We find your model to be great base-model

We find your model to be the 1th best base model over roberta-base architecture.
(Means that using your model as a starting point for finetuning is great)
We suggest to add the following Evaluation to your README.md page
. For any question please contact eladv@il.ibm.com

README.md

@@ -51,8 +51,14 @@ output = model(encoded_input)
 ```
 
 ## Evaluation results
-See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
-When fine-tuned on downstream tasks, this model achieves the following results:
+Evaluation on 36 dataset using ibm/ColD-Fusion-itr13-seed2 as a base model, yield average score of 78.72.
+According to [website](https://ibm.github.io/model-recycling/), this is the 1th best model for roberta-base models (updated to 11/12/2022)
+
+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 |
+|---------------:|----------:|-----------------------:|--------:|--------:|--------:|-------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|-------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
+|        86.3648 |      89.3 |                  66.72 | 53.0937 | 82.0183 | 89.2857 | 83.605 |     73 |   77.4667 | 91.0423 |                   87.3 | 93.868 | 73.1421 | 87.3881 | 87.7451 |   63.6757 |          88.4615 | 92.678 | 91.0809 |           91.4634 | 83.3935 | 95.2982 |     58.1448 | 91.6334 |            97 |          91 |            44.95 |            83.0401 |         52.5589 |          77.0408 |              86.0465 |              69.7818 | 70.0627 | 49.2958 | 63.4615 |         72.5667 |