Datasets:
nkasmanoff
/
huggingface-datasets

Dataset card Data Studio Files Community
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usc-isi/WikiConvert
false
[ "task_categories:fill-mask", "task_categories:other", "task_categories:text-generation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|wikipedia", "language:en", "license:mit", "numeracy", "natural-language-understanding", "tokenization" ]
Language Modelling with Cardinal Number Annotations.
0
1
uva-irlab/canard_quretec
false
[ "arxiv:2005.11723" ]
CANARD has been preprocessed by Voskarides et al. to train and evaluate their Query Resolution Term Classification model (QuReTeC). CANARD is a dataset for question-in-context rewriting that consists of questions each given in a dialog context together with a context-independent rewriting of the question. The context of each question is the dialog utterences that precede the question. CANARD can be used to evaluate question rewriting models that handle important linguistic phenomena such as coreference and ellipsis resolution.
4
0
uva-irlab/trec-cast-2019-multi-turn
false
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "multilinguality:monolingual", "size_categories:10M<n<100M", "language:en" ]
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets.
0
0
uyeongjae/load_klue_re_agmented
false
[]
null
0
0
valurank/12-factor
false
[ "multilinguality:monolingual", "language:en", "license:other" ]
null
0
0
valurank/PoliticalBias
false
[ "multilinguality:monolingual", "language:en", "license:other" ]
null
0
0
valurank/PoliticalBias_AllSides_Txt
false
[ "multilinguality:monolingual", "language:en", "license:other" ]
null
1
1
valurank/PoliticalBias_Sources
false
[ "multilinguality:monolingual", "language:en", "license:other" ]
null
2
0
valurank/hate-multi
false
[ "task_categories:text-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:derived", "language:en", "license:other" ]
null
0
0
valurank/news-12factor
false
[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "language:en", "license:other" ]
null
0
0
valurank/offensive-multi
false
[ "task_categories:text-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:derived", "language:en", "license:other" ]
null
0
0
vanadhi/finlitqa
false
[]
null
0
0
vannacute/AmazonReviewHelpfulness
false
[]
null
0
0
vannynakamura/leish
false
[]
null
0
0
vasilis/et_corpora_parliament_processed
false
[]
null
0
0
vasudevgupta/amazon-ml-hack
false
[]
null
0
0
vasudevgupta/bigbird-tokenized-natural-questions
false
[]
null
0
0
vasudevgupta/data
false
[]
null
2
0
vasudevgupta/fairseq-ljspeech
false
[]
null
0
0
vasudevgupta/gsoc-librispeech
false
[]
null
0
0
vasudevgupta/natural-questions-validation
false
[]
null
0
0
vasudevgupta/prml_data_contest
false
[]
null
0
0
vasudevgupta/temperature-distribution-2d-plate
false
[]
null
0
0
vasudevgupta/temperature-distribution-3d-cylinder
false
[]
null
0
0
iitm-ddp/iiith-indic-speech
false
[]
null
0
0
vblagoje/lfqa
false
[]
null
35
9
vblagoje/lfqa_support_docs
false
[]
null
84
4
vblagoje/wikipedia_snippets_streamed
false
[]
The dataset was built from the Wikipedia dump (https://dumps.wikimedia.org/). Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).
365
0
vctc92/sdsd
false
[]
null
0
0
vctc92/test
false
[]
null
0
0
vera-pro/ShadowLink
false
[]
null
0
3
versae/bibles
false
[ "language:sq", "language:ar", "language:az", "language:be", "language:bg", "language:ceb", "language:zh", "language:cs", "language:da", "language:en", "language:es", "language:fi", "language:fr", "language:de", "language:el", "language:ht", "language:he", "language:hi", "language:hu", "language:it", "language:ko", "language:la", "language:nl", "language:no", "language:pt", "language:rm", "language:ru", "language:sw", "language:ta", "language:th", "language:tr", "language:vi" ]
Multilingual Bibles
1
0
versae/modernisa
false
[]
Modernisa
0
0
versae/norwegian-t5-dataset-debug
false
[]
null
0
0
versae/norwegian-t5-dataset-debug2
false
[]
null
0
0
versae/norwegian-t5-dataset-debug3
false
[]
null
0
0
vershasaxena91/datasets
false
[]
null
0
0
vershasaxena91/squad_multitask
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
vesteinn/icelandic-ner-MIM-GOLD-NER
false
[]
This Icelandic named entity (NE) corpus, MIM-GOLD-NER, is a version of the MIM-GOLD corpus tagged for NEs. Over 48 thousand NEs are tagged in this corpus of one million tokens, which can be used for training named entity recognizers for Icelandic. The MIM-GOLD-NER corpus was developed at Reykjavik University in 2018–2020, funded by the Strategic Research and Development Programme for Language Technology (LT). Two LT students were in charge of the corpus annotation and of training named entity recognizers using machine learning methods. A semi-automatic approach was used for annotating the corpus. Lists of Icelandic person names, location names, and company names were compiled and used for extracting and classifying as many named entities as possible. Regular expressions were then used to find certain numerical entities in the corpus. After this automatic pre-processing step, the whole corpus was reviewed manually to correct any errors. The corpus is tagged for eight named entity types: PERSON – names of humans, animals and other beings, real or fictional. LOCATION – names of locations, real or fictional, i.e. buildings, street and place names, both real and fictional. All geographical and geopolitical entities such as cities, countries, counties and regions, as well as planet names and other outer space entities. ORGANIZATION – companies and other organizations, public or private, real or fictional. Schools, churches, swimming pools, community centers, musical groups, other affiliations. MISCELLANEOUS – proper nouns that don’t belong to the previous three categories, such as products, books and movie titles, events, such as wars, sports tournaments, festivals, concerts, etc. DATE – absolute temporal units of a full day or longer, such as days, months, years, centuries, both written numerically and alphabetically. TIME – absolute temporal units shorter than a full day, such as seconds, minutes, or hours, both written numerically and alphabetically. MONEY – exact monetary amounts in any currency, both written numerically and alphabetically. PERCENT – percentages, both written numerically and alphabetically MIM-GOLD-NER is intended for training of named entity recognizers for Icelandic. It is in the CoNLL format, and the position of each token within the NE is marked using the BIO tagging format. The corpus can be used in its entirety or by training on subsets of the text types that best fit the intended domain. The Named Entity Corpus corpus is distributed with the same special user license as MIM-GOLD, which is based on the MIM license, since the texts in MIM-GOLD were sampled from the MIM corpus.
0
0
vesteinn/icelandic-qa-NQiI
false
[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:extractive-qa", "annotations_creators:curated", "language_creators:curated", "multilinguality:monolingual", "source_datasets:original", "language:is", "license:cc-by-sa-4.0" ]
\
0
1
victor/autonlp-data-imdb-reviews-sentiment
false
[]
null
2
0
vidhur2k/multilingual-hate-speech
false
[]
null
0
0
vincentclaes/mit_indoor_scenes
false
[]
null
0
0
vishnun/huggingpics-data
false
[]
null
0
0
vivekverma239/question-generation
false
[]
null
0
0
vkhangpham/github-issues
false
[]
null
0
0
vocab-transformers/wiki-en-passages-20210101
false
[]
null
0
0
vs4vijay/VizDS
false
[]
null
0
0
vumichien/common_voice_large
false
[]
null
0
0
vumichien/common_voice_large_jsut_jsss_css10
false
[ "task_categories:automatic-speech-recognition", "language_creators:expert-generated", "multilinguality:monolingual", "language:ja", "license:cc-by-nc-nd-4.0" ]
null
0
0
vumichien/ja_opus100_processed
false
[]
null
0
0
w-nicole/childes_data
false
[]
null
0
0
w-nicole/childes_data_no_tags
false
[]
null
0
0
w-nicole/childes_data_no_tags_
false
[]
null
0
0
w-nicole/childes_data_with_tags
false
[]
null
0
0
w-nicole/childes_data_with_tags_
false
[]
null
0
0
w11wo/imdb-javanese
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:jv", "license:odbl" ]
Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. We translated the original IMDB Dataset to Javanese using the multi-lingual MarianMT Transformer model from `Helsinki-NLP/opus-mt-en-mul`.
0
0
wanagenst/maslow-six-choices
false
[]
null
0
0
wanagenst/maslow-stories
false
[]
null
0
0
wanagenst/plutchik-nine-choices
false
[]
null
1
1
wanagenst/plutchik-stories
false
[]
null
12
1
wanagenst/reiss-stories
false
[]
null
0
0
wanagenst/reiss-twenty-choices
false
[]
null
0
0
wardenga/lsoie
false
[ "task_categories:text-retrieval", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|qa_srl", "language:en", "license:mit", "Open Information Extraction", "arxiv:2101.11177" ]
The Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20 times larger than the next largest human-annotated Open Information Extraction (OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset.
0
0
warwickai/financial_phrasebank_mirror
false
[]
null
0
0
webek18735/ddvoacantonesed
false
[]
null
0
0
webek18735/dhikhscook
false
[]
null
0
0
webimmunization/COVID-19-vaccine-attitude-tweets
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:intent-classification", "annotations_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "size_categories:54KB", "source_datasets:original", "language:en", "license:cc-by-4.0" ]
null
1
1
webis/args_me
false
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0" ]
The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal.
17
1
webis/conclugen
false
[]
The ConcluGen corpus is constructed for the task of argument summarization. It consists of 136,996 pairs of argumentative texts and their conclusions collected from the ChangeMyView subreddit, a web portal for argumentative discussions on controversial topics. The corpus has three variants: aspects, topics, and targets. Each variation encodes the corresponding information via control codes. These provide additional argumentative knowledge for generating more informative conclusions.
0
1
webis/ms-marco-anchor-text
false
[]
null
0
2
weijieliu/senteval_cn
false
[]
null
0
0
wesamhaddad14/testdata
false
[]
null
0
0
wicho/stylekqc-style
false
[ "license:cc-by-sa-4.0" ]
null
2
2
wietsedv/stsbenchmark
false
[ "license:cc-by-sa-4.0" ]
STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums.
97
0
wikilee/ADFA_Mapping
false
[]
null
0
0
wikimedia/wikipedia
false
[]
null
2
0
wikimedia/wikisource
false
[]
null
0
0
winvoker/turkish-sentiment-analysis-dataset
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:tr", "license:cc-by-sa-4.0" ]
null
106
13
wisdomify/story
false
[]
This dataset is designed to provide forward and reverse dictionary of Korean proverbs.
0
0
wmt/europarl
false
[]
null
0
0
wmt/news-commentary
false
[]
null
1
0
wmt/uncorpus
false
[]
null
0
0
wmt/wikititles
false
[]
null
0
0
wmt/wmt10
false
[]
null
0
0
wmt/wmt13
false
[]
null
0
0
wmt/wmt14
false
[]
null
0
0
wmt/wmt15
false
[]
null
0
0
wmt/wmt16
false
[]
null
0
0
wmt/wmt17
false
[]
null
1
0
wmt/wmt18
false
[]
null
0
0
wmt/wmt19
false
[]
null
0
0
wpicard/nostradamus-propheties
false
[ "task_ids:language-modeling", "annotations_creators:no-annotation", "multilinguality:monolingual", "size_categories:unknown", "language:en", "license:unknown" ]
null
0
0
wzkariampuzha/EpiClassifySet
false
[]
null
0
0
wzkariampuzha/EpiExtract4GARD
false
[]
null
0
0
wzywzy/telegram_summary
false
[]
null
0
0
botisan-ai/cantonese-mandarin-translations
false
[ "task_categories:text2text-generation", "task_categories:translation", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:unknown", "source_datasets:original", "language:zh", "license:mit", "conditional-text-generation" ]
null
13
4
xiaj/ds_test
false
[]
null
0
0
xiaj/test0919
false
[]
null
0
0
xiaobendanyn/demo
false
[]
null
0
0