distilhubert-finetuned-stutteringdetection
This model is a fine-tuned version of ntu-spml/distilhubert on the stuttering dataset. It achieves the following results on the evaluation set:
- Loss: 0.5717
- Accuracy: 0.9024
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8357 | 1.0 | 92 | 0.7812 | 0.8659 |
| 0.2951 | 2.0 | 184 | 0.3680 | 0.8902 |
| 0.097 | 3.0 | 276 | 0.4000 | 0.8659 |
| 0.0872 | 4.0 | 368 | 0.3953 | 0.9024 |
| 0.4557 | 5.0 | 460 | 0.4904 | 0.9024 |
| 0.0368 | 6.0 | 552 | 0.4972 | 0.9024 |
| 0.0074 | 7.0 | 644 | 0.5408 | 0.9146 |
| 0.0039 | 8.0 | 736 | 0.5460 | 0.9024 |
| 0.0036 | 9.0 | 828 | 0.5684 | 0.9024 |
| 0.0035 | 10.0 | 920 | 0.5717 | 0.9024 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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