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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: 0.9203
- Accuracy: 0.83
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2556 | 1.0 | 113 | 2.1665 | 0.33 |
| 1.9061 | 2.0 | 226 | 1.7036 | 0.57 |
| 1.6449 | 3.0 | 339 | 1.3540 | 0.54 |
| 1.4074 | 4.0 | 452 | 1.1028 | 0.68 |
| 1.0121 | 5.0 | 565 | 0.9766 | 0.68 |
| 0.7831 | 6.0 | 678 | 0.8285 | 0.75 |
| 0.9829 | 7.0 | 791 | 0.7499 | 0.77 |
| 0.604 | 8.0 | 904 | 0.7093 | 0.77 |
| 0.6329 | 9.0 | 1017 | 0.7041 | 0.82 |
| 0.4323 | 10.0 | 1130 | 0.7244 | 0.83 |
| 0.1782 | 11.0 | 1243 | 0.7925 | 0.8 |
| 0.2571 | 12.0 | 1356 | 0.7031 | 0.84 |
| 0.1453 | 13.0 | 1469 | 0.7866 | 0.8 |
| 0.5919 | 14.0 | 1582 | 0.8135 | 0.82 |
| 0.307 | 15.0 | 1695 | 0.7489 | 0.85 |
| 0.2163 | 16.0 | 1808 | 0.9134 | 0.82 |
| 0.2081 | 17.0 | 1921 | 0.9109 | 0.85 |
| 0.1025 | 18.0 | 2034 | 0.9215 | 0.84 |
| 0.0415 | 19.0 | 2147 | 0.9542 | 0.84 |
| 0.481 | 20.0 | 2260 | 0.9203 | 0.83 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
