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distilhubert-finetuned-gtzan
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 1.0908
- Accuracy: 0.84
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: 5e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1531 | 1.0 | 113 | 2.1667 | 0.41 |
| 1.6622 | 2.0 | 226 | 1.6138 | 0.63 |
| 1.3112 | 3.0 | 339 | 1.2047 | 0.7 |
| 0.9374 | 4.0 | 452 | 0.9595 | 0.73 |
| 0.5475 | 5.0 | 565 | 0.7239 | 0.82 |
| 0.4845 | 6.0 | 678 | 0.7406 | 0.75 |
| 0.2489 | 7.0 | 791 | 0.6838 | 0.78 |
| 0.3272 | 8.0 | 904 | 0.8447 | 0.79 |
| 0.2244 | 9.0 | 1017 | 0.7184 | 0.81 |
| 0.0353 | 10.0 | 1130 | 0.8800 | 0.79 |
| 0.0201 | 11.0 | 1243 | 0.8800 | 0.83 |
| 0.0079 | 12.0 | 1356 | 0.8207 | 0.83 |
| 0.004 | 13.0 | 1469 | 0.9218 | 0.82 |
| 0.003 | 14.0 | 1582 | 1.0004 | 0.83 |
| 0.0024 | 15.0 | 1695 | 0.9446 | 0.84 |
| 0.0021 | 16.0 | 1808 | 0.9802 | 0.85 |
| 0.0018 | 17.0 | 1921 | 0.9766 | 0.84 |
| 0.0017 | 18.0 | 2034 | 1.0597 | 0.84 |
| 0.0014 | 19.0 | 2147 | 0.9541 | 0.84 |
| 0.0012 | 20.0 | 2260 | 1.0408 | 0.84 |
| 0.0011 | 21.0 | 2373 | 1.0364 | 0.84 |
| 0.001 | 22.0 | 2486 | 1.0993 | 0.84 |
| 0.001 | 23.0 | 2599 | 1.0620 | 0.84 |
| 0.0009 | 24.0 | 2712 | 1.0193 | 0.83 |
| 0.0009 | 25.0 | 2825 | 1.0164 | 0.83 |
| 0.0009 | 26.0 | 2938 | 1.0293 | 0.84 |
| 0.0008 | 27.0 | 3051 | 1.0478 | 0.84 |
| 0.0008 | 28.0 | 3164 | 1.0727 | 0.84 |
| 0.0008 | 29.0 | 3277 | 1.0773 | 0.84 |
| 0.0008 | 30.0 | 3390 | 1.0908 | 0.84 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
