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README.md

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+# 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. **Install Dependencies**:
+   You can use the following command to install the necessary libraries:
+   ```bash
+   pip install transformers torch
+
+2. **Load Model**:
+   ```bash
+   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.from_pretrained(model_name)
+    tokenizer = BertTokenizer.from_pretrained(model_name)
+    
+    # Ensure the model is in evaluation mode
+    model.eval()
+
+3. **Use the model for Prediction**:
+   ```bash
+   # 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
+
 ---
 datasets:
 - zefang-liu/phishing-email-dataset
+---
 language:
 - en
+---
 base_model:
 - google-bert/bert-base-uncased
+---
 tags:
 - phishing
 - email