Evaluation results for ibm/ColD-Fusion-itr13-seed2 model as a base model for other tasks

As part of a research effort to identify high quality models in Huggingfacethat can serve as base models for further finetuning,we evaluated this by finetuning on 36 datasets.The model ranks 1st among all tested models for the roberta-base architecture as of 13/12/2022.


To share this information with others in your model card,please add the following evaluation results to your README.md page.

For more information please see https://ibm.github.io/model-recycling/ or contact me.

Best regards,
Elad Venezian
eladv@il.ibm.com
IBM Research AI

README.md

@@ -51,12 +51,11 @@ output = model(encoded_input)
 ```
 
 ## Evaluation results
-## [Model Recycling](https://ibm.github.io/model-recycling/)
+## Model Recycling
 
-Evaluation on 36 datasets using ibm/ColD-Fusion-itr13-seed2 as a base model, yields average score of 78.72 in comparison to 76.22 by roberta-base.
-
-Overall ranking: top 1 model among roberta-base models (updated to 12/12/2022)
+[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.50&mnli_lp=nan&20_newsgroup=1.08&ag_news=-0.47&amazon_reviews_multi=0.14&anli=2.75&boolq=3.32&cb=21.52&cola=0.07&copa=24.30&dbpedia=0.17&esnli=0.05&financial_phrasebank=2.19&imdb=-0.03&isear=0.67&mnli=0.41&mrpc=-0.12&multirc=2.46&poem_sentiment=4.52&qnli=0.27&qqp=0.37&rotten_tomatoes=3.04&rte=10.99&sst2=1.18&sst_5bins=1.47&stsb=1.72&trec_coarse=-0.11&trec_fine=3.24&tweet_ev_emoji=-1.35&tweet_ev_emotion=1.22&tweet_ev_hate=-0.34&tweet_ev_irony=5.48&tweet_ev_offensive=1.49&tweet_ev_sentiment=-1.25&wic=4.58&wnli=-5.49&wsc=0.19&yahoo_answers=0.16&model_name=ibm%2FColD-Fusion-itr13-seed2&base_name=roberta-base) using ibm/ColD-Fusion-itr13-seed2 as a base model. yields average score of 78.72 in comparison to 76.22 by roberta-base.
 
+The model ranks 1st among all tested models for the roberta-base architecture as of 13/12/2022
 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 |
@@ -66,7 +65,6 @@ Results:
 
 For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
 
-
 ```bibtex
 @article{ColDFusion,
   author    = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},