distilhubert-finetuned-gtzan-test
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: 0.6090
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9966 | 1.0 | 113 | 1.8241 | 0.58 |
| 1.2799 | 2.0 | 226 | 1.1448 | 0.7 |
| 0.9706 | 3.0 | 339 | 0.8798 | 0.74 |
| 0.8157 | 4.0 | 452 | 0.7674 | 0.76 |
| 0.4817 | 5.0 | 565 | 0.6875 | 0.76 |
| 0.3667 | 6.0 | 678 | 0.6378 | 0.78 |
| 0.4579 | 7.0 | 791 | 0.5703 | 0.85 |
| 0.1763 | 8.0 | 904 | 0.5789 | 0.83 |
| 0.2325 | 9.0 | 1017 | 0.5849 | 0.82 |
| 0.157 | 10.0 | 1130 | 0.6090 | 0.83 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Base model
ntu-spml/distilhubert