add role play data.

README.md

@@ -30,6 +30,8 @@ Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(
 - DPO方法来自论文[Direct Preference Optimization:Your Language Model is Secretly a Reward Model](https://arxiv.org/pdf/2305.18290.pdf)
 - ORPO方法来自论文[ORPO: Monolithic Preference Optimization without Reference Model](https://arxiv.org/abs/2403.07691)
 ## 🔥 News
+[2024/08/02] v2.2版本：支持了角色扮演模型训练，新增了医患对话SFT数据生成代码，详见[Release-v2.2](https://github.com/shibing624/MedicalGPT/releases/tag/2.2.0)
+
 [2024/06/11] v2.1版本：支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型，详见[Release-v2.1](https://github.com/shibing624/MedicalGPT/releases/tag/2.1.0)
 
 [2024/04/24] v2.0版本：支持了 **[Llama-3](https://huggingface.co/meta-llama)** 系列模型，详见[Release-v2.0](https://github.com/shibing624/MedicalGPT/releases/tag/2.0.0)

role_play_data/README.md

@@ -0,0 +1,35 @@
+
+## 造训练数据
+
+### 数据生成框架
+本数据集使用OpenAI API接口生成，流程：
+
+- **种子特征集和基础设定**：
+   - 手工编写的种子集包含基本角色特征。
+   - LLM从这个种子集生成角色的基础设定。
+- **角色设定的进化**：
+  - 第二个种子集包含指导角色设定进化的指令Prompt。
+  - 这些进化角色的指令Prompt被放到一个指令池中。基于这些进化Prompt，LLM对基础设定实施进化。
+- **反馈循环**：
+  - 由人类评估者和GPT-4组成的混合评价系统。此系统对进化后的设定给出反馈。
+  - 反馈用于迭代更新种子集。如此迭代，我们最终得到一个细致的角色设定数据集。
+- **角色扮演和对话生成**：
+  - 使用self-instruction框架基于角色设定生成角色的对话数据。
+
+
+1. 生成角色设定，分别生成护士角色和患者角色
+```bash
+cd role_play_data
+
+python role_generate.py
+```
+
+
+2. 生成医患之间的多轮对话
+LLM选择：分别用gpt-4o的api和豆包的doubao-character-pro-32k的api生成对话
+```bash
+python roleplay_data_generate_gpt4.py
+
+python roleplay_data_generate_doubao.py
+```
+

role_play_data/role_generate.py

@@ -0,0 +1,72 @@
+import json
+import random
+
+from openai import OpenAI
+from tqdm import tqdm
+
+client = OpenAI()
+print(client)
+
+
+def generate(prompt):
+    print(prompt)
+    messages = [
+        {"role": "user", "content": prompt}
+    ]
+    r = client.chat.completions.create(
+        model='gpt-4o',
+        temperature=1,
+        messages=messages, )
+    response = r.choices[0].message.content
+    print("回答：", response)
+    return response
+
+
+def generate_role(input_file, save_file, total_lines):
+    with open(input_file, "r", encoding="utf-8") as file:
+        lines = file.readlines()
+    with tqdm(total=total_lines, desc="指令进度") as pbar:
+        while pbar.n < total_lines:
+            random.shuffle(lines)
+            i = 0
+            sum_str = ""
+            for line in lines:
+                i += 1
+                try:
+                    data = json.loads(line.strip())
+                except:
+                    print("error:", line.strip())
+                    continue
+                question = data["system_prompt"]
+
+                sum_str += f"{i}.{question}\n\n"
+
+                if i == 5:
+                    res = generate(f'请续写下面内容，不少于10条，增加些多样性。\n\n{sum_str}')
+                    res = res.split("\n\n")
+                    for result in res:
+                        result = result.strip()
+                        prefix_length = len(result.split(".", 1)[0]) + 1  # 获取前缀数字的长度，包括后面的点号
+                        result = result[prefix_length:]
+                        if result == "":
+                            continue
+                        json_data = {'system_prompt': result}
+                        # 将数据写入文件
+                        with open(save_file, 'a', encoding='utf-8') as f:
+                            f.write(json.dumps(json_data, ensure_ascii=False) + '\n')
+
+                        pbar.update(1)
+            if pbar.n >= total_lines:
+                break
+
+
+if __name__ == '__main__':
+    total_lines = 50
+    input_file = "seed_nurse_role.jsonl"
+    save_file = "seed_nurse_role_output.jsonl"
+    generate_role(input_file, save_file, total_lines)
+
+    total_lines = 50
+    input_file = "seed_patient_role.jsonl"
+    save_file = "seed_patient_role_output.jsonl"
+    generate_role(input_file, save_file, total_lines)

role_play_data/roleplay_data_generate_doubao.py

@@ -0,0 +1,81 @@
+import json
+import random
+
+from openai import OpenAI
+from tqdm import tqdm
+
+client = OpenAI(
+    api_key="xxx",
+    base_url="https://ark.cn-beijing.volces.com/api/v3",
+)
+print(client)
+
+
+def generate(prompt, system_prompt=''):
+    print('提示：', prompt)
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": prompt}
+    ]
+    completion = client.chat.completions.create(
+        #pro-32k: ep-20240623141021-r77gl
+        #lite-4k:ep-20240623140948-92n2g
+        model="ep-20240623141021-r77gl",  # your model endpoint ID
+        messages=messages,
+        max_tokens=3048,
+    )
+    response = completion.choices[0].message.content
+    print("生成的对话：", response)
+    return response
+
+
+file_role1 = "seed_nurse_role.jsonl"
+file_role2 = "seed_patient_role.jsonl"
+with open(file_role1, "r", encoding="utf-8") as file:
+    role1s = file.readlines()
+with open(file_role2, "r", encoding="utf-8") as file:
+    role2s = file.readlines()
+
+save_file = "roleplay_train_data_v2.jsonl"
+total_lines = 1000  # 10000
+max_history_len = 10
+
+with tqdm(total=total_lines, desc="生成对话") as pbar:
+    while pbar.n < total_lines:
+        role1 = random.choice(role1s)
+        role2 = random.choice(role2s)
+        data1 = json.loads(role1.strip())['system_prompt']
+        data2 = json.loads(role2.strip())['system_prompt']
+        p = "你是护士，跟患者对话。\n\n护士角色：" + data1 + '\n患者角色：' + data2
+        conversation = {"id": str(pbar.n), "system_prompt": p, "conversations": []}
+
+        system_prompt = f"护士角色：{data1}\n患者角色：{data2}\n"
+        print('------' * 10)
+        print('system_prompt:', system_prompt)
+        history = []
+
+        for i in range(6):
+            patient_prompt = f"要求你扮演患者，并且根据角色的设定内容模仿 角色相应的对话口吻和风格。你说一句话，完成本轮对话即可。"
+            for history_turn in history[-max_history_len:]:
+                patient_prompt += history_turn + '\n'
+            patient_prompt += "患者:"
+
+            patient_response = generate(patient_prompt, system_prompt)
+            conversation["conversations"].append({"from": "human", "value": patient_response.strip()})
+            history.append("患者:" + patient_response.strip())
+
+            nurse_prompt = f"要求你扮演护士，并且根据角色的设定内容模仿 角色相应的对话口吻和风格。你说一句话,完成本轮对话即可。\n"
+            for history_turn in history[-max_history_len:]:
+                nurse_prompt += history_turn + '\n'
+            nurse_prompt += "护士:"
+
+            nurse_response = generate(nurse_prompt, system_prompt)
+            conversation["conversations"].append({"from": "gpt", "value": nurse_response.strip()})
+            history.append("护士: " + nurse_response.strip())
+
+        with open(save_file, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(conversation, ensure_ascii=False) + '\n')
+
+        pbar.update(1)
+        if pbar.n >= total_lines:
+            break

role_play_data/roleplay_data_generate_gpt4.py

@@ -0,0 +1,65 @@
+import json
+import random
+
+from openai import OpenAI
+from tqdm import tqdm
+
+client = OpenAI()
+print(client)
+
+
+def generate(prompt):
+    print('提示：', prompt)
+    messages = [
+        {"role": "user", "content": prompt}
+    ]
+    r = client.chat.completions.create(
+        model='gpt-4o',
+        messages=messages,
+        temperature=1,
+        max_tokens=3048,  # 增加max_tokens以生成更长的对话
+    )
+    response = r.choices[0].message.content
+    print("生成的对话：", response)
+    return response
+
+
+file_role1 = "seed_nurse_role.jsonl"
+file_role2 = "seed_patient_role.jsonl"
+with open(file_role1, "r", encoding="utf-8") as file:
+    role1s = file.readlines()
+with open(file_role2, "r", encoding="utf-8") as file:
+    role2s = file.readlines()
+
+save_file = "roleplay_train_data_v1.jsonl"
+total_lines = 1000
+
+with tqdm(total=total_lines, desc="生成对话") as pbar:
+    while pbar.n < total_lines:
+        role1 = random.choice(role1s)
+        role2 = random.choice(role2s)
+        data1 = json.loads(role1.strip())['system_prompt']
+        data2 = json.loads(role2.strip())['system_prompt']
+        p = "你是护士，跟患者对话。\n\n护士角色：" + data1 + '\n患者角色：' + data2
+        conversation = {"id": str(pbar.n), "system_prompt": p, "conversations": []}
+
+        combined_prompt = f"你扮演一个护士，以下对话是你和患者之间的对话。\n护士角色：{data1}\n患者角色：{data2}\n"
+        combined_prompt += "进行多轮问答（6轮以上）。患者说话以`患者:`开头，护士说话以`护士:`开头。患者先提问。\n"
+
+        prompt = combined_prompt + "\n对话开始：\n "
+        response = generate(prompt)
+
+        # 解析生成的多轮对话
+        lines = response.strip().split('\n')
+        for line in lines:
+            if line.startswith("患者"):
+                conversation["conversations"].append({"from": "human", "value": line.split("患者")[1].strip()[1:]})
+            elif line.startswith("护士"):
+                conversation["conversations"].append({"from": "gpt", "value": line.split("护士")[1].strip()[1:]})
+
+        with open(save_file, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(conversation, ensure_ascii=False) + '\n')
+
+        pbar.update(1)
+        if pbar.n >= total_lines:
+            break