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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-finetuned-xsum |
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results: [] |
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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-small-finetuned-xsum |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9389 |
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- Rouge1: 0.2199 |
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- Rouge2: 0.0413 |
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- Rougel: 0.1739 |
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- Rougelsum: 0.1836 |
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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: 0.0002 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- training_steps: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 3.9818 | 1.0 | 1 | 3.5789 | 0.1857 | 0.0245 | 0.1420 | 0.1556 | |
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| 3.5098 | 2.0 | 2 | 3.4107 | 0.1863 | 0.0245 | 0.1391 | 0.1564 | |
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| 3.1669 | 3.0 | 3 | 3.2830 | 0.2008 | 0.0254 | 0.1466 | 0.1703 | |
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| 2.8568 | 4.0 | 4 | 3.1945 | 0.1980 | 0.0222 | 0.1411 | 0.1622 | |
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| 2.7102 | 5.0 | 5 | 3.1215 | 0.2019 | 0.0222 | 0.1472 | 0.1609 | |
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| 2.4563 | 6.0 | 6 | 3.0798 | 0.2167 | 0.0189 | 0.1533 | 0.1737 | |
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| 2.3367 | 7.0 | 7 | 3.0364 | 0.2050 | 0.0139 | 0.1420 | 0.1577 | |
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| 2.269 | 8.0 | 8 | 3.0071 | 0.2041 | 0.0139 | 0.1435 | 0.1561 | |
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| 2.0398 | 9.0 | 9 | 2.9865 | 0.2246 | 0.0139 | 0.1510 | 0.1721 | |
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| 1.9314 | 10.0 | 10 | 2.9783 | 0.2076 | 0.0139 | 0.1542 | 0.1681 | |
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| 1.9148 | 11.0 | 11 | 2.9684 | 0.2076 | 0.0139 | 0.1542 | 0.1681 | |
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| 1.8131 | 12.0 | 12 | 2.9598 | 0.2076 | 0.0139 | 0.1542 | 0.1681 | |
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| 1.7866 | 13.0 | 13 | 2.9497 | 0.2195 | 0.0184 | 0.1501 | 0.1722 | |
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| 1.689 | 14.0 | 14 | 2.9451 | 0.2067 | 0.0203 | 0.1453 | 0.1621 | |
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| 1.7257 | 15.0 | 15 | 2.9405 | 0.2155 | 0.0321 | 0.1599 | 0.1777 | |
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| 1.6441 | 16.0 | 16 | 2.9405 | 0.2155 | 0.0321 | 0.1599 | 0.1777 | |
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| 1.574 | 17.0 | 17 | 2.9409 | 0.2155 | 0.0321 | 0.1599 | 0.1777 | |
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| 1.587 | 18.0 | 18 | 2.9393 | 0.2260 | 0.0388 | 0.1678 | 0.1860 | |
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| 1.5362 | 19.0 | 19 | 2.9387 | 0.2199 | 0.0413 | 0.1739 | 0.1836 | |
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| 1.5133 | 20.0 | 20 | 2.9389 | 0.2199 | 0.0413 | 0.1739 | 0.1836 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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