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: 0.3616
- Accuracy: 0.9475
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: 9e-05
- train_batch_size: 6
- eval_batch_size: 6
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2804 | 1.0 | 100 | 0.3327 | 0.9475 |
| 0.4089 | 2.0 | 200 | 0.3448 | 0.955 |
| 0.0564 | 3.0 | 300 | 0.3446 | 0.95 |
| 0.0 | 4.0 | 400 | 0.3417 | 0.9475 |
| 0.0 | 5.0 | 500 | 0.3616 | 0.9475 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Base model
ntu-spml/distilhubert