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: 0.3539
- Accuracy: 0.91
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: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 18
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2281 | 1.0 | 112 | 2.1128 | 0.26 |
| 1.7082 | 2.0 | 225 | 1.6252 | 0.52 |
| 1.267 | 3.0 | 337 | 1.3100 | 0.54 |
| 1.1791 | 4.0 | 450 | 1.0496 | 0.71 |
| 1.1765 | 5.0 | 562 | 0.8928 | 0.74 |
| 0.5714 | 6.0 | 675 | 0.8298 | 0.77 |
| 0.4869 | 7.0 | 787 | 0.7145 | 0.79 |
| 0.4967 | 8.0 | 900 | 0.6990 | 0.82 |
| 0.8314 | 9.0 | 1012 | 0.5657 | 0.83 |
| 0.4633 | 10.0 | 1125 | 0.4589 | 0.89 |
| 0.5547 | 11.0 | 1237 | 0.4919 | 0.86 |
| 0.4827 | 12.0 | 1350 | 0.4069 | 0.92 |
| 0.324 | 13.0 | 1462 | 0.4634 | 0.87 |
| 0.5224 | 14.0 | 1575 | 0.4419 | 0.86 |
| 0.1873 | 15.0 | 1687 | 0.3988 | 0.89 |
| 0.2852 | 16.0 | 1800 | 0.3788 | 0.9 |
| 0.3169 | 17.0 | 1912 | 0.3526 | 0.89 |
| 0.4491 | 17.92 | 2016 | 0.3539 | 0.91 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3