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---
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license: mit
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-7B
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---
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# 对数据任务类型分类，比如"情感分析"、"文本分类"、"翻译"，"总结"、"数学问答"....
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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import json
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from tqdm import tqdm
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from loguru import logger
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model_name = "Laurie/Qwen2.5-7b-data-classification"
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# 加载模型和 tokenizer，同时调整 padding_side 为 left（适用于 decoder-only 模型）
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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# attn_implementation="flash_attention_2"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left") # batch 推理时要左填充
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# 对话模板
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system_message = [{"role": "system", "content": "你是一个数据分类专家，请根据对话内容判断其所属的类别。"}]
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last_query = [{"role": "user", "content": "现在请输出你的判断结果："}]
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def prepare_text(messages: list[dict]) -> str:
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"""
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将 messages 中的 "from"/"value" 键转为 "role"/"content"，并构造完整对话文本
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"""
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messages = [{"role": item["from"], "content": item["value"]} for item in messages]
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messages = system_message + messages + last_query
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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return text
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def generate_task_types_batch(messages_batch: list[list[dict]]) -> list[str]:
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"""
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对一个 batch 的对话列表进行推理生成，并返回每个对话中 assistant 的回答部分
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"""
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# 将每个消息列表转换为完整文本
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texts = [prepare_text(messages) for messages in messages_batch]
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# 使用批量编码，并进行 padding 以适应批量输入
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model_inputs = tokenizer(
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texts,
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return_tensors="pt",
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padding=True,
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truncation=True
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).to(model.device)
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with torch.no_grad():
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=32,
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eos_token_id=[151643, 151645],
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pad_token_id=151643,
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do_sample=True,
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repetition_penalty=1.05,
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temperature=0.7,
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top_p=0.8,
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top_k=20
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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task_types = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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return task_types
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def process_json(json_path: str, save_path: str, batch_size: int = 8):
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"""
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读取 JSON 文件，对数据进行批量推理处理，
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并将结果写回保存。
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"""
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with open(json_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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# 分批处理，batch_size 可根据 GPU 显存情况进行调整
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for i in tqdm(range(0, len(data_slice), batch_size)):
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batch = data_slice[i : i + batch_size]
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conversations_batch = [item["conversations"] for item in batch]
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task_types = generate_task_types_batch(conversations_batch)
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for item, answer in zip(batch, task_types):
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item["task_type"] = answer
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with open(save_path, "w", encoding="utf-8") as f:
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json.dump(data_slice, f, ensure_ascii=False, indent=4)
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logger.info(f"已处理 {len(data_slice)} 条数据，保存到 {save_path}")
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if __name__ == "__main__":
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json_path = "./qwen_bench_300k.json"
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save_path = "./qwen_bench_300k_cls.json"
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process_json(json_path, save_path, batch_size=16) |
