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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: 0.6333
- Accuracy: 0.87
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: 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2417 | 1.0 | 57 | 2.1896 | 0.42 |
| 1.8003 | 2.0 | 114 | 1.6369 | 0.52 |
| 1.3938 | 3.0 | 171 | 1.2560 | 0.72 |
| 1.2724 | 4.0 | 228 | 1.1942 | 0.68 |
| 0.9682 | 5.0 | 285 | 0.8864 | 0.8 |
| 0.7111 | 6.0 | 342 | 0.7542 | 0.82 |
| 0.6339 | 7.0 | 399 | 0.7712 | 0.81 |
| 0.4599 | 8.0 | 456 | 0.6080 | 0.84 |
| 0.3261 | 9.0 | 513 | 0.5998 | 0.84 |
| 0.2991 | 10.0 | 570 | 0.6767 | 0.79 |
| 0.1615 | 11.0 | 627 | 0.5817 | 0.87 |
| 0.0854 | 12.0 | 684 | 0.5859 | 0.83 |
| 0.0752 | 13.0 | 741 | 0.5681 | 0.85 |
| 0.0341 | 14.0 | 798 | 0.5916 | 0.88 |
| 0.0331 | 15.0 | 855 | 0.6028 | 0.87 |
| 0.02 | 16.0 | 912 | 0.6283 | 0.85 |
| 0.0175 | 17.0 | 969 | 0.6103 | 0.88 |
| 0.0151 | 18.0 | 1026 | 0.6244 | 0.88 |
| 0.014 | 19.0 | 1083 | 0.6293 | 0.86 |
| 0.0181 | 20.0 | 1140 | 0.6333 | 0.87 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
