_debug | false | [] | null | 0 | 0 |
yuvalkirstain/quality_squad | false | [] | null | 0 | 0 |
yuvalkirstain/quality_squad_debug | false | [] | null | 0 | 0 |
yuvalkirstain/quality_t5 | false | [] | null | 0 | 0 |
yuvalkirstain/quality_t5_lm | false | [] | null | 0 | 0 |
yuvalkirstain/scrolls_t5 | false | [] | null | 0 | 0 |
yuvalkirstain/squad_full_doc | false | [] | null | 0 | 0 |
yuvalkirstain/squad_seq2seq | false | [] | null | 2 | 0 |
yuvalkirstain/squad_t5 | false | [] | null | 0 | 0 |
yuvalkirstain/summ_screen_fd_t5 | false | [] | null | 0 | 0 |
yuvalkirstain/summ_screen_fd_t5_lm | false | [] | null | 0 | 0 |
yxchar/ag-tlm | false | [] | null | 0 | 0 |
yxchar/amazon-tlm | false | [] | null | 0 | 0 |
yxchar/chemprot-tlm | false | [] | null | 8 | 0 |
yxchar/citation_intent-tlm | false | [] | null | 0 | 1 |
yxchar/hyp-tlm | false | [] | null | 0 | 0 |
yxchar/imdb-tlm | false | [] | null | 2 | 0 |
yxchar/rct-20k-tlm | false | [] | null | 0 | 0 |
yxchar/sciie-tlm | false | [] | null | 0 | 0 |
z-uo/female-LJSpeech-italian | false | [
"multilinguality:monolingual",
"language:it"
] | null | 0 | 0 |
z-uo/male-LJSpeech-italian | false | [
"multilinguality:monolingual",
"language:it"
] | null | 4 | 0 |
z-uo/squad-it | false | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"size_categories:8k<n<10k",
"language:it"
] | null | 0 | 0 |
zapsdcn/ag | false | [] | null | 0 | 0 |
zapsdcn/amazon | false | [] | null | 0 | 0 |
zapsdcn/chemprot | false | [] | null | 198 | 0 |
zapsdcn/citation_intent | false | [] | null | 233 | 0 |
zapsdcn/hyperpartisan_news | false | [] | null | 22 | 0 |
zapsdcn/imdb | false | [] | null | 0 | 0 |
zapsdcn/rct-20k | false | [] | null | 0 | 0 |
zapsdcn/sciie | false | [] | null | 12 | 0 |
zf-org/org_dataset | false | [] | null | 0 | 0 |
zfaB4Hmm/test | false | [] | null | 0 | 0 |
zhangruihan1/face-recognition-validation | false | [] | null | 1 | 1 |
zhangruihan1/face-recognition | false | [] | null | 0 | 1 |
zhangruihan1/fr-cfp_fp | false | [] | null | 0 | 0 |
zhoujun/hitab | false | [] | null | 1 | 0 |
zhufy/xquad_split | false | [] | XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel
across 12 languages. | 0 | 0 |
zj88zj/PubMed_200k_RCT | false | [] | null | 3 | 1 |
zj88zj/SCIERC | false | [] | null | 0 | 0 |
zloelias/kinopoisk-reviews-short | false | [] | null | 0 | 0 |
zloelias/kinopoisk-reviews | false | [] | null | 2 | 0 |
zloelias/lenta-ru-short | false | [] | null | 0 | 0 |
zloelias/lenta-ru | false | [] | null | 1 | 0 |
zwang199/autonlp-data-traffic_nlp_binary | false | [
"task_categories:text-classification",
"language:en"
] | null | 0 | 0 |
fancyerii/test | false | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"size_categories:10K<n<100K"
] | null | 0 | 0 |
ArnavL/finetune_preprocessed_yelp | false | [] | null | 0 | 0 |
huggan/anime-faces | false | [
"license:cc0-1.0"
] | null | 17 | 4 |
GEM-submissions/lewtun__this-is-a-test__1646314818 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
GEM-submissions/lewtun__this-is-a-test__1646316929 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
v-card/lol | false | [] | null | 0 | 0 |
fuliucansheng/wheat | false | [] | null | 0 | 0 |
davanstrien/testhugit | false | [] | null | 0 | 0 |
testst/dsdfasdfsaf | false | [] | null | 0 | 0 |
firzens/authors | false | [] | null | 0 | 0 |
NLPC-UOM/Sinhala-Tamil-Aligned-Parallel-Corpus | false | [
"language:si",
"license:mit"
] | null | 0 | 0 |
NLPC-UOM/AnanyaSinhalaNERDataset | false | [] | null | 0 | 0 |
openclimatefix/gfs-reforecast | false | [] | This dataset consists of various NOAA datasets related to operational forecasts, including FNL Analysis files,
GFS operational forecasts, and the raw observations used to initialize the grid. | 0 | 1 |
nlpaueb/finer-139 | false | [
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2203.06482"
] | FiNER-139 is a named entity recognition dataset consisting of 10K annual
and quarterly English reports (filings) of publicly traded companies
downloaded from the U.S. Securities and Exchange Commission (SEC)
annotated with 139 XBRL tags in the IOB2 format. | 803 | 8 |
GEM-submissions/ratishsp__seqplan__1646397329 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
GEM-submissions/ratishsp__seqplan__1646397829 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
Alvenir/alvenir_asr_da_eval | false | [
"license:cc-by-4.0"
] | Dataset of a little bit more than 5hours primarily intended as an evaluation dataset for Danish. | 14 | 5 |
google/xtreme_s | false | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|multilingual_librispeech",
"source_datasets:extended|covost2",
"language:afr",
"language:amh",
"language:ara",
"language:asm",
"language:ast",
"language:azj",
"language:bel",
"language:ben",
"language:bos",
"language:cat",
"language:ceb",
"language:cmn",
"language:ces",
"language:cym",
"language:dan",
"language:deu",
"language:ell",
"language:eng",
"language:spa",
"language:est",
"language:fas",
"language:ful",
"language:fin",
"language:tgl",
"language:fra",
"language:gle",
"language:glg",
"language:guj",
"language:hau",
"language:heb",
"language:hin",
"language:hrv",
"language:hun",
"language:hye",
"language:ind",
"language:ibo",
"language:isl",
"language:ita",
"language:jpn",
"language:jav",
"language:kat",
"language:kam",
"language:kea",
"language:kaz",
"language:khm",
"language:kan",
"language:kor",
"language:ckb",
"language:kir",
"language:ltz",
"language:lug",
"language:lin",
"language:lao",
"language:lit",
"language:luo",
"language:lav",
"language:mri",
"language:mkd",
"language:mal",
"language:mon",
"language:mar",
"language:msa",
"language:mlt",
"language:mya",
"language:nob",
"language:npi",
"language:nld",
"language:nso",
"language:nya",
"language:oci",
"language:orm",
"language:ory",
"language:pan",
"language:pol",
"language:pus",
"language:por",
"language:ron",
"language:rus",
"language:bul",
"language:snd",
"language:slk",
"language:slv",
"language:sna",
"language:som",
"language:srp",
"language:swe",
"language:swh",
"language:tam",
"language:tel",
"language:tgk",
"language:tha",
"language:tur",
"language:ukr",
"language:umb",
"language:urd",
"language:uzb",
"language:vie",
"language:wol",
"language:xho",
"language:yor",
"language:yue",
"language:zul",
"license:cc-by-4.0",
"arxiv:2203.10752",
"arxiv:2205.12446",
"arxiv:2007.10310"
] | XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102
languages from 10+ language families, 3 different domains and 4
task families, XTREME-S aims to simplify multilingual speech
representation evaluation, as well as catalyze research in “universal” speech representation learning. | 367 | 24 |
anjandash/java-8m-methods-v1 | false | [
"multilinguality:monolingual",
"language:java",
"license:mit"
] | null | 0 | 0 |
PhilSad/data-guided-scp-gptj-lit | false | [] | null | 0 | 0 |
elkarhizketak | false | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:eu",
"license:cc-by-sa-4.0",
"dialogue-qa"
] | ElkarHizketak is a low resource conversational Question Answering
(QA) dataset in Basque created by Basque speaker volunteers. The
dataset contains close to 400 dialogues and more than 1600 question
and answers, and its small size presents a realistic low-resource
scenario for conversational QA systems. The dataset is built on top of
Wikipedia sections about popular people and organizations. The
dialogues involve two crowd workers: (1) a student ask questions after
reading a small introduction about the person, but without seeing the
section text; and (2) a teacher answers the questions selecting a span
of text of the section. | 0 | 1 |
ruanchaves/hashset_distant_sampled | false | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:hi",
"language:en",
"license:unknown",
"word-segmentation",
"arxiv:2201.06741"
] | Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
as a good benchmark for hashtag segmentation tasks.
HashSet Distant: 3.3M loosely collected camel cased hashtags containing hashtag and their segmentation.
HashSet Distant Sampled is a sample of 20,000 camel cased hashtags from the HashSet Distant dataset. | 2 | 0 |
ruanchaves/hashset_distant | false | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:hi",
"language:en",
"license:unknown",
"word-segmentation",
"arxiv:2201.06741"
] | Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
as a good benchmark for hashtag segmentation tasks.
HashSet Distant: 3.3M loosely collected camel cased hashtags containing hashtag and their segmentation. | 2 | 0 |
ruanchaves/hashset_manual | false | [
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:hi",
"language:en",
"license:unknown",
"word-segmentation",
"arxiv:2201.06741"
] | Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
as a good benchmark for hashtag segmentation tasks.
HashSet Manual: contains 1.9k manually annotated hashtags. Each row consists of the hashtag, segmented
hashtag ,named entity annotations, a list storing whether the hashtag contains mix of hindi and english
tokens and/or contains non-english tokens. | 0 | 0 |
ruanchaves/stan_large | false | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:agpl-3.0",
"word-segmentation"
] | The description below was taken from the paper "Multi-task Pairwise Neural Ranking for Hashtag Segmentation"
by Maddela et al..
"STAN large, our new expert curated dataset, which includes all 12,594 unique English hashtags and their
associated tweets from the same Stanford dataset.
STAN small is the most commonly used dataset in previous work. However, after reexamination, we found annotation
errors in 6.8% of the hashtags in this dataset, which is significant given that the error rate of the state-of-the art
models is only around 10%. Most of the errors were related to named entities. For example, #lionhead,
which refers to the “Lionhead” video game company, was labeled as “lion head”.
We therefore constructed the STAN large dataset of 12,594 hashtags with additional quality control for human annotations." | 0 | 0 |
ruanchaves/stan_small | false | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"word-segmentation",
"arxiv:1501.03210"
] | Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al.. | 2 | 0 |
ruanchaves/boun | false | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"word-segmentation"
] | Dev-BOUN Development set that includes 500 manually segmented hashtags. These are selected from tweets about movies,
tv shows, popular people, sports teams etc. Test-BOUN Test set that includes 500 manually segmented hashtags.
These are selected from tweets about movies, tv shows, popular people, sports teams etc. | 2 | 1 |
ruanchaves/dev_stanford | false | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"word-segmentation"
] | 1000 hashtags manually segmented by Çelebi et al. for development purposes,
randomly selected from the Stanford Sentiment Tweet Corpus by Sentiment140. | 2 | 0 |
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