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: 0.5716
- Accuracy: 0.82
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 |
|---|---|---|---|---|
| 1.7297 | 1.0 | 113 | 1.8011 | 0.44 |
| 1.24 | 2.0 | 226 | 1.3045 | 0.64 |
| 0.9805 | 3.0 | 339 | 0.9888 | 0.7 |
| 0.6853 | 4.0 | 452 | 0.7508 | 0.79 |
| 0.4502 | 5.0 | 565 | 0.6224 | 0.81 |
| 0.3015 | 6.0 | 678 | 0.5411 | 0.83 |
| 0.2244 | 7.0 | 791 | 0.6293 | 0.78 |
| 0.3108 | 8.0 | 904 | 0.5857 | 0.81 |
| 0.1644 | 9.0 | 1017 | 0.5355 | 0.83 |
| 0.1198 | 10.0 | 1130 | 0.5716 | 0.82 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.1.0.dev20230607+cu121
- Datasets 2.13.1.dev0
- Tokenizers 0.13.3