distilhubert-finetuned-gtzan-barra
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: 0.9337
- Accuracy: 0.76
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.906 | 1.0 | 75 | 1.8057 | 0.55 |
| 1.3207 | 2.0 | 150 | 1.3071 | 0.64 |
| 1.1483 | 3.0 | 225 | 1.1382 | 0.68 |
| 0.8834 | 4.0 | 300 | 0.9863 | 0.73 |
| 0.7823 | 5.0 | 375 | 0.9337 | 0.76 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for victorbarra/distilhubert-finetuned-gtzan-barra
Base model
ntu-spml/distilhubert