t5-small-finetuned-xsum
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.9389
- Rouge1: 0.2199
- Rouge2: 0.0413
- Rougel: 0.1739
- Rougelsum: 0.1836
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 3.9818 | 1.0 | 1 | 3.5789 | 0.1857 | 0.0245 | 0.1420 | 0.1556 |
| 3.5098 | 2.0 | 2 | 3.4107 | 0.1863 | 0.0245 | 0.1391 | 0.1564 |
| 3.1669 | 3.0 | 3 | 3.2830 | 0.2008 | 0.0254 | 0.1466 | 0.1703 |
| 2.8568 | 4.0 | 4 | 3.1945 | 0.1980 | 0.0222 | 0.1411 | 0.1622 |
| 2.7102 | 5.0 | 5 | 3.1215 | 0.2019 | 0.0222 | 0.1472 | 0.1609 |
| 2.4563 | 6.0 | 6 | 3.0798 | 0.2167 | 0.0189 | 0.1533 | 0.1737 |
| 2.3367 | 7.0 | 7 | 3.0364 | 0.2050 | 0.0139 | 0.1420 | 0.1577 |
| 2.269 | 8.0 | 8 | 3.0071 | 0.2041 | 0.0139 | 0.1435 | 0.1561 |
| 2.0398 | 9.0 | 9 | 2.9865 | 0.2246 | 0.0139 | 0.1510 | 0.1721 |
| 1.9314 | 10.0 | 10 | 2.9783 | 0.2076 | 0.0139 | 0.1542 | 0.1681 |
| 1.9148 | 11.0 | 11 | 2.9684 | 0.2076 | 0.0139 | 0.1542 | 0.1681 |
| 1.8131 | 12.0 | 12 | 2.9598 | 0.2076 | 0.0139 | 0.1542 | 0.1681 |
| 1.7866 | 13.0 | 13 | 2.9497 | 0.2195 | 0.0184 | 0.1501 | 0.1722 |
| 1.689 | 14.0 | 14 | 2.9451 | 0.2067 | 0.0203 | 0.1453 | 0.1621 |
| 1.7257 | 15.0 | 15 | 2.9405 | 0.2155 | 0.0321 | 0.1599 | 0.1777 |
| 1.6441 | 16.0 | 16 | 2.9405 | 0.2155 | 0.0321 | 0.1599 | 0.1777 |
| 1.574 | 17.0 | 17 | 2.9409 | 0.2155 | 0.0321 | 0.1599 | 0.1777 |
| 1.587 | 18.0 | 18 | 2.9393 | 0.2260 | 0.0388 | 0.1678 | 0.1860 |
| 1.5362 | 19.0 | 19 | 2.9387 | 0.2199 | 0.0413 | 0.1739 | 0.1836 |
| 1.5133 | 20.0 | 20 | 2.9389 | 0.2199 | 0.0413 | 0.1739 | 0.1836 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2