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: 0.7277
- 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: 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.8325 | 1.0 | 225 | 1.6828 | 0.51 |
| 1.1105 | 2.0 | 450 | 1.1369 | 0.66 |
| 0.6095 | 3.0 | 675 | 0.8092 | 0.77 |
| 0.2526 | 4.0 | 900 | 0.6534 | 0.81 |
| 0.3619 | 5.0 | 1125 | 0.6683 | 0.78 |
| 0.0294 | 6.0 | 1350 | 0.5738 | 0.83 |
| 0.429 | 7.0 | 1575 | 0.5983 | 0.84 |
| 0.2307 | 8.0 | 1800 | 0.7582 | 0.85 |
| 0.008 | 9.0 | 2025 | 0.7387 | 0.83 |
| 0.0078 | 10.0 | 2250 | 0.7277 | 0.82 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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