distilhubert-music-classification
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: 0.7110
- Accuracy: 0.86
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: 16
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1284 | 1.0 | 113 | 1.9802 | 0.5 |
| 1.435 | 2.0 | 226 | 1.3403 | 0.65 |
| 1.0235 | 3.0 | 339 | 0.9941 | 0.74 |
| 0.8973 | 4.0 | 452 | 0.9184 | 0.69 |
| 0.7312 | 5.0 | 565 | 0.6918 | 0.79 |
| 0.4306 | 6.0 | 678 | 0.6343 | 0.78 |
| 0.4204 | 7.0 | 791 | 0.6174 | 0.83 |
| 0.1326 | 8.0 | 904 | 0.5888 | 0.83 |
| 0.0766 | 9.0 | 1017 | 0.5939 | 0.84 |
| 0.0308 | 10.0 | 1130 | 0.7191 | 0.86 |
| 0.0318 | 11.0 | 1243 | 0.7308 | 0.84 |
| 0.0657 | 12.0 | 1356 | 0.7222 | 0.81 |
| 0.0096 | 13.0 | 1469 | 0.7075 | 0.84 |
| 0.0077 | 14.0 | 1582 | 0.7268 | 0.84 |
| 0.0073 | 15.0 | 1695 | 0.6957 | 0.85 |
| 0.0066 | 16.0 | 1808 | 0.7110 | 0.86 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3