Update README.md

README.md

@@ -38,7 +38,7 @@ https://www.linkedin.com/in/r-jagan-raj/
 
 TrainOutput(global_step=6297, training_loss=0.07093968526965307, metrics={'train_runtime': 5545.442, 'train_samples_per_second': 9.08, 'train_steps_per_second': 1.136, 'total_flos': 1.32489571926528e+16, 'train_loss': 0.07093968526965307, 'epoch': 3.0})
 
-### How to Use
+## How to Use
 
 **Step 1:** Installing Dependencies: Use the command below to install all the required libraries:
 
@@ -97,7 +97,7 @@ print(f"Prediction: {result}")
 
 ```
 
-## Model Summary:
+# Model Summary:
 This fine-tuned BERT model is designed to detect phishing emails. Built on the powerful BERT (Bidirectional Encoder Representations from Transformers) architecture, it performs binary classification to label emails as either phishing or legitimate.
 
 The model has been fine-tuned using a dataset of phishing and legitimate emails, ensuring it understands patterns and linguistic cues commonly found in phishing content. By leveraging contextual understanding, it can identify subtle differences in text that distinguish malicious intent from normal communication. This makes it an effective tool for email security and anti-phishing defenses.