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Baseline_10Kphish_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: 0.0869
- Accuracy: 0.991
- F1: 0.8960
- Precision: 0.9966
- Recall: 0.8138
- Roc Auc Score: 0.9068
- Tpr At Fpr 0.01: 0.7918
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.0115 | 1.0 | 6563 | 0.0605 | 0.9872 | 0.8462 | 0.9938 | 0.7368 | 0.8683 | 0.6832 |
| 0.006 | 2.0 | 13126 | 0.0538 | 0.9911 | 0.8975 | 0.9946 | 0.8176 | 0.9087 | 0.7928 |
| 0.0033 | 3.0 | 19689 | 0.0496 | 0.9917 | 0.9049 | 0.9959 | 0.8292 | 0.9145 | 0.805 |
| 0.001 | 4.0 | 26252 | 0.0791 | 0.9911 | 0.8970 | 0.9959 | 0.816 | 0.9079 | 0.7806 |
| 0.0002 | 5.0 | 32815 | 0.0869 | 0.991 | 0.8960 | 0.9966 | 0.8138 | 0.9068 | 0.7918 |
Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
