mimii-distilhubert-anomaly
This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2391
- Accuracy: 0.9327
- Precision: 0.9382
- Recall: 0.9327
- F1: 0.9289
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4865 | 1.0 | 52 | 0.3645 | 0.8942 | 0.9073 | 0.8942 | 0.8835 |
| 0.3024 | 2.0 | 104 | 0.2163 | 0.9327 | 0.9382 | 0.9327 | 0.9289 |
| 0.3103 | 3.0 | 156 | 0.2125 | 0.9327 | 0.9382 | 0.9327 | 0.9289 |
| 0.1539 | 4.0 | 208 | 0.2687 | 0.9231 | 0.9302 | 0.9231 | 0.9180 |
| 0.1924 | 5.0 | 260 | 0.2391 | 0.9327 | 0.9382 | 0.9327 | 0.9289 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2