Datasets:
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HuggingBench-Classification

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model:domischwimmbeck/bert-base-german-cased-20000-ner-uncased [42]
model:Davlan/afro-xlmr-large [8]
model:connectivity/feather_berts_28 [32]
model:connectivity/bert_ft_qqp-1 [32]
model:connectivity/bert_ft_qqp-7 [32]
model:north/t5_large_NCC_modern [40]
model:connectivity/bert_ft_qqp-17 [32]
model:connectivity/bert_ft_qqp-25 [32]
model:strickvl/nlp-redaction-classifier [32]
model:assamim/mt5-small-indonesian [40]
model:connectivity/cola_6ep_ft-10 [32]
model:connectivity/cola_6ep_ft-22 [32]
model:connectivity/cola_6ep_ft-33 [32]
model:connectivity/bert_ft_qqp-94 [32]
model:connectivity/bert_ft_qqp-96 [32]
model:Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish [4]
model:eslamxm/mt5-base-finetuned-arur [26, 40]
model:nickmuchi/swin-tiny-patch4-window7-224-finetuned-eurosat [10]
model:Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition [1, 7]
model:Aniemore/rubert-tiny2-russian-emotion-detection [32]
model:Nanatan/distilbert-base-uncased-finetuned-emotion [32]
model:viviastaari/finetuning-sentiment-analysis-en-id [32]
model:KoichiYasuoka/deberta-small-japanese-aozora [8]
model:csebuetnlp/banglat5 [40]
model:NlpHUST/gpt2-vietnamese [33]
model:fabraz/distilbert-base-uncased-finetunned-emotion [32]
model:pszemraj/opt-peter-2.7B [33]
model:andywedlake/test-finetuned-imdb [8]
model:KoichiYasuoka/deberta-small-japanese-upos [42]
model:KoichiYasuoka/deberta-small-japanese-luw-upos [42]
model:KoichiYasuoka/deberta-base-japanese-aozora [8]
model:KoichiYasuoka/deberta-base-japanese-luw-upos [42]
model:KoichiYasuoka/deberta-base-japanese-upos [42]
model:Servinform/wav2vec2-large-xlsr-53-spanish [4]
model:joaobarroca/distilbert-base-uncased-finetuned-massive-intent-detection-english [32]
model:aramka/arka [22]
model:autoevaluate/binary-classification [32]
model:ccdv/lsg-bart-base-4096-wcep [26, 40]
model:uygarkurt/distilbert-base-uncased-finetuned-emotion [32]
model:KoichiYasuoka/deberta-large-japanese-aozora [8]
model:KoichiYasuoka/deberta-large-japanese-luw-upos [42]
model:KoichiYasuoka/deberta-large-japanese-upos [42]
model:lmqg/mt5-base-jaquad-qg [40]
model:zenkri/autotrain-Arabic_Poetry_by_Subject-920730230 [32]
model:KoichiYasuoka/deberta-small-coptic [8]
model:KoichiYasuoka/deberta-small-coptic-upos [42]
model:KoichiYasuoka/deberta-base-coptic [8]
model:KoichiYasuoka/deberta-base-coptic-upos [42]
model:sharif-dal/dal-bert [8]
model:KoichiYasuoka/deberta-base-thai [8]
model:KoichiYasuoka/deberta-base-thai-upos [42]
model:inkoziev/rugpt_interpreter [33]
model:nickmuchi/deberta-v3-base-finetuned-finance-text-classification [32]
model:ccdv/lsg-bart-base-4096-mediasum [26, 40]
model:jkhan447/sarcasm-detection-Bert-base-uncased [32]
model:jkhan447/sarcasm-detection-RoBerta-base [32]
model:M47Labs/spanish_news_classification_headlines_untrained [32]
model:jkhan447/sarcasm-detection-RoBerta-base-CR [32]
model:jkhan447/sarcasm-detection-Bert-base-uncased-CR [32]
model:apple/mobilevit-x-small [10]
model:apple/deeplabv3-mobilevit-x-small [12]
model:apple/deeplabv3-mobilevit-xx-small [12]
model:joebobby/finetuning-sentiment-model-5000-samples3 [32]
model:nghuyong/ernie-health-zh [7]
model:yukta10/finetuning-sentiment-model-3000-samples [32]
model:classla/bcms-bertic-parlasent-bcs-ter [32]
model:KM4STfulltext/SSCI-SciBERT-e2 [8]
model:KM4STfulltext/SSCI-SciBERT-e4 [8]
model:facebook/levit-256 [10]
model:facebook/levit-128 [10]
model:BM-K/KoSimCSE-bert-multitask [7]
model:research-backup/t5-large-subjqa-restaurants-qg [40]
model:research-backup/t5-large-subjqa-books-qg [40]
model:research-backup/t5-large-subjqa-tripadvisor-qg [40]
model:research-backup/t5-large-subjqa-movies-qg [40]
model:research-backup/t5-large-subjqa-electronics-qg [40]
model:research-backup/bart-large-subjqa-restaurants-qg [40]
model:research-backup/bart-large-subjqa-electronics-qg [40]
model:research-backup/t5-base-subjqa-books-qg [40]
model:research-backup/bart-base-subjqa-electronics-qg [40]
model:research-backup/t5-base-subjqa-restaurants-qg [40]
model:research-backup/bart-large-subjqa-grocery-qg [40]
model:research-backup/t5-small-subjqa-restaurants-qg [40]
model:research-backup/bart-base-subjqa-grocery-qg [40]
model:research-backup/t5-base-subjqa-electronics-qg [40]
model:research-backup/bart-base-subjqa-restaurants-qg [40]
model:research-backup/bart-base-subjqa-tripadvisor-qg [40]
model:research-backup/t5-base-subjqa-tripadvisor-qg [40]
model:research-backup/t5-small-subjqa-books-qg [40]
model:research-backup/bart-large-subjqa-tripadvisor-qg [40]
model:research-backup/t5-small-subjqa-grocery-qg [40]
model:research-backup/bart-base-subjqa-books-qg [40]
model:research-backup/t5-small-subjqa-movies-qg [40]
model:research-backup/bart-large-subjqa-books-qg [40]
model:research-backup/t5-base-subjqa-grocery-qg [40]
model:research-backup/t5-base-subjqa-movies-qg [40]
model:research-backup/t5-small-subjqa-electronics-qg [40]
model:research-backup/t5-small-subjqa-tripadvisor-qg [40]
model:research-backup/bart-large-subjqa-movies-qg [40]
model:research-backup/bart-base-subjqa-movies-qg [40]