Hugging Face model: sileod/deberta-v3-large-tasksource-nli
Author: sileod
Created: 2023-03-27 08:47:29
Last modified: 2024-02-17 05:12:52
Pipeline: zero-shot-classification
Library: transformers
Likes: 40 | Downloads: 3375
Tags: transformers, pytorch, safetensors, deberta-v2, text-classification, deberta-v3-large, nli, natural-language-inference, multitask, multi-task, pipeline, extreme-multi-task, extreme-mtl, tasksource, zero-shot, rlhf, zero-shot-classification, en, dataset:glue, dataset:super_glue, dataset:anli, dataset:metaeval/babi_nli, dataset:sick, dataset:snli, dataset:scitail, dataset:hans, dataset:alisawuffles/WANLI, dataset:metaeval/recast, dataset:sileod/probability_words_nli, dataset:joey234/nan-nli, dataset:pietrolesci/nli_fever, dataset:pietrolesci/breaking_nli, dataset:pietrolesci/conj_nli, dataset:pietrolesci/fracas, dataset:pietrolesci/dialogue_nli, dataset:pietrolesci/mpe, dataset:pietrolesci/dnc, dataset:pietrolesci/gpt3_nli, dataset:pietrolesci/recast_white, dataset:pietrolesci/joci, dataset:martn-nguyen/contrast_nli, dataset:pietrolesci/robust_nli, dataset:pietrolesci/robust_nli_is_sd, dataset:pietrolesci/robust_nli_li_ts, dataset:pietrolesci/gen_debiased_nli, dataset:pietrolesci/add_one_rte, dataset:metaeval/imppres, dataset:pietrolesci/glue_diagnostics, dataset:hlgd, dataset:paws, dataset:quora, dataset:medical_questions_pairs, dataset:conll2003, dataset:Anthropic/hh-rlhf, dataset:Anthropic/model-written-evals, dataset:truthful_qa, dataset:nightingal3/fig-qa, dataset:tasksource/bigbench, dataset:bigbench, dataset:blimp, dataset:cos_e, dataset:cosmos_qa, dataset:dream, dataset:openbookqa, dataset:qasc, dataset:quartz, dataset:quail, dataset:head_qa, dataset:sciq, dataset:social_i_qa, dataset:wiki_hop, dataset:wiqa, dataset:piqa, dataset:hellaswag, dataset:pkavumba/balanced-copa, dataset:12ml/e-CARE, dataset:art, dataset:tasksource/mmlu, dataset:winogrande, dataset:codah, dataset:ai2_arc, dataset:definite_pronoun_resolution, dataset:swag, dataset:math_qa, dataset:metaeval/utilitarianism, dataset:mteb/amazon_counterfactual, dataset:SetFit/insincere-questions, dataset:SetFit/toxic_conversations, dataset:turingbench/TuringBench, dataset:trec, dataset:tals/vitaminc, dataset:hope_edi, dataset:strombergnlp/rumoureval_2019, dataset:ethos, dataset:tweet_eval, dataset:discovery, dataset:pragmeval, dataset:silicone, dataset:lex_glue, dataset:papluca/language-identification, dataset:imdb, dataset:rotten_tomatoes, dataset:ag_news, dataset:yelp_review_full, dataset:financial_phrasebank, dataset:poem_sentiment, dataset:dbpedia_14, dataset:amazon_polarity, dataset:app_reviews, dataset:hate_speech18, dataset:sms_spam, dataset:humicroedit, dataset:snips_built_in_intents, dataset:banking77, dataset:hate_speech_offensive, dataset:yahoo_answers_topics, dataset:pacovaldez/stackoverflow-questions, dataset:zapsdcn/hyperpartisan_news, dataset:zapsdcn/sciie, dataset:zapsdcn/citation_intent, dataset:go_emotions, dataset:scicite, dataset:liar, dataset:relbert/lexical_relation_classification, dataset:metaeval/linguisticprobing, dataset:metaeval/crowdflower, dataset:metaeval/ethics, dataset:emo, dataset:google_wellformed_query, dataset:tweets_hate_speech_detection, dataset:has_part, dataset:wnut_17, dataset:ncbi_disease, dataset:acronym_identification, dataset:jnlpba, dataset:species_800, dataset:SpeedOfMagic/ontonotes_english, dataset:blog_authorship_corpus, dataset:launch/open_question_type, dataset:health_fact, dataset:commonsense_qa, dataset:mc_taco, dataset:ade_corpus_v2, dataset:prajjwal1/discosense, dataset:circa, dataset:YaHi/EffectiveFeedbackStudentWriting, dataset:Ericwang/promptSentiment, dataset:Ericwang/promptNLI, dataset:Ericwang/promptSpoke, dataset:Ericwang/promptProficiency, dataset:Ericwang/promptGrammar, dataset:Ericwang/promptCoherence, dataset:PiC/phrase_similarity, dataset:copenlu/scientific-exaggeration-detection, dataset:quarel, dataset:mwong/fever-evidence-related, dataset:numer_sense, dataset:dynabench/dynasent, dataset:raquiba/Sarcasm_News_Headline, dataset:sem_eval_2010_task_8, dataset:demo-org/auditor_review, dataset:medmcqa, dataset:aqua_rat, dataset:RuyuanWan/Dynasent_Disagreement, dataset:RuyuanWan/Politeness_Disagreement, dataset:RuyuanWan/SBIC_Disagreement, dataset:RuyuanWan/SChem_Disagreement, dataset:RuyuanWan/Dilemmas_Disagreement, dataset:lucasmccabe/logiqa, dataset:wiki_qa, dataset:metaeval/cycic_classification, dataset:metaeval/cycic_multiplechoice, dataset:metaeval/sts-companion, dataset:metaeval/commonsense_qa_2.0, dataset:metaeval/lingnli, dataset:metaeval/monotonicity-entailment, dataset:metaeval/arct, dataset:metaeval/scinli, dataset:metaeval/naturallogic, dataset:onestop_qa, dataset:demelin/moral_stories, dataset:corypaik/prost, dataset:aps/dynahate, dataset:metaeval/syntactic-augmentation-nli, dataset:metaeval/autotnli, dataset:lasha-nlp/CONDAQA, dataset:openai/webgpt_comparisons, dataset:Dahoas/synthetic-instruct-gptj-pairwise, dataset:metaeval/scruples, dataset:metaeval/wouldyourather, dataset:sileod/attempto-nli, dataset:metaeval/defeasible-nli, dataset:metaeval/help-nli, dataset:metaeval/nli-veridicality-transitivity, dataset:metaeval/natural-language-satisfiability, dataset:metaeval/lonli, dataset:metaeval/dadc-limit-nli, dataset:ColumbiaNLP/FLUTE, dataset:metaeval/strategy-qa, dataset:openai/summarize_from_feedback, dataset:metaeval/folio, dataset:metaeval/tomi-nli, dataset:metaeval/avicenna, dataset:stanfordnlp/SHP, dataset:GBaker/MedQA-USMLE-4-options-hf, dataset:sileod/wikimedqa, dataset:declare-lab/cicero, dataset:amydeng2000/CREAK, dataset:metaeval/mutual, dataset:inverse-scaling/NeQA, dataset:inverse-scaling/quote-repetition, dataset:inverse-scaling/redefine-math, dataset:metaeval/puzzte, dataset:metaeval/implicatures, dataset:race, dataset:metaeval/spartqa-yn, dataset:metaeval/spartqa-mchoice, dataset:metaeval/temporal-nli, arxiv:2301.05948, license:apache-2.0, endpoints_compatible, region:us, deploy:azure
Card metadata:
license: apache-2.0
language: en
tags: deberta-v3-large, text-classification, nli, natural-language-inference, multitask, multi-task, pipeline, extreme-multi-task, extreme-mtl, tasksource, zero-shot, rlhf
pipeline_tag: zero-shot-classification
datasets: glue, super_glue, anli, metaeval/babi_nli, sick, snli, scitail, hans, alisawuffles/WANLI, metaeval/recast, sileod/probability_words_nli, joey234/nan-nli, pietrolesci/nli_fever, pietrolesci/breaking_nli, pietrolesci/conj_nli, pietrolesci/fracas, pietrolesci/dialogue_nli, pietrolesci/mpe, pietrolesci/dnc, pietrolesci/gpt3_nli, pietrolesci/recast_white, pietrolesci/joci, martn-nguyen/contrast_nli, pietrolesci/robust_nli, pietrolesci/robust_nli_is_sd, pietrolesci/robust_nli_li_ts, pietrolesci/gen_debiased_nli, pietrolesci/add_one_rte, metaeval/imppres, pietrolesci/glue_diagnostics, hlgd, paws, quora, medical_questions_pairs, conll2003, Anthropic/hh-rlhf, Anthropic/model-written-evals, truthful_qa, nightingal3/fig-qa, tasksource/bigbench, bigbench, blimp, cos_e, cosmos_qa, dream, openbookqa, qasc, quartz, quail, head_qa, sciq, social_i_qa, wiki_hop, wiqa, piqa, hellaswag, pkavumba/balanced-copa, 12ml/e-CARE, art, tasksource/mmlu, winogrande, codah, ai2_arc, definite_pronoun_resolution, swag, math_qa, metaeval/utilitarianism, mteb/amazon_counterfactual, SetFit/insincere-questions, SetFit/toxic_conversations, turingbench/TuringBench, trec, tals/vitaminc, hope_edi, strombergnlp/rumoureval_2019, ethos, tweet_eval, discovery, pragmeval, silicone, lex_glue, papluca/language-identification, imdb, rotten_tomatoes, ag_news, yelp_review_full, financial_phrasebank, poem_sentiment, dbpedia_14, amazon_polarity, app_reviews, hate_speech18, sms_spam, humicroedit, snips_built_in_intents, banking77, hate_speech_offensive, yahoo_answers_topics, pacovaldez/stackoverflow-questions, zapsdcn/hyperpartisan_news, zapsdcn/sciie, zapsdcn/citation_intent, go_emotions, scicite, liar, relbert/lexical_relation_classification, metaeval/linguisticprobing, metaeval/crowdflower, metaeval/ethics, emo, google_wellformed_query, tweets_hate_speech_detection, has_part, wnut_17, ncbi_disease, acronym_identification, jnlpba, species_800, SpeedOfMagic/ontonotes_english, blog_authorship_corpus, launch/open_question_type, health_fact, commonsense_qa, mc_taco, ade_corpus_v2, prajjwal1/discosense, circa, YaHi/EffectiveFeedbackStudentWriting, Ericwang/promptSentiment, Ericwang/promptNLI, Ericwang/promptSpoke, Ericwang/promptProficiency, Ericwang/promptGrammar, Ericwang/promptCoherence, PiC/phrase_similarity, copenlu/scientific-exaggeration-detection, quarel, mwong/fever-evidence-related, numer_sense, dynabench/dynasent, raquiba/Sarcasm_News_Headline, sem_eval_2010_task_8, demo-org/auditor_review, medmcqa, aqua_rat, RuyuanWan/Dynasent_Disagreement, RuyuanWan/Politeness_Disagreement, RuyuanWan/SBIC_Disagreement, RuyuanWan/SChem_Disagreement, RuyuanWan/Dilemmas_Disagreement, lucasmccabe/logiqa, wiki_qa, metaeval/cycic_classification, metaeval/cycic_multiplechoice, metaeval/sts-companion, metaeval/commonsense_qa_2.0, metaeval/lingnli, metaeval/monotonicity-entailment, metaeval/arct, metaeval/scinli, metaeval/naturallogic, onestop_qa, demelin/moral_stories, corypaik/prost, aps/dynahate, metaeval/syntactic-augmentation-nli, metaeval/autotnli, lasha-nlp/CONDAQA, openai/webgpt_comparisons, Dahoas/synthetic-instruct-gptj-pairwise, metaeval/scruples, metaeval/wouldyourather, sileod/attempto-nli, metaeval/defeasible-nli, metaeval/help-nli, metaeval/nli-veridicality-transitivity, metaeval/natural-language-satisfiability, metaeval/lonli, metaeval/dadc-limit-nli, ColumbiaNLP/FLUTE, metaeval/strategy-qa, openai/summarize_from_feedback, metaeval/folio, metaeval/tomi-nli, metaeval/avicenna, stanfordnlp/SHP, GBaker/MedQA-USMLE-4-options-hf, sileod/wikimedqa, declare-lab/cicero, amydeng2000/CREAK, metaeval/mutual, inverse-scaling/NeQA, inverse-scaling/quote-repetition, inverse-scaling/redefine-math, metaeval/puzzte, metaeval/implicatures, race, metaeval/spartqa-yn, metaeval/spartqa-mchoice, metaeval/temporal-nli
metrics: accuracy
library_name: transformers