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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: 0.7537
- 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: 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 |
|---|---|---|---|---|
| 1.9647 | 1.0 | 113 | 1.8614 | 0.52 |
| 1.3987 | 2.0 | 226 | 1.3098 | 0.61 |
| 0.8809 | 3.0 | 339 | 0.8631 | 0.76 |
| 0.7643 | 4.0 | 452 | 0.8114 | 0.77 |
| 0.5958 | 5.0 | 565 | 0.7013 | 0.81 |
| 0.4405 | 6.0 | 678 | 0.5860 | 0.84 |
| 0.2183 | 7.0 | 791 | 0.6114 | 0.82 |
| 0.1587 | 8.0 | 904 | 0.5141 | 0.85 |
| 0.0899 | 9.0 | 1017 | 0.4760 | 0.87 |
| 0.0575 | 10.0 | 1130 | 0.5759 | 0.86 |
| 0.0647 | 11.0 | 1243 | 0.6467 | 0.86 |
| 0.0061 | 12.0 | 1356 | 0.6372 | 0.88 |
| 0.0029 | 13.0 | 1469 | 0.6721 | 0.88 |
| 0.0018 | 14.0 | 1582 | 0.7565 | 0.89 |
| 0.0013 | 15.0 | 1695 | 0.7537 | 0.88 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
