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: 0.5486
- Accuracy: 0.85
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9391 | 1.0 | 113 | 1.7463 | 0.42 |
| 1.3438 | 2.0 | 226 | 1.1953 | 0.64 |
| 1.04 | 3.0 | 339 | 0.9436 | 0.72 |
| 0.8608 | 4.0 | 452 | 0.8464 | 0.74 |
| 0.5644 | 5.0 | 565 | 0.6846 | 0.8 |
| 0.4311 | 6.0 | 678 | 0.7118 | 0.77 |
| 0.509 | 7.0 | 791 | 0.5660 | 0.83 |
| 0.2548 | 8.0 | 904 | 0.5486 | 0.85 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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