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: 0.7392
- Accuracy: 0.81
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3055 | 0.97 | 7 | 1.2863 | 0.73 |
| 1.2903 | 1.93 | 14 | 1.2504 | 0.7 |
| 1.2118 | 2.9 | 21 | 1.1450 | 0.77 |
| 1.1443 | 4.0 | 29 | 1.1224 | 0.74 |
| 1.006 | 4.97 | 36 | 1.0376 | 0.79 |
| 1.0174 | 5.93 | 43 | 0.9681 | 0.8 |
| 0.9155 | 6.9 | 50 | 0.9322 | 0.81 |
| 0.8781 | 8.0 | 58 | 0.9266 | 0.78 |
| 0.819 | 8.97 | 65 | 0.8473 | 0.79 |
| 0.7984 | 9.93 | 72 | 0.8225 | 0.77 |
| 0.7254 | 10.9 | 79 | 0.8096 | 0.81 |
| 0.6752 | 12.0 | 87 | 0.7801 | 0.81 |
| 0.6132 | 12.97 | 94 | 0.7687 | 0.8 |
| 0.615 | 13.93 | 101 | 0.7603 | 0.79 |
| 0.6162 | 14.9 | 108 | 0.7599 | 0.82 |
| 0.5678 | 16.0 | 116 | 0.7414 | 0.81 |
| 0.548 | 16.97 | 123 | 0.7423 | 0.81 |
| 0.5495 | 17.93 | 130 | 0.7378 | 0.81 |
| 0.5185 | 18.9 | 137 | 0.7396 | 0.81 |
| 0.5544 | 19.31 | 140 | 0.7392 | 0.81 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
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Base model
ntu-spml/distilhubert