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: 1.0795
- 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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_steps: 100
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.3659 | 1.0 | 113 | 1.5530 | 0.53 |
| 2.1453 | 2.0 | 226 | 1.1867 | 0.61 |
| 1.2708 | 3.0 | 339 | 0.8824 | 0.74 |
| 1.0719 | 4.0 | 452 | 0.8946 | 0.73 |
| 0.6139 | 5.0 | 565 | 0.8437 | 0.69 |
| 0.5381 | 6.0 | 678 | 0.7437 | 0.76 |
| 0.2904 | 7.0 | 791 | 0.8278 | 0.82 |
| 0.0359 | 8.0 | 904 | 1.1736 | 0.79 |
| 0.0341 | 9.0 | 1017 | 1.0795 | 0.83 |
| 0.0031 | 10.0 | 1130 | 1.1588 | 0.8 |
| 0.0020 | 11.0 | 1243 | 1.1727 | 0.81 |
| 0.0016 | 12.0 | 1356 | 1.2418 | 0.83 |
| 0.0013 | 13.0 | 1469 | 1.2516 | 0.82 |
| 0.0015 | 14.0 | 1582 | 1.3328 | 0.82 |
| 0.0010 | 15.0 | 1695 | 1.3100 | 0.82 |
| 0.0009 | 16.0 | 1808 | 1.3831 | 0.8 |
| 0.0009 | 17.0 | 1921 | 1.2942 | 0.82 |
| 0.0007 | 18.0 | 2034 | 1.3562 | 0.81 |
| 0.0007 | 19.0 | 2147 | 1.3046 | 0.81 |
| 0.0007 | 20.0 | 2260 | 1.3027 | 0.82 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.1