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t5-small-finetuned-pytorch-final
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6589
- Rouge1: 25.8199
- Rouge2: 14.6736
- Rougel: 22.3682
- Rougelsum: 23.917
- Gen Len: 19.0
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.0989 | 1.0 | 503 | 1.8152 | 24.4365 | 13.3113 | 20.9862 | 22.6351 | 19.0 |
| 1.9704 | 2.0 | 1006 | 1.7720 | 24.8843 | 13.7084 | 21.4551 | 22.9787 | 19.0 |
| 1.9241 | 3.0 | 1509 | 1.7468 | 25.1988 | 14.052 | 21.7041 | 23.2981 | 19.0 |
| 1.8856 | 4.0 | 2012 | 1.7268 | 25.502 | 14.3573 | 21.9449 | 23.5741 | 19.0 |
| 1.8653 | 5.0 | 2515 | 1.7156 | 25.6461 | 14.3872 | 22.0824 | 23.6502 | 19.0 |
| 1.8367 | 6.0 | 3018 | 1.7057 | 25.6513 | 14.4842 | 22.2314 | 23.7009 | 19.0 |
| 1.8183 | 7.0 | 3521 | 1.6993 | 25.6377 | 14.481 | 22.185 | 23.6855 | 19.0 |
| 1.8068 | 8.0 | 4024 | 1.6945 | 25.5275 | 14.3184 | 22.03 | 23.5524 | 19.0 |
| 1.7959 | 9.0 | 4527 | 1.6885 | 25.4232 | 14.2443 | 21.9691 | 23.4711 | 19.0 |
| 1.7741 | 10.0 | 5030 | 1.6840 | 25.5169 | 14.2654 | 22.0518 | 23.5864 | 19.0 |
| 1.7665 | 11.0 | 5533 | 1.6817 | 25.5237 | 14.3758 | 22.094 | 23.5891 | 19.0 |
| 1.7541 | 12.0 | 6036 | 1.6779 | 25.2572 | 14.1939 | 21.816 | 23.3577 | 19.0 |
| 1.7479 | 13.0 | 6539 | 1.6761 | 25.3922 | 14.4173 | 22.0299 | 23.5163 | 19.0 |
| 1.7308 | 14.0 | 7042 | 1.6742 | 25.3631 | 14.2906 | 22.0221 | 23.5128 | 19.0 |
| 1.7261 | 15.0 | 7545 | 1.6717 | 25.4318 | 14.3493 | 22.0454 | 23.5278 | 19.0 |
| 1.7181 | 16.0 | 8048 | 1.6691 | 25.4043 | 14.325 | 22.0252 | 23.5423 | 19.0 |
| 1.7048 | 17.0 | 8551 | 1.6691 | 25.6406 | 14.5424 | 22.2377 | 23.7325 | 19.0 |
| 1.7064 | 18.0 | 9054 | 1.6671 | 25.4986 | 14.3177 | 22.0629 | 23.5943 | 19.0 |
| 1.7003 | 19.0 | 9557 | 1.6687 | 25.6196 | 14.4546 | 22.2079 | 23.7184 | 19.0 |
| 1.6858 | 20.0 | 10060 | 1.6660 | 25.6864 | 14.5874 | 22.3071 | 23.8151 | 19.0 |
| 1.6861 | 21.0 | 10563 | 1.6648 | 25.6698 | 14.5281 | 22.2717 | 23.797 | 19.0 |
| 1.684 | 22.0 | 11066 | 1.6635 | 25.7104 | 14.5393 | 22.2573 | 23.829 | 19.0 |
| 1.6751 | 23.0 | 11569 | 1.6615 | 25.7254 | 14.5923 | 22.2509 | 23.8439 | 19.0 |
| 1.6741 | 24.0 | 12072 | 1.6624 | 25.7821 | 14.663 | 22.3164 | 23.8809 | 19.0 |
| 1.6765 | 25.0 | 12575 | 1.6621 | 25.7689 | 14.5796 | 22.2779 | 23.8765 | 19.0 |
| 1.6562 | 26.0 | 13078 | 1.6616 | 25.7856 | 14.6224 | 22.3298 | 23.9215 | 19.0 |
| 1.6636 | 27.0 | 13581 | 1.6610 | 25.83 | 14.6569 | 22.4229 | 23.9404 | 19.0 |
| 1.671 | 28.0 | 14084 | 1.6599 | 25.6857 | 14.5126 | 22.2093 | 23.788 | 19.0 |
| 1.6467 | 29.0 | 14587 | 1.6602 | 25.8111 | 14.6784 | 22.3599 | 23.9132 | 19.0 |
| 1.6556 | 30.0 | 15090 | 1.6594 | 25.8887 | 14.7244 | 22.3998 | 23.9739 | 19.0 |
| 1.6463 | 31.0 | 15593 | 1.6594 | 25.8534 | 14.6966 | 22.3867 | 23.9439 | 19.0 |
| 1.6548 | 32.0 | 16096 | 1.6600 | 25.839 | 14.674 | 22.3763 | 23.9438 | 19.0 |
| 1.6458 | 33.0 | 16599 | 1.6590 | 25.8364 | 14.6589 | 22.3512 | 23.9243 | 19.0 |
| 1.6431 | 34.0 | 17102 | 1.6590 | 25.8314 | 14.6733 | 22.3526 | 23.9288 | 19.0 |
| 1.6637 | 35.0 | 17605 | 1.6589 | 25.8199 | 14.6736 | 22.3682 | 23.917 | 19.0 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
