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mt5-small
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3524
- Rouge1: 21.18
- Rouge2: 6.37
- Rougel: 20.84
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: 0.0001
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|---|---|---|---|---|---|---|
| 4.6211 | 1.45 | 500 | 2.5968 | 16.96 | 4.86 | 16.73 |
| 3.1269 | 2.9 | 1000 | 2.4790 | 17.62 | 5.0 | 17.58 |
| 2.884 | 4.35 | 1500 | 2.4077 | 17.67 | 5.06 | 17.4 |
| 2.7627 | 5.8 | 2000 | 2.4003 | 18.67 | 5.42 | 18.26 |
| 2.638 | 7.25 | 2500 | 2.3953 | 18.76 | 5.49 | 18.44 |
| 2.5427 | 8.7 | 3000 | 2.3837 | 18.97 | 6.04 | 18.62 |
| 2.4846 | 10.14 | 3500 | 2.3957 | 20.17 | 6.23 | 19.88 |
| 2.3867 | 11.59 | 4000 | 2.3558 | 19.5 | 6.24 | 19.1 |
| 2.3651 | 13.04 | 4500 | 2.3225 | 19.6 | 6.18 | 19.2 |
| 2.2846 | 14.49 | 5000 | 2.3385 | 19.34 | 6.3 | 18.9 |
| 2.2351 | 15.94 | 5500 | 2.3413 | 20.42 | 6.44 | 19.93 |
| 2.1862 | 17.39 | 6000 | 2.3418 | 20.04 | 6.35 | 19.51 |
| 2.1375 | 18.84 | 6500 | 2.3438 | 21.02 | 6.56 | 20.45 |
| 2.0961 | 20.29 | 7000 | 2.3451 | 20.82 | 6.81 | 20.6 |
| 2.0686 | 21.74 | 7500 | 2.3571 | 20.46 | 6.57 | 20.03 |
| 2.0253 | 23.19 | 8000 | 2.3672 | 20.49 | 6.21 | 20.16 |
| 1.9997 | 24.64 | 8500 | 2.3524 | 21.18 | 6.37 | 20.84 |
| 1.9627 | 26.09 | 9000 | 2.3780 | 20.9 | 5.96 | 20.4 |
| 1.9561 | 27.54 | 9500 | 2.3808 | 21.06 | 6.59 | 20.76 |
| 1.902 | 28.99 | 10000 | 2.3739 | 20.73 | 6.09 | 20.41 |
| 1.8837 | 30.43 | 10500 | 2.3786 | 20.65 | 6.27 | 20.35 |
| 1.8587 | 31.88 | 11000 | 2.3853 | 20.44 | 6.23 | 20.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
