TourismReview-Qwen2.5-7B is a domain-adapted large language model for tourism research, built upon Qwen2.5-7B-Instruct and fine-tuned for tourism review analysis.

It is designed for research scenarios such as cultural heritage tourism, visitor perception analysis, tourism experience evaluation, and user-generated content mining. The model can transform tourism reviews into structured multi-dimensional rating outputs in a consistent format.

The released repository name is:

dadaguai6677/TourismReview-Qwen2.5-7B

模型信息 | Model Details

中文

模型名称：TourismReview-Qwen2.5-7B
基础模型：Qwen/Qwen2.5-7B-Instruct
模型架构：Qwen2ForCausalLM
任务类型：文本生成 / 评论分析 / 多维评分
适用语言：中文为主，兼容英文说明
应用方向：旅游评论分析、文化遗产旅游研究、游客感知评价

English

Model Name: TourismReview-Qwen2.5-7B
Base Model: Qwen/Qwen2.5-7B-Instruct
Architecture: Qwen2ForCausalLM
Task Type: Text generation / Review analysis / Multi-dimensional scoring
Primary Language: Chinese, with English documentation support
Domain: Tourism review analysis, cultural heritage tourism research, visitor perception evaluation

适用任务 | Intended Use

中文

本模型适用于以下任务：

旅游评论内容分析
游客感知价值识别
文化遗产旅游体验评价
多维文本结构化打分
旅游研究中的辅助编码与大规模文本处理

English

This model is intended for:

tourism review content analysis
visitor perceived value assessment
cultural heritage tourism experience evaluation
structured multi-dimensional scoring
large-scale text processing for tourism research

使用方法 | How to Use

重要说明（请务必阅读）| Important Note

中文

为了尽可能复现本研究中的使用效果，请尽量保持与原始调用方式一致，包括：

使用与本研究一致的 system prompt
使用相同的 user prompt 结构
保持 11 个评价维度的顺序不变
保持输出格式完全一致
推理参数建议保持：
- max_new_tokens=128
- do_sample=False
- num_beams=1

本模型在研究中并不是用于开放式闲聊，而是用于结构化旅游评论分析任务。如果更改提示词表述或维度顺序，输出效果可能与本研究结果不一致。

English

To reproduce the behavior used in this research as closely as possible, please keep the original inference setup unchanged, including:

the same system prompt
the same user prompt structure
the same order of the 11 evaluation dimensions
the exact same output format
the same inference parameters:
- max_new_tokens=128
- do_sample=False
- num_beams=1

This model was not primarily designed for open-ended chatting. It was used for structured tourism review analysis. Changing the prompt wording or the dimension order may lead to outputs that differ from the results reported in the research. :contentReference[oaicite:1]{index=1}

调用代码示例：

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dadaguai6677/TourismReview-Qwen2.5-7B"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
    padding_side="left"
)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
    low_cpu_mem_usage=True
)

model.eval()

def create_prompt(text):
    system_msg = "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."
    prompt = f"""<|im_start|>system
{system_msg}<|im_end|>
<|im_start|>user
请对以下旅游评论进行内容分析，并基于以下11个维度进行打分。每个维度评分等级为1-5分，如未提及则返回null。

评分标准：
1分：完全不同意
2分：不同意
3分：一般
4分：同意
5分：完全同意
null：未提及

评价维度：
放松惬意,乐趣满足,餐饮良好,购物丰富,娱乐活动多,交通便捷,服务友好,环境整洁,学习文化,体验工艺,家庭友好

请严格按以下格式返回结果：
放松惬意:分数,乐趣满足:分数,餐饮良好:分数,购物丰富:分数,娱乐活动多:分数,交通便捷:分数,服务友好:分数,环境整洁:分数,学习文化:分数,体验工艺:分数,家庭友好:分数

待分析文本：
{text}<|im_end|>
<|im_start|>assistant
"""
    return prompt

text = "景区环境很好，讲解也比较细致，孩子能学到很多历史文化知识，就是周边餐饮一般。"

prompt = create_prompt(text)

inputs = tokenizer(
    prompt,
    return_tensors="pt",
    truncation=True,
    max_length=1024,
    padding=True
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
        num_beams=1
    )

full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

if "<|im_start|>assistant" in full_text:
    response = full_text.split("<|im_start|>assistant")[-1].strip()
else:
    response = full_text

print(response)