metadata

license: apache-2.0
language: en
tags:
  - deberta-v3-base
  - deberta-v3
  - deberta
  - text-classification
  - nli
  - natural-language-inference
  - multitask
  - multi-task
  - pipeline
  - extreme-multi-task
  - extreme-mtl
  - tasksource
  - zero-shot
  - rlhf
model-index:
  - name: deberta-v3-base-tasksource-nli
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: glue
          type: glue
          config: rte
          split: validation
        metrics:
          - type: accuracy
            value: 0.89
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: anli-r3
          type: anli
          config: plain_text
          split: validation
        metrics:
          - type: accuracy
            value: 0.52
            name: Accuracy
datasets:
  - glue
  - super_glue
  - anli
  - tasksource/babi_nli
  - sick
  - snli
  - scitail
  - OpenAssistant/oasst1
  - universal_dependencies
  - hans
  - qbao775/PARARULE-Plus
  - 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
  - PolyAI/banking77
  - paws
  - quora
  - medical_questions_pairs
  - conll2003
  - nlpaueb/finer-139
  - Anthropic/hh-rlhf
  - 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
  - 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
  - allenai/scicite
  - liar
  - relbert/lexical_relation_classification
  - metaeval/linguisticprobing
  - tasksource/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
  - 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
  - tasksource/dadc-limit-nli
  - ColumbiaNLP/FLUTE
  - metaeval/strategy-qa
  - openai/summarize_from_feedback
  - tasksource/folio
  - metaeval/tomi-nli
  - metaeval/avicenna
  - stanfordnlp/SHP
  - GBaker/MedQA-USMLE-4-options-hf
  - GBaker/MedQA-USMLE-4-options
  - sileod/wikimedqa
  - declare-lab/cicero
  - amydeng2000/CREAK
  - metaeval/mutual
  - inverse-scaling/NeQA
  - inverse-scaling/quote-repetition
  - inverse-scaling/redefine-math
  - tasksource/puzzte
  - metaeval/implicatures
  - race
  - metaeval/spartqa-yn
  - metaeval/spartqa-mchoice
  - metaeval/temporal-nli
  - metaeval/ScienceQA_text_only
  - AndyChiang/cloth
  - metaeval/logiqa-2.0-nli
  - tasksource/oasst1_dense_flat
  - metaeval/boolq-natural-perturbations
  - metaeval/path-naturalness-prediction
  - riddle_sense
  - Jiangjie/ekar_english
  - metaeval/implicit-hate-stg1
  - metaeval/chaos-mnli-ambiguity
  - IlyaGusev/headline_cause
  - metaeval/race-c
  - metaeval/equate
  - metaeval/ambient
  - AndyChiang/dgen
  - metaeval/clcd-english
  - civil_comments
  - metaeval/acceptability-prediction
  - maximedb/twentyquestions
  - metaeval/counterfactually-augmented-snli
  - tasksource/I2D2
  - sileod/mindgames
  - metaeval/counterfactually-augmented-imdb
  - metaeval/cnli
  - metaeval/reclor
  - tasksource/oasst1_pairwise_rlhf_reward
  - tasksource/zero-shot-label-nli
  - webis/args_me
  - webis/Touche23-ValueEval
  - tasksource/starcon
  - tasksource/ruletaker
  - lighteval/lsat_qa
  - tasksource/ConTRoL-nli
  - tasksource/tracie
  - tasksource/sherliic
  - tasksource/sen-making
  - tasksource/winowhy
  - mediabiasgroup/mbib-base
  - tasksource/robustLR
  - CLUTRR/v1
  - tasksource/logical-fallacy
  - tasksource/parade
  - tasksource/cladder
  - tasksource/subjectivity
  - tasksource/MOH
  - tasksource/VUAC
  - tasksource/TroFi
  - sharc_modified
  - tasksource/conceptrules_v2
  - tasksource/disrpt
  - conll2000
  - DFKI-SLT/few-nerd
  - tasksource/com2sense
  - tasksource/scone
  - tasksource/winodict
  - tasksource/fool-me-twice
  - tasksource/monli
  - tasksource/corr2cause
  - tasksource/apt
  - zeroshot/twitter-financial-news-sentiment
  - tasksource/icl-symbol-tuning-instruct
  - tasksource/SpaceNLI
  - sihaochen/propsegment
  - HannahRoseKirk/HatemojiBuild
  - tasksource/regset
  - tasksource/babi_nli
  - lmsys/chatbot_arena_conversations
metrics:
  - accuracy
library_name: transformers
pipeline_tag: zero-shot-classification

Model Card for DeBERTa-v3-base-tasksource-nli

This is DeBERTa-v3-base fine-tuned with multi-task learning on 600 tasks of the tasksource collection. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:

Zero-shot entailment-based classification pipeline (similar to bart-mnli), see [ZS]([ZS] Zero-shot classification pipeline).
Natural language inference, and many other tasks with tasksource-adapters, see [TA]([TA] Tasksource-adapters: 1 line access to hundreds of tasks).
Further fine-tuning with a new task (classification, token classification or multiple-choice) [FT]([FT] Tasknet: 3 lines fine-tuning).

[ZS] Zero-shot classification pipeline

from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="sileod/deberta-v3-base-tasksource-nli")

text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)

NLI training data of this model includes label-nli, a NLI dataset specially constructed to improve this kind of zero-shot classification.

[TA] Tasksource-adapters: 1 line access to hundreds of tasks

# !pip install tasknet
import tasknet as tn
pipe = tn.load_pipeline('sileod/deberta-v3-base-tasksource-nli','glue/sst2') # works for 500+ tasksource tasks
pipe(['That movie was great !', 'Awful movie.'])
# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}]

The list of tasks is available in model config.json. This is more efficient than ZS since it requires only one forward pass per example, but it is less flexible.

[FT] Tasknet: 3 lines fine-tuning

# !pip install tasknet
import tasknet as tn
hparams=dict(model_name='sileod/deberta-v3-base-tasksource-nli', learning_rate=2e-5)
model, trainer = tn.Model_Trainer([tn.AutoTask("glue/rte")], hparams)
trainer.train()

Evaluation

This model ranked 1st among all models with the microsoft/deberta-v3-base architecture according to the IBM model recycling evaluation. https://ibm.github.io/model-recycling/

Software and training details

The model was trained on 600 tasks for 200k steps with a batch size of 384 and a peak learning rate of 2e-5. Training took 12 days on Nvidia A30 24GB gpu. This is the shared model with the MNLI classifier on top. 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.

https://github.com/sileod/tasksource/
https://github.com/sileod/tasknet/
Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing

Citation

Model Card Contact

[email protected]