Evaluation results for RERobbins/qg_T5_triviaqa model as a base model for other tasks
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README.md
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# RERobbins/qg_T5_triviaqa 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=7.99&mnli_lp=nan&20_newsgroup=4.75&ag_news=1.56&amazon_reviews_multi=0.23&anli=15.10&boolq=8.53&cb=26.70&cola=8.82&copa=15.50&dbpedia=6.87&esnli=5.16&financial_phrasebank=19.36&imdb=0.81&isear=1.43&mnli=12.61&mrpc=14.18&multirc=1.15&poem_sentiment=19.42&qnli=3.93&qqp=6.52&rotten_tomatoes=4.10&rte=11.62&sst2=0.55&sst_5bins=5.03&stsb=18.48&trec_coarse=4.75&trec_fine=9.73&tweet_ev_emoji=13.49&tweet_ev_emotion=6.02&tweet_ev_hate=1.85&tweet_ev_irony=9.04&tweet_ev_offensive=2.97&tweet_ev_sentiment=1.12&wic=10.78&wnli=2.39&wsc=8.41&yahoo_answers=4.81&model_name=RERobbins%2Fqg_T5_triviaqa&base_name=google%2Ft5-v1_1-base) using RERobbins/qg_T5_triviaqa as a base model yields average score of 76.82 in comparison to 68.82 by google/t5-v1_1-base.
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The model is ranked 3rd 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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| 87.6261 | 89.7333 | 67.14 | 53.1563 | 74.0979 | 82.1429 | 79.0029 | 56 | 77.6333 | 90.7471 | 86.1 | 93.8 | 72.4902 | 88.1204 | 87.0098 | 57.2814 | 87.5 | 93.3004 | 90.1187 | 90.1501 | 72.2022 | 94.2661 | 56.8778 | 87.2745 | 98 | 91.8 | 46.95 | 81.6327 | 53.367 | 76.6582 | 85.5814 | 71.0029 | 66.6144 | 49.2958 | 56.7308 | 74.0667 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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