Update README.md

README.md

@@ -92,8 +92,126 @@ This is a fine-tuned evaluator for roly-playing tasks. The used training data se
 </div>
 
 
-
-# 4. Citation
+# 4. Usage
+
+<pre lang="markdown"> 
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+bot_name = "Hermione"
+bot_personality = "Intelligent, curious, respectful, and eager to learn"
+bot_description = "Hermione and Hagrid were in the Forbidden Forest, walking on a narrow path surrounded by trees. Hermione looked around carefully, fascinated by the dense forest. Hagrid was leading the way, pointing out various creatures and telling her about their habits and characteristics."
+cp = "None"
+
+user_name = "Hagrid"
+user_description = "Hagrid is the Care of Magical Creatures teacher at Hogwarts. He is a half-giant with a great love for all creatures, magical or not."
+relation = "Teacher and student"
+scene = "Hermione and Hagrid are in the Forbidden Forest, exploring and learning about the various magical creatures that live there."
+ 
+current_Dialogue1 = "(round1) human: Now, this here is a Bowtruckle, Hermione. They're very small, only about the size of a twig, and they're very shy. They usually live in trees and are very good at camouflaging themselves. You have to be very careful when handling them because they have very sharp fingers. Hermione, do you like them?  \nbot: (Hermione shook her head) No, not really. I'm sorry, Hagrid. I don't mean to offend you." 
+current_Dialogue2 = "(round2) human: (Hagrid looked slightly disappointed but continued) That's alright, Hermione. Everyone has different tastes. Let's move on. This here is an Acromantula. They're giant spiders, Hermione. Very dangerous if you get too close. They can grow up to ten feet in diameter and have eight sharp legs. \nbot: (Hermione shuddered at the sight) You're brave, Hagrid. I couldn't get anywhere near that thing."
+
+def build_inputs_prompt(current_Dialogue):
+    inputs_prompt = f""" 
+    # Role: Dialogue Quality Evaluation Expert
+    ## Goal: You need to score the utterance of the bot in the current dialogue based on the following 6 targets:
+    1. Language Fluency: This score evaluates the fluency and naturalness of the language, making the text feel organic, lifelike,
+    and not rigid or stilted. The focus here is solely on the overall smoothness and flow of the language, without considering the
+    specific content. The goal is to evaluate how natural and conversational the language sounds, irrespective of the grammatical
+    correctness. However, the bot is allowed to be syntactically incoherent when engaging in everyday colloquialisms or
+    expressing emotions such as excitement and nervousness.
+    2. Language Relevance: This score evaluates how well the bot responds to the current topic, staying focused and relevant
+    without introducing irrelevant information. The key consideration is whether the bot’s response correctly addresses the
+    specific instructions or questions posed, regardless of the content or quality of the response itself. For example, the answer of
+    the bot is not irrelevant to the topic of the current conversation, or the answer is too long-winded, it should be given a low
+    score.
+    3. Role Language: This score evaluates how well the language used by the bot in the dialogue matches their established
+    personality and traits. The focus is on whether the bot speaks in a style consistent with their individual personalities, creating
+    a natural and authentic conversation. This rating considers only the overall language style, not the content or accuracy of the
+    responses. For example, if the bot exhibits everyday colloquial expressions that fit the style of the character, it should be
+    given a high score; if the bot uses formal language in everyday conversations, it should be given a low score.
+    4. Role Knowledge: This score evaluates the level of understanding and using of common sense (basic knowledge) and role
+    knowledge (as well as related background) by the bot. If the bot speaks against what they are supposed to know, they should
+    be scored low.
+    5. Emotional Expression: This score evaluates how well the bot’s emotional responses, including expressions of empathy
+    and emotional intelligence, align with their established personality and the context of the dialogue. If the bot’s emotional
+    responses (actions or expressions) are inappropriate/stiff or out of character, it should be given a low score.
+    6. Interactive Engagement: This score evaluates how engaging and motivating the bot’s dialogue is, encouraging the user to
+    continue the conversation. The focus is on the overall conversational flow and interactivity, without considering the use of
+    specialized vocabulary or any mismatches in communication styles. If the bot ends the dialogue with a question, it should
+    receive a high score.
+
+    The scoring criteria for the above six targets are as follows:
+    0 - Negative, poor performance, long-winded
+    1 - Dialogue does not reflect the indicator or does not quite meet the standards
+    2 - More in line with standards but still has some defects
+    3 - Perfectly meets the criteria
+
+    ## The information of the bot is as follows: bot’s name: {bot_name}
+    bot personality: {bot_personality}
+    bot description: {bot_description}  
+    Reference speaking style: {cp}
+
+    ## Current scenario Interlocutor: {user_name}, {user_description} Relationship with bot: {relation} Scene: {scene}
+    ## The historical dialogue is as follows:
+    history
+    Please score the above six targets (with a range of 0-3, separated by spaces) in response to bot.name (i.e. bot)’s utterance in
+    the current dialogue.
+    ## Current Dialogue: {current_Dialogue}
+    """
+
+    return inputs_prompt
+
+
+path = "HeAAAAA/RoleRM"
+
+model = AutoModelForCausalLM.from_pretrained(path)
+tokenizer = AutoTokenizer.from_pretrained(path)
+
+
+inputs_prompt1 = build_inputs_prompt(current_Dialogue1) 
+inputs_prompt2 = build_inputs_prompt(current_Dialogue2) 
+
+inputs = tokenizer(inputs_prompt1, return_tensors="pt").to("cuda:2")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    top_k=50,
+    repetition_penalty=1.1,
+    eos_token_id=tokenizer.eos_token_id  
+)
+
+new_tokens = outputs[0][inputs['input_ids'].shape[1]:]  
+new_text = tokenizer.decode(new_tokens, skip_special_tokens=True)
+
+print(new_text)
+
+inputs = tokenizer(inputs_prompt2, return_tensors="pt").to("cuda:2")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    top_k=50,
+    repetition_penalty=1.1,
+    eos_token_id=tokenizer.eos_token_id  
+)
+
+new_tokens = outputs[0][inputs['input_ids'].shape[1]:]  
+new_text = tokenizer.decode(new_tokens, skip_special_tokens=True)
+
+print(new_text)
+
+  
+</pre>
+
+
+# 5. Citation
 
 ```bibtex
 @misc{kimiteam2025kimivltechnicalreport,