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Create README.md
### **🔒 BERT Phishing Detection Model**
This repository hosts a fine-tuned **BERT-based** model optimized for **phishing URL detection** using a cybersecurity dataset. The model classifies URLs as either **phishing (malicious)** or **safe (benign)**.
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## **📚 Model Details**
- **Model Architecture**: BERT (Based on BERT)
- **Task**: Binary Classification (Phishing vs. Safe)
- **Dataset**: Custom cybersecurity dataset (11,431 URLs, 89 features)
- **Framework**: PyTorch & Hugging Face Transformers
- **Input Data**: URL strings & extracted numerical features
- **Number of Classes**: 2 (**Phishing, Safe**)
- **Quantization**: FP16 (for efficiency)
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## **🚀 Usage**
### **Installation**
```bash
pip install torch transformers scikit-learn pandas
```
### **Loading the Model**
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load the fine-tuned model and tokenizer
model_path = "./fine_tuned_SecureBERT… -
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