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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- bills-summarization |
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metrics: |
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- rouge |
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model-index: |
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- name: ft-t5-with-dill-sum |
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results: |
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- task: |
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name: Summarization |
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type: summarization |
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dataset: |
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name: billsum |
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type: bills-summarization |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.1886 |
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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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# ft-t5-with-dill-sum |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3109 |
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- Rouge1: 0.1886 |
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- Rouge2: 0.104 |
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- Rougel: 0.166 |
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- Rougelsum: 0.1659 |
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- Gen Len: 19.0 |
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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: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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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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| 2.5462 | 1.0 | 31 | 2.4185 | 0.187 | 0.1023 | 0.1637 | 0.1639 | 19.0 | |
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| 2.5478 | 2.0 | 62 | 2.4166 | 0.187 | 0.1018 | 0.1637 | 0.1639 | 19.0 | |
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| 2.5729 | 3.0 | 93 | 2.4114 | 0.1868 | 0.1015 | 0.1637 | 0.1638 | 19.0 | |
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| 2.5806 | 4.0 | 124 | 2.4072 | 0.1855 | 0.1006 | 0.1626 | 0.1627 | 19.0 | |
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| 2.5231 | 5.0 | 155 | 2.4025 | 0.1877 | 0.1042 | 0.165 | 0.165 | 19.0 | |
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| 2.5245 | 6.0 | 186 | 2.3948 | 0.1869 | 0.1024 | 0.1642 | 0.1642 | 19.0 | |
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| 2.5273 | 7.0 | 217 | 2.3860 | 0.1886 | 0.1032 | 0.1652 | 0.1653 | 19.0 | |
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| 2.4941 | 8.0 | 248 | 2.3765 | 0.188 | 0.1033 | 0.1649 | 0.165 | 19.0 | |
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| 2.4612 | 9.0 | 279 | 2.3698 | 0.19 | 0.1057 | 0.1671 | 0.1671 | 19.0 | |
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| 2.463 | 10.0 | 310 | 2.3578 | 0.1882 | 0.1039 | 0.1662 | 0.1663 | 19.0 | |
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| 2.4539 | 11.0 | 341 | 2.3491 | 0.1898 | 0.1057 | 0.1667 | 0.1667 | 19.0 | |
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| 2.441 | 12.0 | 372 | 2.3392 | 0.1901 | 0.1055 | 0.1669 | 0.1668 | 19.0 | |
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| 2.4389 | 13.0 | 403 | 2.3292 | 0.1893 | 0.1053 | 0.1666 | 0.1665 | 19.0 | |
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| 2.3945 | 14.0 | 434 | 2.3203 | 0.1903 | 0.1051 | 0.1676 | 0.1675 | 19.0 | |
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| 2.4148 | 15.0 | 465 | 2.3109 | 0.1886 | 0.104 | 0.166 | 0.1659 | 19.0 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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