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# ffgcc/InfoCSE-bert-base model
This model is based on bert-base-uncased pretrained model.
## Model Recycling
[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.08&mnli_lp=nan&20_newsgroup=-0.67&ag_news=-0.26&amazon_reviews_multi=0.42&anli=1.27&boolq=2.36&cb=7.05&cola=2.16&copa=11.55&dbpedia=-1.00&esnli=0.59&financial_phrasebank=15.07&imdb=-0.70&isear=2.70&mnli=0.60&mrpc=2.08&multirc=-1.37&poem_sentiment=8.32&qnli=1.26&qqp=0.40&rotten_tomatoes=0.98&rte=1.75&sst2=0.57&sst_5bins=1.46&stsb=1.12&trec_coarse=1.14&trec_fine=8.87&tweet_ev_emoji=0.81&tweet_ev_emotion=1.23&tweet_ev_hate=1.25&tweet_ev_irony=-2.33&tweet_ev_offensive=-0.02&tweet_ev_sentiment=1.02&wic=3.68&wnli=0.14&wsc=1.35&yahoo_answers=-0.12&model_name=ffgcc%2FInfoCSE-bert-base&base_name=bert-base-uncased) using ffgcc/InfoCSE-bert-base as a base model yields average score of 74.28 in comparison to 72.20 by bert-base-uncased.
The model is ranked 1st among all tested models for the bert-base-uncased architecture as of 21/12/2022
Results:
| 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 |
|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
| 82.3818 | 89.3333 | 66.34 | 48.2188 | 71.315 | 71.4286 | 83.9885 | 61 | 77.1667 | 90.2891 | 83.6 | 90.872 | 71.7731 | 84.3267 | 84.0686 | 58.6015 | 75 | 91.1404 | 90.6752 | 85.8349 | 61.7329 | 92.5459 | 54.2534 | 86.9799 | 97.2 | 77.2 | 36.82 | 81.14 | 54.1077 | 65.4337 | 85.3488 | 70.4982 | 66.9279 | 50.7042 | 63.4615 | 72.2 |
For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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