Evaluation results for gustavecortal/roberta_emo model as a base model for other tasks
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eladven
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README.md
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@@ -46,3 +46,17 @@ The following hyperparameters were used during training:
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- Pytorch 1.13.1
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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- Pytorch 1.13.1
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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## Model Recycling
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.24&mnli_lp=nan&20_newsgroup=0.54&ag_news=0.46&amazon_reviews_multi=-0.50&anli=1.81&boolq=2.93&cb=21.52&cola=-0.12&copa=22.30&dbpedia=0.20&esnli=-0.30&financial_phrasebank=0.99&imdb=-0.12&isear=0.54&mnli=-0.16&mrpc=0.37&multirc=2.85&poem_sentiment=4.52&qnli=0.47&qqp=0.24&rotten_tomatoes=2.95&rte=10.99&sst2=1.64&sst_5bins=0.79&stsb=1.59&trec_coarse=0.09&trec_fine=3.44&tweet_ev_emoji=-0.31&tweet_ev_emotion=0.65&tweet_ev_hate=-0.40&tweet_ev_irony=4.08&tweet_ev_offensive=2.08&tweet_ev_sentiment=-0.16&wic=3.02&wnli=-8.31&wsc=0.19&yahoo_answers=-0.14&model_name=gustavecortal%2Froberta_emo&base_name=roberta-base) using gustavecortal/roberta_emo as a base model yields average score of 78.47 in comparison to 76.22 by roberta-base.
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The model is ranked 2nd among all tested models for the roberta-base architecture as of 18/01/2023
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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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|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
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| 85.8205 | 90.2333 | 66.08 | 52.1563 | 81.6208 | 89.2857 | 83.4132 | 71 | 77.5 | 90.6963 | 86.1 | 93.776 | 73.0117 | 86.8186 | 88.2353 | 64.0677 | 88.4615 | 92.8794 | 90.9523 | 91.3696 | 83.3935 | 95.7569 | 57.4661 | 91.5106 | 97.2 | 91.2 | 45.994 | 82.4771 | 52.4916 | 75.6378 | 86.6279 | 70.8727 | 68.4953 | 46.4789 | 63.4615 | 72.2667 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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