Evaluation results for ibm/ColD-Fusion-itr13-seed2 model as a base model for other tasks
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README.md
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.50&mnli_lp=nan&20_newsgroup=1.08&ag_news=-0.47&amazon_reviews_multi=0.14&anli=2.75&boolq=3.32&cb=21.52&cola=0.07&copa=24.30&dbpedia=0.17&esnli=0.05&financial_phrasebank=2.19&imdb=-0.03&isear=0.67&mnli=0.41&mrpc=-0.12&multirc=2.46&poem_sentiment=4.52&qnli=0.27&qqp=0.37&rotten_tomatoes=3.04&rte=10.99&sst2=1.18&sst_5bins=1.47&stsb=1.72&trec_coarse=-0.11&trec_fine=3.24&tweet_ev_emoji=-1.35&tweet_ev_emotion=1.22&tweet_ev_hate=-0.34&tweet_ev_irony=5.48&tweet_ev_offensive=1.49&tweet_ev_sentiment=-1.25&wic=4.58&wnli=-5.49&wsc=0.19&yahoo_answers=0.16&model_name=ibm%2FColD-Fusion-itr13-seed2&base_name=roberta-base) using ibm/ColD-Fusion-itr13-seed2 as a base model yields average score of 78.72 in comparison to 76.22 by roberta-base.
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The model
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Results:
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| 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 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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@article{ColDFusion,
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author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
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title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
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journal = {CoRR},
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volume = {abs/2212.01378},
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.50&mnli_lp=nan&20_newsgroup=1.08&ag_news=-0.47&amazon_reviews_multi=0.14&anli=2.75&boolq=3.32&cb=21.52&cola=0.07&copa=24.30&dbpedia=0.17&esnli=0.05&financial_phrasebank=2.19&imdb=-0.03&isear=0.67&mnli=0.41&mrpc=-0.12&multirc=2.46&poem_sentiment=4.52&qnli=0.27&qqp=0.37&rotten_tomatoes=3.04&rte=10.99&sst2=1.18&sst_5bins=1.47&stsb=1.72&trec_coarse=-0.11&trec_fine=3.24&tweet_ev_emoji=-1.35&tweet_ev_emotion=1.22&tweet_ev_hate=-0.34&tweet_ev_irony=5.48&tweet_ev_offensive=1.49&tweet_ev_sentiment=-1.25&wic=4.58&wnli=-5.49&wsc=0.19&yahoo_answers=0.16&model_name=ibm%2FColD-Fusion-itr13-seed2&base_name=roberta-base) using ibm/ColD-Fusion-itr13-seed2 as a base model yields average score of 78.72 in comparison to 76.22 by roberta-base.
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The model is ranked 1st among all tested models for the roberta-base architecture as of 13/12/2022
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Results:
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| 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 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
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journal = {CoRR},
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volume = {abs/2212.01378},
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