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Baseline_20Kphish_benignWinter_20_20_20
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0591
- Accuracy: 0.9939
- F1: 0.9315
- Precision: 0.9986
- Recall: 0.8728
- Roc Auc Score: 0.9364
- Tpr At Fpr 0.01: 0.8742
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|---|---|---|---|---|---|---|---|---|---|
| 0.0092 | 1.0 | 13125 | 0.0432 | 0.9899 | 0.8824 | 0.9957 | 0.7922 | 0.8960 | 0.7636 |
| 0.0038 | 2.0 | 26250 | 0.0458 | 0.9935 | 0.9273 | 0.9956 | 0.8678 | 0.9338 | 0.8316 |
| 0.0015 | 3.0 | 39375 | 0.0518 | 0.9938 | 0.9303 | 0.9968 | 0.8722 | 0.9360 | 0.8686 |
| 0.0013 | 4.0 | 52500 | 0.0500 | 0.9941 | 0.9339 | 0.9977 | 0.8778 | 0.9389 | 0.8768 |
| 0.0002 | 5.0 | 65625 | 0.0591 | 0.9939 | 0.9315 | 0.9986 | 0.8728 | 0.9364 | 0.8742 |
Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
