# adit94/nlpcharade model
This model is based on t5-base pretrained model.


## Model Recycling

[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.78&mnli_lp=nan&20_newsgroup=-29.01&ag_news=2.38&amazon_reviews_multi=4.40&anli=1.58&boolq=10.84&cb=-8.92&cola=-2.62&copa=39.82&dbpedia=12.81&esnli=0.60&financial_phrasebank=1.31&imdb=-10.84&isear=26.32&mnli=8.64&mrpc=3.06&multirc=12.08&poem_sentiment=-29.04&qnli=-34.05&qqp=1.74&rotten_tomatoes=-36.72&rte=16.64&sst2=-9.88&sst_5bins=18.68&stsb=-5.99&trec_coarse=-30.77&trec_fine=-0.01&tweet_ev_emoji=47.56&tweet_ev_emotion=10.81&tweet_ev_hate=21.50&tweet_ev_irony=10.21&tweet_ev_offensive=-13.09&tweet_ev_sentiment=16.40&wic=4.61&wnli=0.99&wsc=17.17&yahoo_answers=21.01&model_name=adit94%2Fnlpcharade&base_name=t5-base) using adit94/nlpcharade as a base model yields average score of 78.23 in comparison to 75.45 by t5-base.

The model is ranked 1st among all tested models for the t5-base 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 |
|---------------:|----------:|-----------------------:|-------:|--------:|--------:|--------:|--------:|----------:|--------:|-----------------------:|--------:|--------:|--------:|--------:|----------:|-----------------:|-------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|------:|-------:|--------:|----------------:|
|        56.1086 |      91.8 |                70.9459 | 48.625 |    87.5 | 66.6144 | 79.2905 | 89.4667 |    89.212 | 90.3196 |                86.6151 | 81.4919 |    97.6 | 92.4401 | 88.7255 |   72.3598 |            45.38 | 56.338 | 90.6752 |           51.8855 | 90.3196 | 83.9535 |     74.2347 | 79.3272 |         66.44 |     92.3165 |          92.4401 |            90.3196 |         74.2347 |          83.9535 |              70.9459 |              86.6151 |  71.8 | 56.338 | 77.1667 |          92.596 |


For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)