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
pipeline_tag: zero-shot-classification
datasets:
- glue
- super_glue
- anli
- metaeval/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
- paws
- quora
- medical_questions_pairs
- conll2003
- 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
- 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
Model Card for DeBERTa-v3-base-tasksource-nli
DeBERTa-v3-base fine-tuned with multi-task learning on 560 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 NLI pipeline (similar to bart-mnli but better). You can also load other tasks (see next paragraph) or further fine-tune the encoder for new classification, token or multiple-choice.
Tasksource-adapters: 1 line access to 500 tasks
!pip install tasknet tasksource
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': 0.9956129789352417}, {'label': 'negative', 'score': 0.9967049956321716}]
Evaluation
This model ranked 1st among all models with the microsoft/deberta-v3-base architecture according to IBM model recycling evaluation. Results: Evaluation on 36 datasets 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.
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
For more information, see: Model Recycling
Software and training details
https://github.com/sileod/tasksource/
https://github.com/sileod/tasknet/
Training took 7 days on RTX6000 24GB gpu.
Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
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. 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 45k steps with a batch size of 384, and a peak learning rate of 2e-5.
The list of tasks is available in model config.
Citation
More details on this article:
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}