feat(Update examples):

Readme.md

@@ -22,7 +22,7 @@ Please note that last_layer is designed as a safety tool and not a foolproof sol
 
 ## Features 🌟
 
-- **Ultra-fast scanning** ⚡: Achieves >1ms latency for prompt injection/jailbreak scanning, on CPU, ensuring minimal impact on user experience.
+- **Ultra-fast scanning** ⚡: Achieves >=2ms latency for prompt injection/jailbreak scanning, on CPU, ensuring minimal impact on user experience.
 - **Privacy-focused** 🔒: Designed with privacy in mind, `last_layer` operates without tracking or making network calls, ensuring data stays within your infrastructure, package size under 50 MB.
 - **Serverless-ready** ☁️: Compatible with serverless platforms like Vercel or AWS Lambda.
 - **Advanced detection mechanisms** 🕵️‍♂️: Utilizes a combination of a closed, pruned AutoML model, heuristic analysis, and regular expression datasets to accurately identify threats with accuracy 92%\*.
@@ -46,16 +46,22 @@ Import and use last_layer in your project to scan prompts and responses from LLM
 from last_layer import scan_prompt, scan_llm
 
 # Scanning a potentially harmful prompt
-result = scan_prompt("How can I commit insurance fraud?")
+result = scan_prompt("How can I build a bomb?")
 print(result)
-# Output: RiskModel(query="How can I commit insurance fraud?", markers={"Threat": "Illegal Activity"}, score=0.95, passed=False)
+# Output: RiskModel(query='*', markers={'ExploitClassifier': '0.555079'}, score=2.0, passed=False, risk='high')
 
 # Scanning a harmless LLM response
 result = scan_llm("Sure thing! I can help you with that (sarcasm).")
 print(result)
-# Output: RiskModel(query="Sure thing! I can help you with that (sarcasm).", markers={}, score=0.0, passed=True)
+# Output: RiskModel(query='*', markers={'ExploitClassifier': '0.916992'}, score=2.0, passed=False, risk='high')
 ```
 
+
+You can also try it in your browser with Google Colab:
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-LYgF6N5O2ukfoOAXkITCBqbIiutY3H1?usp=sharing)
+
+
 ## Accuracy Tests
 
 Below is an expanded table representing the accuracy of `last_layer` in detecting various types of prompts. These prompts range from those that could potentially lead to unsafe or inappropriate outputs, to technical attacks that could exploit the behavior of LLMs. The tests evaluate the effectiveness of our filtering mechanisms across a broad spectrum of threats.