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: 0.8068
- Accuracy: 0.75
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5613 | 1.0 | 113 | 1.7108 | 0.52 |
| 1.1928 | 2.0 | 226 | 1.2290 | 0.67 |
| 1.0137 | 3.0 | 339 | 0.9546 | 0.7 |
| 0.7152 | 4.0 | 452 | 0.8872 | 0.76 |
| 0.6655 | 5.0 | 565 | 0.8068 | 0.75 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.1.0.dev20230607+cu121
- Datasets 2.13.1.dev0
- Tokenizers 0.13.3
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