Update README.md

README.md

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 ## 🔍 Overview
 
-PromptShield is a multilingual prompt classification model designed to detect **unsafe or adversarial prompts**, particularly those that may lead to prompt injection or misuse of generative AI systems. It distinguishes between *safe* and *unsafe* inputs with over **99% accuracy**.
+ 🛡️ PromptShield
 
-Whether you're deploying a chatbot, a content moderation tool, or an LLM firewall, PromptShield provides an essential layer of safety assurance.
+**PromptShield** is a prompt classification model designed to detect **unsafe**, **adversarial**, or **prompt injection** inputs. Built on the `xlm-roberta-base` transformer, it delivers high-accuracy performance in distinguishing between **safe** and **unsafe** prompts — achieving **99.33% accuracy** during training.
+
+---
+
+## 📌 Overview
+
+PromptShield is a robust binary classification model built on FacebookAI's `xlm-roberta-base`. Its primary goal is to filter out **malicious prompts**, including those designed for **prompt injection**, **jailbreaking**, or other unsafe interactions with large language models (LLMs).
+
+Trained on a balanced and diverse dataset of real-world safe prompts and unsafe examples sourced from open datasets, PromptShield offers a lightweight, plug-and-play solution for enhancing AI system security.
+
+Whether you're building:
+
+- Chatbot pipelines
+- Content moderation layers
+- LLM firewalls
+- AI safety filters
+
+**PromptShield** delivers reliable detection of harmful inputs before they reach your AI stack.
 
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@@ -83,3 +100,20 @@ Sumit Ranjan
 
 Raj Bapodra
 
+🛡️ Ideal Use Cases
+LLM Firewalls & Guardrails
+
+AI Content Moderation
+
+Prompt Validation Pipelines
+
+Multi-Agent System Safety
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+AI Red Teaming Pre-filters
+
+📄 License
+MIT License (or your preferred open-source license here)
+
+⭐️ Citation
+If you use PromptShield, please consider citing this work or linking back to the Hugging Face model page.
+