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: 0.5008
- Accuracy: 0.88
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: 3e-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: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1457 | 1.0 | 113 | 2.0729 | 0.51 |
| 1.5675 | 2.0 | 226 | 1.5158 | 0.59 |
| 1.2498 | 3.0 | 339 | 1.2242 | 0.67 |
| 1.1526 | 4.0 | 452 | 1.0223 | 0.72 |
| 0.8489 | 5.0 | 565 | 0.8472 | 0.77 |
| 0.8115 | 6.0 | 678 | 0.7177 | 0.82 |
| 0.6554 | 7.0 | 791 | 0.6736 | 0.83 |
| 0.45 | 8.0 | 904 | 0.5767 | 0.88 |
| 0.4059 | 9.0 | 1017 | 0.5429 | 0.88 |
| 0.3081 | 10.0 | 1130 | 0.5187 | 0.88 |
| 0.3331 | 11.0 | 1243 | 0.5111 | 0.86 |
| 0.2877 | 12.0 | 1356 | 0.5008 | 0.88 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Base model
ntu-spml/distilhubert