nkasmanoff/huggingface-datasets · Datasets at Hugging Face

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
jakemarcus/MATH	false	[]	null	134	0
jamescalam/climate-fever-similarity	false	[]	null	128	0
jamol1741/test_dataset	false	[]	null	128	0
jcmc/ga-IE_opus_dgt_train	false	[]	null	258	0
jcmc/ga_mc4_processed	false	[]	null	258	0
jdepoix/junit_test_completion	false	[]	null	130	0
jegormeister/dutch-snli	false	[]	This is the Dutch version of the original SNLI dataset. The translation was performed using Google Translate. Original SNLI available at https://nlp.stanford.edu/projects/snli/	260	0
jel/covid	false	[]	null	128	0
jeree/fr_corpora_parliament_processed	false	[]	null	258	0
jfarray/TFM	false	[]	null	128	0
jfrenz/legalglue	false	[ "task_categories:text-classification", "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:multi-label-classification", "task_ids:topic-classification", "multilinguality:multilingual", "source_datasets:extended", "language:en", "language:da", "language:de", "language:nl", "language:sv", "language:bg", "language:cs", "language:hr", "language:pl", "language:sk", "language:sl", "language:es", "language:fr", "language:it", "language:pt", "language:ro", "language:et", "language:fi", "language:hu", "language:lt", "language:lv", "language:el", "language:mt", "german-ler", "lener-br", "arxiv:2003.13016", "arxiv:2110.00806", "arxiv:2109.00904" ]	\ Legal General Language Understanding Evaluation (LegalGLUE) benchmark is a collection of datasets for evaluating model performance across a diverse set of legal NLP tasks	3,993	5
jgammack/MTL-abstracts	false	[]	null	258	0
jgammack/SAE-door-abstracts	false	[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:unknown" ]	null	261	0
jgammack/THESES-abstracts	false	[]	null	263	0
jglaser/binding_affinity	false	[ "molecules", "chemistry", "SMILES" ]	A dataset to fine-tune language models on protein-ligand binding affinity prediction.	480	1
jhonparra18/spanish_billion_words_clean	false	[]	null	257	2
jhqwqq/2	false	[]	null	130	0
jianhong/dateset1	false	[]	null	130	0
jianhong/dateset2	false	[]	null	130	0
jiminsun/atc0_demo	false	[]	null	130	0
jimregan/clarinpl_sejmsenat	false	[ "task_categories:other", "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:other" ]	A collection of 97 hours of parliamentary speeches published on the ClarinPL website Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```	256	0
jimregan/clarinpl_studio	false	[ "task_categories:other", "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:other", "arxiv:1706.00245" ]	The corpus consists of 317 speakers recorded in 554 sessions, where each session consists of 20 read sentences and 10 phonetically rich words. The size of the audio portion of the corpus amounts to around 56 hours, with transcriptions containing 356674 words from a vocabulary of size 46361. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```	256	1
jimregan/foinse	false	[]	Foinse was an Irish-language magazine site. This script uses a list of articles retrieved from the Wayback Machine to build a corpus	130	0
jimregan/lasid	false	[]	Linguistic Atlas and Survey of Irish Dialects, volume 1	129	0
jinmang2/KorQuADv1	false	[]	KorQuAD 1.0 (Korean Question Answering Dataset v1.0) KorQuAD 1.0 is a dataset created for Korean Machine Reading Comprehension. The answers to all your questions are made up of some subareas in the corresponding Wikipedia article paragraphs. It is structured in the same way as the Stanford Question Answering Dataset (SQuAD) v1.0.	92	0
jinmang2/common-sense-mrc	false	[]	null	0	0
jinmang2/load_klue_re	false	[]	KLUE (Korean Language Understanding Evaluation) Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models.	3	0
jinmang2/medical-mask	false	[]	null	0	0
jinmang2/pred	false	[]	""" _LICENSE = "CC-BY-SA-4.0" _URL = "https://github.com/boostcampaitech2/data-annotation-nlp-level3-nlp-14" _DATA_URLS = { "train": "https://huggingface.co/datasets/jinmang2/pred/resolve/main/train.csv", "dev": "https://huggingface.co/datasets/jinmang2/pred/resolve/main/dev.csv", } _VERSION = "0.0.0" _LABEL = [ '관계_없음', '이론:대체어', '이론:상위_이론', '이론:하위_이론', '이론:상위_학문분야', '학문분야:하위_이론', '인물:소속이론또는학문분야', '용어:치료기법', '용어:약', '용어:증상또는질환', '용어:대체어', ] class PredConfig(datasets.BuilderConfig): def __init__(self, data_url, kwargs): super().__init__(version=datasets.Version(_VERSION), kwargs) self.data_url = data_url class Pred(datasets.GeneratorBasedBuilder): DEFAULT_CONFIG_NAME = "pred" BUILDER_CONFIGS = [ PredConfig( name="pred", data_url=_DATA_URLS, description=_DESCRIPTION, ) ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "sentence": datasets.Value("string"), "subject_entity": { "word": datasets.Value("string"), "start_idx": datasets.Value("int32"), "end_idx": datasets.Value("int32"), "type": datasets.Value("string"), }, "object_entity": { "word": datasets.Value("string"), "start_idx": datasets.Value("int32"), "end_idx": datasets.Value("int32"), "type": datasets.Value("string"), }, "label": datasets.ClassLabel(names=_LABEL), } ), homepage=_URL, license=_LICENSE, citation=_CITATION, supervised_keys=None, ) def _split_generators(self, dl_manager):	1	0
jiyoojeong/targetizer	false	[]	null	1	0
jlh/coco	false	[]	null	2	0
jmamou/augmented-glue-sst2	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en-US", "license:unknown" ]	null	5	0
joelito/ler	false	[]	We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.	1	0
joelito/sem_eval_2010_task_8	false	[]	The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research.	1	0
johnpaulbin/autonlp-data-asag-v2	false	[]	null	89	0
jonatli/youtube-sponsor	false	[]	null	1	0
jonfd/ICC	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100M<n<1B", "source_datasets:original", "language:is", "license:cc-by-4.0" ]	null	0	1
jozierski/ecomwebtexts-pl	false	[]	null	1	0
jpcorb20/multidogo	false	[ "task_categories:text-classification", "task_categories:other", "task_ids:intent-classification", "task_ids:dialogue-modeling", "task_ids:slot-filling", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10k<n<100k", "source_datasets:original", "language:en", "license:other" ]	null	0	0
jsfactory/mental_health_reddit_posts	false	[]	null	0	0
ju-bezdek/conll2003-SK-NER	false	[ "task_categories:other", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended\|conll2003", "language:sk", "license:unknown", "structure-prediction" ]	This is translated version of the original CONLL2003 dataset (translated from English to Slovak via Google translate) Annotation was done mostly automatically with word matching scripts. Records where some tags were not matched, were annotated manually (10%) Unlike the original Conll2003 dataset, this one contains only NER tags	2	0
julien-c/dummy-dataset-from-colab	false	[]	null	1	0
julien-c/persistent-space-dataset	false	[]	null	0	2
julien-c/reactiongif	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "arxiv:2105.09967" ]	null	1	1
juliensimon/autonlp-data-song-lyrics-demo	false	[ "task_categories:text-classification", "language:en" ]	null	0	0
juliensimon/autonlp-data-song-lyrics	false	[ "task_categories:text-classification", "language:en" ]	null	2	0
juniorrios/roi_leish_test	false	[]	null	0	0
juny116/few_glue	false	[ "arxiv:2012.15723" ]	SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard.	152	1
justinqbui/covid_fact_checked_google_api	false	[]	null	0	0
justinqbui/covid_fact_checked_polifact	false	[]	null	0	1
k-halid/ar	false	[]	The corpus is a part of the MultiUN corpus.It is a collection of translated documents from the United Nations.The corpus is download from the following website : [open parallel corpus](http://opus.datasetsl.eu/) \	0	0
k0t1k/test	false	[]	null	0	0
karinev/lanuitdudroit	false	[]	null	0	0
kartikay/review-summarizer	false	[]	null	0	1
katanaml/cord	false	[]	https://huggingface.co/datasets/katanaml/cord	188	1
katoensp/VR-OP	false	[]	null	0	0
kaushikacharya/github-issues	false	[]	null	0	0
kenlevine/CUAD	false	[]	null	0	0
keshan/clean-si-mc4	false	[]	A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI.	0	0
keshan/large-sinhala-asr-dataset	false	[]	This data set contains ~185K transcribed audio data for Sinhala. The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. See LICENSE.txt file for license information. Copyright 2016, 2017, 2018 Google, Inc.	0	0
keshan/multispeaker-tts-sinhala	false	[]	\\nThis data set contains multi-speaker high quality transcribed audio data for Sinhala. The data set consists of wave files, and a TSV file. The file si_lk.lines.txt contains a FileID, which in tern contains the UserID and the Transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Part of this dataset was collected by Google in Sri Lanka and the rest was contributed by Path to Nirvana organization.	0	0
keshan/wit-dataset	false	[]	\\nWikipedia-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.	0	1
kevinassobo/sales_2015_dataset	false	[]	null	0	0
kevinjesse/ManyTypes4TypeScript	false	[ "annotations_creators:found", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:code", "license:cc-by-4.0" ]	null	0	1
kevinlu1248/personificationgen	false	[]	null	0	0
khalidsaifullaah/detecThreats	false	[]	null	0	0
khanbaba/online_love	false	[]	null	0	0
kiamehr74/CoarseWSD-20	false	[]	The CoarseWSD-20 dataset is a coarse-grained sense disambiguation built from Wikipedia (nouns only) targetting 2 to 5 senses of 20 ambiguous words. It was specifically designed to provide an ideal setting for evaluating WSD models (e.g. no senses in test sets missing from training), both quantitavely and qualitatively.	0	1
kingabzpro/Rick-bot-flags	false	[]	null	0	1
kingabzpro/ar_corpora_parliament_processed	false	[]	null	0	0
kingabzpro/ga_corpora_parliament_processed	false	[]	null	0	0
kingabzpro/pan_corpora_parliament_processed	false	[]	null	0	0
kingabzpro/savtadepth-flags	false	[]	null	0	1
kingabzpro/tt_corpora_parliament_processed	false	[]	null	0	0
kiyoung2/aistage-mrc	false	[]	null	0	4
kiyoung2/temp	false	[]	null	0	0
kleinay/qa_srl	false	[]	The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence. This dataset loads the train split from "QASRL Bank", a.k.a "QASRL-v2" or "QASRL-LS" (Large Scale), which was constructed via crowdsourcing and presented at (FitzGeralds et. al., ACL 2018), and the dev and test splits from QASRL-GS (Gold Standard), introduced in (Roit et. al., ACL 2020).	0	0
kmfoda/booksum	false	[ "license:bsd-3-clause", "arxiv:2105.08209" ]	null	1,287	11
kmfoda/name_finder_v1	false	[]	null	0	0
kmyoo/klue-tc-dev	false	[]	null	0	0
knilakshan20/wikigold	false	[]	WikiGold dataset,Origianl dataset labels converted to IOB-format. Dataloading file based on https://github.com/huggingface/datasets/blob/master/datasets/conllpp/conllpp.py and https://huggingface.co/docs/datasets/add_dataset.html	3	0
krandiash/sc09	false	[]	null	0	0
kresnik/librispeech_asr_test	false	[]	\ 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. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```	20	2
kresnik/zeroth_korean	false	[]	This is Zeroth-Korean corpus, licensed under Attribution 4.0 International (CC BY 4.0) The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor). Zeroth project introduces free Korean speech corpus and aims to make Korean speech recognition more broadly accessible to everyone. This project was developed in collaboration between Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.). Contact: Lucas Jo([email protected]), Wonkyum Lee([email protected])	91	5
kroshan/BioASQ	false	[]	null	1	1
kroshan/qa_evaluator	false	[]	null	0	0
kudo-research/mustc-en-es-text-only	false	[ "annotations_creators:other", "language_creators:other", "multilinguality:translation", "size_categories:unknown", "language:en", "language:es", "license:cc-by-nc-nd-4.0" ]	null	0	0
kyryl0s/ukbbc	false	[ "license:wtfpl" ]	null	0	3
laion/filtered-wit	false	[ "arxiv:2103.00020" ]	null	0	2
laion/laion_100m_vqgan_f8	false	[]	null	0	2
lara-martin/Scifi_TV_Shows	false	[ "task_categories:other", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-4.0", "Story Generation" ]	null	12	2
larcane/ko-WIT	false	[]	null	0	0
laugustyniak/abusive-clauses-pl	false	[ "task_categories:text-classification", "annotations_creators:hired_annotators", "language_creators:found", "multilinguality:monolingual", "size_categories:10<n<10K", "language:pl", "license:cc-by-nc-sa-4.0" ]	null	58	2
lavis-nlp/german_legal_sentences	false	[ "task_categories:text-retrieval", "task_ids:semantic-similarity-scoring", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n>1M", "source_datasets:original", "language:de", "license:unknown", "arxiv:2005.13342", "arxiv:2010.10252" ]	German Legal Sentences (GLS) is an automatically generated training dataset for semantic sentence matching in the domain in german legal documents. It follows the concept of weak supervision, where imperfect labels are generated using multiple heuristics. For this purpose we use a combination of legal citation matching and BM25 similarity. The contained sentences and their citations are parsed from real judicial decisions provided by [Open Legal Data](http://openlegaldata.io/)	5	2
layboard/layboard.in	false	[]	null	0	1
lbox/lbox_open	false	[ "license:cc-by-nc-4.0" ]	null	182	1
lc-col/sv_corpora_parliament_processed	false	[]	null	0	0
leetdavid/celera	false	[]	null	0	0
leetdavid/market-positivity-bert-tokenized	false	[]	null	0	0
leiping/jj	false	[]	null	0	0

jakemarcus/MATH

false

[]

null

134

0

jamescalam/climate-fever-similarity

false

[]

null

128

0

jamol1741/test_dataset

false

[]

null

128

0

jcmc/ga-IE_opus_dgt_train

false

[]

null

258

0

jcmc/ga_mc4_processed

false

[]

null

258

0

jdepoix/junit_test_completion

false

[]

null

130

0

jegormeister/dutch-snli

false

[]

This is the Dutch version of the original SNLI dataset. The translation was performed using Google Translate. Original SNLI available at https://nlp.stanford.edu/projects/snli/

260

0

jel/covid

false

[]

null

128

0

jeree/fr_corpora_parliament_processed

false

[]

null

258

0

jfarray/TFM

false

[]

null

128

0

jfrenz/legalglue

false

[ "task_categories:text-classification", "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:multi-label-classification", "task_ids:topic-classification", "multilinguality:multilingual", "source_datasets:extended", "language:en", "language:da", "language:de", "language:nl", "language:sv", "language:bg", "language:cs", "language:hr", "language:pl", "language:sk", "language:sl", "language:es", "language:fr", "language:it", "language:pt", "language:ro", "language:et", "language:fi", "language:hu", "language:lt", "language:lv", "language:el", "language:mt", "german-ler", "lener-br", "arxiv:2003.13016", "arxiv:2110.00806", "arxiv:2109.00904" ]

\ Legal General Language Understanding Evaluation (LegalGLUE) benchmark is a collection of datasets for evaluating model performance across a diverse set of legal NLP tasks

3,993

5

jgammack/MTL-abstracts

false

[]

null

258

0

jgammack/SAE-door-abstracts

false

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:unknown" ]

null

261

0

jgammack/THESES-abstracts

false

[]

null

263

0

jglaser/binding_affinity

false

[ "molecules", "chemistry", "SMILES" ]

A dataset to fine-tune language models on protein-ligand binding affinity prediction.

480

1

jhonparra18/spanish_billion_words_clean

false

[]

null

257

2

jhqwqq/2

false

[]

null

130

0

jianhong/dateset1

false

[]

null

130

0

jianhong/dateset2

false

[]

null

130

0

jiminsun/atc0_demo

false

[]

null

130

0

jimregan/clarinpl_sejmsenat

false

[ "task_categories:other", "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:other" ]

A collection of 97 hours of parliamentary speeches published on the ClarinPL website Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

256

0

jimregan/clarinpl_studio

false

[ "task_categories:other", "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:other", "arxiv:1706.00245" ]

The corpus consists of 317 speakers recorded in 554 sessions, where each session consists of 20 read sentences and 10 phonetically rich words. The size of the audio portion of the corpus amounts to around 56 hours, with transcriptions containing 356674 words from a vocabulary of size 46361. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

256

1

jimregan/foinse

false

[]

Foinse was an Irish-language magazine site. This script uses a list of articles retrieved from the Wayback Machine to build a corpus

130

0

jimregan/lasid

false

[]

Linguistic Atlas and Survey of Irish Dialects, volume 1

129

0

jinmang2/KorQuADv1

false

[]

KorQuAD 1.0 (Korean Question Answering Dataset v1.0) KorQuAD 1.0 is a dataset created for Korean Machine Reading Comprehension. The answers to all your questions are made up of some subareas in the corresponding Wikipedia article paragraphs. It is structured in the same way as the Stanford Question Answering Dataset (SQuAD) v1.0.

92

0

jinmang2/common-sense-mrc

false

[]

null

0

jinmang2/load_klue_re

false

[]

KLUE (Korean Language Understanding Evaluation) Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models.

3

0

jinmang2/medical-mask

false

[]

null

0

jinmang2/pred

false

[]

""" _LICENSE = "CC-BY-SA-4.0" _URL = "https://github.com/boostcampaitech2/data-annotation-nlp-level3-nlp-14" _DATA_URLS = { "train": "https://huggingface.co/datasets/jinmang2/pred/resolve/main/train.csv", "dev": "https://huggingface.co/datasets/jinmang2/pred/resolve/main/dev.csv", } _VERSION = "0.0.0" _LABEL = [ '관계_없음', '이론:대체어', '이론:상위_이론', '이론:하위_이론', '이론:상위_학문분야', '학문분야:하위_이론', '인물:소속이론또는학문분야', '용어:치료기법', '용어:약', '용어:증상또는질환', '용어:대체어', ] class PredConfig(datasets.BuilderConfig): def __init__(self, data_url, **kwargs): super().__init__(version=datasets.Version(_VERSION), **kwargs) self.data_url = data_url class Pred(datasets.GeneratorBasedBuilder): DEFAULT_CONFIG_NAME = "pred" BUILDER_CONFIGS = [ PredConfig( name="pred", data_url=_DATA_URLS, description=_DESCRIPTION, ) ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "sentence": datasets.Value("string"), "subject_entity": { "word": datasets.Value("string"), "start_idx": datasets.Value("int32"), "end_idx": datasets.Value("int32"), "type": datasets.Value("string"), }, "object_entity": { "word": datasets.Value("string"), "start_idx": datasets.Value("int32"), "end_idx": datasets.Value("int32"), "type": datasets.Value("string"), }, "label": datasets.ClassLabel(names=_LABEL), } ), homepage=_URL, license=_LICENSE, citation=_CITATION, supervised_keys=None, ) def _split_generators(self, dl_manager):

1

0

jiyoojeong/targetizer

false

[]

null

1

0

jlh/coco

false

[]

null

2

0

jmamou/augmented-glue-sst2

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en-US", "license:unknown" ]

null

5

0

joelito/ler

false

[]

We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.

1

0

joelito/sem_eval_2010_task_8

false

[]

The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research.

1

0

johnpaulbin/autonlp-data-asag-v2

false

[]

null

89

0

jonatli/youtube-sponsor

false

[]

null

1

0

jonfd/ICC

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100M<n<1B", "source_datasets:original", "language:is", "license:cc-by-4.0" ]

null

0

1

jozierski/ecomwebtexts-pl

false

[]

null

1

0

jpcorb20/multidogo

false

[ "task_categories:text-classification", "task_categories:other", "task_ids:intent-classification", "task_ids:dialogue-modeling", "task_ids:slot-filling", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10k<n<100k", "source_datasets:original", "language:en", "license:other" ]

null

0

jsfactory/mental_health_reddit_posts

false

[]

null

0

ju-bezdek/conll2003-SK-NER

false

[ "task_categories:other", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|conll2003", "language:sk", "license:unknown", "structure-prediction" ]

This is translated version of the original CONLL2003 dataset (translated from English to Slovak via Google translate) Annotation was done mostly automatically with word matching scripts. Records where some tags were not matched, were annotated manually (10%) Unlike the original Conll2003 dataset, this one contains only NER tags

2

0

julien-c/dummy-dataset-from-colab

false

[]

null

1

0

julien-c/persistent-space-dataset

false

[]

null

0

2

julien-c/reactiongif

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "arxiv:2105.09967" ]

null

1

juliensimon/autonlp-data-song-lyrics-demo

false

[ "task_categories:text-classification", "language:en" ]

null

0

juliensimon/autonlp-data-song-lyrics

false

[ "task_categories:text-classification", "language:en" ]

null

2

0

juniorrios/roi_leish_test

false

[]

null

0

juny116/few_glue

false

[ "arxiv:2012.15723" ]

SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard.

152

1

justinqbui/covid_fact_checked_google_api

false

[]

null

0

justinqbui/covid_fact_checked_polifact

false

[]

null

0

1

k-halid/ar

false

[]

The corpus is a part of the MultiUN corpus.It is a collection of translated documents from the United Nations.The corpus is download from the following website : [open parallel corpus](http://opus.datasetsl.eu/) \

0

k0t1k/test

false

[]

null

0

karinev/lanuitdudroit

false

[]

null

0

kartikay/review-summarizer

false

[]

null

0

1

katanaml/cord

false

[]

https://huggingface.co/datasets/katanaml/cord

188

1

katoensp/VR-OP

false

[]

null

0

kaushikacharya/github-issues

false

[]

null

0

kenlevine/CUAD

false

[]

null

0

keshan/clean-si-mc4

false

[]

A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI.

0

keshan/large-sinhala-asr-dataset

false

[]

This data set contains ~185K transcribed audio data for Sinhala. The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. See LICENSE.txt file for license information. Copyright 2016, 2017, 2018 Google, Inc.

0

keshan/multispeaker-tts-sinhala

false

[]

\\nThis data set contains multi-speaker high quality transcribed audio data for Sinhala. The data set consists of wave files, and a TSV file. The file si_lk.lines.txt contains a FileID, which in tern contains the UserID and the Transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Part of this dataset was collected by Google in Sri Lanka and the rest was contributed by Path to Nirvana organization.

0

keshan/wit-dataset

false

[]

\\nWikipedia-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.

0

1

kevinassobo/sales_2015_dataset

false

[]

null

0

kevinjesse/ManyTypes4TypeScript

false

[ "annotations_creators:found", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:code", "license:cc-by-4.0" ]

null

0

1

kevinlu1248/personificationgen

false

[]

null

0

khalidsaifullaah/detecThreats

false

[]

null

0

khanbaba/online_love

false

[]

null

0

kiamehr74/CoarseWSD-20

false

[]

The CoarseWSD-20 dataset is a coarse-grained sense disambiguation built from Wikipedia (nouns only) targetting 2 to 5 senses of 20 ambiguous words. It was specifically designed to provide an ideal setting for evaluating WSD models (e.g. no senses in test sets missing from training), both quantitavely and qualitatively.

0

1

kingabzpro/Rick-bot-flags

false

[]

null

0

1

kingabzpro/ar_corpora_parliament_processed

false

[]

null

0

kingabzpro/ga_corpora_parliament_processed

false

[]

null

0

kingabzpro/pan_corpora_parliament_processed

false

[]

null

0

kingabzpro/savtadepth-flags

false

[]

null

0

1

kingabzpro/tt_corpora_parliament_processed

false

[]

null

0

kiyoung2/aistage-mrc

false

[]

null

0

4

kiyoung2/temp

false

[]

null

0

kleinay/qa_srl

false

[]

The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence. This dataset loads the train split from "QASRL Bank", a.k.a "QASRL-v2" or "QASRL-LS" (Large Scale), which was constructed via crowdsourcing and presented at (FitzGeralds et. al., ACL 2018), and the dev and test splits from QASRL-GS (Gold Standard), introduced in (Roit et. al., ACL 2020).

0

kmfoda/booksum

false

[ "license:bsd-3-clause", "arxiv:2105.08209" ]

null

1,287

11

kmfoda/name_finder_v1

false

[]

null

0

kmyoo/klue-tc-dev

false

[]

null

0

knilakshan20/wikigold

false

[]

WikiGold dataset,Origianl dataset labels converted to IOB-format. Dataloading file based on https://github.com/huggingface/datasets/blob/master/datasets/conllpp/conllpp.py and https://huggingface.co/docs/datasets/add_dataset.html

3

0

krandiash/sc09

false

[]

null

0

kresnik/librispeech_asr_test

false

[]

\ 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. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

20

2

kresnik/zeroth_korean

false

[]

This is Zeroth-Korean corpus, licensed under Attribution 4.0 International (CC BY 4.0) The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor). Zeroth project introduces free Korean speech corpus and aims to make Korean speech recognition more broadly accessible to everyone. This project was developed in collaboration between Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.). Contact: Lucas Jo([email protected]), Wonkyum Lee([email protected])

91

5

kroshan/BioASQ

false

[]

null

1

kroshan/qa_evaluator

false

[]

null

0

kudo-research/mustc-en-es-text-only

false

[ "annotations_creators:other", "language_creators:other", "multilinguality:translation", "size_categories:unknown", "language:en", "language:es", "license:cc-by-nc-nd-4.0" ]

null

0

kyryl0s/ukbbc

false

[ "license:wtfpl" ]

null

0

3

laion/filtered-wit

false

[ "arxiv:2103.00020" ]

null

0

2

laion/laion_100m_vqgan_f8

false

[]

null

0

2

lara-martin/Scifi_TV_Shows

false

[ "task_categories:other", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-4.0", "Story Generation" ]

null

12

2

larcane/ko-WIT

false

[]

null

0

laugustyniak/abusive-clauses-pl

false

[ "task_categories:text-classification", "annotations_creators:hired_annotators", "language_creators:found", "multilinguality:monolingual", "size_categories:10<n<10K", "language:pl", "license:cc-by-nc-sa-4.0" ]

null

58

2

lavis-nlp/german_legal_sentences

false

[ "task_categories:text-retrieval", "task_ids:semantic-similarity-scoring", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n>1M", "source_datasets:original", "language:de", "license:unknown", "arxiv:2005.13342", "arxiv:2010.10252" ]

German Legal Sentences (GLS) is an automatically generated training dataset for semantic sentence matching in the domain in german legal documents. It follows the concept of weak supervision, where imperfect labels are generated using multiple heuristics. For this purpose we use a combination of legal citation matching and BM25 similarity. The contained sentences and their citations are parsed from real judicial decisions provided by [Open Legal Data](http://openlegaldata.io/)

5

2

layboard/layboard.in

false

[]

null

0

1

lbox/lbox_open

false

[ "license:cc-by-nc-4.0" ]

null

182

1

lc-col/sv_corpora_parliament_processed

false

[]

null

0

leetdavid/celera

false

[]

null

0

leetdavid/market-positivity-bert-tokenized

false

[]

null

0

leiping/jj

false

[]

null

0