---
license: apache-2.0
widget:
- text: "https://www.facebook.com/"
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: bert-large-multilingual-finetuned-phishing
  results: []
datasets:
- huynq3Cyradar/Phishing_Detection_Dataset
---

<!-- 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. -->

# bert-large-finetuned-phishing

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/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