distilbert-base-uncased-finetuned-radiology-txt
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3534
- F1: 0.5200
- Avg Roc Auc: 0.6870
- Accuracy: 0.3145
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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Avg Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.4674 | 1.0 | 147 | 0.4190 | 0.4385 | 0.6434 | 0.2559 |
| 0.4122 | 2.0 | 294 | 0.3847 | 0.4603 | 0.6541 | 0.2923 |
| 0.3826 | 3.0 | 441 | 0.3659 | 0.4621 | 0.6543 | 0.3134 |
| 0.3657 | 4.0 | 588 | 0.3593 | 0.4987 | 0.6746 | 0.3126 |
| 0.3565 | 5.0 | 735 | 0.3561 | 0.5311 | 0.6950 | 0.3055 |
| 0.3528 | 6.0 | 882 | 0.3542 | 0.5227 | 0.6890 | 0.3113 |
| 0.3482 | 7.0 | 1029 | 0.3534 | 0.5200 | 0.6870 | 0.3145 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.2