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: 0.5277
- 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: 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.978 | 1.0 | 113 | 1.8421 | 0.37 |
| 1.3409 | 2.0 | 226 | 1.2195 | 0.59 |
| 1.04 | 3.0 | 339 | 0.9709 | 0.71 |
| 0.9141 | 4.0 | 452 | 0.8523 | 0.79 |
| 0.5192 | 5.0 | 565 | 0.6483 | 0.83 |
| 0.3506 | 6.0 | 678 | 0.5827 | 0.84 |
| 0.3316 | 7.0 | 791 | 0.4703 | 0.88 |
| 0.1275 | 8.0 | 904 | 0.4937 | 0.86 |
| 0.2109 | 9.0 | 1017 | 0.4971 | 0.86 |
| 0.1213 | 10.0 | 1130 | 0.5277 | 0.87 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0