Evaluation results for MoritzLaurer/DeBERTa-v3-base-mnli model as a base model for other tasks

README.md

@@ -58,4 +58,17 @@ If you want to cite this model, please cite the original DeBERTa paper, the resp
 If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
 
 ### Debugging and issues
-Note that DeBERTa-v3 was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues. 
+Note that DeBERTa-v3 was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues. 
+## Model Recycling
+
+[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.97&mnli_lp=nan&20_newsgroup=-0.39&ag_news=0.19&amazon_reviews_multi=0.10&anli=1.31&boolq=0.81&cb=8.93&cola=0.01&copa=13.60&dbpedia=-0.23&esnli=-0.51&financial_phrasebank=0.61&imdb=-0.26&isear=-0.35&mnli=-0.34&mrpc=1.24&multirc=1.50&poem_sentiment=-0.19&qnli=0.30&qqp=0.13&rotten_tomatoes=-0.55&rte=3.57&sst2=0.35&sst_5bins=0.39&stsb=1.10&trec_coarse=-0.36&trec_fine=-0.02&tweet_ev_emoji=1.11&tweet_ev_emotion=-0.35&tweet_ev_hate=1.43&tweet_ev_irony=-2.65&tweet_ev_offensive=-1.69&tweet_ev_sentiment=-1.51&wic=0.57&wnli=-2.61&wsc=9.95&yahoo_answers=-0.33&model_name=MoritzLaurer%2FDeBERTa-v3-base-mnli&base_name=microsoft%2Fdeberta-v3-base) using MoritzLaurer/DeBERTa-v3-base-mnli as a base model yields average score of 80.01 in comparison to 79.04 by microsoft/deberta-v3-base.
+
+The model is ranked 1st among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023
+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 |
+|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|-------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
+|        86.0196 |   90.6333 |                  66.96 | 60.0938 |  83.792 | 83.9286 | 86.5772 |     72 |      79.2 |  91.419 |                   85.1 | 94.232 | 71.5124 | 89.4426 | 90.4412 |   63.7583 |          86.5385 | 93.8129 | 91.9144 |           89.8687 | 85.9206 | 95.4128 |     57.3756 | 91.377 |          97.4 |          91 |           47.302 |            83.6031 |         57.6431 |          77.1684 |              83.3721 |              70.2947 | 71.7868 | 67.6056 | 74.0385 |            71.7 |
+
+
+For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)