modernbert-phishing-classifier
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3066
- Accuracy: 0.9
- Auc: 0.965
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: 8
- seed: 42
- 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
- num_epochs: 15
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
---|---|---|---|---|---|
0.3714 | 1.0 | 263 | 0.2936 | 0.869 | 0.949 |
0.2622 | 2.0 | 526 | 0.2681 | 0.884 | 0.96 |
0.2405 | 3.0 | 789 | 0.2642 | 0.898 | 0.961 |
0.2091 | 4.0 | 1052 | 0.2688 | 0.893 | 0.963 |
0.2078 | 5.0 | 1315 | 0.3813 | 0.882 | 0.962 |
0.1887 | 6.0 | 1578 | 0.2667 | 0.9 | 0.965 |
0.1695 | 7.0 | 1841 | 0.2851 | 0.902 | 0.964 |
0.1654 | 8.0 | 2104 | 0.2935 | 0.902 | 0.964 |
0.157 | 9.0 | 2367 | 0.3169 | 0.904 | 0.966 |
0.158 | 10.0 | 2630 | 0.3190 | 0.896 | 0.964 |
0.149 | 11.0 | 2893 | 0.3019 | 0.893 | 0.965 |
0.1437 | 12.0 | 3156 | 0.2995 | 0.9 | 0.965 |
0.1365 | 13.0 | 3419 | 0.3048 | 0.9 | 0.965 |
0.1312 | 14.0 | 3682 | 0.3090 | 0.898 | 0.965 |
0.1304 | 15.0 | 3945 | 0.3066 | 0.9 | 0.965 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
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Base model
answerdotai/ModernBERT-base