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distilhubert-finetuned-gtzan-bs-16-fp16-false
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: 0.5225
- 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1955 | 1.0 | 57 | 2.1121 | 0.44 |
| 1.6787 | 2.0 | 114 | 1.5681 | 0.62 |
| 1.1812 | 3.0 | 171 | 1.1904 | 0.74 |
| 1.0922 | 4.0 | 228 | 0.9769 | 0.7 |
| 0.7733 | 5.0 | 285 | 0.8104 | 0.78 |
| 0.6225 | 6.0 | 342 | 0.6892 | 0.83 |
| 0.609 | 7.0 | 399 | 0.6572 | 0.85 |
| 0.4223 | 8.0 | 456 | 0.5775 | 0.83 |
| 0.3115 | 9.0 | 513 | 0.5956 | 0.81 |
| 0.1942 | 10.0 | 570 | 0.5556 | 0.82 |
| 0.1602 | 11.0 | 627 | 0.6075 | 0.83 |
| 0.1206 | 12.0 | 684 | 0.5676 | 0.85 |
| 0.1265 | 13.0 | 741 | 0.5585 | 0.85 |
| 0.0776 | 14.0 | 798 | 0.5220 | 0.84 |
| 0.1085 | 15.0 | 855 | 0.5225 | 0.84 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
