Evaluation results for RERobbins/qg_T5_nq model as a base model for other tasks
Browse filesAs part of a research effort to identify high quality models in Huggingface that can serve as base models for further finetuning, we evaluated this by finetuning on 36 datasets. The model ranks 2nd among all tested models for the google/t5-v1_1-base architecture as of 18/01/2023.
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
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# RERobbins/qg_T5_nq model
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This model is based on google/t5-v1_1-base pretrained model.
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## Model Recycling
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=8.37&mnli_lp=nan&20_newsgroup=4.19&ag_news=1.52&amazon_reviews_multi=-0.13&anli=13.06&boolq=12.35&cb=30.27&cola=9.40&copa=8.50&dbpedia=6.63&esnli=5.31&financial_phrasebank=20.66&imdb=0.80&isear=2.61&mnli=11.88&mrpc=14.91&multirc=5.37&poem_sentiment=16.54&qnli=3.67&qqp=4.70&rotten_tomatoes=3.64&rte=14.87&sst2=0.55&sst_5bins=4.76&stsb=18.60&trec_coarse=4.75&trec_fine=9.93&tweet_ev_emoji=13.56&tweet_ev_emotion=6.59&tweet_ev_hate=2.08&tweet_ev_irony=9.67&tweet_ev_offensive=2.04&tweet_ev_sentiment=1.56&wic=13.60&wnli=6.62&wsc=12.26&yahoo_answers=4.11&model_name=RERobbins%2Fqg_T5_nq&base_name=google%2Ft5-v1_1-base) using RERobbins/qg_T5_nq as a base model yields average score of 77.20 in comparison to 68.82 by google/t5-v1_1-base.
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The model is ranked 2nd among all tested models for the google/t5-v1_1-base architecture as of 18/01/2023
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Results:
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| 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 |
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|---------------:|----------:|-----------------------:|-------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
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| 87.0685 | 89.7 | 66.78 | 51.125 | 77.9205 | 85.7143 | 79.5781 | 49 | 77.4 | 90.8897 | 87.4 | 93.788 | 73.6636 | 87.3881 | 87.7451 | 61.5099 | 84.6154 | 93.0441 | 88.2958 | 89.6811 | 75.4513 | 94.2661 | 56.6063 | 87.3921 | 98 | 92 | 47.02 | 82.1956 | 53.6027 | 77.2959 | 84.6512 | 71.4425 | 69.4357 | 53.5211 | 60.5769 | 73.3667 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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