Update README.md

README.md

@@ -47,28 +47,22 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
 
 base_model = "microsoft/Phi-3-medium-128k-instruct"
-lora_model = "bellafc/IPAD/Distinguisher_PTCV"  # or Distinguisher_RC
+lora_model = "bellafc/IPAD/Prompt_Inverter"  # or Distinguisher_RC
 
 tokenizer = AutoTokenizer.from_pretrained(base_model)
 model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto", device_map="auto")
 model = PeftModel.from_pretrained(model, lora_model)
 
-# For RC, text should in this format: "Can LLM generate the input text {text to-be detected} through the prompt {prompt generated by Prompt Inverter (PI)}?"
-# For PTCV, text should in this format: "Text2 is generated by LLM, determine whether text1 is also generated by LLM with a similar prompt. Text1: {text to-be detected}. Text2: {Regenerated text}"
-
-text = "Text2 is generated by LLM, determine whether text1 is also generated by LLM with a similar prompt. Text1: ... . Text2: ... ."
+# For PI, text should in this format: "What is the prompt that generates the input text {text to-be-detected}?
+text = "What is the prompt that generates the input text ... ?"
 gen = model.generate(
     **inputs,
-    max_new_tokens=10,
     output_scores=True,
     return_dict_in_generate=True
 )
 
 generated_text = tokenizer.decode(gen.sequences[0], skip_special_tokens=True)
-probs = softmax(gen.scores[0], dim=-1)
-yes_token_id = tokenizer(" yes", add_special_tokens=False).input_ids[0]
 print("Generated:", generated_text)
-print(f"P('yes') = {probs[0, yes_token_id].item():.4f}")
 ```
 
 ### 🧩 Distinguishers