distilhubert-finetuned-babycry-v5
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: 0.8441
- Accuracy: {'accuracy': 0.782608695652174}
- F1: 0.6872
- Precision: 0.6125
- Recall: 0.7826
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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.7047 | 1.0870 | 25 | 0.9225 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
| 0.6071 | 2.1739 | 50 | 0.9175 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
| 0.6525 | 3.2609 | 75 | 0.8866 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
| 0.6558 | 4.3478 | 100 | 0.8433 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
| 0.5577 | 5.4348 | 125 | 0.8705 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
| 0.7055 | 6.5217 | 150 | 0.8323 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
| 0.6092 | 7.6087 | 175 | 0.8440 | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125 | 0.7826 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1