215 lines
5.9 KiB
Markdown
215 lines
5.9 KiB
Markdown
# MultiNERD
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## 概述
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MultiNERD 是一个大规模、多语言、多体裁的细粒度命名实体识别（Named Entity Recognition, NER）数据集，通过维基百科（Wikipedia）和维基新闻（Wikinews）自动生成。该数据集涵盖 10 种语言和 15 个不同的实体类别。
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## 任务描述
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- **任务类型**：细粒度多语言命名实体识别（NER）
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- **输入**：维基百科和维基新闻文本
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- **输出**：识别出的实体片段及其对应的 15 种细粒度类型
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- **领域**：通用知识、新闻、百科类内容
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## 主要特性
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- 大规模自动构建的语料库
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- 支持 10 种语言
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- 包含 15 种细粒度实体类别
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- 数据来源为维基百科和维基新闻
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- 实体类型覆盖全面
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## 评估说明
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- 默认配置使用 **5-shot** 评估方式
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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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- 实体类型包括：PER（人物）、ORG（组织）、LOC（地点）、ANIM（动物）、BIO（生物）、CEL（细胞）、DIS（疾病）、EVE（事件）、FOOD（食物）、INST（机构）、MEDIA（媒体）、MYTH（神话）、PLANT（植物）、TIME（时间）、VEHI（交通工具）
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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** | `multi_nerd` |
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| **数据集ID** | [extraordinarylab/multi-nerd](https://modelscope.cn/datasets/extraordinarylab/multi-nerd/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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| **默认示例数量** | 5-shot |
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| **评估划分** | `test` |
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| **训练划分** | `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 | 167,993 |
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| 提示词长度（平均） | 4016.26 字符 |
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| 提示词长度（最小/最大） | 3915 / 4501 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": "56cf0758",
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"content": "Here are some examples of named entity recognition:\n\nInput:\n2002 ging er ins Ausland und wechselte für 750.000 Pfund Sterling zu Manchester City .\n\nOutput:\n<response>2002 ging er ins Ausland und wechselte für 750.000 Pfund Sterling zu <organi ... [TRUNCATED] ... Ensure every opening tag has a matching closing tag.\n\nText to process:\nIn der Wissenschaft und dort vor allem in der Soziologie wird der Begriff Lebensführung traditionell stark mit der religionshistorischen Arbeit von Max Weber verbunden .\n"
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}
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],
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"target": "<response>In der Wissenschaft und dort vor allem in der Soziologie wird der Begriff Lebensführung traditionell stark mit der religionshistorischen Arbeit von <person>Max Weber</person> verbunden .</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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"In",
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"der",
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"Wissenschaft",
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"und",
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"dort",
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"vor",
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"allem",
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"in",
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"der",
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"Soziologie",
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"wird",
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"der",
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"Begriff",
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"Lebensführung",
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"traditionell",
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"stark",
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"mit",
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"der",
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"religionshistorischen",
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"Arbeit",
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"von",
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"Max",
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"Weber",
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"verbunden",
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"."
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],
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"ner_tags": [
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"B-PER",
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"I-PER",
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"O",
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"O"
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]
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}
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}
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```
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*注：部分内容因展示需要已被截断。*
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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 multi_nerd \
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--limit 10 # 正式评估时请删除此行
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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=['multi_nerd'],
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |