---
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