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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: 0.6228
- Accuracy: 0.85
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: 4e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2841 | 0.98 | 28 | 2.2578 | 0.22 |
| 2.108 | 2.0 | 57 | 2.0031 | 0.55 |
| 1.7117 | 2.98 | 85 | 1.6220 | 0.65 |
| 1.4624 | 4.0 | 114 | 1.4061 | 0.7 |
| 1.2607 | 4.98 | 142 | 1.1969 | 0.69 |
| 1.1162 | 6.0 | 171 | 1.0955 | 0.75 |
| 1.0 | 6.98 | 199 | 0.9670 | 0.78 |
| 0.8864 | 8.0 | 228 | 0.9192 | 0.77 |
| 0.8583 | 8.98 | 256 | 0.8475 | 0.78 |
| 0.8147 | 10.0 | 285 | 0.8214 | 0.77 |
| 0.6572 | 10.98 | 313 | 0.7754 | 0.78 |
| 0.5958 | 12.0 | 342 | 0.7187 | 0.79 |
| 0.4196 | 12.98 | 370 | 0.6732 | 0.83 |
| 0.4515 | 14.0 | 399 | 0.7272 | 0.8 |
| 0.4256 | 14.98 | 427 | 0.6507 | 0.84 |
| 0.3734 | 16.0 | 456 | 0.6587 | 0.83 |
| 0.3541 | 16.98 | 484 | 0.6244 | 0.86 |
| 0.312 | 18.0 | 513 | 0.6363 | 0.84 |
| 0.3287 | 18.98 | 541 | 0.6226 | 0.86 |
| 0.313 | 19.65 | 560 | 0.6228 | 0.85 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
