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
Lacito/pangloss	false	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "source_datasets:original", "language:jya", "language:nru", "license:cc-by-nc-sa-4.0" ]	These datasets are extracts from the Pangloss collection and have been preprocessed for ASR experiments in Na and Japhug.	278	2
Binbin/my_dataset	false	[]	null	150	0
BlakesOrb6/Fred-Flintstone	false	[]	null	151	0
Bosio/pacman	false	[]	null	299	0
Bosio/pacman_descriptions	false	[]	null	299	0
BritishLibraryLabs/EThOS-PhD-metadata	false	[ "task_categories:text-classification", "task_categories:fill-mask", "task_ids:multi-label-classification", "task_ids:masked-language-modeling", "multilinguality:monolingual", "language:en" ]	The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included.	445	1
CAGER/rick	false	[]	null	151	0
CALM/arwiki	false	[ "multilinguality:monolingual", "language:ar", "license:unknown" ]	null	297	1
CAiRE/ASCEND	false	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:zh", "license:cc-by-sa-4.0", "speech-recognition", "code-switching", "arxiv:2112.06223" ]	ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.	386	7
CShorten/KerasBERT	false	[]	null	151	2
ChadxxxxHall/Inter-vision	false	[]	null	151	0
Champion/vpc2020_clear_anon_speech	false	[]	null	149	0
Check/a_re_gi	false	[]	null	151	0
Check/region_1	false	[]	null	299	0
Check/region_2	false	[]	null	299	0
Check/region_3	false	[]	null	299	0
Check/region_4	false	[]	null	299	0
Check/region_5	false	[]	null	298	0
Check/region_6	false	[]	null	297	0
Check/region_7	false	[]	null	298	0
Check/region_8	false	[]	null	297	0
Check/region_9	false	[]	null	299	0
Check/regions	false	[]	null	298	0
Check/vverify	false	[]	null	297	0
Cheranga/test	false	[ "license:afl-3.0" ]	null	148	0
ChristophSchuhmann/MS_COCO_2017_URL_TEXT	false	[]	null	337	5
Chun/dataset	false	[]	null	299	0
Chuu/Vhh	false	[]	null	151	0
CodedotAI/code-clippy-tfrecords	false	[]	null	300	0
CodedotAI/code_clippy	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:gpl-3.0", "arxiv:2107.03374" ]	This dataset was generated by selecting GitHub repositories from a large collection of repositories. These repositories were collected from https://seart-ghs.si.usi.ch/ and Github portion of [The Pile](https://github.com/EleutherAI/github-downloader) (performed on July 7th, 2021). The goal of this dataset is to provide a training set for pretraining large language models on code data for helping software engineering researchers better understand their impacts on software related tasks such as autocompletion of code. The dataset is split into train, validation, and test splits. There is a version containing duplicates (209GBs compressed) and ones where exact duplicates (132GBs compressed) are removed. Contains mostly JavaScript and Python code, but other programming languages are included as well to various degrees.	152	8
CodedotAI/code_clippy_github	false	[ "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:mit", "arxiv:2107.03374" ]	The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totalling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BiqQuery.	160	9
Crives/haha	false	[]	null	298	1
Cropinky/flatearther	false	[]	null	151	0
Cropinky/rap_lyrics_english	false	[]	null	308	0
Cropinky/wow_fishing_bobber	false	[]	null	298	0
Cyberfish/pos_tagger	false	[]	null	297	0
Cyberfish/text_error_correction	false	[]	null	298	0
CyranoB/polarity	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:1509.01626" ]	The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review.	297	1
DDSC/angry-tweets	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]	null	297	1
DDSC/europarl	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]	null	297	2
DDSC/lcc	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]	null	309	3
DDSC/partial-danish-gigaword-no-twitter	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:da", "license:cc-by-4.0" ]	null	321	3
DDSC/reddit-da-asr-preprocessed	false	[]	null	299	0
DDSC/reddit-da	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:da", "license:mit" ]	null	297	2
DDSC/twitter-sent	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]	null	297	2
DELith/github-issues	false	[]	null	299	0
DSCI511G1/COP26_Energy_Transition_Tweets	false	[]	null	299	1
DanL/scientific-challenges-and-directions-dataset	false	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "multilinguality:monolingual", "source_datasets:CORD-19", "language:en", "arxiv:2108.13751", "arxiv:2004.10706" ]	null	266	1
Daniele/dante-corpus	false	[]	null	282	0
Darren/data	false	[]	null	135	0
Datatang/accented_english	false	[]	null	267	4
Datatang/accented_mandarin	false	[]	null	267	3
Datatang/chinese_dialect	false	[]	null	268	5
Datatang/mandarin_chinese	false	[]	null	279	5
Datatang/mixed_speech_chinese_english	false	[]	null	265	4
Datatang/multi_language	false	[]	null	266	3
Datatang/multi_language_conversation	false	[]	null	267	5
Davlan/conll2003_de_noMISC	false	[]	null	267	0
Davlan/conll2003_noMISC	false	[]	null	268	0
Davlan/masakhanerV1	false	[]	null	135	0
DelgadoPanadero/Pokemon	false	[]	null	267	3
DeskDown/ALTDataset	false	[]	null	311	0
DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm	false	[]	null	267	0
DiFronzo/Human_Activity_Recognition	false	[]	null	267	1
Dmitriy612/1	false	[]	null	135	0
DoctorSlimm/yipee	false	[]	null	135	0
Doohae/klue-mrc-bm25	false	[]	null	267	0
Doohae/modern_music_re	false	[]	null	293	0
DoyyingFace/github-embeddings-doy	false	[]	null	267	0
DoyyingFace/github-issues-doy	false	[]	null	135	0
DrishtiSharma/as_opus100_processed	false	[]	null	266	0
DrishtiSharma/bg_opus100_processed	false	[]	null	265	0
DrishtiSharma/br_opus100_processed	false	[]	null	265	0
DrishtiSharma/hi_opus100_processed	false	[]	null	265	0
DrishtiSharma/kk_opus100_processed	false	[]	null	266	0
DrishtiSharma/mr_opus100_processed	false	[]	null	266	0
DrishtiSharma/or_opus100_processed	false	[]	null	267	0
DrishtiSharma/sl_opus100_processed	false	[]	null	267	0
DrishtiSharma/sr_opus100_processed	false	[]	null	268	0
Dumiiii/common-voice-romaniarss	false	[]	null	267	0
EMBO/biolang	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n>1M", "language:en", "license:cc-by-4.0" ]	This dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology.	663	0
EMBO/sd-nlp	false	[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:named-entity-recognition", "task_ids:parsing", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0" ]	This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.	793	0
ESZER/H	false	[]	null	135	0
Emanuel/UD_Portuguese-Bosque	false	[ "language:pt" ]	null	265	1
Emma121/aaaaa	false	[ "license:bsd-3-clause-clear" ]	null	137	0
Emma121/testtest	false	[]	null	135	0
Enes3774/data	false	[]	null	135	0
Exr0n/wiki-entity-similarity	false	[ "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:mit", "named entities", "similarity", "paraphrasing", "synonyms", "wikipedia", "arxiv:2004.04906", "arxiv:2202.13581" ]	null	936	4
Eymen3455/xsum_tr	false	[]	null	135	0
FIG-Loneliness/FIG-Loneliness	false	[]	null	267	1
FL33TW00D/test-dataset	false	[]	null	135	0
FRTNX/cosuju	false	[]	Court Summaries and Judgements (CoSuJu) Dataset	400	0
FRTNX/worldbank-projects	false	[]	null	135	0
Felix-ML/quoteli3	false	[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0" ]	This dataset is a representation of Muzny et al.'s QuoteLi3 dataset as a Huggingface dataset. It can be best used for quote attribution.	135	0
Finnish-NLP/mc4_fi_cleaned	false	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|mc4", "language:fi" ]	null	265	3
Firoj/HumAID	false	[]	The HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english tweets and it is the largest dataset for crisis informatics so far. Humanitarian categories - Caution and advice - Displaced people and evacuations - Dont know cant judge - Infrastructure and utility damage - Injured or dead people - Missing or found people - Not humanitarian - Other relevant information - Requests or urgent needs - Rescue volunteering or donation effort - Sympathy and support	267	1
Francois/futures_es	false	[]	null	134	0
Fraser/mnist-text-default	false	[]	MNIST dataset adapted to a text-based representation. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Data format: - text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example: ``` 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 03 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 04 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 05 down ! ! ! ! ! ! ! ! ! ! ! ! ! % % % @ C L ' J a ^ @ ! ! ! ! 06 down ! ! ! ! ! ! ! ! ( * 8 G K ` ` ` ` ` Y L ` ] Q 1 ! ! ! ! 07 down ! ! ! ! ! ! ! - \ ` ` ` ` ` ` ` ` _ 8 5 5 / * ! ! ! ! ! 08 down ! ! ! ! ! ! ! % W ` ` ` ` ` R N ^ ] ! ! ! ! ! ! ! ! ! ! 09 down ! ! ! ! ! ! ! ! 5 H ; ` ` T # ! + G ! ! ! ! ! ! ! ! ! ! 10 down ! ! ! ! ! ! ! ! ! $ ! G ` 7 ! ! ! ! ! ! ! ! ! ! ! ! ! ! 11 down ! ! ! ! ! ! ! ! ! ! ! C ` P ! ! ! ! ! ! ! ! ! ! ! ! ! ! 12 down ! ! ! ! ! ! ! ! ! ! ! # P ` 2 ! ! ! ! ! ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ) ] Y I < ! ! ! ! ! ! ! ! ! ! ! 14 down ! ! ! ! ! ! ! ! ! ! ! ! ! 5 ] ` ` > ' ! ! ! ! ! ! ! ! ! 15 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , O ` ` F ' ! ! ! ! ! ! ! ! 16 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! % 8 ` ` O ! ! ! ! ! ! ! ! 17 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! _ ` _ 1 ! ! ! ! ! ! ! 18 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , A N ` ` T ! ! ! ! ! ! ! ! 19 down ! ! ! ! ! ! ! ! ! ! ! ! * F Z ` ` ` _ N ! ! ! ! ! ! ! ! 20 down ! ! ! ! ! ! ! ! ! ! ' = X ` ` ` ` S 4 ! ! ! ! ! ! ! ! ! 21 down ! ! ! ! ! ! ! ! & 1 V ` ` ` ` R 5 ! ! ! ! ! ! ! ! ! ! ! 22 down ! ! ! ! ! ! % K W ` ` ` ` Q 5 # ! ! ! ! ! ! ! ! ! ! ! ! 23 down ! ! ! ! . L Y ` ` ` ` ^ B # ! ! ! ! ! ! ! ! ! ! ! ! ! ! 24 down ! ! ! ! C ` ` ` V B B % ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 25 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 26 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 27 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ``` - label: Just a number with the texts matching label.	266	0
Fraser/mnist-text-no-spaces	false	[]	MNIST dataset adapted to a text-based representation. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Removed spaces to get better BPE compression on sequences. Should only be used with a trained tokenizer. Data format: - text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example: ``` 00down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 01down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 02down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 03down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 04down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 05down!!!!!!!!!!!!!%%%@CL'Ja^@!!!! 06down!!!!!!!!(8GK`````YL`]Q1!!!! 07down!!!!!!!-\\````````_855/!!!!! 08down!!!!!!!%W`````RN^]!!!!!!!!!! 09down!!!!!!!!5H;``T#!+G!!!!!!!!!! 10down!!!!!!!!!$!G`7!!!!!!!!!!!!!! 11down!!!!!!!!!!!C`P!!!!!!!!!!!!!! 12down!!!!!!!!!!!#P`2!!!!!!!!!!!!! 13down!!!!!!!!!!!!)]YI<!!!!!!!!!!! 14down!!!!!!!!!!!!!5]``>'!!!!!!!!! 15down!!!!!!!!!!!!!!,O``F'!!!!!!!! 16down!!!!!!!!!!!!!!!%8``O!!!!!!!! 17down!!!!!!!!!!!!!!!!!_`_1!!!!!!! 18down!!!!!!!!!!!!!!,AN``T!!!!!!!! 19down!!!!!!!!!!!!*FZ```_N!!!!!!!! 20down!!!!!!!!!!'=X````S4!!!!!!!!! 21down!!!!!!!!&1V````R5!!!!!!!!!!! 22down!!!!!!%KW````Q5#!!!!!!!!!!!! 23down!!!!.LY````^B#!!!!!!!!!!!!!! 24down!!!!C```VBB%!!!!!!!!!!!!!!!! 25down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 26down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 27down!!!!!!!!!!!!!!!!!!!!!!!!!!!! ``` - label: Just a number with the texts matching label.	267	0
Fraser/mnist-text-small	false	[]	MNIST dataset adapted to a text-based representation. Modified images to be ~1/4 the original area. Done by taking a max pool. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Data format: - text: (16 x 14 tokens, 224 tokens total): Textual representation of MNIST digit, for example: ``` 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! % % C L a ^ ! ! 03 down ! ! ! - ` ` ` ` ` Y ` Q ! ! 04 down ! ! ! % ` ` ` R ^ ! ! ! ! ! 05 down ! ! ! ! $ G ` ! ! ! ! ! ! ! 06 down ! ! ! ! ! # ` Y < ! ! ! ! ! 07 down ! ! ! ! ! ! 5 ` ` F ! ! ! ! 08 down ! ! ! ! ! ! ! % ` ` 1 ! ! ! 09 down ! ! ! ! ! ! F ` ` ` ! ! ! ! 10 down ! ! ! ! 1 ` ` ` ` 4 ! ! ! ! 11 down ! ! L ` ` ` ` 5 ! ! ! ! ! ! 12 down ! ! ` ` V B ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ``` - label: Just a number with the texts matching label.	329	0

Lacito/pangloss

false

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "source_datasets:original", "language:jya", "language:nru", "license:cc-by-nc-sa-4.0" ]

These datasets are extracts from the Pangloss collection and have been preprocessed for ASR experiments in Na and Japhug.

278

2

Binbin/my_dataset

false

[]

null

150

0

BlakesOrb6/Fred-Flintstone

false

[]

null

151

0

Bosio/pacman

false

[]

null

299

0

Bosio/pacman_descriptions

false

[]

null

299

0

BritishLibraryLabs/EThOS-PhD-metadata

false

[ "task_categories:text-classification", "task_categories:fill-mask", "task_ids:multi-label-classification", "task_ids:masked-language-modeling", "multilinguality:monolingual", "language:en" ]

The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included.

445

1

CAGER/rick

false

[]

null

151

0

CALM/arwiki

false

[ "multilinguality:monolingual", "language:ar", "license:unknown" ]

null

297

1

CAiRE/ASCEND

false

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:zh", "license:cc-by-sa-4.0", "speech-recognition", "code-switching", "arxiv:2112.06223" ]

ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.

386

7

CShorten/KerasBERT

false

[]

null

151

2

ChadxxxxHall/Inter-vision

false

[]

null

151

0

Champion/vpc2020_clear_anon_speech

false

[]

null

149

0

Check/a_re_gi

false

[]

null

151

0

Check/region_1

false

[]

null

299

0

Check/region_2

false

[]

null

299

0

Check/region_3

false

[]

null

299

0

Check/region_4

false

[]

null

299

0

Check/region_5

false

[]

null

298

0

Check/region_6

false

[]

null

297

0

Check/region_7

false

[]

null

298

0

Check/region_8

false

[]

null

297

0

Check/region_9

false

[]

null

299

0

Check/regions

false

[]

null

298

0

Check/vverify

false

[]

null

297

0

Cheranga/test

false

[ "license:afl-3.0" ]

null

148

0

ChristophSchuhmann/MS_COCO_2017_URL_TEXT

false

[]

null

337

5

Chun/dataset

false

[]

null

299

0

Chuu/Vhh

false

[]

null

151

0

CodedotAI/code-clippy-tfrecords

false

[]

null

300

0

CodedotAI/code_clippy

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:gpl-3.0", "arxiv:2107.03374" ]

This dataset was generated by selecting GitHub repositories from a large collection of repositories. These repositories were collected from https://seart-ghs.si.usi.ch/ and Github portion of [The Pile](https://github.com/EleutherAI/github-downloader) (performed on July 7th, 2021). The goal of this dataset is to provide a training set for pretraining large language models on code data for helping software engineering researchers better understand their impacts on software related tasks such as autocompletion of code. The dataset is split into train, validation, and test splits. There is a version containing duplicates (209GBs compressed) and ones where exact duplicates (132GBs compressed) are removed. Contains mostly JavaScript and Python code, but other programming languages are included as well to various degrees.

152

8

CodedotAI/code_clippy_github

false

[ "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:mit", "arxiv:2107.03374" ]

The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totalling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BiqQuery.

160

9

Crives/haha

false

[]

null

298

1

Cropinky/flatearther

false

[]

null

151

0

Cropinky/rap_lyrics_english

false

[]

null

308

0

Cropinky/wow_fishing_bobber

false

[]

null

298

0

Cyberfish/pos_tagger

false

[]

null

297

0

Cyberfish/text_error_correction

false

[]

null

298

0

CyranoB/polarity

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:1509.01626" ]

The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review.

297

1

DDSC/angry-tweets

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]

null

297

1

DDSC/europarl

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]

null

297

2

DDSC/lcc

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]

null

309

3

DDSC/partial-danish-gigaword-no-twitter

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:da", "license:cc-by-4.0" ]

null

321

3

DDSC/reddit-da-asr-preprocessed

false

[]

null

299

0

DDSC/reddit-da

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:da", "license:mit" ]

null

297

2

DDSC/twitter-sent

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0" ]

null

297

2

DELith/github-issues

false

[]

null

299

0

DSCI511G1/COP26_Energy_Transition_Tweets

false

[]

null

299

1

DanL/scientific-challenges-and-directions-dataset

false

[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "multilinguality:monolingual", "source_datasets:CORD-19", "language:en", "arxiv:2108.13751", "arxiv:2004.10706" ]

null

266

1

Daniele/dante-corpus

false

[]

null

282

0

Darren/data

false

[]

null

135

0

Datatang/accented_english

false

[]

null

267

4

Datatang/accented_mandarin

false

[]

null

267

3

Datatang/chinese_dialect

false

[]

null

268

5

Datatang/mandarin_chinese

false

[]

null

279

5

Datatang/mixed_speech_chinese_english

false

[]

null

265

4

Datatang/multi_language

false

[]

null

266

3

Datatang/multi_language_conversation

false

[]

null

267

5

Davlan/conll2003_de_noMISC

false

[]

null

267

0

Davlan/conll2003_noMISC

false

[]

null

268

0

Davlan/masakhanerV1

false

[]

null

135

0

DelgadoPanadero/Pokemon

false

[]

null

267

3

DeskDown/ALTDataset

false

[]

null

311

0

DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm

false

[]

null

267

0

DiFronzo/Human_Activity_Recognition

false

[]

null

267

1

Dmitriy612/1

false

[]

null

135

0

DoctorSlimm/yipee

false

[]

null

135

0

Doohae/klue-mrc-bm25

false

[]

null

267

0

Doohae/modern_music_re

false

[]

null

293

0

DoyyingFace/github-embeddings-doy

false

[]

null

267

0

DoyyingFace/github-issues-doy

false

[]

null

135

0

DrishtiSharma/as_opus100_processed

false

[]

null

266

0

DrishtiSharma/bg_opus100_processed

false

[]

null

265

0

DrishtiSharma/br_opus100_processed

false

[]

null

265

0

DrishtiSharma/hi_opus100_processed

false

[]

null

265

0

DrishtiSharma/kk_opus100_processed

false

[]

null

266

0

DrishtiSharma/mr_opus100_processed

false

[]

null

266

0

DrishtiSharma/or_opus100_processed

false

[]

null

267

0

DrishtiSharma/sl_opus100_processed

false

[]

null

267

0

DrishtiSharma/sr_opus100_processed

false

[]

null

268

0

Dumiiii/common-voice-romaniarss

false

[]

null

267

0

EMBO/biolang

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n>1M", "language:en", "license:cc-by-4.0" ]

This dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology.

663

0

EMBO/sd-nlp

false

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:named-entity-recognition", "task_ids:parsing", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0" ]

This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.

793

0

ESZER/H

false

[]

null

135

0

Emanuel/UD_Portuguese-Bosque

false

[ "language:pt" ]

null

265

1

Emma121/aaaaa

false

[ "license:bsd-3-clause-clear" ]

null

137

0

Emma121/testtest

false

[]

null

135

0

Enes3774/data

false

[]

null

135

0

Exr0n/wiki-entity-similarity

false

[ "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:mit", "named entities", "similarity", "paraphrasing", "synonyms", "wikipedia", "arxiv:2004.04906", "arxiv:2202.13581" ]

null

936

4

Eymen3455/xsum_tr

false

[]

null

135

0

FIG-Loneliness/FIG-Loneliness

false

[]

null

267

1

FL33TW00D/test-dataset

false

[]

null

135

0

FRTNX/cosuju

false

[]

Court Summaries and Judgements (CoSuJu) Dataset

400

0

FRTNX/worldbank-projects

false

[]

null

135

0

Felix-ML/quoteli3

false

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0" ]

This dataset is a representation of Muzny et al.'s QuoteLi3 dataset as a Huggingface dataset. It can be best used for quote attribution.

135

0

Finnish-NLP/mc4_fi_cleaned

false

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|mc4", "language:fi" ]

null

265

3

Firoj/HumAID

false

[]

The HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english tweets and it is the largest dataset for crisis informatics so far. ** Humanitarian categories ** - Caution and advice - Displaced people and evacuations - Dont know cant judge - Infrastructure and utility damage - Injured or dead people - Missing or found people - Not humanitarian - Other relevant information - Requests or urgent needs - Rescue volunteering or donation effort - Sympathy and support

267

1

Francois/futures_es

false

[]

null

134

0

Fraser/mnist-text-default

false

[]

MNIST dataset adapted to a text-based representation. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Data format: - text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example: ``` 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 03 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 04 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 05 down ! ! ! ! ! ! ! ! ! ! ! ! ! % % % @ C L ' J a ^ @ ! ! ! ! 06 down ! ! ! ! ! ! ! ! ( * 8 G K ` ` ` ` ` Y L ` ] Q 1 ! ! ! ! 07 down ! ! ! ! ! ! ! - \ ` ` ` ` ` ` ` ` _ 8 5 5 / * ! ! ! ! ! 08 down ! ! ! ! ! ! ! % W ` ` ` ` ` R N ^ ] ! ! ! ! ! ! ! ! ! ! 09 down ! ! ! ! ! ! ! ! 5 H ; ` ` T # ! + G ! ! ! ! ! ! ! ! ! ! 10 down ! ! ! ! ! ! ! ! ! $ ! G ` 7 ! ! ! ! ! ! ! ! ! ! ! ! ! ! 11 down ! ! ! ! ! ! ! ! ! ! ! C ` P ! ! ! ! ! ! ! ! ! ! ! ! ! ! 12 down ! ! ! ! ! ! ! ! ! ! ! # P ` 2 ! ! ! ! ! ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ) ] Y I < ! ! ! ! ! ! ! ! ! ! ! 14 down ! ! ! ! ! ! ! ! ! ! ! ! ! 5 ] ` ` > ' ! ! ! ! ! ! ! ! ! 15 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , O ` ` F ' ! ! ! ! ! ! ! ! 16 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! % 8 ` ` O ! ! ! ! ! ! ! ! 17 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! _ ` _ 1 ! ! ! ! ! ! ! 18 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , A N ` ` T ! ! ! ! ! ! ! ! 19 down ! ! ! ! ! ! ! ! ! ! ! ! * F Z ` ` ` _ N ! ! ! ! ! ! ! ! 20 down ! ! ! ! ! ! ! ! ! ! ' = X ` ` ` ` S 4 ! ! ! ! ! ! ! ! ! 21 down ! ! ! ! ! ! ! ! & 1 V ` ` ` ` R 5 ! ! ! ! ! ! ! ! ! ! ! 22 down ! ! ! ! ! ! % K W ` ` ` ` Q 5 # ! ! ! ! ! ! ! ! ! ! ! ! 23 down ! ! ! ! . L Y ` ` ` ` ^ B # ! ! ! ! ! ! ! ! ! ! ! ! ! ! 24 down ! ! ! ! C ` ` ` V B B % ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 25 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 26 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 27 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ``` - label: Just a number with the texts matching label.

266

0

Fraser/mnist-text-no-spaces

false

[]

MNIST dataset adapted to a text-based representation. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Removed spaces to get better BPE compression on sequences. **Should only be used with a trained tokenizer.** Data format: - text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example: ``` 00down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 01down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 02down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 03down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 04down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 05down!!!!!!!!!!!!!%%%@CL'Ja^@!!!! 06down!!!!!!!!(*8GK`````YL`]Q1!!!! 07down!!!!!!!-\\````````_855/*!!!!! 08down!!!!!!!%W`````RN^]!!!!!!!!!! 09down!!!!!!!!5H;``T#!+G!!!!!!!!!! 10down!!!!!!!!!$!G`7!!!!!!!!!!!!!! 11down!!!!!!!!!!!C`P!!!!!!!!!!!!!! 12down!!!!!!!!!!!#P`2!!!!!!!!!!!!! 13down!!!!!!!!!!!!)]YI<!!!!!!!!!!! 14down!!!!!!!!!!!!!5]``>'!!!!!!!!! 15down!!!!!!!!!!!!!!,O``F'!!!!!!!! 16down!!!!!!!!!!!!!!!%8``O!!!!!!!! 17down!!!!!!!!!!!!!!!!!_`_1!!!!!!! 18down!!!!!!!!!!!!!!,AN``T!!!!!!!! 19down!!!!!!!!!!!!*FZ```_N!!!!!!!! 20down!!!!!!!!!!'=X````S4!!!!!!!!! 21down!!!!!!!!&1V````R5!!!!!!!!!!! 22down!!!!!!%KW````Q5#!!!!!!!!!!!! 23down!!!!.LY````^B#!!!!!!!!!!!!!! 24down!!!!C```VBB%!!!!!!!!!!!!!!!! 25down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 26down!!!!!!!!!!!!!!!!!!!!!!!!!!!! 27down!!!!!!!!!!!!!!!!!!!!!!!!!!!! ``` - label: Just a number with the texts matching label.

267

0

Fraser/mnist-text-small

false

[]

MNIST dataset adapted to a text-based representation. *Modified images to be ~1/4 the original area.* Done by taking a max pool. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Data format: - text: (16 x 14 tokens, 224 tokens total): Textual representation of MNIST digit, for example: ``` 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! % % C L a ^ ! ! 03 down ! ! ! - ` ` ` ` ` Y ` Q ! ! 04 down ! ! ! % ` ` ` R ^ ! ! ! ! ! 05 down ! ! ! ! $ G ` ! ! ! ! ! ! ! 06 down ! ! ! ! ! # ` Y < ! ! ! ! ! 07 down ! ! ! ! ! ! 5 ` ` F ! ! ! ! 08 down ! ! ! ! ! ! ! % ` ` 1 ! ! ! 09 down ! ! ! ! ! ! F ` ` ` ! ! ! ! 10 down ! ! ! ! 1 ` ` ` ` 4 ! ! ! ! 11 down ! ! L ` ` ` ` 5 ! ! ! ! ! ! 12 down ! ! ` ` V B ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ``` - label: Just a number with the texts matching label.

329

0