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: 0.6298
- Accuracy: 0.82
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 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9297 | 1.0 | 113 | 1.8304 | 0.52 |
| 1.1908 | 2.0 | 226 | 1.2680 | 0.62 |
| 0.997 | 3.0 | 339 | 1.0931 | 0.66 |
| 0.6903 | 4.0 | 452 | 0.8967 | 0.71 |
| 0.5372 | 5.0 | 565 | 0.7350 | 0.8 |
| 0.4878 | 6.0 | 678 | 0.6679 | 0.82 |
| 0.2688 | 7.0 | 791 | 0.6463 | 0.8 |
| 0.1214 | 8.0 | 904 | 0.6345 | 0.82 |
| 0.1472 | 9.0 | 1017 | 0.6368 | 0.83 |
| 0.0925 | 10.0 | 1130 | 0.6298 | 0.82 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.3
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Base model
ntu-spml/distilhubert