60 lines
2.7 KiB
Python
60 lines
2.7 KiB
Python
import argparse
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import openai
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import yaml
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import sys
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import random
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def return_random_prompt():
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system_prompt = "你需要尽可能给出多样化的任务指令和对应的回答。我们将用于人工评估ChatGPT模型对指令的完成情况。要求:\n"
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# generate random topics
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system_prompt += "1. 主题多样化，涵盖法律诉讼的各个领域，例如：刑法、民法、行政法等。\n"
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# generate random tasks
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task_list = ["开放式生成", "分类", "问答", "编辑", "摘要",
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"写作", "翻译", "分析", "常识推理", "写信", "抽取", "推荐"]
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system_prompt += "2. 表述多样化，结合真实问题；指令类型多样化，例如：" + \
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"、".join(random.sample(task_list, 10)) + "等。\n"
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# other requirements
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system_prompt += "3. 如果遇到无法处理的指令（只靠文本无法回答），给出无法处理的回复。\n"
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system_prompt += "4. 除非特别要求，请使用中文，指令可以是命令句、疑问句、或其他合适的类型。\n"
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system_prompt += "5. 为指令生成一个适当且涉及真实情况的<input>，不应该只包含简单的占位符。<input>应提供实质性的内容，具有挑战性。字数不超过" + \
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str(random.randint(80, 120)) + "字。\n"
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system_prompt += "6. <output>应该是对指令的适当且真实的回应，不能只回复答应或拒绝请求。如果需要额外信息才能回复时，请努力预测用户意图并尝试回复。<output>的内容应少于" + \
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str(random.randint(128, 512)) + "字。\n\n"
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system_prompt += "请给出满足条件的20条JSON格式数据：\n"
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return system_prompt
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--cfg_path', default='../config.yaml', type=str)
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parser.add_argument('--save_path', default='./output.json', type=str)
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args = parser.parse_args()
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with open(args.cfg_path, 'r') as f:
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cfg = yaml.load(f, Loader=yaml.FullLoader)
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openai.api_key = cfg['API_KEY']
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openai.api_base = cfg['API_BASE_URL']
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output_file = open(args.save_path, 'w')
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# number of data to generate (each prompt contains 20 JSON-formatted data)
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# TODO: 改成流式的，不然会中途断掉
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MAX_EPOCHS = 1
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for k in range(MAX_EPOCHS):
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response = openai.ChatCompletion.create(
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# here we use `gpt-3.5-turbo` model, while Stanford-Alpaca uses `text-davinci-003`
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model="gpt-3.5-turbo",
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messages=[
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{"role": "user", "content": return_random_prompt()},
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]
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)
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output_file.write(response["choices"][0]["message"]["content"] + '\n')
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output_file.close()
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