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sentence-transformers/NQ-retrieval | false | [] | null | 8 | 0 |
GEM-submissions/lewtun__this-is-a-test-name__1648111972 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
M-Quan/sv_corpora_parliament_processed | false | [] | null | 0 | 0 |
huggan/CelebA-HQ | false | [
"arxiv:1710.10196"
] | null | 87 | 3 |
Jira/mao | false | [
"license:gpl"
] | null | 0 | 0 |
huggan/cartoon-faces | false | [] | null | 1 | 0 |
huggan/cats | false | [] | null | 2 | 0 |
Gare/Classical_Chinese_to_Modern_Chinese | false | [
"license:mit"
] | null | 2 | 0 |
Vipitis/Shadertoys-bimodal | false | [] | null | 0 | 0 |
ebrigham/NOS-news | false | [] | null | 0 | 0 |
GEM-submissions/lewtun__this-is-a-test-name__1648137608 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
wesamhaddad14/spanishNLP | false | [] | null | 0 | 0 |
Openmindedness/mc_chat_scraped_from_toxigon_anarchy | false | [
"license:cc"
] | null | 0 | 0 |
huggan/AFHQ | false | [] | null | 5,624 | 0 |
DrishtiSharma/MESD-Processed-Dataset-v2 | false | [] | null | 0 | 0 |
beyond/20NG | false | [] | null | 1 | 0 |
huggan/AFHQv2 | false | [] | null | 13 | 0 |
DFKI-SLT/scidtb | false | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
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] | Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question
answering. SciDTB is a domain-specific discourse treebank annotated on scientific articles.
Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is
flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework,
annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating
discourse dependency parsers, on which we provide several baselines as fundamental work. | 0 | 2 |
huggan/metfaces | false | [] | null | 0 | 0 |
pietrolesci/nli_fever | false | [] | null | 43 | 1 |
pietrolesci/conj_nli | false | [] | null | 161 | 0 |
Fatima-Gh/GLARE | false | [] | null | 0 | 0 |
sosuke/dataset_for_ease | false | [] | null | 0 | 1 |
GEM-submissions/lewtun__this-is-a-test-name__1648220072 | false | [
"benchmark:gem",
"evaluation",
"benchmark"
] | null | 0 | 0 |
roman_urdu_hate_speech | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ur",
"license:mit",
"binary classification"
] | The Roman Urdu Hate-Speech and Offensive Language Detection (RUHSOLD) dataset is a Roman Urdu dataset of tweets annotated by experts in the relevant language. The authors develop the gold-standard for two sub-tasks. First sub-task is based on binary labels of Hate-Offensive content and Normal content (i.e., inoffensive language). These labels are self-explanatory. The authors refer to this sub-task as coarse-grained classification. Second sub-task defines Hate-Offensive content with four labels at a granular level. These labels are the most relevant for the demographic of users who converse in RU and are defined in related literature. The authors refer to this sub-task as fine-grained classification. The objective behind creating two gold-standards is to enable the researchers to evaluate the hate speech detection approaches on both easier (coarse-grained) and challenging (fine-grained) scenarios. \ | 51 | 1 |
JuanJoseMV/CIE10-classifier-Test_Dataset | false | [] | null | 0 | 0 |
nndhung/garlic | false | [] | null | 0 | 0 |
avacaondata/lfqa_squad | false | [] | null | 0 | 0 |
Splend1dchan/NMSQA_w2v2-st-ft | false | [] | null | 0 | 0 |
Gare/github-issues | false | [] | null | 1 | 0 |
benjamin/ner-uk | false | [
"task_categories:token-classification",
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"multilinguality:monolingual",
"language:uk",
"license:cc-by-nc-sa-4.0"
] | null | 0 | 0 |
laion/laion2B-en-safety | false | [
"license:cc-by-4.0"
] | null | 0 | 0 |
laion/laion2B-multi-safety | false | [
"license:cc-by-4.0"
] | null | 0 | 0 |
laion/laion1B-nolang-safety | false | [
"license:cc-by-4.0"
] | null | 2 | 0 |
laion/laion5B-index | false | [
"license:cc-by-4.0"
] | null | 0 | 12 |
Marmoot/Fake_News_jpposadas | false | [
"license:cc-by-4.0"
] | null | 0 | 0 |
Marmoot/Kaggle_1 | false | [] | null | 0 | 0 |
Georgii/russianPoetry | false | [
"license:mit"
] | null | 2 | 1 |
MorVentura/TRBLLmaker | false | [] | null | 0 | 1 |
jglaser/pdbbind_complexes | false | [
"molecules",
"chemistry",
"SMILES"
] | A dataset to fine-tune language models on protein-ligand binding affinity and contact prediction. | 0 | 0 |
ashishpapanai/inverted_vs_normal | false | [] | null | 0 | 0 |
Jiangjie/ekar_chinese | false | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_ids:explanation-generation",
"size_categories:1K<n<2K",
"source_datasets:original",
"language:zh",
"license:afl-3.0"
] | null | 1 | 4 |
Jiangjie/ekar_english | false | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_ids:explanation-generation",
"size_categories:1K<n<2K",
"source_datasets:original",
"language:en",
"license:afl-3.0"
] | null | 0 | 3 |
atenglens/taiwanese_english_translation | false | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:translation",
"task_ids:language-modeling",
"language_creators:other",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|other",
"language:tw",
"language:en",
"conditional-text-generation"
] | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | 3 | 1 |
nadhifikbarw/id_ohsuhmed | false | [
"task_categories:text-classification",
"language:id"
] | null | 8 | 0 |
tomekkorbak/pile-toxic-chunk-0 | false | [] | null | 0 | 0 |
UrukHan/wav2vec2-russian | false | [] | null | 0 | 0 |
T-202/github-issues | false | [] | null | 0 | 0 |
TzRain/AMPs | false | [] | null | 0 | 0 |
smilegate-ai/kor_unsmile | false | [] | null | 450 | 0 |
UrukHan/t5-russian-spell_I | false | [] | null | 6 | 0 |
UrukHan/t5-russian-spell_II | false | [] | null | 0 | 0 |
UrukHan/t5-russian-spell_III | false | [] | null | 0 | 0 |
stjokerli/TextToText_DocNLI_seqio | false | [] | null | 0 | 0 |
laion/conceptual-captions-12m-webdataset | false | [] | null | 1 | 3 |
leonadase/fdRE | false | [] | \
fdRE是一个中文的轴承故障诊断领域的关系抽取数据集
该数据集主要包含正向从属、反向从属以及无关三类标签 | 0 | 0 |
IIC/spanish_biomedical_crawled_corpus_splitted | false | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:IIC/spanish_biomedical_crawled_corpus",
"language:es",
"arxiv:2109.07765"
] | null | 0 | 0 |
mrm8488/AnswerSum | false | [] | null | 0 | 0 |
IIC/ms_marco_es | false | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ms_marco",
"language:es"
] | null | 0 | 0 |
stjokerli/TextToText_squad_seqio | false | [] | null | 0 | 0 |
sac3tf/roman_urdu | false | [] | null | 0 | 0 |
adv_glue | false | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:sentiment-classification",
"annotations_creators:other",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|glue",
"language:en",
"license:cc-by-sa-4.0",
"paraphrase-identification",
"qa-nli",
"arxiv:2111.02840"
] | Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark
that focuses on the adversarial robustness evaluation of language models. It covers five
natural language understanding tasks from the famous GLUE tasks and is an adversarial
version of GLUE benchmark. | 133 | 2 |
sichenzhong/squad_v2_synonym_aug | false | [] | null | 0 | 0 |
carolina-c4ai/corpus-carolina | false | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1B<n<10B",
"source_datasets:original",
"language:pt",
"license:cc-by-nc-sa-4.0"
] | Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
robust volume of texts of varied typology in contemporary Brazilian Portuguese
(1970-2021). | 37 | 4 |
wrapper228/arxiv_data_extended | false | [] | null | 0 | 0 |
nobodylll/test_huggingface_dataset | false | [] | null | 0 | 0 |
laion/laion-synthetic-115m | false | [] | null | 3 | 2 |
IIC/msmarco_es | false | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ms_marco",
"language:es"
] | null | 0 | 0 |
laion/laion2B-en-watermark | false | [
"license:cc-by-4.0"
] | null | 1 | 0 |
KeithHorgan98/autotrain-data-TweetClimateAnalysis | false | [
"task_categories:text-classification"
] | null | 0 | 0 |
laion/water-vit-webdataset | false | [] | null | 5 | 1 |
Splend1dchan/NMSQA_w2v2-st-ft2 | false | [] | null | 0 | 0 |
Pavithra/sampled-code-parrot-ds-train | false | [] | null | 0 | 0 |
Pavithra/sampled-code-parrot-ds-valid | false | [] | null | 0 | 0 |
M-Quan/sv_corpora_parliament_processe | false | [] | null | 0 | 0 |
hackathon-pln-es/Dataset-Acoso-Twitter-Es | false | [
"license:gpl-3.0"
] | null | 0 | 2 |
abdusah/arabic_speech_massive_sm | false | [] | null | 0 | 1 |
huggan/horse2zebra | false | [
"arxiv:1703.10593"
] | null | 5 | 0 |
tskolm/youtube_top_popular_videos_comments | false | [] | null | 0 | 0 |
huggan/monet2photo | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
huggan/cezanne2photo | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
huggan/ukiyoe2photo | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
huggan/vangogh2photo | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
huggan/apple2orange | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
huggan/iphone2dslr_flower | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
huggan/summer2winter_yosemite | false | [
"arxiv:1703.10593"
] | null | 2 | 0 |
huggan/grumpifycat | false | [
"arxiv:1703.10593"
] | null | 0 | 0 |
malay-huggingface/jelapang-padi | false | [] | null | 0 | 0 |
rzhang123/UScourt | false | [] | null | 0 | 0 |
marksverdhei/clickbait_title_classification | false | [
"license:mit",
"arxiv:1610.09786"
] | null | 19 | 3 |
laion/laion2B-en-joined | false | [
"license:cc-by-4.0"
] | null | 179 | 4 |
laion/laion2B-multi-joined | false | [
"license:cc-by-4.0"
] | null | 0 | 2 |
laion/laion1B-nolang-joined | false | [] | null | 0 | 0 |
liza-alx/tokenized_data_yahoo | false | [] | null | 0 | 0 |
liza-alx/tokenized_data | false | [] | null | 0 | 0 |
laion/laion2B-multi-watermark | false | [
"license:cc-by-4.0"
] | null | 3 | 1 |
laion/laion1B-nolang-watermark | false | [
"license:cc-by-4.0"
] | null | 0 | 1 |
hackathon-pln-es/nli-es | false | [
"arxiv:1809.05053"
] | null | 12 | 3 |
sichenzhong/squad_v2_word2vec_aug | false | [] | null | 0 | 0 |
vumichien/pitch_japanese_data | false | [] | null | 0 | 2 |
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