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: 0.6904
- Accuracy: 0.79
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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0975 | 1.0 | 113 | 1.8922 | 0.52 |
| 1.5102 | 2.0 | 226 | 1.2591 | 0.65 |
| 1.1763 | 3.0 | 339 | 0.9755 | 0.72 |
| 0.9729 | 4.0 | 452 | 0.8941 | 0.75 |
| 0.7546 | 5.0 | 565 | 0.7343 | 0.78 |
| 0.5046 | 6.0 | 678 | 0.7324 | 0.78 |
| 0.6277 | 7.0 | 791 | 0.6579 | 0.8 |
| 0.3472 | 8.0 | 904 | 0.6160 | 0.81 |
| 0.5776 | 9.0 | 1017 | 0.7029 | 0.78 |
| 0.3091 | 10.0 | 1130 | 0.6904 | 0.79 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for pmano2/distilhubert-finetuned-gtzan
Base model
ntu-spml/distilhubert