210 lines
6.1 KiB
Markdown
210 lines
6.1 KiB
Markdown
---
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language:
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- zh
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- tourism
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- cultural-heritage
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- review-analysis
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- qwen2
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- lora
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- chinese
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base_model: Qwen/Qwen2.5-7B-Instruct
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model_name: TourismReview-Qwen2.5-7B
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---
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# TourismReview-Qwen2.5-7B
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## 中文介绍
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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。
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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。
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本仓库发布的模型名称为：
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**`dadaguai6677/TourismReview-Qwen2.5-7B`**
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---
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## English Introduction
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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.
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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.
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The released repository name is:
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**`dadaguai6677/TourismReview-Qwen2.5-7B`**
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---
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# 模型信息 | Model Details
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## 中文
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- **模型名称**：TourismReview-Qwen2.5-7B
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- **基础模型**：Qwen/Qwen2.5-7B-Instruct
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- **模型架构**：Qwen2ForCausalLM
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- **任务类型**：文本生成 / 评论分析 / 多维评分
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- **适用语言**：中文为主，兼容英文说明
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- **应用方向**：旅游评论分析、文化遗产旅游研究、游客感知评价
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## English
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- **Model Name**: TourismReview-Qwen2.5-7B
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- **Base Model**: Qwen/Qwen2.5-7B-Instruct
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- **Architecture**: Qwen2ForCausalLM
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- **Task Type**: Text generation / Review analysis / Multi-dimensional scoring
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- **Primary Language**: Chinese, with English documentation support
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- **Domain**: Tourism review analysis, cultural heritage tourism research, visitor perception evaluation
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---
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# 适用任务 | Intended Use
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## 中文
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本模型适用于以下任务：
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- 旅游评论内容分析
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- 游客感知价值识别
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- 文化遗产旅游体验评价
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- 多维文本结构化打分
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- 旅游研究中的辅助编码与大规模文本处理
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## English
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This model is intended for:
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- tourism review content analysis
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- visitor perceived value assessment
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- cultural heritage tourism experience evaluation
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- structured multi-dimensional scoring
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- large-scale text processing for tourism research
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---
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# 使用方法 | How to Use
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## 重要说明（请务必阅读）| Important Note
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### 中文
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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：
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1. 使用与本研究一致的 system prompt
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2. 使用相同的 user prompt 结构
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3. 保持 11 个评价维度的顺序不变
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4. 保持输出格式完全一致
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5. 推理参数建议保持：
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- `max_new_tokens=128`
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- `do_sample=False`
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- `num_beams=1`
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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。
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### English
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To reproduce the behavior used in this research as closely as possible, please keep the original inference setup unchanged, including:
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1. the same system prompt
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2. the same user prompt structure
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3. the same order of the 11 evaluation dimensions
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4. the exact same output format
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5. the same inference parameters:
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- `max_new_tokens=128`
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- `do_sample=False`
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- `num_beams=1`
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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}
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---
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## 调用代码示例：
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "dadaguai6677/TourismReview-Qwen2.5-7B"
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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trust_remote_code=True,
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padding_side="left"
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True,
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low_cpu_mem_usage=True
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)
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model.eval()
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def create_prompt(text):
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system_msg = "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."
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prompt = f"""<|im_start|>system
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{system_msg}<|im_end|>
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<|im_start|>user
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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。
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评分标准：
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1分：完全不同意
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2分：不同意
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3分：一般
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4分：同意
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5分：完全同意
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null：未提及
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评价维度：
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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
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请严格按以下格式返回结果：
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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
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待分析文本：
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{text}<|im_end|>
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<|im_start|>assistant
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"""
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return prompt
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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。"
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prompt = create_prompt(text)
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inputs = tokenizer(
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prompt,
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return_tensors="pt",
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truncation=True,
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max_length=1024,
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padding=True
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).to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=False,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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num_beams=1
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)
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full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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if "<|im_start|>assistant" in full_text:
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response = full_text.split("<|im_start|>assistant")[-1].strip()
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else:
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response = full_text
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print(response) |