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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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- billsum |
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metrics: |
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- rouge |
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model-index: |
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- name: T5_summ_gen_v1 |
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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: billsum |
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type: billsum |
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config: default |
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split: ca_test |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.1986 |
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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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# T5_summ_gen_v1 |
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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: 2.0950 |
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- Rouge1: 0.1986 |
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- Rouge2: 0.1044 |
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- Rougel: 0.1726 |
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- Rougelsum: 0.1727 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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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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| No log | 1.0 | 62 | 2.2294 | 0.1988 | 0.1023 | 0.1715 | 0.1714 | 19.0 | |
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| No log | 2.0 | 124 | 2.2038 | 0.1998 | 0.1024 | 0.1727 | 0.1725 | 19.0 | |
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| No log | 3.0 | 186 | 2.1890 | 0.2011 | 0.1049 | 0.1744 | 0.1746 | 19.0 | |
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| No log | 4.0 | 248 | 2.1767 | 0.2002 | 0.1059 | 0.1736 | 0.1737 | 19.0 | |
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| No log | 5.0 | 310 | 2.1593 | 0.2015 | 0.1064 | 0.1739 | 0.1741 | 19.0 | |
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| No log | 6.0 | 372 | 2.1522 | 0.2022 | 0.1059 | 0.1747 | 0.175 | 19.0 | |
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| No log | 7.0 | 434 | 2.1404 | 0.2028 | 0.1078 | 0.1746 | 0.1748 | 19.0 | |
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| No log | 8.0 | 496 | 2.1369 | 0.2015 | 0.1061 | 0.1735 | 0.1737 | 19.0 | |
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| 2.382 | 9.0 | 558 | 2.1299 | 0.1999 | 0.1053 | 0.1723 | 0.1725 | 19.0 | |
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| 2.382 | 10.0 | 620 | 2.1205 | 0.2003 | 0.1058 | 0.173 | 0.1729 | 19.0 | |
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| 2.382 | 11.0 | 682 | 2.1170 | 0.1998 | 0.105 | 0.1727 | 0.1727 | 19.0 | |
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| 2.382 | 12.0 | 744 | 2.1122 | 0.2003 | 0.1057 | 0.1734 | 0.1734 | 19.0 | |
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| 2.382 | 13.0 | 806 | 2.1084 | 0.1993 | 0.1042 | 0.1725 | 0.1726 | 19.0 | |
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| 2.382 | 14.0 | 868 | 2.1046 | 0.1988 | 0.1037 | 0.1723 | 0.1725 | 19.0 | |
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| 2.382 | 15.0 | 930 | 2.1023 | 0.1992 | 0.1047 | 0.1727 | 0.1729 | 19.0 | |
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| 2.382 | 16.0 | 992 | 2.1006 | 0.1992 | 0.1047 | 0.1727 | 0.1729 | 19.0 | |
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| 2.2855 | 17.0 | 1054 | 2.0979 | 0.1983 | 0.1034 | 0.1722 | 0.1723 | 19.0 | |
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| 2.2855 | 18.0 | 1116 | 2.0961 | 0.1988 | 0.1046 | 0.1729 | 0.173 | 19.0 | |
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| 2.2855 | 19.0 | 1178 | 2.0953 | 0.1986 | 0.1044 | 0.1725 | 0.1726 | 19.0 | |
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| 2.2855 | 20.0 | 1240 | 2.0950 | 0.1986 | 0.1044 | 0.1726 | 0.1727 | 19.0 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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