medical_teacher_update_reomved_v0
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: 0.0290
- Accuracy: 0.9967
- Recall: 0.9967
- Precision: 0.9967
- F1: 0.9967
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: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0055 | 0.4009 | 375 | 0.0742 | 0.9893 | 0.9893 | 0.9896 | 0.9893 |
| 0.0026 | 0.8017 | 750 | 0.0705 | 0.9877 | 0.9877 | 0.9878 | 0.9877 |
| 0.0027 | 1.2020 | 1125 | 0.0316 | 0.9967 | 0.9967 | 0.9967 | 0.9967 |
| 0.0026 | 1.6029 | 1500 | 0.0290 | 0.9967 | 0.9967 | 0.9967 | 0.9967 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2