distilhubert-finetuned-gtzan-15sec
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: 0.8519
- Accuracy: 0.8492
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|---|---|---|---|---|
| 3.1339 | 1.0 | 112 | 1.3512 | 0.6583 |
| 1.6185 | 2.0 | 224 | 0.8620 | 0.7085 |
| 1.0020 | 3.0 | 336 | 0.5799 | 0.8090 |
| 0.7062 | 4.0 | 448 | 0.6624 | 0.8040 |
| 0.5191 | 5.0 | 560 | 0.6755 | 0.8191 |
| 0.2967 | 6.0 | 672 | 0.7785 | 0.7990 |
| 0.0821 | 7.0 | 784 | 0.8235 | 0.8342 |
| 0.0686 | 8.0 | 896 | 0.8519 | 0.8492 |
| 0.0156 | 9.0 | 1008 | 0.9783 | 0.8191 |
| 0.0019 | 10.0 | 1120 | 1.0472 | 0.8191 |
| 0.0015 | 11.0 | 1232 | 1.1903 | 0.8090 |
| 0.0862 | 12.0 | 1344 | 1.1610 | 0.8191 |
| 0.0011 | 13.0 | 1456 | 1.1986 | 0.8191 |
| 0.0008 | 14.0 | 1568 | 1.2203 | 0.8141 |
| 0.0010 | 15.0 | 1680 | 1.4290 | 0.7889 |
| 0.0007 | 16.0 | 1792 | 1.3051 | 0.8090 |
| 0.0007 | 17.0 | 1904 | 1.1839 | 0.8291 |
| 0.0077 | 18.0 | 2016 | 1.1803 | 0.8342 |
| 0.0007 | 19.0 | 2128 | 1.1789 | 0.8291 |
| 0.0006 | 20.0 | 2240 | 1.1757 | 0.8342 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.1