bert-small-phishing
This model is a fine-tuned version of prajjwal1/bert-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1006
- Accuracy: 0.9766
- Precision: 0.9713
- Recall: 0.9669
- F1: 0.9691
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.202 | 1.0 | 762 | 0.0941 | 0.9717 | 0.9728 | 0.9520 | 0.9623 |
| 0.077 | 2.0 | 1524 | 0.0964 | 0.9764 | 0.9757 | 0.9617 | 0.9686 |
| 0.0428 | 3.0 | 2286 | 0.0992 | 0.9786 | 0.9739 | 0.9695 | 0.9717 |
| 0.0248 | 4.0 | 3048 | 0.1006 | 0.9766 | 0.9713 | 0.9669 | 0.9691 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2