Evaluation results for jfarmerphd/bert-finetuned-squad-accelerate model as a base model for other tasks
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README.md
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# jfarmerphd/bert-finetuned-squad-accelerate model
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This model is based on bert-base-cased pretrained model.
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## Model Recycling
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=1.62&mnli_lp=nan&20_newsgroup=-0.65&ag_news=-0.33&amazon_reviews_multi=0.13&anli=0.84&boolq=3.17&cb=7.95&cola=-0.26&copa=-2.15&dbpedia=-1.14&esnli=-0.13&financial_phrasebank=14.24&imdb=-0.13&isear=1.04&mnli=-0.33&mrpc=5.31&multirc=0.12&poem_sentiment=5.38&qnli=0.98&qqp=-0.09&rotten_tomatoes=0.35&rte=6.68&sst2=0.37&sst_5bins=0.04&stsb=1.04&trec_coarse=0.37&trec_fine=7.42&tweet_ev_emoji=-0.22&tweet_ev_emotion=-1.08&tweet_ev_hate=0.62&tweet_ev_irony=2.66&tweet_ev_offensive=0.98&tweet_ev_sentiment=0.27&wic=0.58&wnli=4.01&wsc=1.54&yahoo_answers=-1.19&model_name=jfarmerphd%2Fbert-finetuned-squad-accelerate&base_name=bert-base-cased) using jfarmerphd/bert-finetuned-squad-accelerate as a base model yields average score of 74.05 in comparison to 72.43 by bert-base-cased.
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The model is ranked 3rd among all tested models for the bert-base-cased architecture as of 09/01/2023
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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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| 81.094 | 88.7333 | 65.84 | 47.4062 | 71.4373 | 71.4286 | 81.5916 | 50 | 77.6333 | 89.5053 | 82.6 | 91.012 | 69.4263 | 83.0553 | 88.2353 | 60.5817 | 73.0769 | 90.9757 | 89.8541 | 84.8968 | 69.3141 | 91.8578 | 51.448 | 85.562 | 97 | 80.4 | 44.018 | 77.7621 | 53.4007 | 67.8571 | 85.2326 | 68.4956 | 65.3605 | 56.338 | 63.4615 | 69.8333 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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