readme: ---
  license: apache-2.0
  language: en
  tags:
  - deberta-v3-base
  - text-classification
  - nli
  - natural-language-inference
  - multitask
  - multi-task
  - extreme-multi-task
  - extreme-mtl
  - deberta-v3-base
  - tasksource
  pipeline_tag: zero-shot-classification
  datasets:
  - hellaswag
  - ag_news
  - pietrolesci/nli_fever
  - numer_sense
  - go_emotions
  - Ericwang/promptProficiency
  - poem_sentiment
  - pietrolesci/robust_nli_is_sd
  - sileod/probability_words_nli
  - social_i_qa
  - trec
  - imppres
  - pietrolesci/gen_debiased_nli
  - snips_built_in_intents
  - metaeval/imppres
  - metaeval/crowdflower
  - tals/vitaminc
  - dream
  - metaeval/babi_nli
  - Ericwang/promptSpoke
  - metaeval/ethics
  - art
  - ai2_arc
  - discovery
  - Ericwang/promptGrammar
  - code_x_glue_cc_clone_detection_big_clone_bench
  - prajjwal1/discosense
  - pietrolesci/joci
  - Anthropic/model-written-evals
  - utilitarianism
  - emo
  - tweets_hate_speech_detection
  - piqa
  - blog_authorship_corpus
  - SpeedOfMagic/ontonotes_english
  - circa
  - app_reviews
  - anli
  - Ericwang/promptSentiment
  - codah
  - definite_pronoun_resolution
  - health_fact
  - tweet_eval
  - hate_speech18
  - glue
  - hendrycks_test
  - paws
  - bigbench
  - hate_speech_offensive
  - blimp
  - sick
  - turingbench/TuringBench
  - martn-nguyen/contrast_nli
  - Anthropic/hh-rlhf
  - openbookqa
  - species_800
  - alisawuffles/WANLI
  - ethos
  - pietrolesci/mpe
  - wiki_hop
  - pietrolesci/glue_diagnostics
  - mc_taco
  - quarel
  - PiC/phrase_similarity
  - strombergnlp/rumoureval_2019
  - quail
  - acronym_identification
  - pietrolesci/robust_nli
  - quora
  - wnut_17
  - dynabench/dynasent
  - pietrolesci/gpt3_nli
  - truthful_qa
  - pietrolesci/add_one_rte
  - pietrolesci/breaking_nli
  - copenlu/scientific-exaggeration-detection
  - medical_questions_pairs
  - rotten_tomatoes
  - scicite
  - scitail
  - pietrolesci/dialogue_nli
  - code_x_glue_cc_defect_detection
  - nightingal3/fig-qa
  - pietrolesci/conj_nli
  - liar
  - sciq
  - head_qa
  - pietrolesci/dnc
  - quartz
  - wiqa
  - code_x_glue_cc_code_refinement
  - Ericwang/promptCoherence
  - joey234/nan-nli
  - hope_edi
  - jnlpba
  - yelp_review_full
  - pietrolesci/recast_white
  - swag
  - banking77
  - cosmos_qa
  - financial_phrasebank
  - hans
  - pietrolesci/fracas
  - math_qa
  - conll2003
  - qasc
  - ncbi_disease
  - mwong/fever-evidence-related
  - YaHi/EffectiveFeedbackStudentWriting
  - ade_corpus_v2
  - amazon_polarity
  - pietrolesci/robust_nli_li_ts
  - super_glue
  - adv_glue
  - Ericwang/promptNLI
  - cos_e
  - launch/open_question_type
  - lex_glue
  - has_part
  - pragmeval
  - sem_eval_2010_task_8
  - imdb
  - humicroedit
  - sms_spam
  - dbpedia_14
  - commonsense_qa
  - hlgd
  - snli
  - hyperpartisan_news_detection
  - google_wellformed_query
  - raquiba/Sarcasm_News_Headline
  - metaeval/recast
  - winogrande
  - relbert/lexical_relation_classification
  - metaeval/linguisticprobing
  metrics:
  - accuracy
  library_name: transformers
  ---
  
  # Model Card for DeBERTa-v3-base-tasksource-nli
  
  DeBERTa-v3-base fine-tuned jointly fine-tuned on 444 tasks of the [tasksource collection](https://github.com/sileod/tasksource/)
  You can fine-tune this model to use it for any classification or multiple-choice task.
  This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI).
  The untuned model CLS embedding also has strong linear probing performance (90% on MNLI), due to the multitask training.
  
  This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic/hh-rlhf... alongside many NLI and classification tasks with a SequenceClassification heads while using only one shared encoder.
  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.
  The number of examples per task was capped to 64k. The model was trained for 20k steps with a batch size of 384, and a peak learning rate of 2e-5.
  
  The list of tasks is available in tasks.md
  
  tasksource training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
  
  ### Software
  https://github.com/sileod/tasksource/ \
  https://github.com/sileod/tasknet/ \
  Training took 7 days on RTX6000 24GB gpu.
  
  ## Model Recycling
  An earlier (weaker) version model is ranked 1st among all models with the microsoft/deberta-v3-base architecture as of 10/01/2023
  Results:
  [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