Model: zeroMN/SHMT
Author: zeroMN
Pipeline: audio-text-to-text
Library: transformers
Tags: transformers, transformer, multimodal, vqa, text, audio, audio-text-to-text, en, zh, dataset:zeroMN/nlp_corpus_zh, dataset:zeroMN/hanlp_date-zh, dataset:nyu-mll/glue, dataset:aps/super_glue, dataset:facebook/anli, dataset:tasksource/babi_nli, dataset:zeroMN/AVEdate, dataset:sick, dataset:snli, dataset:scitail, dataset:hans, dataset:alisawuffles/WANLI, dataset:tasksource/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/recast_white, dataset:pietrolesci/joci, 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:tasksource/imppres, dataset:hlgd, dataset:paws, dataset:medical_questions_pairs, dataset:Anthropic/model-written-evals, dataset:truthful_qa, dataset:nightingal3/fig-qa, dataset:tasksource/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: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: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:allenai/scicite, dataset:liar, dataset:relbert/lexical_relation_classification, dataset:tasksource/linguisticprobing, dataset:tasksource/crowdflower, dataset:metaeval/ethics, dataset:emo, dataset:google_wellformed_query, dataset:tweets_hate_speech_detection, dataset:has_part, 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: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: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:tasksource/cycic_classification, dataset:tasksource/cycic_multiplechoice, dataset:tasksource/sts-companion, dataset:tasksource/commonsense_qa_2.0, dataset:tasksource/lingnli, dataset:tasksource/monotonicity-entailment, dataset:tasksource/arct, dataset:tasksource/scinli, dataset:tasksource/naturallogic, dataset:onestop_qa, dataset:demelin/moral_stories, dataset:corypaik/prost, dataset:aps/dynahate, dataset:metaeval/syntactic-augmentation-nli, dataset:tasksource/autotnli, dataset:lasha-nlp/CONDAQA, dataset:openai/webgpt_comparisons, dataset:Dahoas/synthetic-instruct-gptj-pairwise, dataset:metaeval/scruples, dataset:metaeval/wouldyourather, dataset:metaeval/defeasible-nli, dataset:tasksource/help-nli, dataset:metaeval/nli-veridicality-transitivity, dataset:tasksource/lonli, dataset:tasksource/dadc-limit-nli, dataset:ColumbiaNLP/FLUTE, dataset:tasksource/strategy-qa, dataset:openai/summarize_from_feedback, dataset:tasksource/folio, dataset:yale-nlp/FOLIO, dataset:tasksource/tomi-nli, dataset:tasksource/avicenna, dataset:stanfordnlp/SHP, dataset:GBaker/MedQA-USMLE-4-options-hf, dataset:sileod/wikimedqa, dataset:declare-lab/cicero, dataset:amydeng2000/CREAK, dataset:tasksource/mutual, dataset:inverse-scaling/NeQA, dataset:inverse-scaling/quote-repetition, dataset:inverse-scaling/redefine-math, dataset:tasksource/puzzte, dataset:tasksource/implicatures, dataset:race, dataset:tasksource/race-c, dataset:tasksource/spartqa-yn, dataset:tasksource/spartqa-mchoice, dataset:tasksource/temporal-nli, dataset:riddle_sense, dataset:tasksource/clcd-english, dataset:maximedb/twentyquestions, dataset:metaeval/reclor, dataset:tasksource/counterfactually-augmented-imdb, dataset:tasksource/counterfactually-augmented-snli, dataset:metaeval/cnli, dataset:tasksource/boolq-natural-perturbations, dataset:metaeval/acceptability-prediction, dataset:metaeval/equate, dataset:tasksource/ScienceQA_text_only, dataset:Jiangjie/ekar_english, dataset:tasksource/implicit-hate-stg1, dataset:metaeval/chaos-mnli-ambiguity, dataset:IlyaGusev/headline_cause, dataset:tasksource/logiqa-2.0-nli, dataset:tasksource/oasst2_dense_flat, dataset:sileod/mindgames, dataset:metaeval/ambient, dataset:metaeval/path-naturalness-prediction, dataset:civil_comments, dataset:AndyChiang/cloth, dataset:AndyChiang/dgen, dataset:tasksource/I2D2, dataset:webis/args_me, dataset:webis/Touche23-ValueEval, dataset:tasksource/starcon, dataset:PolyAI/banking77, dataset:tasksource/ConTRoL-nli, dataset:tasksource/tracie, dataset:tasksource/sherliic, dataset:tasksource/sen-making, dataset:tasksource/winowhy, dataset:tasksource/robustLR, dataset:CLUTRR/v1, dataset:tasksource/logical-fallacy, dataset:tasksource/parade, dataset:tasksource/cladder, dataset:tasksource/subjectivity, dataset:tasksource/MOH, dataset:tasksource/VUAC, dataset:tasksource/TroFi, dataset:sharc_modified, dataset:tasksource/conceptrules_v2, dataset:metaeval/disrpt, dataset:tasksource/zero-shot-label-nli, dataset:tasksource/com2sense, dataset:tasksource/scone, dataset:tasksource/winodict, dataset:tasksource/fool-me-twice, dataset:tasksource/monli, dataset:tasksource/corr2cause, dataset:lighteval/lsat_qa, dataset:tasksource/apt, dataset:zeroshot/twitter-financial-news-sentiment, dataset:tasksource/icl-symbol-tuning-instruct, dataset:tasksource/SpaceNLI, dataset:sihaochen/propsegment, dataset:HannahRoseKirk/HatemojiBuild, dataset:tasksource/regset, dataset:tasksource/esci, dataset:lmsys/chatbot_arena_conversations, dataset:neurae/dnd_style_intents, dataset:hitachi-nlp/FLD.v2, dataset:tasksource/SDOH-NLI, dataset:allenai/scifact_entailment, dataset:tasksource/feasibilityQA, dataset:tasksource/simple_pair, dataset:tasksource/AdjectiveScaleProbe-nli, dataset:tasksource/resnli, dataset:tasksource/SpaRTUN, dataset:tasksource/ReSQ, dataset:tasksource/semantic_fragments_nli, dataset:MoritzLaurer/dataset_train_nli, dataset:tasksource/stepgame, dataset:tasksource/nlgraph, dataset:tasksource/oasst2_pairwise_rlhf_reward, dataset:tasksource/hh-rlhf, dataset:tasksource/ruletaker, dataset:qbao775/PARARULE-Plus, dataset:tasksource/proofwriter, dataset:tasksource/logical-entailment, dataset:tasksource/nope, dataset:tasksource/LogicNLI, dataset:kiddothe2b/contract-nli, dataset:AshtonIsNotHere/nli4ct_semeval2024, dataset:tasksource/lsat-ar, dataset:tasksource/lsat-rc, dataset:AshtonIsNotHere/biosift-nli, dataset:tasksource/brainteasers, dataset:Anthropic/persuasion, dataset:erbacher/AmbigNQ-clarifying-question, dataset:tasksource/SIGA-nli, dataset:unigram/FOL-nli, dataset:tasksource/goal-step-wikihow, dataset:GGLab/PARADISE, dataset:tasksource/doc-nli, dataset:tasksource/mctest-nli, dataset:tasksource/patent-phrase-similarity, dataset:tasksource/natural-language-satisfiability, dataset:tasksource/idioms-nli, dataset:tasksource/lifecycle-entailment, dataset:nvidia/HelpSteer, dataset:nvidia/HelpSteer2, dataset:sadat2307/MSciNLI, dataset:pushpdeep/UltraFeedback-paired, dataset:tasksource/AES2-essay-scoring, dataset:tasksource/english-grading, dataset:tasksource/wice, dataset:Dzeniks/hover, dataset:sileod/missing-item-prediction, dataset:tasksource/tasksource_dpo_pairs, license:apache-2.0, model-index, endpoints_compatible, region:us
Card metadata: widget: text: My name is Sylvain and I live in Paris
example_title: Parisian, text: My name is Sarah and I live in London
example_title: Londoner
datasets: zeroMN/nlp_corpus_zh, zeroMN/hanlp_date-zh, nyu-mll/glue, aps/super_glue, facebook/anli, tasksource/babi_nli, zeroMN/AVEdate, sick, snli, scitail, hans, alisawuffles/WANLI, tasksource/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/recast_white, pietrolesci/joci, pietrolesci/robust_nli, pietrolesci/robust_nli_is_sd, pietrolesci/robust_nli_li_ts, pietrolesci/gen_debiased_nli, pietrolesci/add_one_rte, tasksource/imppres, hlgd, paws, medical_questions_pairs, Anthropic/model-written-evals, truthful_qa, nightingal3/fig-qa, tasksource/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, 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, hate_speech_offensive, yahoo_answers_topics, pacovaldez/stackoverflow-questions, zapsdcn/hyperpartisan_news, zapsdcn/sciie, zapsdcn/citation_intent, go_emotions, allenai/scicite, liar, relbert/lexical_relation_classification, tasksource/linguisticprobing, tasksource/crowdflower, metaeval/ethics, emo, google_wellformed_query, tweets_hate_speech_detection, has_part, blog_authorship_corpus, launch/open_question_type, health_fact, commonsense_qa, mc_taco, ade_corpus_v2, prajjwal1/discosense, circa, 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, RuyuanWan/Dynasent_Disagreement, RuyuanWan/Politeness_Disagreement, RuyuanWan/SBIC_Disagreement, RuyuanWan/SChem_Disagreement, RuyuanWan/Dilemmas_Disagreement, lucasmccabe/logiqa, wiki_qa, tasksource/cycic_classification, tasksource/cycic_multiplechoice, tasksource/sts-companion, tasksource/commonsense_qa_2.0, tasksource/lingnli, tasksource/monotonicity-entailment, tasksource/arct, tasksource/scinli, tasksource/naturallogic, onestop_qa, demelin/moral_stories, corypaik/prost, aps/dynahate, metaeval/syntactic-augmentation-nli, tasksource/autotnli, lasha-nlp/CONDAQA, openai/webgpt_comparisons, Dahoas/synthetic-instruct-gptj-pairwise, metaeval/scruples, metaeval/wouldyourather, metaeval/defeasible-nli, tasksource/help-nli, metaeval/nli-veridicality-transitivity, tasksource/lonli, tasksource/dadc-limit-nli, ColumbiaNLP/FLUTE, tasksource/strategy-qa, openai/summarize_from_feedback, tasksource/folio, yale-nlp/FOLIO, tasksource/tomi-nli, tasksource/avicenna, stanfordnlp/SHP, GBaker/MedQA-USMLE-4-options-hf, sileod/wikimedqa, declare-lab/cicero, amydeng2000/CREAK, tasksource/mutual, inverse-scaling/NeQA, inverse-scaling/quote-repetition, inverse-scaling/redefine-math, tasksource/puzzte, tasksource/implicatures, race, tasksource/race-c, tasksource/spartqa-yn, tasksource/spartqa-mchoice, tasksource/temporal-nli, riddle_sense, tasksource/clcd-english, maximedb/twentyquestions, metaeval/reclor, tasksource/counterfactually-augmented-imdb, tasksource/counterfactually-augmented-snli, metaeval/cnli, tasksource/boolq-natural-perturbations, metaeval/acceptability-prediction, metaeval/equate, tasksource/ScienceQA_text_only, Jiangjie/ekar_english, tasksource/implicit-hate-stg1, metaeval/chaos-mnli-ambiguity, IlyaGusev/headline_cause, tasksource/logiqa-2.0-nli, tasksource/oasst2_dense_flat, sileod/mindgames, metaeval/ambient, metaeval/path-naturalness-prediction, civil_comments, AndyChiang/cloth, AndyChiang/dgen, tasksource/I2D2, webis/args_me, webis/Touche23-ValueEval, tasksource/starcon, PolyAI/banking77, tasksource/ConTRoL-nli, tasksource/tracie, tasksource/sherliic, tasksource/sen-making, tasksource/winowhy, tasksource/robustLR, CLUTRR/v1, tasksource/logical-fallacy, tasksource/parade, tasksource/cladder, tasksource/subjectivity, tasksource/MOH, tasksource/VUAC, tasksource/TroFi, sharc_modified, tasksource/conceptrules_v2, metaeval/disrpt, tasksource/zero-shot-label-nli, tasksource/com2sense, tasksource/scone, tasksource/winodict, tasksource/fool-me-twice, tasksource/monli, tasksource/corr2cause, lighteval/lsat_qa, tasksource/apt, zeroshot/twitter-financial-news-sentiment, tasksource/icl-symbol-tuning-instruct, tasksource/SpaceNLI, sihaochen/propsegment, HannahRoseKirk/HatemojiBuild, tasksource/regset, tasksource/esci, lmsys/chatbot_arena_conversations, neurae/dnd_style_intents, hitachi-nlp/FLD.v2, tasksource/SDOH-NLI, allenai/scifact_entailment, tasksource/feasibilityQA, tasksource/simple_pair, tasksource/AdjectiveScaleProbe-nli, tasksource/resnli, tasksource/SpaRTUN, tasksource/ReSQ, tasksource/semantic_fragments_nli, MoritzLaurer/dataset_train_nli, tasksource/stepgame, tasksource/nlgraph, tasksource/oasst2_pairwise_rlhf_reward, tasksource/hh-rlhf, tasksource/ruletaker, qbao775/PARARULE-Plus, tasksource/proofwriter, tasksource/logical-entailment, tasksource/nope, tasksource/LogicNLI, kiddothe2b/contract-nli, AshtonIsNotHere/nli4ct_semeval2024, tasksource/lsat-ar, tasksource/lsat-rc, AshtonIsNotHere/biosift-nli, tasksource/brainteasers, Anthropic/persuasion, erbacher/AmbigNQ-clarifying-question, tasksource/SIGA-nli, unigram/FOL-nli, tasksource/goal-step-wikihow, GGLab/PARADISE, tasksource/doc-nli, tasksource/mctest-nli, tasksource/patent-phrase-similarity, tasksource/natural-language-satisfiability, tasksource/idioms-nli, tasksource/lifecycle-entailment, nvidia/HelpSteer, nvidia/HelpSteer2, sadat2307/MSciNLI, pushpdeep/UltraFeedback-paired, tasksource/AES2-essay-scoring, tasksource/english-grading, tasksource/wice, Dzeniks/hover, sileod/missing-item-prediction, tasksource/tasksource_dpo_pairs
language: en, zh
license: apache-2.0
library_name: transformers
tags: multimodal, vqa, text, audio
metrics: accuracy, bleu, wer
model-index: name: Evolutionary Multi-Modal Model
results: task: type: vqa
name: Visual Question Answering
dataset: type: synthetic-dataset
name: Synthetic Multimodal Dataset
split: test
metrics: type: accuracy
value: 85
verified: no
pipeline_tag: audio-text-to-text
Likes: 1 | All-time downloads: 497
Created: 2025-01-06 04:33:44