mt5-small-finetuned-easy-read
This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6421
- Rouge1: 12.4785
- Rouge2: 7.297
- Rougel: 11.2459
- Rougelsum: 11.8977
- Gen Len: 20.0
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 450 | 2.1751 | 3.0651 | 1.4242 | 2.7239 | 2.9793 | 8.91 |
| 8.7064 | 2.0 | 900 | 2.0039 | 7.9637 | 3.5462 | 7.2607 | 7.7483 | 17.7633 |
| 3.0928 | 3.0 | 1350 | 1.9316 | 9.9888 | 4.4076 | 9.0744 | 9.736 | 19.6767 |
| 2.7145 | 4.0 | 1800 | 1.8842 | 10.663 | 4.7603 | 9.6266 | 10.3352 | 19.975 |
| 2.5488 | 5.0 | 2250 | 1.8269 | 12.2099 | 6.5741 | 10.9447 | 11.7951 | 19.9583 |
| 2.4292 | 6.0 | 2700 | 1.8062 | 12.3732 | 6.5612 | 11.0588 | 11.9345 | 19.9683 |
| 2.3469 | 7.0 | 3150 | 1.7903 | 12.393 | 6.8323 | 11.0593 | 11.918 | 19.97 |
| 2.2962 | 8.0 | 3600 | 1.7783 | 12.5416 | 7.129 | 11.2943 | 12.0636 | 19.9883 |
| 2.2392 | 9.0 | 4050 | 1.7542 | 12.569 | 7.3297 | 11.3892 | 12.1161 | 19.9833 |
| 2.1997 | 10.0 | 4500 | 1.7367 | 12.2996 | 7.0573 | 11.1477 | 11.7957 | 19.9817 |
| 2.1997 | 11.0 | 4950 | 1.7292 | 12.3945 | 7.0875 | 11.2196 | 11.8729 | 19.9983 |
| 2.167 | 12.0 | 5400 | 1.7141 | 12.5922 | 7.3044 | 11.3837 | 12.0437 | 20.0 |
| 2.1364 | 13.0 | 5850 | 1.7055 | 12.7888 | 7.4252 | 11.4793 | 12.2255 | 20.0 |
| 2.1001 | 14.0 | 6300 | 1.7050 | 12.8441 | 7.5659 | 11.4877 | 12.2913 | 20.0 |
| 2.0948 | 15.0 | 6750 | 1.6888 | 12.4564 | 7.2591 | 11.2335 | 11.9194 | 20.0 |
| 2.0641 | 16.0 | 7200 | 1.6814 | 12.6283 | 7.3345 | 11.3652 | 12.0453 | 20.0 |
| 2.0493 | 17.0 | 7650 | 1.6792 | 12.521 | 7.2919 | 11.2711 | 11.9666 | 20.0 |
| 2.0278 | 18.0 | 8100 | 1.6707 | 12.6528 | 7.4801 | 11.4206 | 12.1064 | 20.0 |
| 2.0158 | 19.0 | 8550 | 1.6567 | 12.3323 | 7.1938 | 11.1236 | 11.7917 | 20.0 |
| 2.0094 | 20.0 | 9000 | 1.6614 | 12.3774 | 7.2186 | 11.1618 | 11.8052 | 20.0 |
| 2.0094 | 21.0 | 9450 | 1.6608 | 12.4445 | 7.3107 | 11.1788 | 11.8749 | 20.0 |
| 1.9939 | 22.0 | 9900 | 1.6520 | 12.4746 | 7.2967 | 11.2498 | 11.9068 | 20.0 |
| 1.9834 | 23.0 | 10350 | 1.6478 | 12.4374 | 7.252 | 11.198 | 11.8311 | 20.0 |
| 1.976 | 24.0 | 10800 | 1.6477 | 12.3398 | 7.1823 | 11.1211 | 11.7798 | 20.0 |
| 1.9779 | 25.0 | 11250 | 1.6486 | 12.2964 | 7.1372 | 11.1039 | 11.7536 | 20.0 |
| 1.9566 | 26.0 | 11700 | 1.6430 | 12.4138 | 7.2163 | 11.2127 | 11.8479 | 20.0 |
| 1.9639 | 27.0 | 12150 | 1.6422 | 12.4914 | 7.2668 | 11.2499 | 11.922 | 20.0 |
| 1.9595 | 28.0 | 12600 | 1.6441 | 12.5039 | 7.3107 | 11.2503 | 11.9109 | 20.0 |
| 1.9539 | 29.0 | 13050 | 1.6422 | 12.4762 | 7.2738 | 11.2411 | 11.905 | 20.0 |
| 1.9546 | 30.0 | 13500 | 1.6421 | 12.4785 | 7.297 | 11.2459 | 11.8977 | 20.0 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.0.0+cu117
- Datasets 3.4.0
- Tokenizers 0.21.0