medical_teacher_updatev0_v2
This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0120
- Accuracy: 0.9975
- Recall: 0.9975
- Precision: 0.9975
- F1: 0.9975
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 2
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 0.2054 | 0.4017 | 94 | 0.1333 | 0.9713 | 0.9713 | 0.9737 | 0.9713 |
| 0.0097 | 0.8034 | 188 | 0.0294 | 0.9959 | 0.9959 | 0.9959 | 0.9959 |
| 0.0542 | 1.2051 | 282 | 0.0194 | 0.9959 | 0.9959 | 0.9960 | 0.9959 |
| 0.0483 | 1.6068 | 376 | 0.0120 | 0.9975 | 0.9975 | 0.9975 | 0.9975 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2