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: 0.8934
- 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: 2
- eval_batch_size: 2
- 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 |
|---|---|---|---|---|
| 1.681 | 1.0 | 450 | 1.7351 | 0.5 |
| 1.5534 | 2.0 | 900 | 1.2192 | 0.66 |
| 0.6835 | 3.0 | 1350 | 1.0462 | 0.71 |
| 1.069 | 4.0 | 1800 | 0.5503 | 0.83 |
| 0.1563 | 5.0 | 2250 | 0.9394 | 0.78 |
| 0.0077 | 6.0 | 2700 | 0.9394 | 0.81 |
| 0.7444 | 7.0 | 3150 | 0.8934 | 0.82 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.13.1
- Tokenizers 0.13.2