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: 0.6492
- 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch 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 |
|---|---|---|---|---|
| 2.2305 | 1.0 | 200 | 2.1948 | 0.37 |
| 1.8318 | 2.0 | 400 | 1.7088 | 0.555 |
| 1.44 | 3.0 | 600 | 1.3910 | 0.565 |
| 1.0672 | 4.0 | 800 | 1.0886 | 0.725 |
| 0.6987 | 5.0 | 1000 | 1.0055 | 0.72 |
| 0.6847 | 6.0 | 1200 | 0.8528 | 0.755 |
| 0.8731 | 7.0 | 1400 | 0.7888 | 0.78 |
| 0.7602 | 8.0 | 1600 | 0.6399 | 0.81 |
| 0.4625 | 9.0 | 1800 | 0.6973 | 0.795 |
| 0.1884 | 10.0 | 2000 | 0.6347 | 0.815 |
| 0.0945 | 11.0 | 2200 | 0.6723 | 0.805 |
| 0.0334 | 12.0 | 2400 | 0.7025 | 0.815 |
| 0.2925 | 13.0 | 2600 | 0.5688 | 0.835 |
| 0.0322 | 14.0 | 2800 | 0.5742 | 0.845 |
| 0.0166 | 15.0 | 3000 | 0.5655 | 0.86 |
| 0.1173 | 16.0 | 3200 | 0.5723 | 0.855 |
| 0.0223 | 17.0 | 3400 | 0.6601 | 0.83 |
| 0.0103 | 18.0 | 3600 | 0.6294 | 0.835 |
| 0.0096 | 19.0 | 3800 | 0.6413 | 0.835 |
| 0.0093 | 20.0 | 4000 | 0.6492 | 0.84 |
Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.8.0.dev20250404+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1