Evaluation results for momtaz/bert-finetuned-squad model as a base model for other tasks
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eladven
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README.md
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@@ -52,3 +52,17 @@ The following hyperparameters were used during training:
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- Pytorch 1.13.1+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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- Pytorch 1.13.1+cu116
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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=1.66&mnli_lp=nan&20_newsgroup=-0.18&ag_news=0.31&amazon_reviews_multi=0.01&anli=1.65&boolq=4.52&cb=9.73&cola=0.80&copa=0.85&dbpedia=0.10&esnli=-0.15&financial_phrasebank=12.94&imdb=0.15&isear=1.24&mnli=-0.46&mrpc=2.61&multirc=0.49&poem_sentiment=0.58&qnli=0.59&qqp=0.20&rotten_tomatoes=-0.21&rte=2.35&sst2=0.60&sst_5bins=0.81&stsb=1.71&trec_coarse=0.17&trec_fine=7.02&tweet_ev_emoji=0.74&tweet_ev_emotion=0.19&tweet_ev_hate=2.17&tweet_ev_irony=1.77&tweet_ev_offensive=-0.41&tweet_ev_sentiment=0.88&wic=0.58&wnli=4.01&wsc=1.54&yahoo_answers=-0.16&model_name=momtaz%2Fbert-finetuned-squad&base_name=bert-base-cased) using momtaz/bert-finetuned-squad as a base model yields average score of 74.08 in comparison to 72.43 by bert-base-cased.
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The model is ranked 2nd among all tested models for the bert-base-cased architecture as of 22/01/2023
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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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| 81.5587 | 89.3667 | 65.72 | 48.2188 | 72.7829 | 73.2143 | 82.6462 | 53 | 78.8667 | 89.4849 | 81.3 | 91.292 | 69.6219 | 82.9231 | 85.5392 | 60.953 | 68.2692 | 90.5913 | 90.1459 | 84.334 | 64.9819 | 92.0872 | 52.2172 | 86.2269 | 96.8 | 80 | 44.982 | 79.0289 | 54.9495 | 66.9643 | 83.8372 | 69.1062 | 65.3605 | 56.338 | 63.4615 | 70.8667 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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