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wav2vec2-base-finetuned-mednames
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1443
- Accuracy: 1.0
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: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- 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 |
|---|---|---|---|---|
| 2.2789 | 1.0 | 36 | 2.2639 | 0.2222 |
| 2.1925 | 2.0 | 72 | 2.1404 | 0.3833 |
| 1.8763 | 3.0 | 108 | 1.7607 | 0.5556 |
| 1.5035 | 4.0 | 144 | 1.3599 | 0.7667 |
| 1.0598 | 5.0 | 180 | 0.9607 | 0.8778 |
| 0.8303 | 6.0 | 216 | 0.7327 | 0.8722 |
| 0.6418 | 7.0 | 252 | 0.5544 | 0.8778 |
| 0.4481 | 8.0 | 288 | 0.4377 | 0.9722 |
| 0.3438 | 9.0 | 324 | 0.2963 | 0.9833 |
| 0.1831 | 10.0 | 360 | 0.1443 | 1.0 |
| 0.1129 | 11.0 | 396 | 0.0890 | 1.0 |
| 0.0877 | 12.0 | 432 | 0.0664 | 1.0 |
| 0.0657 | 13.0 | 468 | 0.0509 | 1.0 |
| 0.0528 | 14.0 | 504 | 0.0406 | 1.0 |
| 0.0428 | 15.0 | 540 | 0.0388 | 1.0 |
| 0.0369 | 16.0 | 576 | 0.0526 | 0.9944 |
| 0.0858 | 17.0 | 612 | 0.0539 | 0.9944 |
| 0.0294 | 18.0 | 648 | 0.0231 | 1.0 |
| 0.0272 | 19.0 | 684 | 0.0204 | 1.0 |
| 0.0247 | 20.0 | 720 | 0.0188 | 1.0 |
| 0.0227 | 21.0 | 756 | 0.0172 | 1.0 |
| 0.0211 | 22.0 | 792 | 0.0161 | 1.0 |
| 0.0195 | 23.0 | 828 | 0.0152 | 1.0 |
| 0.0684 | 24.0 | 864 | 0.0144 | 1.0 |
| 0.0175 | 25.0 | 900 | 0.0139 | 1.0 |
| 0.0756 | 26.0 | 936 | 0.0134 | 1.0 |
| 0.017 | 27.0 | 972 | 0.0131 | 1.0 |
| 0.0163 | 28.0 | 1008 | 0.0128 | 1.0 |
| 0.0158 | 29.0 | 1044 | 0.0126 | 1.0 |
| 0.0159 | 30.0 | 1080 | 0.0126 | 1.0 |
Framework versions
- Transformers 4.28.1
- Pytorch 1.11.0+cu102
- Datasets 2.11.0
- Tokenizers 0.13.3
