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--- |
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license: apache-2.0 |
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base_model: t5-base |
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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: my_awesome_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: 0.2033 |
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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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# my_awesome_billsum_model |
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6638 |
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- Rouge1: 0.2033 |
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- Rouge2: 0.1149 |
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- Rougel: 0.1762 |
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- Rougelsum: 0.1759 |
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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: 4 |
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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: 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 | 248 | 1.9584 | 0.1999 | 0.1073 | 0.1716 | 0.1717 | 19.0 | |
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| No log | 2.0 | 496 | 1.8621 | 0.195 | 0.1045 | 0.1685 | 0.1682 | 19.0 | |
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| 2.2512 | 3.0 | 744 | 1.8095 | 0.1973 | 0.1109 | 0.1728 | 0.1727 | 19.0 | |
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| 2.2512 | 4.0 | 992 | 1.7797 | 0.1989 | 0.1102 | 0.1724 | 0.1724 | 19.0 | |
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| 1.8144 | 5.0 | 1240 | 1.7505 | 0.1997 | 0.112 | 0.1735 | 0.1736 | 19.0 | |
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| 1.8144 | 6.0 | 1488 | 1.7308 | 0.2003 | 0.1134 | 0.1746 | 0.1744 | 19.0 | |
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| 1.6898 | 7.0 | 1736 | 1.7145 | 0.199 | 0.1114 | 0.1732 | 0.173 | 19.0 | |
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| 1.6898 | 8.0 | 1984 | 1.7083 | 0.1977 | 0.1106 | 0.1718 | 0.1716 | 19.0 | |
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| 1.5997 | 9.0 | 2232 | 1.6983 | 0.2014 | 0.1127 | 0.175 | 0.175 | 19.0 | |
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| 1.5997 | 10.0 | 2480 | 1.6923 | 0.2014 | 0.1153 | 0.1754 | 0.1753 | 19.0 | |
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| 1.5403 | 11.0 | 2728 | 1.6826 | 0.2009 | 0.1134 | 0.1752 | 0.1751 | 19.0 | |
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| 1.5403 | 12.0 | 2976 | 1.6768 | 0.2003 | 0.1125 | 0.1745 | 0.1744 | 19.0 | |
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| 1.491 | 13.0 | 3224 | 1.6722 | 0.2016 | 0.1146 | 0.1756 | 0.1755 | 19.0 | |
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| 1.491 | 14.0 | 3472 | 1.6750 | 0.2039 | 0.1164 | 0.1773 | 0.177 | 19.0 | |
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| 1.4496 | 15.0 | 3720 | 1.6679 | 0.2023 | 0.1147 | 0.1765 | 0.1763 | 19.0 | |
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| 1.4496 | 16.0 | 3968 | 1.6677 | 0.2032 | 0.1148 | 0.177 | 0.1768 | 19.0 | |
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| 1.4241 | 17.0 | 4216 | 1.6640 | 0.2021 | 0.1135 | 0.1752 | 0.175 | 19.0 | |
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| 1.4241 | 18.0 | 4464 | 1.6645 | 0.2027 | 0.1155 | 0.1766 | 0.1764 | 19.0 | |
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| 1.4025 | 19.0 | 4712 | 1.6632 | 0.2028 | 0.1149 | 0.1761 | 0.1757 | 19.0 | |
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| 1.4025 | 20.0 | 4960 | 1.6638 | 0.2033 | 0.1149 | 0.1762 | 0.1759 | 19.0 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.1 |
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- Tokenizers 0.13.3 |
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