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hubert-base-ls960-finetuned-gtzan
This model is a fine-tuned version of facebook/hubert-base-ls960 on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.6645
- Accuracy: 0.88
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2685 | 1.0 | 56 | 2.2069 | 0.44 |
| 2.0208 | 1.99 | 112 | 1.8352 | 0.46 |
| 1.7603 | 2.99 | 168 | 1.5275 | 0.49 |
| 1.4843 | 4.0 | 225 | 1.4296 | 0.52 |
| 1.347 | 5.0 | 281 | 1.2222 | 0.52 |
| 1.2364 | 5.99 | 337 | 1.1477 | 0.62 |
| 1.2082 | 6.99 | 393 | 1.0181 | 0.67 |
| 0.9861 | 8.0 | 450 | 0.9598 | 0.71 |
| 0.752 | 9.0 | 506 | 0.7499 | 0.77 |
| 1.006 | 9.99 | 562 | 0.8190 | 0.79 |
| 0.6725 | 10.99 | 618 | 0.8798 | 0.75 |
| 0.7457 | 12.0 | 675 | 0.6276 | 0.81 |
| 0.4605 | 13.0 | 731 | 0.6086 | 0.85 |
| 0.5751 | 13.99 | 787 | 0.6894 | 0.75 |
| 0.4886 | 14.99 | 843 | 0.6109 | 0.83 |
| 0.2429 | 16.0 | 900 | 0.6076 | 0.85 |
| 0.3084 | 17.0 | 956 | 0.4646 | 0.86 |
| 0.3762 | 17.99 | 1012 | 0.8349 | 0.81 |
| 0.2897 | 18.99 | 1068 | 0.4509 | 0.89 |
| 0.1296 | 20.0 | 1125 | 0.6791 | 0.86 |
| 0.1291 | 21.0 | 1181 | 0.6466 | 0.85 |
| 0.3784 | 21.99 | 1237 | 0.6272 | 0.88 |
| 0.1156 | 22.99 | 1293 | 0.7916 | 0.85 |
| 0.2093 | 24.0 | 1350 | 0.6536 | 0.85 |
| 0.2167 | 25.0 | 1406 | 0.7050 | 0.87 |
| 0.1095 | 25.99 | 1462 | 0.6128 | 0.88 |
| 0.1004 | 26.99 | 1518 | 0.6092 | 0.89 |
| 0.0897 | 28.0 | 1575 | 0.6730 | 0.88 |
| 0.083 | 29.0 | 1631 | 0.6396 | 0.89 |
| 0.0343 | 29.87 | 1680 | 0.6645 | 0.88 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.14.1
- Tokenizers 0.13.3
