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Model: dadaguai6677/TourismReview-Qwen2.5-7B
Source: Original Platform

README.md

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+---
+language:
+- zh
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- tourism
+- cultural-heritage
+- review-analysis
+- qwen2
+- lora
+- chinese
+base_model: Qwen/Qwen2.5-7B-Instruct
+model_name: TourismReview-Qwen2.5-7B
+---
+
+# TourismReview-Qwen2.5-7B
+
+## 中文介绍
+
+TourismReview-Qwen2.5-7B 是一个面向旅游研究场景的大语言模型，基于 **Qwen2.5-7B-Instruct** 进行微调，主要用于旅游评论文本的内容分析与多维度评分任务。
+
+本模型重点服务于文化遗产旅游、旅游体验评价、游客认知分析、UGC文本挖掘等研究场景，可用于对游客评论进行结构化解析，并输出统一格式的多维评分结果。
+
+本仓库发布的模型名称为：
+
+**`dadaguai6677/TourismReview-Qwen2.5-7B`**
+
+---
+
+## English Introduction
+
+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：
+
+1. 使用与本研究一致的 system prompt  
+2. 使用相同的 user prompt 结构  
+3. 保持 11 个评价维度的顺序不变  
+4. 保持输出格式完全一致  
+5. 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:
+
+1. the same system prompt  
+2. the same user prompt structure  
+3. the same order of the 11 evaluation dimensions  
+4. the exact same output format  
+5. 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)