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: 0.6551
- Accuracy: 0.83
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 1.9627 | 1.0 | 113 | 1.9008 | 0.51 |
| 1.2185 | 2.0 | 226 | 1.2541 | 0.68 |
| 0.8868 | 3.0 | 339 | 1.0025 | 0.7 |
| 0.6438 | 4.0 | 452 | 0.8160 | 0.77 |
| 0.4255 | 5.0 | 565 | 0.7545 | 0.79 |
| 0.3223 | 6.0 | 678 | 0.6584 | 0.82 |
| 0.2756 | 7.0 | 791 | 0.7826 | 0.76 |
| 0.186 | 8.0 | 904 | 0.6439 | 0.82 |
| 0.1233 | 9.0 | 1017 | 0.6260 | 0.85 |
| 0.0971 | 10.0 | 1130 | 0.6551 | 0.83 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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Base model
ntu-spml/distilhubert