BERT Model for Phishing Detection

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

   1pip install transformers torch 2
2. Load Model:
   bash

   1from transformers import BertForSequenceClassification, BertTokenizer 2 import torch 3 4 # Replace with your Hugging Face model repo name 5 model_name = 'ElSlay/BERT-Phishing-Email-Model' 6 7 # Load the pre-trained model and tokenizer 8 model = BertForSequenceClassification.from_pretrained(model_name) 9 tokenizer = BertTokenizer.from_pretrained(model_name) 10 11 # Ensure the model is in evaluation mode 12 model.eval() 13
3. Use the model for Prediction:
   bash

   1# Input email text 2 email_text = "Your email content here" 3 4 # Tokenize and preprocess the input text 5 inputs = tokenizer(email_text, return_tensors="pt", truncation=True, padding='max_length', max_length=512) 6 7 # Make the prediction 8 with torch.no_grad(): 9 outputs = model(**inputs) 10 logits = outputs.logits 11 predictions = torch.argmax(logits, dim=-1) 12 13 # Interpret the prediction 14 result = "Phishing" if predictions.item() == 1 else "Legitimate" 15 print(f"Prediction: {result}") 16
4. Expected Outputs: 1: Phishing 0: Legitimate