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: 0.7235
- Accuracy: 0.87
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.152 | 1.0 | 113 | 2.0748 | 0.59 |
| 1.3845 | 2.0 | 226 | 1.4518 | 0.61 |
| 1.1096 | 3.0 | 339 | 1.0450 | 0.68 |
| 0.703 | 4.0 | 452 | 0.8385 | 0.77 |
| 0.5479 | 5.0 | 565 | 0.7331 | 0.82 |
| 0.4713 | 6.0 | 678 | 0.5985 | 0.8 |
| 0.2392 | 7.0 | 791 | 0.5582 | 0.8 |
| 0.088 | 8.0 | 904 | 0.7288 | 0.83 |
| 0.0976 | 9.0 | 1017 | 0.6088 | 0.83 |
| 0.0575 | 10.0 | 1130 | 0.5913 | 0.86 |
| 0.0153 | 11.0 | 1243 | 0.7503 | 0.85 |
| 0.0073 | 12.0 | 1356 | 0.8163 | 0.86 |
| 0.0058 | 13.0 | 1469 | 0.7235 | 0.87 |
| 0.0049 | 14.0 | 1582 | 0.8402 | 0.86 |
| 0.0036 | 15.0 | 1695 | 0.8322 | 0.86 |
| 0.0035 | 16.0 | 1808 | 0.7559 | 0.87 |
| 0.0033 | 17.0 | 1921 | 0.7148 | 0.87 |
| 0.003 | 18.0 | 2034 | 0.8381 | 0.87 |
| 0.0029 | 19.0 | 2147 | 0.8051 | 0.87 |
| 0.0029 | 20.0 | 2260 | 0.7847 | 0.87 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.2