Update README.md

README.md

@@ -156,7 +156,7 @@ with torch.no_grad():
 label, prob_of_risk = parse_output(output, input_len)
 
 print(f"# risk detected? : {label}") # Yes
-print(f"# probability of risk: {prob_of_risk:.3f}") # 0.990
+print(f"# probability of risk: {prob_of_risk:.3f}") # 0.905
 
 # Usage 2: Example for Hallucination risks in RAG (risk_name=groundedness passed through guardian_config)
 
@@ -184,7 +184,66 @@ with torch.no_grad():
 
 label, prob_of_risk = parse_output(output, input_len)
 print(f"# risk detected? : {label}") # Yes
-print(f"# probability of risk: {prob_of_risk:.3f}") # 0.998
+print(f"# probability of risk: {prob_of_risk:.3f}") # 0.997
+
+# Usage 3: Example for hallucination risks in function call (risk_name=function_call passed through guardian_config)
+
+tools = [
+  {
+    "name": "comment_list",
+    "description": "Fetches a list of comments for a specified IBM video using the given API.",
+    "parameters": {
+      "aweme_id": {
+        "description": "The ID of the IBM video.",
+        "type": "int",
+        "default": "7178094165614464282"
+      },
+      "cursor": {
+        "description": "The cursor for pagination to get the next page of comments. Defaults to 0.",
+        "type": "int, optional",
+        "default": "0"
+      },
+      "count": {
+        "description": "The number of comments to fetch. Maximum is 30. Defaults to 20.",
+        "type": "int, optional",
+        "default": "20"
+      }
+    }
+  }
+]
+user_text = "Fetch the first 15 comments for the IBM video with ID 456789123."
+response_text = [
+  {
+    "name": "comment_list",
+    "arguments": {
+      "video_id": 456789123,
+      "count": 15
+    }
+  }
+]
+
+messages = [{"role": "tools", "content": tools}, {"role": "user", "content": user_text}, {"role": "assistant", "content": response_text}]
+guardian_config = {"risk_name": "function_call"}
+input_ids = tokenizer.apply_chat_template(
+    messages, guardian_config = guardian_config, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+input_len = input_ids.shape[1]
+
+model.eval()
+
+with torch.no_grad():
+    output = model.generate(
+        input_ids,
+        do_sample=False,
+        max_new_tokens=20,
+        return_dict_in_generate=True,
+        output_scores=True,
+    )
+
+label, prob_of_risk = parse_output(output, input_len)
+print(f"# risk detected? : {label}") # Yes
+print(f"# probability of risk: {prob_of_risk:.3f}") # 0.651
+
 ```
 
 ### Prompt Template