| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: phishing-binary-classification-bert |
| results: [] |
| datasets: |
| - aisuko/phishing-binary-classification |
| language: |
| - en |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # phishing-binary-classification-bert |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on [aisuko/phishing-binary-classification](https://huggingface.co/datasets/aisuko/phishing-binary-classification) dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4878 |
| - Accuracy: 0.82 |
| - Auc: 0.919 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| [aisuko/phishing-binary-classification](https://huggingface.co/datasets/aisuko/phishing-binary-classification) dataset |
|
|
| ## Training procedure |
|
|
| Please check Kaggle notbebook [FT Google Bert for Binary Classification](https://www.kaggle.com/code/aisuko/ft-google-bert-for-binary-classification) |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0002 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----:| |
| | 0.6681 | 1.0 | 1250 | 0.6198 | 0.69 | 0.885 | |
| | 0.6185 | 2.0 | 2500 | 0.5813 | 0.712 | 0.897 | |
| | 0.5907 | 3.0 | 3750 | 0.5478 | 0.82 | 0.9 | |
| | 0.5693 | 4.0 | 5000 | 0.5267 | 0.815 | 0.908 | |
| | 0.5608 | 5.0 | 6250 | 0.5193 | 0.787 | 0.91 | |
| | 0.5486 | 6.0 | 7500 | 0.5168 | 0.769 | 0.915 | |
| | 0.5409 | 7.0 | 8750 | 0.5034 | 0.79 | 0.916 | |
| | 0.5338 | 8.0 | 10000 | 0.5016 | 0.784 | 0.918 | |
| | 0.5331 | 9.0 | 11250 | 0.4947 | 0.796 | 0.919 | |
| | 0.5308 | 10.0 | 12500 | 0.4878 | 0.82 | 0.919 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.45.1 |
| - Pytorch 2.4.0 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.0 |
