distilhubert-finetuned-gtzan-v2
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: 0.4006
- Accuracy: 0.89
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4786 | 1.0 | 225 | 1.3772 | 0.67 |
| 1.0539 | 2.0 | 450 | 0.8660 | 0.78 |
| 0.8426 | 3.0 | 675 | 0.7087 | 0.79 |
| 0.5203 | 4.0 | 900 | 0.6213 | 0.8 |
| 0.2969 | 5.0 | 1125 | 0.5474 | 0.8 |
| 0.2166 | 6.0 | 1350 | 0.5594 | 0.86 |
| 0.0563 | 7.0 | 1575 | 0.3808 | 0.91 |
| 0.1048 | 8.0 | 1800 | 0.4006 | 0.89 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
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