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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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- billsum |
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metrics: |
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- rouge |
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model-index: |
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- name: custom_billsum_model |
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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: 0.1968 |
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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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# custom_billsum_model |
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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: 2.2150 |
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- Rouge1: 0.1968 |
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- Rouge2: 0.0981 |
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- Rougel: 0.1672 |
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- Rougelsum: 0.167 |
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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 | 2.4551 | 0.1626 | 0.0663 | 0.135 | 0.135 | 19.0 | |
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| No log | 2.0 | 124 | 2.3987 | 0.1882 | 0.0866 | 0.1577 | 0.1577 | 19.0 | |
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| No log | 3.0 | 186 | 2.3639 | 0.1964 | 0.0937 | 0.1652 | 0.165 | 19.0 | |
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| No log | 4.0 | 248 | 2.3370 | 0.1943 | 0.0931 | 0.164 | 0.1638 | 19.0 | |
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| No log | 5.0 | 310 | 2.3135 | 0.1942 | 0.0938 | 0.1646 | 0.1643 | 19.0 | |
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| No log | 6.0 | 372 | 2.2949 | 0.195 | 0.0938 | 0.1648 | 0.1648 | 19.0 | |
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| No log | 7.0 | 434 | 2.2809 | 0.1937 | 0.0944 | 0.1643 | 0.1642 | 19.0 | |
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| No log | 8.0 | 496 | 2.2676 | 0.1949 | 0.0957 | 0.1664 | 0.166 | 19.0 | |
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| 2.5047 | 9.0 | 558 | 2.2582 | 0.1954 | 0.097 | 0.1665 | 0.1662 | 19.0 | |
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| 2.5047 | 10.0 | 620 | 2.2510 | 0.1951 | 0.0966 | 0.1661 | 0.166 | 19.0 | |
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| 2.5047 | 11.0 | 682 | 2.2416 | 0.1962 | 0.0979 | 0.1673 | 0.1671 | 19.0 | |
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| 2.5047 | 12.0 | 744 | 2.2360 | 0.196 | 0.0975 | 0.1664 | 0.1663 | 19.0 | |
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| 2.5047 | 13.0 | 806 | 2.2302 | 0.1965 | 0.098 | 0.1667 | 0.1666 | 19.0 | |
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| 2.5047 | 14.0 | 868 | 2.2262 | 0.1973 | 0.0985 | 0.1673 | 0.1671 | 19.0 | |
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| 2.5047 | 15.0 | 930 | 2.2224 | 0.197 | 0.0976 | 0.1668 | 0.1667 | 19.0 | |
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| 2.5047 | 16.0 | 992 | 2.2200 | 0.1973 | 0.0984 | 0.1673 | 0.1671 | 19.0 | |
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| 2.3391 | 17.0 | 1054 | 2.2183 | 0.1967 | 0.0974 | 0.1669 | 0.1666 | 19.0 | |
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| 2.3391 | 18.0 | 1116 | 2.2164 | 0.1968 | 0.0974 | 0.1669 | 0.1666 | 19.0 | |
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| 2.3391 | 19.0 | 1178 | 2.2152 | 0.1969 | 0.0982 | 0.1673 | 0.1671 | 19.0 | |
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| 2.3391 | 20.0 | 1240 | 2.2150 | 0.1968 | 0.0981 | 0.1672 | 0.167 | 19.0 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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