229 lines
6.3 KiB
Markdown
229 lines
6.3 KiB
Markdown
# OntoNotes5
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## 概述
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OntoNotes Release 5.0 是一个大规模、多语言语料库，包含英语、中文和阿拉伯语文本，涵盖多种体裁。该语料库包含丰富的多层次语言学标注信息，包括句法、谓词-论元结构、词义、命名实体和共指关系。
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## 任务描述
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- **任务类型**：多体裁命名实体识别（NER）
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- **输入**：来自新闻、博客、广播对话的文本
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- **输出**：细粒度命名实体片段
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- **语言**：英语、中文、阿拉伯语
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## 主要特点
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- 大规模多语言语料库
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- 多种体裁（新闻、博客、广播）
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- 18 种细粒度实体类型
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- 丰富的语言学标注
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- 命名实体识别评估的标准基准
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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：PERSON（人物）、NORP（民族/宗教/政治团体）、FAC（设施）、ORG（组织）、GPE（地缘政治实体）、LOC（地点）、PRODUCT（产品）、EVENT（事件）、WORK_OF_ART（艺术作品）、LAW（法律）、LANGUAGE（语言）、DATE（日期）、TIME（时间）、PERCENT（百分比）、MONEY（货币）、QUANTITY（数量）、ORDINAL（序数）、CARDINAL（基数）
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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** | `ontonotes5` |
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| **数据集ID** | [extraordinarylab/ontonotes5](https://modelscope.cn/datasets/extraordinarylab/ontonotes5/summary) |
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| **论文** | N/A |
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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 | 8,262 |
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| 提示词长度（平均） | 3364.28 字符 |
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| 提示词长度（最小/最大） | 3253 / 4171 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": "6215273c",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nPeople start their own businesses for many reasons .\n\nOutput:\n<response>People start their own businesses for many reasons .</response>\n\nInput:\nBut a chance to fill out sales - tax r ... [TRUNCATED] ... ening tag has a matching closing tag.\n\nText to process:\nThe following were among Friday 's offerings and pricings in the U.S. and non-U.S. capital markets , with terms and syndicate manager , as compiled by Dow Jones Capital Markets Report :\n"
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}
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],
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"target": "<response>The following were among <date>Friday</date> 's offerings and pricings in the <geopolitical_entity>U.S.</geopolitical_entity> and <geopolitical_entity>non-U.S.</geopolitical_entity> capital markets , with terms and syndicate manager , as compiled by <organization>Dow Jones Capital Markets Report</organization> :</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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"The",
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"following",
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"were",
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"among",
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"Friday",
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"'s",
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"offerings",
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"and",
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"pricings",
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"in",
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"the",
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"U.S.",
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"and",
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"non-U.S.",
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"capital",
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"markets",
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",",
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"with",
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"terms",
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"and",
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"syndicate",
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"manager",
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",",
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"as",
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"compiled",
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"by",
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"Dow",
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"Jones",
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"Capital",
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"Markets",
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"Report",
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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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"B-DATE",
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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-GPE",
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"O",
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"B-GPE",
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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-ORG",
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"I-ORG",
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"I-ORG",
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"I-ORG",
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"I-ORG",
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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>少样本提示模板</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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### 使用命令行接口（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 ontonotes5 \
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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=['ontonotes5'],
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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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``` |