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--- |
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license: apache-2.0 |
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language: en |
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tags: |
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- deberta-v3-base |
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- deberta-v3 |
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- deberta |
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- text-classification |
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- nli |
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- natural-language-inference |
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- multitask |
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- multi-task |
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- pipeline |
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- extreme-multi-task |
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- extreme-mtl |
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- tasksource |
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- zero-shot |
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- rlhf |
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pipeline_tag: zero-shot-classification |
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datasets: |
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- glue |
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- super_glue |
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- anli |
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- metaeval/babi_nli |
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- sick |
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- snli |
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- scitail |
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- OpenAssistant/oasst1 |
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- universal_dependencies |
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- hans |
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- qbao775/PARARULE-Plus |
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- alisawuffles/WANLI |
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- metaeval/recast |
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- sileod/probability_words_nli |
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- joey234/nan-nli |
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- pietrolesci/nli_fever |
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- pietrolesci/breaking_nli |
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- pietrolesci/conj_nli |
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- pietrolesci/fracas |
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- pietrolesci/dialogue_nli |
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- pietrolesci/mpe |
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- pietrolesci/dnc |
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- pietrolesci/gpt3_nli |
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- pietrolesci/recast_white |
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- pietrolesci/joci |
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- martn-nguyen/contrast_nli |
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- pietrolesci/robust_nli |
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- pietrolesci/robust_nli_is_sd |
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- pietrolesci/robust_nli_li_ts |
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- pietrolesci/gen_debiased_nli |
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- pietrolesci/add_one_rte |
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- metaeval/imppres |
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- pietrolesci/glue_diagnostics |
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- hlgd |
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- paws |
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- quora |
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- medical_questions_pairs |
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- conll2003 |
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- Anthropic/hh-rlhf |
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- Anthropic/model-written-evals |
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- truthful_qa |
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- nightingal3/fig-qa |
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- tasksource/bigbench |
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- blimp |
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- cos_e |
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- cosmos_qa |
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- dream |
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- openbookqa |
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- qasc |
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- quartz |
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- quail |
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- head_qa |
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- sciq |
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- social_i_qa |
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- wiki_hop |
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- wiqa |
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- piqa |
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- hellaswag |
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- pkavumba/balanced-copa |
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- 12ml/e-CARE |
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- art |
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- tasksource/mmlu |
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- winogrande |
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- codah |
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- ai2_arc |
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- definite_pronoun_resolution |
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- swag |
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- math_qa |
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- metaeval/utilitarianism |
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- mteb/amazon_counterfactual |
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- SetFit/insincere-questions |
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- SetFit/toxic_conversations |
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- turingbench/TuringBench |
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- trec |
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- tals/vitaminc |
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- hope_edi |
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- strombergnlp/rumoureval_2019 |
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- ethos |
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- tweet_eval |
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- discovery |
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- pragmeval |
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- silicone |
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- lex_glue |
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- papluca/language-identification |
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- imdb |
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- rotten_tomatoes |
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- ag_news |
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- yelp_review_full |
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- financial_phrasebank |
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- poem_sentiment |
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- dbpedia_14 |
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- amazon_polarity |
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- app_reviews |
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- hate_speech18 |
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- sms_spam |
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- humicroedit |
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- snips_built_in_intents |
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- banking77 |
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- hate_speech_offensive |
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- yahoo_answers_topics |
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- pacovaldez/stackoverflow-questions |
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- zapsdcn/hyperpartisan_news |
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- zapsdcn/sciie |
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- zapsdcn/citation_intent |
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- go_emotions |
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- scicite |
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- liar |
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- relbert/lexical_relation_classification |
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- metaeval/linguisticprobing |
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- metaeval/crowdflower |
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- metaeval/ethics |
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- emo |
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- google_wellformed_query |
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- tweets_hate_speech_detection |
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- has_part |
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- wnut_17 |
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- ncbi_disease |
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- acronym_identification |
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- jnlpba |
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- species_800 |
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- SpeedOfMagic/ontonotes_english |
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- blog_authorship_corpus |
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- launch/open_question_type |
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- health_fact |
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- commonsense_qa |
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- mc_taco |
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- ade_corpus_v2 |
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- prajjwal1/discosense |
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- circa |
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- YaHi/EffectiveFeedbackStudentWriting |
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- Ericwang/promptSentiment |
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- Ericwang/promptNLI |
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- Ericwang/promptSpoke |
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- Ericwang/promptProficiency |
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- Ericwang/promptGrammar |
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- Ericwang/promptCoherence |
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- PiC/phrase_similarity |
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- copenlu/scientific-exaggeration-detection |
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- quarel |
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- mwong/fever-evidence-related |
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- numer_sense |
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- dynabench/dynasent |
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- raquiba/Sarcasm_News_Headline |
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- sem_eval_2010_task_8 |
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- demo-org/auditor_review |
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- medmcqa |
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- aqua_rat |
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- RuyuanWan/Dynasent_Disagreement |
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- RuyuanWan/Politeness_Disagreement |
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- RuyuanWan/SBIC_Disagreement |
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- RuyuanWan/SChem_Disagreement |
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- RuyuanWan/Dilemmas_Disagreement |
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- lucasmccabe/logiqa |
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- wiki_qa |
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- metaeval/cycic_classification |
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- metaeval/cycic_multiplechoice |
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- metaeval/sts-companion |
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- metaeval/commonsense_qa_2.0 |
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- metaeval/lingnli |
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- metaeval/monotonicity-entailment |
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- metaeval/arct |
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- metaeval/scinli |
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- metaeval/naturallogic |
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- onestop_qa |
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- demelin/moral_stories |
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- corypaik/prost |
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- aps/dynahate |
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- metaeval/syntactic-augmentation-nli |
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- metaeval/autotnli |
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- lasha-nlp/CONDAQA |
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- openai/webgpt_comparisons |
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- Dahoas/synthetic-instruct-gptj-pairwise |
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- metaeval/scruples |
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- metaeval/wouldyourather |
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- sileod/attempto-nli |
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- metaeval/defeasible-nli |
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- metaeval/help-nli |
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- metaeval/nli-veridicality-transitivity |
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- metaeval/natural-language-satisfiability |
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- metaeval/lonli |
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- metaeval/dadc-limit-nli |
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- ColumbiaNLP/FLUTE |
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- metaeval/strategy-qa |
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- openai/summarize_from_feedback |
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- metaeval/folio |
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- metaeval/tomi-nli |
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- metaeval/avicenna |
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- stanfordnlp/SHP |
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- GBaker/MedQA-USMLE-4-options-hf |
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- sileod/wikimedqa |
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- declare-lab/cicero |
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- amydeng2000/CREAK |
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- metaeval/mutual |
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- inverse-scaling/NeQA |
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- inverse-scaling/quote-repetition |
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- inverse-scaling/redefine-math |
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- metaeval/puzzte |
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- metaeval/implicatures |
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- race |
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- metaeval/spartqa-yn |
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- metaeval/spartqa-mchoice |
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- metaeval/temporal-nli |
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metrics: |
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- accuracy |
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library_name: transformers |
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--- |
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# Model Card for DeBERTa-v3-base-tasksource-nli |
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This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 560 tasks of the [tasksource collection](https://github.com/sileod/tasksource/) |
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This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for zero-shot NLI pipeline (similar to bart-mnli but better). |
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You can also load other tasks (see next paragraph) or further fine-tune the encoder for new classification, token classification or multiple-choice tasks. |
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# Tasksource-adapters: 1 line access to 500 tasks |
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```python |
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!pip install tasknet tasksource |
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import tasknet as tn |
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pipe = tn.load_pipeline('sileod/deberta-v3-base-tasksource-nli','glue/sst2') # works for 500+ tasksource tasks |
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pipe(['That movie was great !', 'Awful movie.']) |
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# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}] |
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``` |
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The list of tasks is available in model config.json. |
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## Evaluation |
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This model ranked 1st among all models with the microsoft/deberta-v3-base architecture according to the IBM model recycling evaluation. |
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Results: |
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=1.41&mnli_lp=nan&20_newsgroup=0.63&ag_news=0.46&amazon_reviews_multi=-0.40&anli=0.94&boolq=2.55&cb=10.71&cola=0.49&copa=10.60&dbpedia=0.10&esnli=-0.25&financial_phrasebank=1.31&imdb=-0.17&isear=0.63&mnli=0.42&mrpc=-0.23&multirc=1.73&poem_sentiment=0.77&qnli=0.12&qqp=-0.05&rotten_tomatoes=0.67&rte=2.13&sst2=0.01&sst_5bins=-0.02&stsb=1.39&trec_coarse=0.24&trec_fine=0.18&tweet_ev_emoji=0.62&tweet_ev_emotion=0.43&tweet_ev_hate=1.84&tweet_ev_irony=1.43&tweet_ev_offensive=0.17&tweet_ev_sentiment=0.08&wic=-1.78&wnli=3.03&wsc=9.95&yahoo_answers=0.17&model_name=sileod%2Fdeberta-v3-base_tasksource-420&base_name=microsoft%2Fdeberta-v3-base) using sileod/deberta-v3-base_tasksource-420 as a base model yields average score of 80.45 in comparison to 79.04 by microsoft/deberta-v3-base. |
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| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |
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|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:| |
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| 87.042 | 90.9 | 66.46 | 59.7188 | 85.5352 | 85.7143 | 87.0566 | 69 | 79.5333 | 91.6735 | 85.8 | 94.324 | 72.4902 | 90.2055 | 88.9706 | 63.9851 | 87.5 | 93.6299 | 91.7363 | 91.0882 | 84.4765 | 95.0688 | 56.9683 | 91.6654 | 98 | 91.2 | 46.814 | 84.3772 | 58.0471 | 81.25 | 85.2326 | 71.8821 | 69.4357 | 73.2394 | 74.0385 | 72.2 | |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/) |
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### Software and training details |
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https://github.com/sileod/tasksource/ \ |
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https://github.com/sileod/tasknet/ \ |
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Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing |
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Training took 7 days on RTX6000 24GB gpu. |
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This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic rlhf, anli... alongside many NLI and classification tasks with one shared encoder. |
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Each task had a specific CLS embedding, which is dropped 10% of the time to facilitate model use without it. All multiple-choice model used the same classification layers. For classification tasks, models shared weights if their labels matched. |
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The number of examples per task was capped to 64k. The model was trained for 45k steps with a batch size of 384, and a peak learning rate of 2e-5. |
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# Citation |
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More details on this [article:](https://arxiv.org/abs/2301.05948) |
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```bib |
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@article{sileo2023tasksource, |
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title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation}, |
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author={Sileo, Damien}, |
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url= {https://arxiv.org/abs/2301.05948}, |
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journal={arXiv preprint arXiv:2301.05948}, |
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year={2023} |
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} |
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``` |
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# Model Card Contact |
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damien.sileo@inria.fr |
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</details> |
