| --- |
| language: en |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - deberta-v3-small |
| - deberta-v3 |
| - deberta |
| - text-classification |
| - nli |
| - natural-language-inference |
| - multitask |
| - multi-task |
| - pipeline |
| - extreme-multi-task |
| - extreme-mtl |
| - tasksource |
| - zero-shot |
| - rlhf |
| datasets: |
| - nyu-mll/glue |
| - super_glue |
| - facebook/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 |
| - allenai/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 |
| pipeline_tag: zero-shot-classification |
| --- |
| |
| # Model Card for DeBERTa-v3-small-tasksource-nli |
|
|
| This is [DeBERTa-v3-small](https://hf.co/microsoft/deberta-v3-small) fine-tuned with multi-task learning on 600+ tasks of the [tasksource collection](https://github.com/sileod/tasksource/). |
| This checkpoint has strong zero-shot validation performance on many tasks, and can be used for: |
| - Zero-shot entailment-based classification for arbitrary labels [ZS]. |
| - Natural language inference [NLI] |
| - Hundreds of previous tasks with tasksource-adapters [TA]. |
| - Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT]. |
|
|
| # [ZS] Zero-shot classification pipeline |
| ```python |
| from transformers import pipeline |
| classifier = pipeline("zero-shot-classification",model="sileod/deberta-v3-small-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](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification. |
|
|
| # [NLI] Natural language inference pipeline |
|
|
| ```python |
| from transformers import pipeline |
| pipe = pipeline("text-classification",model="sileod/deberta-v3-small-tasksource-nli") |
| pipe([dict(text='there is a cat', |
| text_pair='there is a black cat')]) #list of (premise,hypothesis) |
| # [{'label': 'neutral', 'score': 0.9952911138534546}] |
| ``` |
|
|
| # [TA] Tasksource-adapters: 1 line access to hundreds of tasks |
|
|
| ```python |
| # !pip install tasknet |
| import tasknet as tn |
| pipe = tn.load_pipeline('sileod/deberta-v3-small-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 |
|
|
| ```python |
| # !pip install tasknet |
| import tasknet as tn |
| hparams=dict(model_name='sileod/deberta-v3-small-tasksource-nli', learning_rate=2e-5) |
| model, trainer = tn.Model_Trainer([tn.AutoTask("glue/rte")], hparams) |
| trainer.train() |
| ``` |
|
|
| ## Evaluation |
| This the base equivalent of this model was 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 |
| |
| More details on this [article:](https://arxiv.org/abs/2301.05948) |
| ``` |
| @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} |
| } |
| ``` |
| |
| |
| # Model Card Contact |
| |
| damien.sileo@inria.fr |
| |
| |
| </details> |
