Transformers
PyTorch
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use andreaparker/flan-t5-base-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreaparker/flan-t5-base-samsum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("andreaparker/flan-t5-base-samsum") model = AutoModelForMultimodalLM.from_pretrained("andreaparker/flan-t5-base-samsum") - Notebooks
- Google Colab
- Kaggle
Evaluation results for andreaparker/flan-t5-base-samsum 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 07/02/2023.
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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## Model Recycling
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=9.04&mnli_lp=nan&20_newsgroup=3.55&ag_news=1.66&amazon_reviews_multi=0.19&anli=14.53&boolq=16.60&cb=24.91&cola=10.35&copa=25.50&dbpedia=5.73&esnli=5.31&financial_phrasebank=19.96&imdb=0.05&isear=0.59&mnli=11.74&mrpc=15.89&multirc=5.99&poem_sentiment=23.27&qnli=3.93&qqp=5.54&rotten_tomatoes=3.54&rte=23.90&sst2=-0.14&sst_5bins=5.12&stsb=20.58&trec_coarse=4.15&trec_fine=10.93&tweet_ev_emoji=12.87&tweet_ev_emotion=6.02&tweet_ev_hate=-0.04&tweet_ev_irony=7.12&tweet_ev_offensive=2.16&tweet_ev_sentiment=-0.00&wic=12.03&wnli=9.44&wsc=9.37&yahoo_answers=3.04&model_name=andreaparker%2Fflan-t5-base-samsum&base_name=google%2Ft5-v1_1-base) using andreaparker/flan-t5-base-samsum as a base model yields average score of 77.86 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 07/02/2023
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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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| 86.4312 | 89.8333 | 67.1 | 52.5937 | 82.1713 | 80.3571 | 80.5369 | 66 | 76.5 | 90.8897 | 86.7 | 93.044 | 71.6428 | 87.2457 | 88.7255 | 62.1287 | 91.3462 | 93.3004 | 89.1393 | 89.5872 | 84.4765 | 93.578 | 56.9683 | 89.3674 | 97.4 | 93 | 46.334 | 81.6327 | 51.4815 | 74.7449 | 84.7674 | 69.8795 | 67.8683 | 56.338 | 57.6923 | 72.3 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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