193 lines
5.2 KiB
Markdown
193 lines
5.2 KiB
Markdown
# CoNLL2003
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## 概述
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CoNLL-2003 是在 2003 年计算自然语言学习会议（Conference on Computational Natural Language Learning）上提出的经典命名实体识别（NER）基准数据集。该数据集包含标注了四种实体类型的新闻文章。
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## 任务描述
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- **任务类型**：命名实体识别（NER）
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- **输入**：待识别实体的文本
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- **输出**：带有类型标签的实体片段
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- **实体类型**：人物（PER）、组织（ORG）、地点（LOC）、其他（MISC）
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## 主要特点
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- 标准 NER 基准，实体类型定义清晰
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- 新闻领域文本，标注质量高
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- 四类实体，定义明确
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- 支持少样本（few-shot）评估
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- 提供全面的评估指标（精确率、召回率、F1 分数、准确率）
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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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- 训练集划分：**train**，评估集划分：**test**
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- 实体类型映射为人类可读的名称
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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** | `conll2003` |
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| **数据集ID** | [extraordinarylab/conll2003](https://modelscope.cn/datasets/extraordinarylab/conll2003/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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| **默认样本数** | 5-shot |
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| **评估集划分** | `test` |
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| **训练集划分** | `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 | 3,453 |
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| 提示词长度（平均） | 2733.98 字符 |
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| 提示词长度（最小/最大） | 2663 / 3275 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": "c8d2b130",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nEU rejects German call to boycott British lamb .\n\nOutput:\n<response><organization>EU</organization> rejects <miscellaneous>German</miscellaneous> call to boycott <miscellaneous>Briti ... [TRUNCATED] ... 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:\nSOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRISE DEFEAT .\n"
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}
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],
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"target": "<response>SOCCER - <location>JAPAN</location> GET LUCKY WIN , <person>CHINA</person> IN SURPRISE DEFEAT .</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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"SOCCER",
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"-",
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"JAPAN",
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"GET",
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"LUCKY",
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"WIN",
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",",
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"CHINA",
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"IN",
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"SURPRISE",
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"DEFEAT",
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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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"B-LOC",
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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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"O",
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"O",
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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 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 conll2003 \
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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=['conll2003'],
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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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``` |