README.md

---
license: apache-2.0
language: en
tags:
- deberta-v3-base
- text-classification
- nli
- natural-language-inference
- multitask
- extreme-mtl
- deberta-v3-base
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 pretrained model 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 multiple-choice or any classification task (e.g. NLI) like any deberta model. 
This model 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 64. The model was trained for 20k steps with a batch size of 384, a peak learning rate of 2e-5.

The list of tasks is available in tasks.md

code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing

### Software

https://github.com/sileod/tasknet/
Training took 7 days on 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_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.



|   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](https://ibm.github.io/model-recycling/)

# Citation [optional]

**BibTeX:**

```bib
@article{sileo2023tasksource,
  title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
  author={Sileo, Damien},
  journal={arXiv preprint arXiv:2301.05948},
  year={2023}
}
```


# Model Card Contact

damien.sileo@inria.fr


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