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
eugenesiow/Set5	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" ]	Set5 is a evaluation dataset with 5 RGB images for the image super resolution task.	443	0
eugenesiow/Urban100	false	[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:cc-by-4.0", "other-image-super-resolution" ]	The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models.	364	0
evageon/IADD	false	[ "license:cc-by-4.0" ]	null	256	0
facebook/multilingual_librispeech	false	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:de", "language:nl", "language:fr", "language:it", "language:es", "language:pt", "language:pl", "license:cc-by-4.0", "arxiv:2012.03411" ]	This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages: English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.	1,161	16
fastjt/fasst	false	[ "license:afl-3.0" ]	null	127	0
fatvvs/autonlp-data-entity_model_conll2003	false	[]	null	256	0
fededeleon/CriteriosClasificacion	false	[ "license:mit" ]	null	257	0
fengzhang/fzTestDatasets	false	[]	null	129	0
fhamborg/news_sentiment_newsmtsc	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit" ]	NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles.	553	5
fighterhitx/test	false	[ "license:cc" ]	null	127	0
fihtrotuld/asu	false	[]	null	127	0
flax-community/code_clippy_data	false	[]	null	128	0
flax-community/conceptual-12m-mbart-50-multilingual	false	[]	null	253	0
flax-community/conceptual-12m-multilingual-marian-128	false	[]	null	255	0
flax-community/conceptual-12m-multilingual-marian-es	false	[]	null	287	0
flax-community/conceptual-12m-multilingual-marian	false	[]	null	253	0
flax-community/conceptual-captions-12	false	[]	null	255	1
flax-community/dummy-oscar-als-32	false	[]	null	255	0
flax-community/german-common-voice-processed	false	[]	null	256	1
flax-community/german_common_crawl	false	[]	German Only Extract from Common Crawl This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German	635	0
flax-community/multilingual-vqa	false	[]	null	256	0
flax-community/norwegian-clean-dummy	false	[]	null	129	0
flax-community/swahili-safi	false	[]	Cleaned dataset for Swahili Language Modeling	267	3
flax-sentence-embeddings/Gender_Bias_Evaluation_Set	false	[ "arxiv:1906.00591" ]	null	256	2
flax-sentence-embeddings/paws-jsonl	false	[]	null	253	0
flax-sentence-embeddings/stackexchange_math_jsonl	false	[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.	549	0
flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl	false	[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.	22,652	1
flax-sentence-embeddings/stackexchange_title_body_jsonl	false	[]	null	259	0
flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl	false	[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.	23,983	8
flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl	false	[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.	22,221	3
flax-sentence-embeddings/stackexchange_xml	false	[]	null	129	1
flexthink/librig2p-nostress-space	false	[]	Grapheme-to-Phoneme training, validation and test sets	261	0
flexthink/librig2p-nostress	false	[]	Grapheme-to-Phoneme training, validation and test sets	261	0
flexthink/ljspeech	false	[]	This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.	253	1
florentgbelidji/test-3	false	[]	null	129	0
florentgbelidji/test-dataset	false	[]	null	129	0
florianbussmann/FUNSD-vu2020revising	false	[ "multilinguality:monolingual", "language:en", "arxiv:2010.05322" ]	\ FUNSD is one of the limited publicly available datasets for information extraction from document images. The information in the FUNSD dataset is defined by text areas of four categories ("key", "value", "header", "other", and "background") and connectivity between areas as key-value relations. Inspecting FUNSD, we found several inconsistency in labeling, which impeded its applicability to the key-value extraction problem. In this report, we described some labeling issues in FUNSD and the revision we made to the dataset.	129	0
florianbussmann/train_tickets-yu2020pick	false	[]	\ The train ticket is fixed layout dataset, however, it contains background noise and imaging distortions. It contains 1,530 synthetic images and 320 real images for training, and 80 real images for testing. Every train ticket has eight key text fields including ticket number, starting station, train number, destination station, date, ticket rates, seat category, and name. This dataset mainly consists of digits, English characters, and Chinese characters.	128	0
flxclxc/encoded_drug_reviews	false	[]	null	256	2
formermagic/github_python_1m	false	[ "task_ids:language-modeling", "task_ids:slot-filling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:py", "license:mit" ]	null	130	1
formu/CVT	false	[]	null	128	0
fractalego/QA_to_statements	false	[ "arxiv:1809.02922", "doi:10.57967/hf/0011" ]	null	256	0
frahman/github-issues	false	[]	null	256	0
frtna/deneme	false	[]	null	256	0
frtna/es_it_Results-base-OPUS_Tatoeba	false	[]	null	256	0
frtna/jwt300_mt	false	[]	This new dataset is designed to be used in the scope of machine translation project.	256	0
frtna/opensubtitles_mt	false	[]	This new dataset is designed to be used in the scope of PhD project.	255	0
frtna/sabahaKKarsi	false	[]	null	256	0
frtna/ted_mt	false	[]	This new dataset is designed to be used in the scope of multilingual model project.	256	0
frtna/test	false	[]	null	127	0
frtna/test2	false	[]	null	129	0
fulai/DuReader	false	[]	null	130	0
fuliucansheng/coco	false	[]	null	129	0
fuliucansheng/minicoco	false	[]	MINICOCO2017	353	0
fuliucansheng/mininlp	false	[]	MiniNLP Data	257	0
fuliucansheng/pascal_voc	false	[]	PASCAL_VOC	164	0
fuyun1107/clip-for-vlp	false	[]	null	255	0
fvillena/cantemist	false	[]	\	256	0
fvillena/spanish_diagnostics	false	[]	null	254	0
gabella/demo_data_raw	false	[]	null	252	0
gabtan99/pex-conversations	false	[ "task_ids:dialogue-modeling", "task_ids:language-modeling", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:tl", "language:fil", "license:unknown", "multi-turn" ]	null	255	1
gagan3012/fake-news	false	[]	null	129	0
gagan3012/grover-data	false	[]	null	256	0
gagan3012/vizwiz	false	[ "license:apache-2.0" ]	null	131	0
gar1t/test	false	[]	null	256	0
gayanin/pubmed-gastro-maskfilling	false	[]	null	256	0
gayanin/pubmed-gastro-paraphrasing	false	[]	null	256	2
gayanin/pubmed-gastro-summarisation	false	[]	null	252	0
gcaillaut/citeseer	false	[]	The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. The README file in the dataset provides more details.	256	0
gcaillaut/cora	false	[]	The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words.	257	0
gcaillaut/frwiki_good_pages_el	false	[ "task_categories:other", "annotations_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:fr-FR", "language:fr", "license:wtfpl" ]	French Wikipedia dataset for Entity Linking	254	1
gcaillaut/pubmed	false	[]	The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.	256	0
geekydevu/mlquestions	false	[]	null	129	0
geninhu/vi_opus100_processed	false	[]	null	256	0
geninhu/vi_vivos-cv-tts-fpt_processed	false	[]	null	257	0
german-nlp-group/german_common_crawl	false	[]	German Only Extract from Common Crawl This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German	264	6
gfigueroa/wikitext_processed	false	[]	null	256	0
gfissore/arxiv-abstracts-2021	false	[ "task_categories:summarization", "task_categories:text-retrieval", "task_categories:text2text-generation", "task_ids:explanation-generation", "task_ids:text-simplification", "task_ids:document-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "arxiv:1905.00075" ]	null	505	6
ghadeermobasher/BC5CDR-Chemical-Disease	false	[]	\	535	3
ghadeermobasher/CRAFT-Chem	false	[]	\	256	0
ghomasHudson/ao3_style_change	false	[]	null	256	0
ghomasHudson/character_id	false	[]	The character types identification dataset consists of movie scripts annotated with character archetypes (Hero, Villain, Mentor, etc.).	256	0
ghomasHudson/hotpotExtended	false	[]	null	255	0
ghomasHudson/long_contra_pro	false	[]	null	256	0
ghomasHudson/muld	false	[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_categories:translation", "task_ids:abstractive-qa", "annotations_creators:found", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:translation", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "source_datasets:extended\|hotpot_qa", "source_datasets:extended\|open_subtitles", "language:en", "language:de", "conditional-text-generation", "arxiv:2202.07362" ]	MuLD: The Multitask Long Document Benchmark A set of NLP tasks where each example is over 10,000 tokens long.	943	3
ghomasHudson/vlsp	false	[ "language:en" ]	Very Long version of the scientific papers summarization dataset. Only includes theses over 10,000 tokens long.	256	0
gigant/african_accented_french	false	[ "task_categories:automatic-speech-recognition", "language:fr", "license:cc" ]	\ This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts: 1. Yaounde Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female. 2. CA16 This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon. 3. Niger This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen.	254	3
gigant/m-ailabs_speech_dataset_fr	false	[ "task_categories:automatic-speech-recognition", "language:fr", "license:cc" ]	\ The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis. Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format. A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian. Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details).	255	0
gigant/ro_corpora_parliament_processed	false	[]	null	252	0
gigant/romanian_speech_synthesis_0_8_1	false	[ "task_categories:automatic-speech-recognition", "language:ro", "license:unknown" ]	\ The Romanian speech synthesis (RSS) corpus was recorded in a hemianechoic chamber (anechoic walls and ceiling; floor partially anechoic) at the University of Edinburgh. We used three high quality studio microphones: a Neumann u89i (large diaphragm condenser), a Sennheiser MKH 800 (small diaphragm condenser with very wide bandwidth) and a DPA 4035 (headset-mounted condenser). Although the current release includes only speech data recorded via Sennheiser MKH 800, we may release speech data recorded via other microphones in the future. All recordings were made at 96 kHz sampling frequency and 24 bits per sample, then downsampled to 48 kHz sampling frequency. For recording, downsampling and bit rate conversion, we used ProTools HD hardware and software. We conducted 8 sessions over the course of a month, recording about 500 sentences in each session. At the start of each session, the speaker listened to a previously recorded sample, in order to attain a similar voice quality and intonation.	267	1
giganticode/java-cmpx-v1	false	[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:unknown", "language:java", "license:mit" ]	null	258	0
giganticode/java-cmpx	false	[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:unknown", "language:java", "license:mit" ]	null	254	0
gj1997/trial	false	[]	null	256	0
gmnlp/tico19	false	[]	In response to the on-going crisis, several academic (Carnegie Mellon University, George Mason University, Johns Hopkins University) and industry (Amazon, Appen, Facebook, Google, Microsoft, Translated) partners have partnered with the Translators without Borders to prepare COVID-19 materials for a variety of the world’s languages to be used by professional translators and for training state-of-the-art Machine Translation (MT) models. The focus is on making emergency and crisis-related content available in as many languages as possible. The collected, curated and translated content across nearly 90 languages will be available to the professional translation as well the MT research community.	4,994	1
gorkemgoknar/tr_ted_talk_translated	false	[ "language:tr", "license:apache-2.0", "dataset", "turkish", "ted-multi", "cleaned" ]	null	258	1
gpt3mix/rt20	false	[]	null	256	0
gpt3mix/sst2	false	[]	null	1,631	0
gsarti/change_it	false	[ "task_categories:summarization", "task_categories:text-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:it", "license:cc-by-nc-sa-4.0", "conditional-text-generation", "style-transfer" ]	The CHANGE-IT dataset contains approximately 152,000 article-headline pairs, collected from two Italian newspapers situated at opposite ends of the political spectrum, namely la Repubblica (left) and Il Giornale (right), with the two newspapers equally represented. The dataset has been used in the context of the CHANGE-IT task (https://sites.google.com/view/change-it) during the Evalita 2020 evaluation campaign (http://www.evalita.it/2020). CHANGE-IT is a generation task for Italian – more specifically, a style transfer task for headlines of Italian newspapers. Given a (collection of) headlines from one newspaper, namely Il Giornale (G) or La Repubblica (R), it challenges automatic systems to change all G-headlines to headlines in style R, and all R-headlines to headlines in style G. Although the task only concerns headline change, the dataset comprehends both the headlines as well as their respective full articles.	383	1
gsarti/clean_mc4_it	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:it", "license:odc-by", "arxiv:1910.10683", "arxiv:2203.03759" ]	A thoroughly cleaned version of the Italian portion of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning detailed in the repository README file.	803	4
gsarti/flores_101	false	[ "task_categories:text-generation", "task_categories:translation", "annotations_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:unknown", "source_datasets:extended\|flores", "language:af", "language:am", "language:ar", "language:hy", "language:as", "language:ast", "language:az", "language:be", "language:bn", "language:bs", "language:bg", "language:my", "language:ca", "language:ceb", "language:zho", "language:hr", "language:cs", "language:da", "language:nl", "language:en", "language:et", "language:tl", "language:fi", "language:fr", "language:ff", "language:gl", "language:lg", "language:ka", "language:de", "language:el", "language:gu", "language:ha", "language:he", "language:hi", "language:hu", "language:is", "language:ig", "language:id", "language:ga", "language:it", "language:ja", "language:jv", "language:kea", "language:kam", "language:kn", "language:kk", "language:km", "language:ko", "language:ky", "language:lo", "language:lv", "language:ln", "language:lt", "language:luo", "language:lb", "language:mk", "language:ms", "language:ml", "language:mt", "language:mi", "language:mr", "language:mn", "language:ne", "language:ns", "language:no", "language:ny", "language:oc", "language:or", "language:om", "language:ps", "language:fa", "language:pl", "language:pt", "language:pa", "language:ro", "language:ru", "language:sr", "language:sn", "language:sd", "language:sk", "language:sl", "language:so", "language:ku", "language:es", "language:sw", "language:sv", "language:tg", "language:ta", "language:te", "language:th", "language:tr", "language:uk", "language:umb", "language:ur", "language:uz", "language:vi", "language:cy", "language:wo", "language:xh", "language:yo", "language:zu", "license:cc-by-sa-4.0", "conditional-text-generation", "arxiv:2106.03193" ]	One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.	16,704	6

eugenesiow/Set5

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" ]

Set5 is a evaluation dataset with 5 RGB images for the image super resolution task.

443

0

eugenesiow/Urban100

false

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

The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models.

364

0

evageon/IADD

false

[ "license:cc-by-4.0" ]

null

256

0

facebook/multilingual_librispeech

false

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:de", "language:nl", "language:fr", "language:it", "language:es", "language:pt", "language:pl", "license:cc-by-4.0", "arxiv:2012.03411" ]

This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages: English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.

1,161

16

fastjt/fasst

false

[ "license:afl-3.0" ]

null

127

0

fatvvs/autonlp-data-entity_model_conll2003

false

[]

null

256

0

fededeleon/CriteriosClasificacion

false

[ "license:mit" ]

null

257

0

fengzhang/fzTestDatasets

false

[]

null

129

0

fhamborg/news_sentiment_newsmtsc

false

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

NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles.

553

5

fighterhitx/test

false

[ "license:cc" ]

null

127

0

fihtrotuld/asu

false

[]

null

127

0

flax-community/code_clippy_data

false

[]

null

128

0

flax-community/conceptual-12m-mbart-50-multilingual

false

[]

null

253

0

flax-community/conceptual-12m-multilingual-marian-128

false

[]

null

255

0

flax-community/conceptual-12m-multilingual-marian-es

false

[]

null

287

0

flax-community/conceptual-12m-multilingual-marian

false

[]

null

253

0

flax-community/conceptual-captions-12

false

[]

null

255

1

flax-community/dummy-oscar-als-32

false

[]

null

255

0

flax-community/german-common-voice-processed

false

[]

null

256

1

flax-community/german_common_crawl

false

[]

German Only Extract from Common Crawl This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German

635

0

flax-community/multilingual-vqa

false

[]

null

256

0

flax-community/norwegian-clean-dummy

false

[]

null

129

0

flax-community/swahili-safi

false

[]

Cleaned dataset for Swahili Language Modeling

267

3

flax-sentence-embeddings/Gender_Bias_Evaluation_Set

false

[ "arxiv:1906.00591" ]

null

256

2

flax-sentence-embeddings/paws-jsonl

false

[]

null

253

0

flax-sentence-embeddings/stackexchange_math_jsonl

false

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]

This new dataset is designed to solve this great NLP task and is crafted with a lot of care.

549

0

flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl

false

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]

This new dataset is designed to solve this great NLP task and is crafted with a lot of care.

22,652

1

flax-sentence-embeddings/stackexchange_title_body_jsonl

false

[]

null

259

0

flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl

false

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]

This new dataset is designed to solve this great NLP task and is crafted with a lot of care.

23,983

8

flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl

false

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0" ]

This new dataset is designed to solve this great NLP task and is crafted with a lot of care.

22,221

3

flax-sentence-embeddings/stackexchange_xml

false

[]

null

129

1

flexthink/librig2p-nostress-space

false

[]

Grapheme-to-Phoneme training, validation and test sets

261

0

flexthink/librig2p-nostress

false

[]

Grapheme-to-Phoneme training, validation and test sets

261

0

flexthink/ljspeech

false

[]

This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.

253

1

florentgbelidji/test-3

false

[]

null

129

0

florentgbelidji/test-dataset

false

[]

null

129

0

florianbussmann/FUNSD-vu2020revising

false

[ "multilinguality:monolingual", "language:en", "arxiv:2010.05322" ]

\ FUNSD is one of the limited publicly available datasets for information extraction from document images. The information in the FUNSD dataset is defined by text areas of four categories ("key", "value", "header", "other", and "background") and connectivity between areas as key-value relations. Inspecting FUNSD, we found several inconsistency in labeling, which impeded its applicability to the key-value extraction problem. In this report, we described some labeling issues in FUNSD and the revision we made to the dataset.

129

0

florianbussmann/train_tickets-yu2020pick

false

[]

\ The train ticket is fixed layout dataset, however, it contains background noise and imaging distortions. It contains 1,530 synthetic images and 320 real images for training, and 80 real images for testing. Every train ticket has eight key text fields including ticket number, starting station, train number, destination station, date, ticket rates, seat category, and name. This dataset mainly consists of digits, English characters, and Chinese characters.

128

0

flxclxc/encoded_drug_reviews

false

[]

null

256

2

formermagic/github_python_1m

false

[ "task_ids:language-modeling", "task_ids:slot-filling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:py", "license:mit" ]

null

130

1

formu/CVT

false

[]

null

128

0

fractalego/QA_to_statements

false

[ "arxiv:1809.02922", "doi:10.57967/hf/0011" ]

null

256

0

frahman/github-issues

false

[]

null

256

0

frtna/deneme

false

[]

null

256

0

frtna/es_it_Results-base-OPUS_Tatoeba

false

[]

null

256

0

frtna/jwt300_mt

false

[]

This new dataset is designed to be used in the scope of machine translation project.

256

0

frtna/opensubtitles_mt

false

[]

This new dataset is designed to be used in the scope of PhD project.

255

0

frtna/sabahaKKarsi

false

[]

null

256

0

frtna/ted_mt

false

[]

This new dataset is designed to be used in the scope of multilingual model project.

256

0

frtna/test

false

[]

null

127

0

frtna/test2

false

[]

null

129

0

fulai/DuReader

false

[]

null

130

0

fuliucansheng/coco

false

[]

null

129

0

fuliucansheng/minicoco

false

[]

MINICOCO2017

353

0

fuliucansheng/mininlp

false

[]

MiniNLP Data

257

0

fuliucansheng/pascal_voc

false

[]

PASCAL_VOC

164

0

fuyun1107/clip-for-vlp

false

[]

null

255

0

fvillena/cantemist

false

[]

\

256

0

fvillena/spanish_diagnostics

false

[]

null

254

0

gabella/demo_data_raw

false

[]

null

252

0

gabtan99/pex-conversations

false

[ "task_ids:dialogue-modeling", "task_ids:language-modeling", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:tl", "language:fil", "license:unknown", "multi-turn" ]

null

255

1

gagan3012/fake-news

false

[]

null

129

0

gagan3012/grover-data

false

[]

null

256

0

gagan3012/vizwiz

false

[ "license:apache-2.0" ]

null

131

0

gar1t/test

false

[]

null

256

0

gayanin/pubmed-gastro-maskfilling

false

[]

null

256

0

gayanin/pubmed-gastro-paraphrasing

false

[]

null

256

2

gayanin/pubmed-gastro-summarisation

false

[]

null

252

0

gcaillaut/citeseer

false

[]

The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. The README file in the dataset provides more details.

256

0

gcaillaut/cora

false

[]

The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words.

257

0

gcaillaut/frwiki_good_pages_el

false

[ "task_categories:other", "annotations_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:fr-FR", "language:fr", "license:wtfpl" ]

French Wikipedia dataset for Entity Linking

254

1

gcaillaut/pubmed

false

[]

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

256

0

geekydevu/mlquestions

false

[]

null

129

0

geninhu/vi_opus100_processed

false

[]

null

256

0

geninhu/vi_vivos-cv-tts-fpt_processed

false

[]

null

257

0

german-nlp-group/german_common_crawl

false

[]

German Only Extract from Common Crawl This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German

264

6

gfigueroa/wikitext_processed

false

[]

null

256

0

gfissore/arxiv-abstracts-2021

false

[ "task_categories:summarization", "task_categories:text-retrieval", "task_categories:text2text-generation", "task_ids:explanation-generation", "task_ids:text-simplification", "task_ids:document-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "arxiv:1905.00075" ]

null

505

6

ghadeermobasher/BC5CDR-Chemical-Disease

false

[]

\

535

3

ghadeermobasher/CRAFT-Chem

false

[]

\

256

0

ghomasHudson/ao3_style_change

false

[]

null

256

0

ghomasHudson/character_id

false

[]

The character types identification dataset consists of movie scripts annotated with character archetypes (Hero, Villain, Mentor, etc.).

256

0

ghomasHudson/hotpotExtended

false

[]

null

255

0

ghomasHudson/long_contra_pro

false

[]

null

256

0

ghomasHudson/muld

false

[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_categories:translation", "task_ids:abstractive-qa", "annotations_creators:found", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:translation", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "source_datasets:extended|hotpot_qa", "source_datasets:extended|open_subtitles", "language:en", "language:de", "conditional-text-generation", "arxiv:2202.07362" ]

MuLD: The Multitask Long Document Benchmark A set of NLP tasks where each example is over 10,000 tokens long.

943

3

ghomasHudson/vlsp

false

[ "language:en" ]

Very Long version of the scientific papers summarization dataset. Only includes theses over 10,000 tokens long.

256

0

gigant/african_accented_french

false

[ "task_categories:automatic-speech-recognition", "language:fr", "license:cc" ]

\ This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts: 1. Yaounde Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female. 2. CA16 This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon. 3. Niger This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen.

254

3

gigant/m-ailabs_speech_dataset_fr

false

[ "task_categories:automatic-speech-recognition", "language:fr", "license:cc" ]

\ The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis. Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format. A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian. Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details).

255

0

gigant/ro_corpora_parliament_processed

false

[]

null

252

0

gigant/romanian_speech_synthesis_0_8_1

false

[ "task_categories:automatic-speech-recognition", "language:ro", "license:unknown" ]

\ The Romanian speech synthesis (RSS) corpus was recorded in a hemianechoic chamber (anechoic walls and ceiling; floor partially anechoic) at the University of Edinburgh. We used three high quality studio microphones: a Neumann u89i (large diaphragm condenser), a Sennheiser MKH 800 (small diaphragm condenser with very wide bandwidth) and a DPA 4035 (headset-mounted condenser). Although the current release includes only speech data recorded via Sennheiser MKH 800, we may release speech data recorded via other microphones in the future. All recordings were made at 96 kHz sampling frequency and 24 bits per sample, then downsampled to 48 kHz sampling frequency. For recording, downsampling and bit rate conversion, we used ProTools HD hardware and software. We conducted 8 sessions over the course of a month, recording about 500 sentences in each session. At the start of each session, the speaker listened to a previously recorded sample, in order to attain a similar voice quality and intonation.

267

1

giganticode/java-cmpx-v1

false

[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:unknown", "language:java", "license:mit" ]

null

258

0

giganticode/java-cmpx

false

[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:unknown", "language:java", "license:mit" ]

null

254

0

gj1997/trial

false

[]

null

256

0

gmnlp/tico19

false

[]

In response to the on-going crisis, several academic (Carnegie Mellon University, George Mason University, Johns Hopkins University) and industry (Amazon, Appen, Facebook, Google, Microsoft, Translated) partners have partnered with the Translators without Borders to prepare COVID-19 materials for a variety of the world’s languages to be used by professional translators and for training state-of-the-art Machine Translation (MT) models. The focus is on making emergency and crisis-related content available in as many languages as possible. The collected, curated and translated content across nearly 90 languages will be available to the professional translation as well the MT research community.

4,994

1

gorkemgoknar/tr_ted_talk_translated

false

[ "language:tr", "license:apache-2.0", "dataset", "turkish", "ted-multi", "cleaned" ]

null

258

1

gpt3mix/rt20

false

[]

null

256

0

gpt3mix/sst2

false

[]

null

1,631

0

gsarti/change_it

false

[ "task_categories:summarization", "task_categories:text-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:it", "license:cc-by-nc-sa-4.0", "conditional-text-generation", "style-transfer" ]

The CHANGE-IT dataset contains approximately 152,000 article-headline pairs, collected from two Italian newspapers situated at opposite ends of the political spectrum, namely la Repubblica (left) and Il Giornale (right), with the two newspapers equally represented. The dataset has been used in the context of the CHANGE-IT task (https://sites.google.com/view/change-it) during the Evalita 2020 evaluation campaign (http://www.evalita.it/2020). CHANGE-IT is a generation task for Italian – more specifically, a style transfer task for headlines of Italian newspapers. Given a (collection of) headlines from one newspaper, namely Il Giornale (G) or La Repubblica (R), it challenges automatic systems to change all G-headlines to headlines in style R, and all R-headlines to headlines in style G. Although the task only concerns headline change, the dataset comprehends both the headlines as well as their respective full articles.

383

1

gsarti/clean_mc4_it

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:it", "license:odc-by", "arxiv:1910.10683", "arxiv:2203.03759" ]

A thoroughly cleaned version of the Italian portion of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning detailed in the repository README file.

803

4

gsarti/flores_101

false

[ "task_categories:text-generation", "task_categories:translation", "annotations_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:unknown", "source_datasets:extended|flores", "language:af", "language:am", "language:ar", "language:hy", "language:as", "language:ast", "language:az", "language:be", "language:bn", "language:bs", "language:bg", "language:my", "language:ca", "language:ceb", "language:zho", "language:hr", "language:cs", "language:da", "language:nl", "language:en", "language:et", "language:tl", "language:fi", "language:fr", "language:ff", "language:gl", "language:lg", "language:ka", "language:de", "language:el", "language:gu", "language:ha", "language:he", "language:hi", "language:hu", "language:is", "language:ig", "language:id", "language:ga", "language:it", "language:ja", "language:jv", "language:kea", "language:kam", "language:kn", "language:kk", "language:km", "language:ko", "language:ky", "language:lo", "language:lv", "language:ln", "language:lt", "language:luo", "language:lb", "language:mk", "language:ms", "language:ml", "language:mt", "language:mi", "language:mr", "language:mn", "language:ne", "language:ns", "language:no", "language:ny", "language:oc", "language:or", "language:om", "language:ps", "language:fa", "language:pl", "language:pt", "language:pa", "language:ro", "language:ru", "language:sr", "language:sn", "language:sd", "language:sk", "language:sl", "language:so", "language:ku", "language:es", "language:sw", "language:sv", "language:tg", "language:ta", "language:te", "language:th", "language:tr", "language:uk", "language:umb", "language:ur", "language:uz", "language:vi", "language:cy", "language:wo", "language:xh", "language:yo", "language:zu", "license:cc-by-sa-4.0", "conditional-text-generation", "arxiv:2106.03193" ]

One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.

16,704

6