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hubert-large-ls960-ft
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the galsenai/waxal_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.3272
- Accuracy: 0.9413
- Precision: 0.9865
- F1: 0.9628
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: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|---|---|---|---|---|---|---|
| 4.7142 | 1.01 | 500 | 5.2765 | 0.0 | 0.0 | 0.0 |
| 4.396 | 2.02 | 1000 | 5.4145 | 0.0 | 0.0 | 0.0 |
| 3.8883 | 3.04 | 1500 | 4.4336 | 0.0474 | 0.0408 | 0.0104 |
| 2.7848 | 4.05 | 2000 | 3.9772 | 0.1300 | 0.1281 | 0.0964 |
| 1.8649 | 5.06 | 2500 | 3.4482 | 0.1576 | 0.3339 | 0.1547 |
| 1.3084 | 6.07 | 3000 | 2.9703 | 0.3081 | 0.5296 | 0.3402 |
| 0.9868 | 7.08 | 3500 | 2.3985 | 0.4687 | 0.8032 | 0.5353 |
| 0.7679 | 8.1 | 4000 | 1.7937 | 0.6521 | 0.8389 | 0.7095 |
| 0.6232 | 9.11 | 4500 | 1.4768 | 0.7389 | 0.8698 | 0.7847 |
| 0.5126 | 10.12 | 5000 | 1.0542 | 0.8287 | 0.9443 | 0.8763 |
| 0.4453 | 11.13 | 5500 | 0.9050 | 0.8518 | 0.9511 | 0.8960 |
| 0.3775 | 12.15 | 6000 | 0.6996 | 0.8928 | 0.9662 | 0.9266 |
| 0.3568 | 13.16 | 6500 | 0.6157 | 0.8958 | 0.9743 | 0.9285 |
| 0.3165 | 14.17 | 7000 | 0.4925 | 0.9151 | 0.9764 | 0.9436 |
| 0.2951 | 15.18 | 7500 | 0.4992 | 0.9038 | 0.9773 | 0.9369 |
| 0.2763 | 16.19 | 8000 | 0.5212 | 0.9072 | 0.9821 | 0.9404 |
| 0.2634 | 17.21 | 8500 | 0.5201 | 0.9087 | 0.9817 | 0.9418 |
| 0.2422 | 18.22 | 9000 | 0.4504 | 0.9235 | 0.9840 | 0.9514 |
| 0.236 | 19.23 | 9500 | 0.3829 | 0.9257 | 0.9825 | 0.9518 |
| 0.2272 | 20.24 | 10000 | 0.4632 | 0.9155 | 0.9822 | 0.9451 |
| 0.226 | 21.25 | 10500 | 0.4731 | 0.9159 | 0.9837 | 0.9470 |
| 0.2129 | 22.27 | 11000 | 0.3814 | 0.9299 | 0.9832 | 0.9549 |
| 0.2009 | 23.28 | 11500 | 0.4119 | 0.9257 | 0.9814 | 0.9515 |
| 0.1973 | 24.29 | 12000 | 0.4310 | 0.9216 | 0.9843 | 0.9493 |
| 0.1965 | 25.3 | 12500 | 0.3272 | 0.9413 | 0.9865 | 0.9628 |
| 0.1989 | 26.32 | 13000 | 0.4231 | 0.9242 | 0.9878 | 0.9528 |
| 0.1916 | 27.33 | 13500 | 0.3978 | 0.9284 | 0.9876 | 0.9559 |
| 0.1849 | 28.34 | 14000 | 0.4529 | 0.9216 | 0.9865 | 0.9507 |
| 0.1844 | 29.35 | 14500 | 0.3854 | 0.9314 | 0.9864 | 0.9566 |
| 0.1831 | 30.36 | 15000 | 0.4178 | 0.9257 | 0.9853 | 0.9528 |
| 0.1778 | 31.38 | 15500 | 0.3737 | 0.9360 | 0.9884 | 0.9606 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
