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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: 0.4759
- 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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 |
|---|---|---|---|---|
| 1.5864 | 1.0 | 112 | 1.4484 | 0.53 |
| 1.1517 | 2.0 | 225 | 1.0442 | 0.66 |
| 0.9177 | 3.0 | 337 | 0.8256 | 0.76 |
| 0.6564 | 4.0 | 450 | 0.6099 | 0.84 |
| 0.5938 | 5.0 | 562 | 0.6822 | 0.78 |
| 0.2182 | 6.0 | 675 | 0.5630 | 0.81 |
| 0.3178 | 7.0 | 787 | 0.4598 | 0.85 |
| 0.1181 | 8.0 | 900 | 0.4580 | 0.86 |
| 0.0377 | 9.0 | 1012 | 0.4716 | 0.88 |
| 0.034 | 9.96 | 1120 | 0.4759 | 0.87 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
