nkasmanoff/huggingface-datasets · Datasets at Hugging Face

id stringlengths 2 115	private bool 1 class	tags sequence	description stringlengths 0 5.93k ⌀	downloads int64 0 1.14M	likes int64 0 1.79k
csikasote/bembaspeech_plus_jw_processed	false	[]	null	264	0
cstrathe435/Task2Dial	false	[]	null	270	0
ctgowrie/chessgames	false	[]	null	135	0
ctu-aic/csfever	false	[ "license:cc-by-sa-3.0", "arxiv:1803.05355", "arxiv:2201.11115" ]	CsFEVER is a Czech localisation of the English FEVER datgaset.	262	1
ctu-aic/csfever_nli	false	[]	CsfeverNLI is a NLI version of the Czech Csfever dataset	265	1
ctu-aic/ctkfacts_nli	false	[ "arxiv:2201.11115" ]	CtkFactsNLI is a NLI version of the Czech CTKFacts dataset	269	1
cyko/books	false	[]	null	134	0
cylee/github-issues	false	[ "arxiv:2005.00614" ]	null	264	0
dalle-mini/YFCC100M_OpenAI_subset	false	[ "arxiv:1503.01817" ]	The YFCC100M is one of the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr, all of which were shared under one of the various Creative Commons licenses. This version is a subset defined in openai/CLIP.	277	5
dalle-mini/open-images	false	[]	null	278	2
dalle-mini/wit	false	[]	null	263	4
damlab/HIV_FLT	false	[]	null	270	0
damlab/HIV_PI	false	[ "license:mit" ]	null	265	0
damlab/HIV_V3_bodysite	false	[]	null	264	0
damlab/HIV_V3_coreceptor	false	[]	null	264	0
dansbecker/hackernews_hiring_posts	false	[]	null	266	0
darentang/generated	false	[]	https://arxiv.org/abs/2103.10213	336	0
darentang/sroie	false	[]	https://arxiv.org/abs/2103.10213	230	0
darkraipro/recipe-instructions	false	[]	null	266	0
dasago78/dasago78dataset	false	[]	null	133	0
dataset/wikipedia_bn	false	[]	Bengali Wikipedia from the dump of 03/20/2021. The data was processed using the huggingface datasets wikipedia script early april 2021. 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.).	268	0
davanstrien/19th-century-ads	false	[]	null	264	0
davanstrien/ads-test	false	[]	null	133	0
davanstrien/beyond_test	false	[]	null	264	0
davanstrien/crowdsourced-keywords	false	[]	null	264	0
davanstrien/embellishments-sample	false	[]	null	265	0
davanstrien/embellishments	false	[]	null	263	0
davanstrien/hipe2020	false	[]	null	133	0
davanstrien/iiif_labeled	false	[]	null	133	0
davanstrien/iiif_manuscripts_label_ge_50	false	[]	null	264	0
davanstrien/kitten	false	[]	null	264	0
davanstrien/manuscript_iiif_test	false	[]	null	264	0
BritishLibraryLabs/BookGenreSnorkelAnnotated	false	[]	null	298	0
davanstrien/test_iiif	false	[]	null	264	0
davanstrien/test_push_to_hub_image	false	[]	null	264	0
davanstrien/testpush	false	[]	null	264	0
david-wb/zeshel	false	[]	null	133	0
davidwisdom/reddit-randomness	false	[]	null	264	0
dcfidalgo/test	false	[]	null	264	0
debajyotidatta/biosses	false	[ "license:gpl-3.0" ]	null	131	0
debatelab/aaac	false	[ "task_categories:summarization", "task_categories:text-retrieval", "task_categories:text-generation", "task_ids:parsing", "task_ids:text-simplification", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "argument-mining", "conditional-text-generation", "structure-prediction", "arxiv:2110.01509" ]	null	264	1
debatelab/deepa2	false	[ "task_categories:text-retrieval", "task_categories:text-generation", "task_ids:text-simplification", "task_ids:parsing", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "language:en", "license:other", "argument-mining", "summarization", "conditional-text-generation", "structure-prediction", "arxiv:2110.01509" ]	null	263	3
deepset/germandpr	false	[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "multilinguality:monolingual", "source_datasets:original", "language:de", "license:cc-by-4.0", "arxiv:2104.12741" ]	We take GermanQuAD as a starting point and add hard negatives from a dump of the full German Wikipedia following the approach of the DPR authors (Karpukhin et al., 2020). The format of the dataset also resembles the one of DPR. GermanDPR comprises 9275 question/answer pairs in the training set and 1025 pairs in the test set. For each pair, there are one positive context and three hard negative contexts.	353	5
deepset/germanquad	false	[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "task_ids:open-domain-qa", "multilinguality:monolingual", "source_datasets:original", "language:de", "license:cc-by-4.0", "arxiv:2104.12741" ]	In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set. The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words.	594	13
dennlinger/klexikon	false	[ "task_categories:summarization", "task_categories:text2text-generation", "task_ids:text-simplification", "annotations_creators:found", "annotations_creators:expert-generated", "language_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "license:cc-by-sa-4.0", "conditional-text-generation", "simplification", "document-level", "arxiv:2201.07198" ]	null	277	5
dev/untitled_imgs	false	[]	null	132	0
dfgvhxfgv/fghghj	false	[]	null	132	0
DFKI-SLT/few-nerd	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended\|wikipedia", "language:en", "license:cc-by-sa-4.0", "structure-prediction" ]	Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).	1,543	5
DFKI-SLT/mobie	false	[ "task_categories:other", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "license:cc-by-4.0", "structure-prediction" ]	MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks.	313	0
dgknrsln/Yorumsepeti	false	[]	null	132	0
diiogo/annotations	false	[]	null	131	0
dispenst/jhghdghfd	false	[]	null	131	0
dispix/test-dataset	false	[]	null	131	0
diwank/hinglish-dump	false	[ "license:mit" ]	Raw merged dump of Hinglish (hi-EN) datasets.	916	1
diwank/silicone-merged	false	[ "license:mit" ]	Merged and simplified dialog act datasets from the silicone collection.	401	1
dk-crazydiv/huggingface-modelhub	false	[]	Metadata information of all the models available on HuggingFace's modelhub	264	2
dlb/plue	false	[ "task_categories:text-classification", "task_ids:acceptability-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:extended\|glue", "language:pt", "license:lgpl-3.0", "paraphrase-identification", "qa-nli", "coreference-nli" ]	PLUE: Portuguese Language Understanding Evaluationis a Portuguese translation of the GLUE benchmark and Scitail using OPUS-MT model and Google Cloud Translation.	2,151	4
dongpil/test	false	[]	null	133	0
dragosnicolae555/RoITD	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:ro-RO", "license:cc-by-4.0" ]	null	262	0
dram-conflict/horror-scripts	false	[]	This dataset is designed to generate scripts.	264	0
dvilasuero/ag_news_error_analysis	false	[]	null	264	0
dvilasuero/ag_news_training_set_losses	false	[]	null	264	0
dvilasuero/test-dataset	false	[]	null	262	0
dweb/squad_with_cola_scores	false	[]	null	262	0
dynabench/dynasent	false	[ "arxiv:2012.15349", "arxiv:1803.09010", "arxiv:1810.03993" ]	Dynabench.DynaSent is a Sentiment Analysis dataset collected using a human-and-model-in-the-loop.	4,770	2
dynabench/qa	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:2002.00293", "arxiv:1606.05250" ]	Dynabench.QA is a Reading Comprehension dataset collected using a human-and-model-in-the-loop.	655	1
eason929/test	false	[]	null	132	0
ebrigham/asr_files	false	[]	null	132	0
ebrigham/labels	false	[]	AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The AG's news topic classification dataset is constructed by Xiang Zhang ([email protected]) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).	262	0
ebrigham/multi_sentiment	false	[]	null	263	0
echarlaix/gqa-lxmert	false	[ "license:apache-2.0" ]	GQA is a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous visual question answering (VQA) datasets.	264	0
echarlaix/gqa	false	[ "license:apache-2.0" ]	GQA is a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous visual question answering (VQA) datasets.	256	0
echarlaix/vqa-lxmert	false	[ "license:apache-2.0" ]	VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.	255	0
echarlaix/vqa	false	[ "license:apache-2.0" ]	VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.	272	0
edbeeching/decision_transformer_gym_replay	false	[ "license:apache-2.0", "arxiv:2004.07219" ]	A subset of the D4RL dataset, used for training Decision Transformers	1,649	1
edbeeching/github-issues	false	[]	null	256	0
edfews/szdfcszdf	false	[]	null	128	0
edge2992/github-issues	false	[]	null	254	0
edge2992/rri-short	false	[]	null	254	0
edge2992/rri_short	false	[]	null	129	0
edsas/fgrdtgrdtdr	false	[]	null	129	0
edsas/grttyi	false	[]	null	129	0
ehcalabres/ravdess_speech	false	[ "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]	null	128	2
ejjaffe/onion_headlines_2_sources	false	[]	null	257	0
eliza-dukim/load_klue_re	false	[]	null	128	0
elonmuskceo/persistent-space-dataset	false	[]	null	253	0
elonmuskceo/wordle	false	[]	null	256	1
elricwan/bert_data	false	[]	null	257	0
emre/Open_SLR108_Turkish_10_hours	false	[ "license:cc-by-4.0", "robust-speech-event", "arxiv:2103.16193" ]	null	255	2
emrecan/stsb-mt-turkish	false	[ "task_categories:text-classification", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "language_creators:machine-generated", "size_categories:1K<n<10K", "source_datasets:extended\|other-sts-b", "language:tr" ]	null	1,527	3
enelpol/czywiesz	false	[ "task_categories:question-answering", "task_ids:open-domain-qa", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:unknown" ]	null	258	0
ervis/aaa	false	[]	null	128	0
ervis/qqq	false	[]	null	128	0
erwanlc/cocktails_recipe	false	[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:2M<n<3M", "language:en", "license:other" ]	null	257	1
erwanlc/cocktails_recipe_no_brand	false	[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:2M<n<3M", "language:en", "license:other" ]	null	256	1
espejelomar/code_search_net_python_10000_examples	false	[ "license:cc" ]	null	282	4
eugenesiow/BSD100	false	[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "image-super-resolution" ]	BSD is a dataset used frequently for image denoising and super-resolution. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.	367	0
eugenesiow/Div2k	false	[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "other-image-super-resolution" ]	DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and CVPR 2018) and @ PIRM (ECCV 2018)	2,197	2
eugenesiow/PIRM	false	[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:cc-by-nc-sa-4.0", "other-image-super-resolution", "arxiv:1809.07517" ]	The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution. This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.	253	0
eugenesiow/Set14	false	[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "other-image-super-resolution" ]	Set14 is an evaluation dataset with 14 RGB images for the image super resolution task.	401	0

csikasote/bembaspeech_plus_jw_processed

false

[]

null

264

0

cstrathe435/Task2Dial

false

[]

null

270

0

ctgowrie/chessgames

false

[]

null

135

0

ctu-aic/csfever

false

[ "license:cc-by-sa-3.0", "arxiv:1803.05355", "arxiv:2201.11115" ]

CsFEVER is a Czech localisation of the English FEVER datgaset.

262

1

ctu-aic/csfever_nli

false

[]

CsfeverNLI is a NLI version of the Czech Csfever dataset

265

1

ctu-aic/ctkfacts_nli

false

[ "arxiv:2201.11115" ]

CtkFactsNLI is a NLI version of the Czech CTKFacts dataset

269

1

cyko/books

false

[]

null

134

0

cylee/github-issues

false

[ "arxiv:2005.00614" ]

null

264

0

dalle-mini/YFCC100M_OpenAI_subset

false

[ "arxiv:1503.01817" ]

The YFCC100M is one of the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr, all of which were shared under one of the various Creative Commons licenses. This version is a subset defined in openai/CLIP.

277

5

dalle-mini/open-images

false

[]

null

278

2

dalle-mini/wit

false

[]

null

263

4

damlab/HIV_FLT

false

[]

null

270

0

damlab/HIV_PI

false

[ "license:mit" ]

null

265

0

damlab/HIV_V3_bodysite

false

[]

null

264

0

damlab/HIV_V3_coreceptor

false

[]

null

264

0

dansbecker/hackernews_hiring_posts

false

[]

null

266

0

darentang/generated

false

[]

https://arxiv.org/abs/2103.10213

336

0

darentang/sroie

false

[]

https://arxiv.org/abs/2103.10213

230

0

darkraipro/recipe-instructions

false

[]

null

266

0

dasago78/dasago78dataset

false

[]

null

133

0

dataset/wikipedia_bn

false

[]

Bengali Wikipedia from the dump of 03/20/2021. The data was processed using the huggingface datasets wikipedia script early april 2021. 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.).

268

0

davanstrien/19th-century-ads

false

[]

null

264

0

davanstrien/ads-test

false

[]

null

133

0

davanstrien/beyond_test

false

[]

null

264

0

davanstrien/crowdsourced-keywords

false

[]

null

264

0

davanstrien/embellishments-sample

false

[]

null

265

0

davanstrien/embellishments

false

[]

null

263

0

davanstrien/hipe2020

false

[]

null

133

0

davanstrien/iiif_labeled

false

[]

null

133

0

davanstrien/iiif_manuscripts_label_ge_50

false

[]

null

264

0

davanstrien/kitten

false

[]

null

264

0

davanstrien/manuscript_iiif_test

false

[]

null

264

0

BritishLibraryLabs/BookGenreSnorkelAnnotated

false

[]

null

298

0

davanstrien/test_iiif

false

[]

null

264

0

davanstrien/test_push_to_hub_image

false

[]

null

264

0

davanstrien/testpush

false

[]

null

264

0

david-wb/zeshel

false

[]

null

133

0

davidwisdom/reddit-randomness

false

[]

null

264

0

dcfidalgo/test

false

[]

null

264

0

debajyotidatta/biosses

false

[ "license:gpl-3.0" ]

null

131

0

debatelab/aaac

false

[ "task_categories:summarization", "task_categories:text-retrieval", "task_categories:text-generation", "task_ids:parsing", "task_ids:text-simplification", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "argument-mining", "conditional-text-generation", "structure-prediction", "arxiv:2110.01509" ]

null

264

1

debatelab/deepa2

false

[ "task_categories:text-retrieval", "task_categories:text-generation", "task_ids:text-simplification", "task_ids:parsing", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "language:en", "license:other", "argument-mining", "summarization", "conditional-text-generation", "structure-prediction", "arxiv:2110.01509" ]

null

263

3

deepset/germandpr

false

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "multilinguality:monolingual", "source_datasets:original", "language:de", "license:cc-by-4.0", "arxiv:2104.12741" ]

We take GermanQuAD as a starting point and add hard negatives from a dump of the full German Wikipedia following the approach of the DPR authors (Karpukhin et al., 2020). The format of the dataset also resembles the one of DPR. GermanDPR comprises 9275 question/answer pairs in the training set and 1025 pairs in the test set. For each pair, there are one positive context and three hard negative contexts.

353

5

deepset/germanquad

false

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "task_ids:open-domain-qa", "multilinguality:monolingual", "source_datasets:original", "language:de", "license:cc-by-4.0", "arxiv:2104.12741" ]

In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set. The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words.

594

13

dennlinger/klexikon

false

[ "task_categories:summarization", "task_categories:text2text-generation", "task_ids:text-simplification", "annotations_creators:found", "annotations_creators:expert-generated", "language_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "license:cc-by-sa-4.0", "conditional-text-generation", "simplification", "document-level", "arxiv:2201.07198" ]

null

277

5

dev/untitled_imgs

false

[]

null

132

0

dfgvhxfgv/fghghj

false

[]

null

132

0

DFKI-SLT/few-nerd

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-sa-4.0", "structure-prediction" ]

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).

1,543

5

DFKI-SLT/mobie

false

[ "task_categories:other", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "license:cc-by-4.0", "structure-prediction" ]

MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks.

313

0

dgknrsln/Yorumsepeti

false

[]

null

132

0

diiogo/annotations

false

[]

null

131

0

dispenst/jhghdghfd

false

[]

null

131

0

dispix/test-dataset

false

[]

null

131

0

diwank/hinglish-dump

false

[ "license:mit" ]

Raw merged dump of Hinglish (hi-EN) datasets.

916

1

diwank/silicone-merged

false

[ "license:mit" ]

Merged and simplified dialog act datasets from the silicone collection.

401

1

dk-crazydiv/huggingface-modelhub

false

[]

Metadata information of all the models available on HuggingFace's modelhub

264

2

dlb/plue

false

[ "task_categories:text-classification", "task_ids:acceptability-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:extended|glue", "language:pt", "license:lgpl-3.0", "paraphrase-identification", "qa-nli", "coreference-nli" ]

PLUE: Portuguese Language Understanding Evaluationis a Portuguese translation of the GLUE benchmark and Scitail using OPUS-MT model and Google Cloud Translation.

2,151

4

dongpil/test

false

[]

null

133

0

dragosnicolae555/RoITD

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:ro-RO", "license:cc-by-4.0" ]

null

262

0

dram-conflict/horror-scripts

false

[]

This dataset is designed to generate scripts.

264

0

dvilasuero/ag_news_error_analysis

false

[]

null

264

0

dvilasuero/ag_news_training_set_losses

false

[]

null

264

0

dvilasuero/test-dataset

false

[]

null

262

0

dweb/squad_with_cola_scores

false

[]

null

262

0

dynabench/dynasent

false

[ "arxiv:2012.15349", "arxiv:1803.09010", "arxiv:1810.03993" ]

Dynabench.DynaSent is a Sentiment Analysis dataset collected using a human-and-model-in-the-loop.

4,770

2

dynabench/qa

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:2002.00293", "arxiv:1606.05250" ]

Dynabench.QA is a Reading Comprehension dataset collected using a human-and-model-in-the-loop.

655

1

eason929/test

false

[]

null

132

0

ebrigham/asr_files

false

[]

null

132

0

ebrigham/labels

false

[]

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The AG's news topic classification dataset is constructed by Xiang Zhang ([email protected]) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

262

0

ebrigham/multi_sentiment

false

[]

null

263

0

echarlaix/gqa-lxmert

false

[ "license:apache-2.0" ]

GQA is a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous visual question answering (VQA) datasets.

264

0

echarlaix/gqa

false

[ "license:apache-2.0" ]

GQA is a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous visual question answering (VQA) datasets.

256

0

echarlaix/vqa-lxmert

false

[ "license:apache-2.0" ]

VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.

255

0

echarlaix/vqa

false

[ "license:apache-2.0" ]

VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.

272

0

edbeeching/decision_transformer_gym_replay

false

[ "license:apache-2.0", "arxiv:2004.07219" ]

A subset of the D4RL dataset, used for training Decision Transformers

1,649

1

edbeeching/github-issues

false

[]

null

256

0

edfews/szdfcszdf

false

[]

null

128

0

edge2992/github-issues

false

[]

null

254

0

edge2992/rri-short

false

[]

null

254

0

edge2992/rri_short

false

[]

null

129

0

edsas/fgrdtgrdtdr

false

[]

null

129

0

edsas/grttyi

false

[]

null

129

0

ehcalabres/ravdess_speech

false

[ "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]

null

128

2

ejjaffe/onion_headlines_2_sources

false

[]

null

257

0

eliza-dukim/load_klue_re

false

[]

null

128

0

elonmuskceo/persistent-space-dataset

false

[]

null

253

0

elonmuskceo/wordle

false

[]

null

256

1

elricwan/bert_data

false

[]

null

257

0

emre/Open_SLR108_Turkish_10_hours

false

[ "license:cc-by-4.0", "robust-speech-event", "arxiv:2103.16193" ]

null

255

2

emrecan/stsb-mt-turkish

false

[ "task_categories:text-classification", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "language_creators:machine-generated", "size_categories:1K<n<10K", "source_datasets:extended|other-sts-b", "language:tr" ]

null

1,527

3

enelpol/czywiesz

false

[ "task_categories:question-answering", "task_ids:open-domain-qa", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:unknown" ]

null

258

0

ervis/aaa

false

[]

null

128

0

ervis/qqq

false

[]

null

128

0

erwanlc/cocktails_recipe

false

[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:2M<n<3M", "language:en", "license:other" ]

null

257

1

erwanlc/cocktails_recipe_no_brand

false

[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:2M<n<3M", "language:en", "license:other" ]

null

256

1

espejelomar/code_search_net_python_10000_examples

false

[ "license:cc" ]

null

282

4

eugenesiow/BSD100

false

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "image-super-resolution" ]

BSD is a dataset used frequently for image denoising and super-resolution. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.

367

0

eugenesiow/Div2k

false

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "other-image-super-resolution" ]

DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and CVPR 2018) and @ PIRM (ECCV 2018)

2,197

2

eugenesiow/PIRM

false

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:cc-by-nc-sa-4.0", "other-image-super-resolution", "arxiv:1809.07517" ]

The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution. This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.

253

0

eugenesiow/Set14

false

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "other-image-super-resolution" ]

Set14 is an evaluation dataset with 14 RGB images for the image super resolution task.

401

0