readme: # 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/)
  