179 lines
5.2 KiB
Markdown
179 lines
5.2 KiB
Markdown
# MIT-Restaurant
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## 概述
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MIT-Restaurant 数据集是一组专门用于训练和测试命名实体识别（NER）自然语言处理（NLP）模型的餐厅评论文本。该数据集包含来自真实评论的句子，并采用 BIO 格式进行标注。
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## 任务描述
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- **任务类型**：餐厅领域命名实体识别（NER）
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- **输入**：餐厅评论文本和查询
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- **输出**：识别出的与餐厅相关的实体片段
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- **领域**：餐饮服务、餐厅评论、对话系统
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## 主要特点
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- 真实的餐厅评论句子
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- 采用 BIO 格式标注
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- 包含八种餐厅特定的实体类型
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- 适用于餐饮服务领域的 NLP 任务
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- 适配于对话式 AI 应用
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## 评估说明
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- 默认配置使用 **5-shot** 评估
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- 评估指标：精确率（Precision）、召回率（Recall）、F1 分数（F1-Score）、准确率（Accuracy）
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- 实体类型：AMENITY（设施）、CUISINE（菜系）、DISH（菜品）、HOURS（营业时间）、LOCATION（位置）、PRICE（价格）、RATING（评分）、RESTAURANT_NAME（餐厅名称）
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `mit_restaurant` |
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| **数据集ID** | [extraordinarylab/mit-restaurant](https://modelscope.cn/datasets/extraordinarylab/mit-restaurant/summary) |
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| **论文** | 无 |
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| **标签** | `Knowledge`, `NER` |
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| **指标** | `precision`, `recall`, `f1_score`, `accuracy` |
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| **默认示例数** | 5-shot |
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| **评估分割** | `test` |
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| **训练分割** | `train` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 1,521 |
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| 提示词长度（平均） | 2383.97 字符 |
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| 提示词长度（最小/最大） | 2338 / 2474 字符 |
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## 样例示例
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**子集**: `default`
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```json
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{
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"input": [
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{
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"id": "9d5a77f1",
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"content": "Here are some examples of named entity recognition:\n\nInput:\ncan you find me the cheapest mexican restaurant nearby\n\nOutput:\n<response>can you find me the <price>cheapest</price> <cuisine>mexican</cuisine> restaurant <location>nearby</location ... [TRUNCATED] ... mes provided.\n5. Do not include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\na four star restaurant with a bar\n"
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}
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],
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"target": "<response>a <rating>four star</rating> restaurant <location>with a</location> <amenity>bar</amenity></response>",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"tokens": [
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"a",
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"four",
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"star",
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"restaurant",
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"with",
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"a",
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"bar"
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],
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"ner_tags": [
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"O",
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"B-RATING",
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"I-RATING",
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"O",
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"B-LOCATION",
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"I-LOCATION",
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"B-AMENITY"
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]
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}
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}
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```
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*注：部分内容因显示需要已被截断。*
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## 提示模板
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**提示模板：**
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```text
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You are a named entity recognition system that identifies the following entity types:
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{entities}
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Process the provided text and mark all named entities with XML-style tags.
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For example:
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<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
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Available entity tags: {entity_list}
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INSTRUCTIONS:
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1. Wrap your entire response in <response>...</response> tags.
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2. Inside these tags, include the original text with entity tags inserted.
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3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
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4. Tag ALL entities you can identify using the exact tag names provided.
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5. Do not include explanations, just the tagged text.
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6. If entity spans overlap, choose the most specific entity type.
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7. Ensure every opening tag has a matching closing tag.
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Text to process:
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{text}
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```
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<details>
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<summary>少样本（Few-shot）模板</summary>
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```text
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Here are some examples of named entity recognition:
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{fewshot}
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You are a named entity recognition system that identifies the following entity types:
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{entities}
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Process the provided text and mark all named entities with XML-style tags.
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For example:
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<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
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Available entity tags: {entity_list}
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INSTRUCTIONS:
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1. Wrap your entire response in <response>...</response> tags.
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2. Inside these tags, include the original text with entity tags inserted.
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3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
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4. Tag ALL entities you can identify using the exact tag names provided.
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5. Do not include explanations, just the tagged text.
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6. If entity spans overlap, choose the most specific entity type.
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7. Ensure every opening tag has a matching closing tag.
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Text to process:
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{text}
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```
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</details>
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## 使用方法
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### 使用命令行（CLI）
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets mit_restaurant \
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--limit 10 # 正式评估时请移除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['mit_restaurant'],
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limit=10, # 正式评估时请移除此行
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)
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run_task(task_cfg=task_cfg)
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``` |