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
NYTK/HuCOLA	false	[ "task_ids:text-simplification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:hu", "license:cc-by-sa-4.0" ]	null	265	0
NYTK/HuCoPA	false	[ "task_categories:other", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|other", "language:hu", "license:bsd-2-clause", "commonsense-reasoning" ]	null	266	0
NYTK/HuRC	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:abstractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|other", "language:hu", "license:cc-by-4.0" ]	null	265	0
NYTK/HuSST	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:text-scoring", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|other", "language:hu", "license:bsd-2-clause" ]	null	265	0
NYTK/HuWNLI	false	[ "task_categories:other", "task_ids:coreference-resolution", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|other", "language:hu", "license:cc-by-sa-4.0", "structure-prediction" ]	null	265	2
NahedAbdelgaber/evaluating-student-writing	false	[]	null	267	0
Narsil/asr_dummy	false	[]	Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```	432	0
Narsil/conversational_dummy	false	[]	null	265	0
Narsil/image_dummy	false	[]	\	266	0
Narsil/test_data	false	[]	null	267	0
Nathanael/NPS	false	[]	null	134	0
Navigator/dodydard-marty	false	[]	null	134	0
NbAiLab/NCC_small_100	false	[ "arxiv:2104.09617" ]	\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters.	265	0
NbAiLab/NCC_small_divided	false	[ "arxiv:2104.09617" ]	\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters.	135	0
NbAiLab/NPSC	false	[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:2G<n<1B", "source_datasets:original", "language:no", "language:nb", "language:nn", "license:cc0-1.0", "speech-modeling", "arxiv:2201.10881" ]	The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models. The corpus is created by Språkbanken at the National Library in Norway. NPSC is based on sound recording from meeting in the Norwegian Parliament. These talks are orthographically transcribed to either Norwegian Bokmål or Norwegian Nynorsk. In addition to the data actually included in this dataset, there is a significant amount of metadata that is included in the original corpus. Through the speaker id there is additional information about the speaker, like gender, age, and place of birth (ie dialect). Through the proceedings id the corpus can be linked to the official proceedings from the meetings. The corpus is in total sound recordings from 40 entire days of meetings. This amounts to 140 hours of speech, 65,000 sentences or 1.2 million words.	271	3
NbAiLab/NPSC_test	false	[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:2G<n<1B", "source_datasets:original", "language:nb", "language:no", "language:nn", "license:cc0-1.0", "speech-modeling" ]	null	265	0
NbAiLab/NPSC_test2	false	[ "license:cc0-1.0" ]	null	133	0
NbAiLab/bokmaal_admin	false	[ "arxiv:2104.09617" ]	\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters.	266	0
NbAiLab/norec_agg	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:en", "license:cc-by-4.0", "arxiv:2011.02686" ]	Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian This dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC_fine and removing sentences with conflicting or no sentiment.	264	0
NbAiLab/norne	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:no", "license:other", "structure-prediction", "arxiv:1911.12146" ]	NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.	134	2
NbAiLab/norwegian_parliament	false	[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:no", "license:cc-by-4.0" ]	The Norwegian Parliament Speeches is a collection of text passages from 1998 to 2016 and pronounced at the Norwegian Parliament (Storting) by members of the two major parties: Fremskrittspartiet and Sosialistisk Venstreparti.	265	0
Niciu/github-issues	false	[]	null	134	0
Niciu/test-cre-dataset-issues	false	[]	null	135	0
Niciu/test-squad	false	[]	null	135	0
NikolajW/NPS_nonNormalized-Cased	false	[]	null	135	0
NishinoTSK/leishmaniaV2	false	[]	null	135	0
NishinoTSK/leishmaniav1	false	[]	null	267	0
Nuwaisir/Quran_speech_recognition_kaggle	false	[]	null	265	0
Ofrit/tmp	false	[]	null	135	0
Omar2027/caner_replicate	false	[]	Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.	267	0
OmarN121/train	false	[]	null	267	0
PDJ107/riot-data	false	[]	null	136	0
Paul/hatecheck	false	[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2012.15606" ]	null	295	2
PaulLerner/triviaqa_for_viquae	false	[]	null	267	0
PaulLerner/triviaqa_splits_for_viquae	false	[]	null	267	0
PaulLerner/viquae_all_images	false	[]	null	135	0
PaulLerner/viquae_dataset	false	[]	null	277	2
PaulLerner/viquae_images	false	[]	null	135	0
PaulLerner/viquae_wikipedia	false	[]	null	273	0
Pengfei/asfwe	false	[]	null	135	0
Pengfei/test	false	[]	null	135	0
Pengfei/test1	false	[]	null	135	0
PereLluis13/parla_text_corpus	false	[]	null	267	0
PereLluis13/spanish_speech_text	false	[]	null	265	0
Perkhad/corejur	false	[]	null	267	0
PlanTL-GOB-ES/SQAC	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "arxiv:1606.05250" ]	This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment. The sources of the contexts are: * Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode). * News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/). * Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode). This dataset can be used to build extractive-QA.	292	2
PlanTL-GOB-ES/cantemist-ner	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish" ]	https://temu.bsc.es/cantemist/	267	1
PlanTL-GOB-ES/pharmaconer	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish" ]	PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje (Plan TL). It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online). The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR. The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each. In terms of training examples, this translates to a total of 8074, 3764 and 3931 annotated sentences in each set. The original dataset was distributed in Brat format (https://brat.nlplab.org/standoff.html). For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to [email protected]	274	0
Plim/common_voice_7_0_fr_processed	false	[ "language:fr" ]	null	265	0
Plim/fr_corpora_parliament_processed	false	[ "language:fr" ]	null	265	0
Plim/fr_wikipedia_processed	false	[ "language:fr" ]	null	265	0
Plim/language_model_fr	false	[ "language:fr" ]	null	264	0
Pongsaky/Wiki_SCG	false	[]	null	135	0
Pratik/Gujarati_OpenSLR	false	[]	null	134	0
Pyjay/emotion_nl	false	[]	null	143	0
Pyke/patent_abstract	false	[]	null	134	0
QA/abk-eng	false	[]	null	135	0
R0bk/XFUN	false	[ "license:mit" ]	null	152	0
RBG-AI/CoRePooL	false	[]	null	135	0
Recognai/ag_news_corrected_labels	false	[]	null	267	0
Recognai/corrected_labels_ag_news	false	[]	null	267	0
Recognai/gutenberg_spacy-ner	false	[]	null	135	0
Recognai/imdb_spacy-ner	false	[]	null	135	0
Recognai/news	false	[]	null	267	0
Recognai/sentiment-banking	false	[]	null	1,691	0
Recognai/veganuary	false	[]	null	267	0
Remesita/tagged_reviews	false	[]	null	134	0
RohanAiLab/persian_blog	false	[ "task_categories:text-generation", "task_ids:language-modeling", "source_datasets:original", "language:fa" ]	persian_blog is a dataset consist of 400K blog posts from various websites and has types of tones. this dataset can be used in different NLG tasks and as a show-case it's is used in training reformer-persian.	327	1
RohanAiLab/persian_daily_news	false	[ "source_datasets:original", "language:fa" ]	Persian Daily News dataset is a collection of 2 million news articles with the headline of each news article. This dataset contains news articles and their summaries for the last 10 years. This dataset is provided by Rohan AI lab for research purposes.	263	0
RohanAiLab/persian_news_dataset	false	[ "task_categories:text-classification", "task_ids:language-modeling", "task_ids:multi-class-classification", "source_datasets:original", "language:fa" ]	persian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. The dataset is provided by Rohan AI lab for research purposes. for more information refer to this link:	266	1
RollingMuffin/test_scripts	false	[]	This dataset is designed to generate lyrics with HuggingArtists.	267	0
RuudVelo/commonvoice_mt_8_processed	false	[]	null	135	0
RuudVelo/commonvoice_nl_8_processed	false	[]	null	135	0
RuudVelo/nl_corpora_parliament_processed	false	[]	null	266	0
SCourthial/test	false	[]	null	135	0
Sabokou/qg_squad_modified	false	[]	null	267	0
Sabokou/qg_squad_modified_dev	false	[]	null	267	0
SajjadAyoubi/persian_qa	false	[]	\\\\\\\Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer".	282	2
Sakonii/nepalitext-language-model-dataset	false	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "language_creators:other", "multilinguality:monolingual", "source_datasets:extended\|oscar", "source_datasets:extended\|cc100", "language:ne", "license:cc0-1.0" ]	null	271	0
Sam2021/Arguement_Mining_CL2017	false	[]	tokens along with chunk id. IOB1 format Begining of arguement denoted by B-ARG,inside arguement denoted by I-ARG, other chunks are O Orginial train,test split as used by the paper is provided	267	1
Samip/func	false	[]	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.	267	0
SaulLu/Natural_Questions_HTML	false	[]	null	266	0
SaulLu/Natural_Questions_HTML_Toy	false	[]	null	267	0
SaulLu/Natural_Questions_HTML_reduced_all	false	[]	null	266	0
SaulLu/test	false	[]	null	135	0
SaulLu/toy_struc_dataset	false	[]	null	267	0
SebastianS/github-issues	false	[ "task_categories:text-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en-US" ]	null	265	0
SergeiGKS/wikiner_fr_job	false	[]	null	136	0
Serhii/Custom_SQuAD	false	[]	Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.	267	0
SetFit/20_newsgroups	false	[]	null	3,021	3
SetFit/TREC-QC	false	[]	null	328	0
SetFit/ag_news	false	[]	null	795	0
SetFit/amazon_counterfactual	false	[ "arxiv:2104.06893" ]	The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).	666	0
SetFit/amazon_counterfactual_en	false	[ "arxiv:2104.06893" ]	null	722	0
SetFit/amazon_polarity	false	[]	null	267	0
SetFit/bbc-news	false	[]	null	443	3
SetFit/emotion	false	[]	null	4,082	4
SetFit/enron_spam	false	[]	null	1,359	5
SetFit/ethos	false	[]	ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech detection on social media platforms, called Ethos. There are two variations of the dataset: Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label about hate speech presence or absence. 565 of them do not contain hate speech, while the rest of them, 433, contain. Ethos_Dataset_Multi_Label: which contains 8 labels for the 433 comments with hate speech content. These labels are violence (if it incites (1) or not (0) violence), directed_vs_general (if it is directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like, gender, race, national_origin, disability, religion and sexual_orientation.	400	0
SetFit/ethos_binary	false	[]	null	275	0

NYTK/HuCOLA

false

[ "task_ids:text-simplification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:hu", "license:cc-by-sa-4.0" ]

null

265

0

NYTK/HuCoPA

false

[ "task_categories:other", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:bsd-2-clause", "commonsense-reasoning" ]

null

266

0

NYTK/HuRC

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:abstractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:cc-by-4.0" ]

null

265

0

NYTK/HuSST

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:text-scoring", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:bsd-2-clause" ]

null

265

0

NYTK/HuWNLI

false

[ "task_categories:other", "task_ids:coreference-resolution", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:cc-by-sa-4.0", "structure-prediction" ]

null

265

2

NahedAbdelgaber/evaluating-student-writing

false

[]

null

267

0

Narsil/asr_dummy

false

[]

Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

432

0

Narsil/conversational_dummy

false

[]

null

265

0

Narsil/image_dummy

false

[]

\

266

0

Narsil/test_data

false

[]

null

267

0

Nathanael/NPS

false

[]

null

134

0

Navigator/dodydard-marty

false

[]

null

134

0

NbAiLab/NCC_small_100

false

[ "arxiv:2104.09617" ]

\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters.

265

0

NbAiLab/NCC_small_divided

false

[ "arxiv:2104.09617" ]

\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters.

135

0

NbAiLab/NPSC

false

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:2G<n<1B", "source_datasets:original", "language:no", "language:nb", "language:nn", "license:cc0-1.0", "speech-modeling", "arxiv:2201.10881" ]

The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models. The corpus is created by Språkbanken at the National Library in Norway. NPSC is based on sound recording from meeting in the Norwegian Parliament. These talks are orthographically transcribed to either Norwegian Bokmål or Norwegian Nynorsk. In addition to the data actually included in this dataset, there is a significant amount of metadata that is included in the original corpus. Through the speaker id there is additional information about the speaker, like gender, age, and place of birth (ie dialect). Through the proceedings id the corpus can be linked to the official proceedings from the meetings. The corpus is in total sound recordings from 40 entire days of meetings. This amounts to 140 hours of speech, 65,000 sentences or 1.2 million words.

271

3

NbAiLab/NPSC_test

false

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:2G<n<1B", "source_datasets:original", "language:nb", "language:no", "language:nn", "license:cc0-1.0", "speech-modeling" ]

null

265

0

NbAiLab/NPSC_test2

false

[ "license:cc0-1.0" ]

null

133

0

NbAiLab/bokmaal_admin

false

[ "arxiv:2104.09617" ]

\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters.

266

0

NbAiLab/norec_agg

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:en", "license:cc-by-4.0", "arxiv:2011.02686" ]

Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian This dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC_fine and removing sentences with conflicting or no sentiment.

264

0

NbAiLab/norne

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:no", "license:other", "structure-prediction", "arxiv:1911.12146" ]

NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.

134

2

NbAiLab/norwegian_parliament

false

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:no", "license:cc-by-4.0" ]

The Norwegian Parliament Speeches is a collection of text passages from 1998 to 2016 and pronounced at the Norwegian Parliament (Storting) by members of the two major parties: Fremskrittspartiet and Sosialistisk Venstreparti.

265

0

Niciu/github-issues

false

[]

null

134

0

Niciu/test-cre-dataset-issues

false

[]

null

135

0

Niciu/test-squad

false

[]

null

135

0

NikolajW/NPS_nonNormalized-Cased

false

[]

null

135

0

NishinoTSK/leishmaniaV2

false

[]

null

135

0

NishinoTSK/leishmaniav1

false

[]

null

267

0

Nuwaisir/Quran_speech_recognition_kaggle

false

[]

null

265

0

Ofrit/tmp

false

[]

null

135

0

Omar2027/caner_replicate

false

[]

Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.

267

0

OmarN121/train

false

[]

null

267

0

PDJ107/riot-data

false

[]

null

136

0

Paul/hatecheck

false

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2012.15606" ]

null

295

2

PaulLerner/triviaqa_for_viquae

false

[]

null

267

0

PaulLerner/triviaqa_splits_for_viquae

false

[]

null

267

0

PaulLerner/viquae_all_images

false

[]

null

135

0

PaulLerner/viquae_dataset

false

[]

null

277

2

PaulLerner/viquae_images

false

[]

null

135

0

PaulLerner/viquae_wikipedia

false

[]

null

273

0

Pengfei/asfwe

false

[]

null

135

0

Pengfei/test

false

[]

null

135

0

Pengfei/test1

false

[]

null

135

0

PereLluis13/parla_text_corpus

false

[]

null

267

0

PereLluis13/spanish_speech_text

false

[]

null

265

0

Perkhad/corejur

false

[]

null

267

0

PlanTL-GOB-ES/SQAC

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "arxiv:1606.05250" ]

This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment. The sources of the contexts are: * Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode). * News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/). * Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode). This dataset can be used to build extractive-QA.

292

2

PlanTL-GOB-ES/cantemist-ner

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish" ]

https://temu.bsc.es/cantemist/

267

1

PlanTL-GOB-ES/pharmaconer

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish" ]

PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje (Plan TL). It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online). The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR. The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each. In terms of training examples, this translates to a total of 8074, 3764 and 3931 annotated sentences in each set. The original dataset was distributed in Brat format (https://brat.nlplab.org/standoff.html). For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to [email protected]

274

0

Plim/common_voice_7_0_fr_processed

false

[ "language:fr" ]

null

265

0

Plim/fr_corpora_parliament_processed

false

[ "language:fr" ]

null

265

0

Plim/fr_wikipedia_processed

false

[ "language:fr" ]

null

265

0

Plim/language_model_fr

false

[ "language:fr" ]

null

264

0

Pongsaky/Wiki_SCG

false

[]

null

135

0

Pratik/Gujarati_OpenSLR

false

[]

null

134

0

Pyjay/emotion_nl

false

[]

null

143

0

Pyke/patent_abstract

false

[]

null

134

0

QA/abk-eng

false

[]

null

135

0

R0bk/XFUN

false

[ "license:mit" ]

null

152

0

RBG-AI/CoRePooL

false

[]

null

135

0

Recognai/ag_news_corrected_labels

false

[]

null

267

0

Recognai/corrected_labels_ag_news

false

[]

null

267

0

Recognai/gutenberg_spacy-ner

false

[]

null

135

0

Recognai/imdb_spacy-ner

false

[]

null

135

0

Recognai/news

false

[]

null

267

0

Recognai/sentiment-banking

false

[]

null

1,691

0

Recognai/veganuary

false

[]

null

267

0

Remesita/tagged_reviews

false

[]

null

134

0

RohanAiLab/persian_blog

false

[ "task_categories:text-generation", "task_ids:language-modeling", "source_datasets:original", "language:fa" ]

persian_blog is a dataset consist of 400K blog posts from various websites and has types of tones. this dataset can be used in different NLG tasks and as a show-case it's is used in training reformer-persian.

327

1

RohanAiLab/persian_daily_news

false

[ "source_datasets:original", "language:fa" ]

Persian Daily News dataset is a collection of 2 million news articles with the headline of each news article. This dataset contains news articles and their summaries for the last 10 years. This dataset is provided by Rohan AI lab for research purposes.

263

0

RohanAiLab/persian_news_dataset

false

[ "task_categories:text-classification", "task_ids:language-modeling", "task_ids:multi-class-classification", "source_datasets:original", "language:fa" ]

persian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. The dataset is provided by Rohan AI lab for research purposes. for more information refer to this link:

266

1

RollingMuffin/test_scripts

false

[]

This dataset is designed to generate lyrics with HuggingArtists.

267

0

RuudVelo/commonvoice_mt_8_processed

false

[]

null

135

0

RuudVelo/commonvoice_nl_8_processed

false

[]

null

135

0

RuudVelo/nl_corpora_parliament_processed

false

[]

null

266

0

SCourthial/test

false

[]

null

135

0

Sabokou/qg_squad_modified

false

[]

null

267

0

Sabokou/qg_squad_modified_dev

false

[]

null

267

0

SajjadAyoubi/persian_qa

false

[]

\\\\\\\Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer".

282

2

Sakonii/nepalitext-language-model-dataset

false

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "language_creators:other", "multilinguality:monolingual", "source_datasets:extended|oscar", "source_datasets:extended|cc100", "language:ne", "license:cc0-1.0" ]

null

271

0

Sam2021/Arguement_Mining_CL2017

false

[]

tokens along with chunk id. IOB1 format Begining of arguement denoted by B-ARG,inside arguement denoted by I-ARG, other chunks are O Orginial train,test split as used by the paper is provided

267

1

Samip/func

false

[]

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

267

0

SaulLu/Natural_Questions_HTML

false

[]

null

266

0

SaulLu/Natural_Questions_HTML_Toy

false

[]

null

267

0

SaulLu/Natural_Questions_HTML_reduced_all

false

[]

null

266

0

SaulLu/test

false

[]

null

135

0

SaulLu/toy_struc_dataset

false

[]

null

267

0

SebastianS/github-issues

false

[ "task_categories:text-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en-US" ]

null

265

0

SergeiGKS/wikiner_fr_job

false

[]

null

136

0

Serhii/Custom_SQuAD

false

[]

Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.

267

0

SetFit/20_newsgroups

false

[]

null

3,021

3

SetFit/TREC-QC

false

[]

null

328

0

SetFit/ag_news

false

[]

null

795

0

SetFit/amazon_counterfactual

false

[ "arxiv:2104.06893" ]

The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).

666

0

SetFit/amazon_counterfactual_en

false

[ "arxiv:2104.06893" ]

null

722

0

SetFit/amazon_polarity

false

[]

null

267

0

SetFit/bbc-news

false

[]

null

443

3

SetFit/emotion

false

[]

null

4,082

4

SetFit/enron_spam

false

[]

null

1,359

5

SetFit/ethos

false

[]

ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech detection on social media platforms, called Ethos. There are two variations of the dataset: Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label about hate speech presence or absence. 565 of them do not contain hate speech, while the rest of them, 433, contain. Ethos_Dataset_Multi_Label: which contains 8 labels for the 433 comments with hate speech content. These labels are violence (if it incites (1) or not (0) violence), directed_vs_general (if it is directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like, gender, race, national_origin, disability, religion and sexual_orientation.

400

0

SetFit/ethos_binary

false

[]

null

275

0