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wanyu/IteraTeR_v2 | false | [
"task_categories:text2text-generation",
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"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"text-editing",
"arxiv:2204.03685"
]
| null | 0 | 1 |
ciroy/mlcommons-test | false | [
"license:cc-by-4.0"
]
| null | 0 | 0 |
gagan3012/k2t-training-data | false | []
| null | 0 | 0 |
Bingsu/Cat_and_Dog | false | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc0-1.0"
]
| null | 582 | 0 |
Bingsu/KSS_Dataset | false | [
"task_categories:text-to-speech",
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"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-nc-sa-4.0"
]
| null | 1 | 2 |
huggan/selfie2anime | false | []
| null | 65 | 0 |
hnchen/Session-search | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
huggingface/image-classification-test-sample | false | []
| null | 3 | 1 |
samwell/en_twi | false | [
"license:mit"
]
| null | 2 | 0 |
google/fleurs | false | [
"task_categories:automatic-speech-recognition",
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"arxiv:2106.03193"
]
| null | 7,470 | 64 |
mteb/twitterurlcorpus-pairclassification | false | []
| null | 1,828 | 0 |
mteb/sprintduplicatequestions-pairclassification | false | [
"language:en"
]
| null | 2,309 | 0 |
mteb/twittersemeval2015-pairclassification | false | []
| null | 1,791 | 0 |
bigscience-historical-texts/Open_Medieval_French | false | [
"language:fro"
]
| null | 0 | 0 |
mteb/askubuntudupquestions-reranking | false | [
"language:en"
]
| null | 1,846 | 0 |
taln-ls2n/wikinews-fr-100 | false | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:fr",
"license:cc-by-4.0"
]
| Wikinews-fr-100 benchmark dataset for keyphrase extraction an generation. | 3 | 1 |
mteb/scidocs-reranking | false | [
"language:en"
]
| null | 2,044 | 0 |
mteb/stackoverflowdupquestions-reranking | false | [
"language:en"
]
| null | 1,679 | 0 |
JEFFREY-VERDIERE/train_X_PJT_NLP | false | []
| null | 0 | 0 |
taln-ls2n/taln-archives | false | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"language:fr",
"language:en",
"license:cc-by-4.0"
]
| TALN Archives benchmark dataset for keyphrase extraction an generation. | 3 | 2 |
mteb/sickr-sts | false | [
"language:en"
]
| null | 1,865 | 0 |
mnazari/urmi-assyrian-voice | false | [
"task_categories:automatic-speech-recognition",
"annotations_creators:Geoffrey Khan",
"annotations_creators:Matthew Nazari",
"language:aii",
"license:cc0-1.0"
]
| null | 0 | 0 |
mteb/biosses-sts | false | [
"language:en"
]
| null | 2,051 | 0 |
SKYKISS/test | false | []
| null | 0 | 0 |
mteb/stsbenchmark-sts | false | [
"language:en"
]
| null | 3,184 | 0 |
arka0821/multi_document_summarization | false | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2010.14235"
]
| Multi-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. | 4 | 0 |
asdAD222/segment_T | false | []
| null | 0 | 0 |
danrambado/SODA | false | [
"license:mit"
]
| null | 2 | 0 |
kniemiec/crack-segmentation | false | []
| null | 0 | 0 |
kniemiec/test | false | []
| null | 0 | 0 |
kniemiec/sidewalk-imagery-test | false | []
| null | 0 | 0 |
Lexi/spanextract_no_qg | false | []
| null | 0 | 0 |
enoriega/biocreative_gene_mention | false | []
| Training and validation datasets for the BioCreative II gene mention task.
The data has been tokenized with [processors](https://github.com/clulab/processors)
## Features:
- __tokens__: Input token sequence
- __folded_tokens__: Same as tokens, but case-folded
- __tags__: POS tags of the input sequence tokens
- __labels__: BIO sequence tags | 3 | 0 |
aspis/car_background_removal | false | []
| null | 0 | 0 |
truongpdd/us_train | false | []
| null | 0 | 0 |
truongpdd/test_us | false | []
| null | 0 | 0 |
truongpdd/train_jp | false | []
| null | 0 | 0 |
truongpdd/test_jp | false | []
| null | 0 | 0 |
truongpdd/train_es | false | []
| null | 0 | 0 |
truongpdd/test_es | false | []
| null | 0 | 0 |
bookbot/id_word2phoneme | false | [
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:id",
"language:ms"
]
| null | 0 | 0 |
hapandya/sqnnr | false | []
| Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. | 0 | 0 |
truongpdd/train_valid_jp | false | []
| null | 0 | 0 |
truongpdd/train_valid_us | false | []
| null | 0 | 0 |
truongpdd/train_valid_es | false | []
| null | 0 | 0 |
Khalsuu/filipino_dataset_script | false | [
"license:apache-2.0"
]
| Magic Hub's initiative to help teach machines how real people speak. They wanted to provide structured data that
will help enthusiasts and researchers to spend more time on training models rather than cleaning and structuring data. | 0 | 0 |
csebuetnlp/CrossSum | false | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:am",
"language:ar",
"language:az",
"language:bn",
"language:my",
"language:zh",
"language:en",
"language:fr",
"language:gu",
"language:ha",
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"language:pa",
"language:ru",
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"language:te",
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"language:ti",
"language:tr",
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"language:ur",
"language:uz",
"language:vi",
"language:cy",
"language:yo",
"license:cc-by-nc-sa-4.0",
"arxiv:2112.08804"
]
| null | 31 | 2 |
UmnatHCU/test | false | [
"license:afl-3.0"
]
| null | 0 | 0 |
osanseviero/test123 | false | []
| null | 0 | 0 |
javilonso/TEST_mex_data_title_with_opinion | false | []
| null | 0 | 0 |
nirmalkumar/old-old-old | false | []
| null | 0 | 0 |
nirmalkumar/cricket-commentary | false | []
| null | 1 | 0 |
mteb/sts12-sts | false | [
"language:en"
]
| null | 3,039 | 2 |
mteb/sts13-sts | false | [
"language:en"
]
| null | 1,861 | 0 |
mteb/sts14-sts | false | [
"language:en"
]
| null | 1,849 | 0 |
mteb/sts15-sts | false | [
"language:en"
]
| null | 1,871 | 0 |
mteb/sts16-sts | false | [
"language:en"
]
| null | 1,856 | 0 |
javilonso/PRED_TEST_mex_data_title_with_opinion | false | []
| null | 0 | 0 |
mwong/climatetext-claim-related-evaluation | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0"
]
| null | 0 | 1 |
NbAiLab/NST | false | [
"license:apache-2.0"
]
| This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Norwegian. In this version, the organization of the data have been altered to improve the usefulness of the database.
The acoustic databases described below were developed by the firm Nordisk språkteknologi holding AS (NST), which went bankrupt in 2003. In 2006, a consortium consisting of the University of Oslo, the University of Bergen, the Norwegian University of Science and Technology, the Norwegian Language Council and IBM bought the bankruptcy estate of NST, in order to ensure that the language resources developed by NST were preserved. In 2009, the Norwegian Ministry of Culture charged the National Library of Norway with the task of creating a Norwegian language bank, which they initiated in 2010. The resources from NST were transferred to the National Library in May 2011, and are now made available in Språkbanken, for the time being without any further modification. Språkbanken is open for feedback from users about how the resources can be improved, and we are also interested in improved versions of the databases that users wish to share with other users. Please send response and feedback to [email protected]. | 1 | 0 |
mwong/climatetext-evidence-related-evaluation | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0"
]
| null | 0 | 1 |
Peihao/CURE-Pretrain | false | [
"license:lgpl"
]
| null | 0 | 0 |
Fin/cat_bs | false | []
| null | 0 | 0 |
davanstrien/test_bl_fickr | false | []
| null | 0 | 0 |
Yaxin/SemEval2016Task5Raw | false | []
| A collection of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis. | 36 | 1 |
enoriega/GENIA-Term-Corpus | false | []
| GENIA Term corpus | 0 | 0 |
Ericblancosf/subtechnie2 | false | []
| null | 0 | 0 |
KeithHorgan/TweetClimateAnalysisData | false | []
| null | 0 | 0 |
crisdev/comentarios | false | [
"license:mit"
]
| null | 2 | 0 |
daniel-dona/tfg-voice-2 | false | [
"license:cc-by-sa-3.0"
]
| null | 0 | 0 |
alisawuffles/WANLI | false | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
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"multilinguality:monolingual",
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"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2201.05955"
]
| null | 954 | 4 |
billray110/corpus-of-diverse-styles | false | [
"task_categories:text-classification",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"arxiv:2010.05700"
]
| null | 0 | 2 |
Kateryna/eva_ru_forum_headlines | false | []
| null | 0 | 0 |
ylacombe/xsum_factuality | false | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-xsum",
"language:en",
"license:cc-by-4.0"
]
| null | 5 | 0 |
cmotions/NL_restaurant_reviews | false | [
"language:nl",
"text-classification",
"sentiment-analysis"
]
| null | 5 | 1 |
AntoineLB/FrozenLakeNotFrozen | false | []
| null | 0 | 0 |
mwong/climatetext-climate_evidence-claim-related-evaluation | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0"
]
| null | 0 | 1 |
mwong/climatetext-claim-climate_evidence-related-evaluation | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0"
]
| null | 0 | 1 |
mwong/climatetext-evidence-claim-pair-related-evaluation | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0"
]
| null | 0 | 1 |
mwong/climatetext-claim-evidence-pair-related-evaluation | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0"
]
| null | 0 | 1 |
mweiss/fashion_mnist_corrupted | false | [
"task_categories:image-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|fashion_mnist",
"language:en",
"license:mit",
"arxiv:1906.02337"
]
| Fashion-MNIST is dataset of fashion images, indended as a drop-in replacement for the MNIST dataset.
This dataset (Fashion-Mnist-Corrupted) provides out-of-distribution data for the Fashion-Mnist
dataset. Fashion-Mnist-Corrupted is based on a similar project for MNIST, called MNIST-C, by Mu et. al. | 43 | 1 |
visual_genome | false | [
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:visual-question-answering",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1602.07332"
]
| Visual Genome enable to model objects and relationships between objects.
They collect dense annotations of objects, attributes, and relationships within each image.
Specifically, the dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. | 329 | 13 |
yanekyuk/wikikey-full | false | []
| null | 0 | 0 |
Yaxin/SemEval2014Task4Raw | false | []
| A collection of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis. | 188 | 3 |
yanekyuk/wikikey-small | false | []
| null | 0 | 0 |
yanekyuk/wikikey-large | false | []
| null | 0 | 0 |
yanekyuk/wikikey-medium | false | []
| null | 0 | 0 |
tsantosh7/COVID-19_Annotations | false | [
"license:cc"
]
| null | 0 | 0 |
Yaxin/SemEval2015Task12Raw | false | []
| A collection of SemEval2015 specifically designed to aid research in Aspect Based Sentiment Analysis. | 0 | 1 |
pie/conll2003 | false | []
| null | 1,108 | 0 |
rvl_cdip | false | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|iit_cdip",
"language:en",
"license:other",
"arxiv:1502.07058"
]
| The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. | 2,206 | 21 |
Goud/Goud-sum | false | [
"task_categories:summarization",
"task_ids:news-articles-headline-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"size_categories:100K<n<1M",
"source_datasets:original"
]
| null | 6 | 0 |
eleldar/sub_train-normal_tests-datasets | false | []
| null | 0 | 0 |
GEM-submissions/ratishsp__seqplan-sportsett__1650556902 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
]
| null | 0 | 0 |
pietrolesci/glue_diagnostics | false | []
| null | 12 | 0 |
patrickvonplaten/librispeech_asr_self_contained | false | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0"
]
| 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 | 0 | 0 |
truongpdd/dataset_clf | false | []
| null | 0 | 0 |
multiIR/testing_ko | false | []
| null | 0 | 0 |
adithya7/xlel_wd_dictionary | false | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:af",
"language:ar",
"language:be",
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"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"language:hi",
"language:hu",
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"language:it",
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"language:ml",
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"language:nl",
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"language:pl",
"language:pt",
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"language:th",
"language:tr",
"language:uk",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"arxiv:2204.06535"
]
| XLEL-WD is a multilingual event linking dataset. This sub-dataset contains a dictionary of events from Wikidata. The multilingual descriptions for Wikidata event items are taken from the corresponding Wikipedia articles. | 1 | 0 |
adithya7/xlel_wd | false | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
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"language:af",
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"language:uk",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"arxiv:2204.06535"
]
| XLEL-WD is a multilingual event linking dataset. This dataset contains mention references from multilingual Wikipedia/Wikinews articles to event items in Wikidata. The text descriptions for Wikidata events are compiled from Wikipedia articles. | 7 | 0 |
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