Update README.md

README.md

@@ -218,7 +218,7 @@ The model was evaluated based on the training loss across 200 steps. Below is th
 
 The model shows a significant reduction in training loss over the first 100 steps, indicating good convergence during the fine-tuning process. After step 100, the training loss becomes more stable but continues to fluctuate slightly. Overall, the model achieved a low loss by the final training step, suggesting effective learning and adaptation to the task of classifying SQL injections.
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
@@ -238,14 +238,14 @@ T4 GPU of Colab
 - **Training Framework:** PEFT.
 
 
-## Glossary [optional]
+## Glossary
 
 - **SQL Injection**: A type of attack where malicious SQL statements are executed in an application’s database.
 - **PEFT**: Parameter Efficient Fine-Tuning, a technique used for fine-tuning large models with fewer parameters.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-Shukdev Datta
+Shukdev Datta(https://www.linkedin.com/in/shukdev-datta-729767144/)
 
 ## Model Card Contact