bert-large-finetuned-phishing
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: 0.1462
- Accuracy: 0.9527
- Precision: 0.9652
- Recall: 0.9030
- False Positive Rate: 0.0187
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: 20
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | False Positive Rate |
|---|---|---|---|---|---|---|---|
| 0.2941 | 1.0 | 673 | 0.1956 | 0.9254 | 0.9662 | 0.8246 | 0.0166 |
| 0.1771 | 2.0 | 1346 | 0.1813 | 0.9364 | 0.9773 | 0.8456 | 0.0113 |
| 0.1208 | 3.0 | 2020 | 0.1498 | 0.9481 | 0.9645 | 0.8907 | 0.0189 |
| 0.1041 | 4.0 | 2692 | 0.1462 | 0.9527 | 0.9652 | 0.9030 | 0.0187 |
Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1
- Datasets 2.12.0
- Tokenizers 0.15.1
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Model tree for huynq3Cyradar/bert-large-finetuned-phishing
Base model
google-bert/bert-base-multilingual-cased