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distilhubert-finetuned-gtzan-30-epochs
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: 1.1939
- Accuracy: 0.81
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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1804 | 1.0 | 113 | 2.1756 | 0.46 |
| 1.7271 | 2.0 | 226 | 1.6973 | 0.53 |
| 1.2703 | 3.0 | 339 | 1.2950 | 0.51 |
| 0.9446 | 4.0 | 452 | 0.9433 | 0.68 |
| 0.6192 | 5.0 | 565 | 0.7885 | 0.73 |
| 0.3628 | 6.0 | 678 | 0.8338 | 0.76 |
| 0.2871 | 7.0 | 791 | 0.8125 | 0.74 |
| 0.0587 | 8.0 | 904 | 0.7500 | 0.8 |
| 0.1316 | 9.0 | 1017 | 0.8711 | 0.79 |
| 0.0175 | 10.0 | 1130 | 0.7429 | 0.82 |
| 0.0818 | 11.0 | 1243 | 0.9848 | 0.81 |
| 0.0049 | 12.0 | 1356 | 1.0498 | 0.76 |
| 0.0034 | 13.0 | 1469 | 1.0422 | 0.84 |
| 0.0028 | 14.0 | 1582 | 1.0919 | 0.83 |
| 0.0023 | 15.0 | 1695 | 1.0565 | 0.82 |
| 0.0019 | 16.0 | 1808 | 1.0797 | 0.84 |
| 0.0769 | 17.0 | 1921 | 1.1430 | 0.82 |
| 0.104 | 18.0 | 2034 | 1.1482 | 0.8 |
| 0.0014 | 19.0 | 2147 | 1.0972 | 0.83 |
| 0.0012 | 20.0 | 2260 | 1.1867 | 0.82 |
| 0.0012 | 21.0 | 2373 | 1.1914 | 0.82 |
| 0.0012 | 22.0 | 2486 | 1.1461 | 0.84 |
| 0.0009 | 23.0 | 2599 | 1.1401 | 0.82 |
| 0.0009 | 24.0 | 2712 | 1.1686 | 0.84 |
| 0.0009 | 25.0 | 2825 | 1.1824 | 0.85 |
| 0.0009 | 26.0 | 2938 | 1.1815 | 0.81 |
| 0.0008 | 27.0 | 3051 | 1.1808 | 0.82 |
| 0.0008 | 28.0 | 3164 | 1.1904 | 0.81 |
| 0.0008 | 29.0 | 3277 | 1.1990 | 0.82 |
| 0.0008 | 30.0 | 3390 | 1.1939 | 0.81 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
