bert-large-finetuned-phishing-webpage-cleaned-version
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0324
- Accuracy: 0.9911
- Precision: 0.9931
- Recall: 0.9883
- False Positive Rate: 0.0063
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
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 | False Positive Rate |
---|---|---|---|---|---|---|---|
0.0931 | 1.0 | 562 | 0.0409 | 0.9861 | 0.9948 | 0.9762 | 0.0047 |
0.0345 | 2.0 | 1124 | 0.0348 | 0.9900 | 0.9918 | 0.9874 | 0.0075 |
0.0224 | 3.0 | 1687 | 0.0324 | 0.9911 | 0.9931 | 0.9883 | 0.0063 |
0.0156 | 4.0 | 2248 | 0.0509 | 0.9913 | 0.9914 | 0.9904 | 0.0079 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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