# devpranjal/deberta-v3-base-devrev-data model
This model is based on microsoft/deberta-v3-base pretrained model.


## Model Recycling

[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.54&mnli_lp=nan&20_newsgroup=-0.92&ag_news=-0.51&amazon_reviews_multi=-0.14&anli=-0.59&boolq=2.27&cb=5.36&cola=-0.28&copa=11.60&dbpedia=-0.40&esnli=-0.74&financial_phrasebank=3.22&imdb=-0.48&isear=-0.61&mnli=-0.30&mrpc=0.75&multirc=1.38&poem_sentiment=-3.08&qnli=0.30&qqp=0.24&rotten_tomatoes=-0.18&rte=1.77&sst2=-0.34&sst_5bins=0.66&stsb=1.16&trec_coarse=-0.56&trec_fine=0.38&tweet_ev_emoji=0.18&tweet_ev_emotion=-0.70&tweet_ev_hate=1.46&tweet_ev_irony=-2.40&tweet_ev_offensive=0.87&tweet_ev_sentiment=-0.87&wic=-0.37&wnli=1.62&wsc=-0.63&yahoo_answers=0.47&model_name=devpranjal%2Fdeberta-v3-base-devrev-data&base_name=microsoft%2Fdeberta-v3-base) using devpranjal/deberta-v3-base-devrev-data as a base model yields average score of 79.58 in comparison to 79.04 by microsoft/deberta-v3-base.

The model is ranked 3rd among all tested models for the microsoft/deberta-v3-base architecture as of 07/02/2023
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 |
|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|-------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|-------:|--------:|----------------:|
|        85.4886 |   89.9333 |                  66.72 | 58.1875 | 85.2599 | 80.3571 | 86.2895 |     70 |   79.0333 | 91.1849 |                   87.7 | 94.012 | 71.2516 | 89.4833 | 89.951 |   63.6345 |          83.6538 | 93.8129 | 92.0257 |           90.2439 | 84.1155 | 94.7248 |     57.6471 | 91.4423 |          97.2 |        91.4 |           46.374 |            83.2512 |         57.6768 |          77.4235 |              85.9302 |              70.9297 | 70.8464 | 71.831 | 63.4615 |            72.5 |


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