ElSlay/BERT-Phishing-Email-Model

BERT Model for Phishing Detection

This repository contains the fine-tuned BERT model for detecting phishing emails. The model has been trained to classify emails as either phishing or legitimate based on their body text.

Model Details

• —Model Type: BERT (Bidirectional Encoder Representations from Transformers)
• —Task: Phishing detection (Binary classification)
• —Fine-Tuning: The model was fine-tuned on a dataset of phishing and legitimate emails.

How to Use

1. 1.Install Dependencies: You can use the following command to install the necessary libraries:

   pip install transformers torch

2. **Load Model**:

from transformers import BertForSequenceClassification, BertTokenizer import torch

# Replace with your Hugging Face model repo name model_name = 'ElSlay/BERT-Phishing-Email-Model'

# Load the pre-trained model and tokenizer model = BertForSequenceClassification.frompretrained(modelname) tokenizer = BertTokenizer.frompretrained(modelname)

# Ensure the model is in evaluation mode model.eval()

1. 1.Use the model for Prediction:

   # Input email text
    email_text = "Your email content here"
    
    # Tokenize and preprocess the input text
    inputs = tokenizer(email_text, return_tensors="pt", truncation=True, padding='max_length', max_length=512)
    
    # Make the prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predictions = torch.argmax(logits, dim=-1)
    
    # Interpret the prediction
    result = "Phishing" if predictions.item() == 1 else "Legitimate"
    print(f"Prediction: {result}")

4. **Expected Outputs**:
   1: Phishing
   0: Legitimate