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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-base-samsum |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: samsum |
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type: samsum |
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config: samsum |
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split: test |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 47.4798 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# flan-t5-base-samsum |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3772 |
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- Rouge1: 47.4798 |
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- Rouge2: 23.9756 |
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- Rougel: 40.0392 |
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- Rougelsum: 43.6545 |
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- Gen Len: 17.3162 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.4403 | 1.0 | 1842 | 1.3829 | 46.5346 | 23.1326 | 39.4401 | 42.8272 | 17.0977 | |
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| 1.3534 | 2.0 | 3684 | 1.3732 | 47.0911 | 23.5074 | 39.5951 | 43.2279 | 17.4554 | |
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| 1.2795 | 3.0 | 5526 | 1.3709 | 46.8895 | 23.3243 | 39.5909 | 43.1286 | 17.2027 | |
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| 1.2313 | 4.0 | 7368 | 1.3736 | 47.4946 | 23.7802 | 39.9999 | 43.5903 | 17.2198 | |
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| 1.1934 | 5.0 | 9210 | 1.3772 | 47.4798 | 23.9756 | 40.0392 | 43.6545 | 17.3162 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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### Papers With Code Results |
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As of 2 February 2023 the Papers with Code page for this task has the following leaderboard. |
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Our score (Rouge 1 score of 47.4798) puts this model's performance between fourth and fifth place on the leaderboard: |
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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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