221 lines
6.1 KiB
Markdown
221 lines
6.1 KiB
Markdown
# CrossNER
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## 概述
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CrossNER 是一个完全标注的命名实体识别（NER）数据集，涵盖五个不同领域：人工智能（AI）、文学（Literature）、音乐（Music）、政治（Politics）和科学（Science）。该数据集支持跨领域 NER 评估和领域自适应研究。
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## 任务描述
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- **任务类型**：跨领域命名实体识别（NER）
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- **输入**：来自五个专业领域的文本
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- **输出**：领域特定的实体片段
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- **领域**：AI、文学、音乐、政治、科学
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## 主要特点
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- 包含五个多样化的领域子集
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- 每个子集具有领域特定的实体类型
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- 支持跨领域迁移能力评估
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- 由专家完全标注
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- 适用于领域自适应研究
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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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- 子集：ai、literature、music、politics、science
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- 实体类型因领域子集而异
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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** | `cross_ner` |
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| **数据集 ID** | [extraordinarylab/cross-ner](https://modelscope.cn/datasets/extraordinarylab/cross-ner/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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| **默认样本数** | 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 | 2,506 |
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| 提示词长度（平均） | 5687.97 字符 |
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| 提示词长度（最小/最大） | 5407 / 6007 字符 |
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**各子集统计数据：**
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| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |
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|--------|---------|-------------|------------|------------|
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| `ai` | 431 | 5562.3 | 5407 | 5878 |
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| `literature` | 416 | 5725.13 | 5566 | 6007 |
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| `music` | 465 | 5737.0 | 5570 | 5962 |
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| `politics` | 651 | 5701.8 | 5527 | 5935 |
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| `science` | 543 | 5700.71 | 5548 | 5995 |
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## 样例示例
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**子集**: `ai`
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```json
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{
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"input": [
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{
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"id": "3a78cbcf",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nPopular approaches of opinion-based recommender system utilize various techniques including text mining , information retrieval , sentiment analysis ( see also Multimodal sentiment a ... [TRUNCATED] ... the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nTypical generative model approaches include naive Bayes classifier s , Gaussian mixture model s , variational autoencoders and others .\n"
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}
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],
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"target": "<response>Typical generative model approaches include <algorithm>naive Bayes classifier</algorithm> s , <algorithm>Gaussian mixture model</algorithm> s , <algorithm>variational autoencoders</algorithm> and others .</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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"Typical",
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"generative",
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"model",
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"approaches",
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"include",
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"naive",
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"Bayes",
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"classifier",
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"s",
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",",
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"Gaussian",
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"mixture",
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"model",
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"s",
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",",
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"variational",
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"autoencoders",
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"and",
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"others",
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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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"B-ALGORITHM",
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"I-ALGORITHM",
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"I-ALGORITHM",
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"O",
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"O",
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"B-ALGORITHM",
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"I-ALGORITHM",
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"I-ALGORITHM",
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"O",
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"O",
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"B-ALGORITHM",
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"I-ALGORITHM",
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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：部分内容为显示目的已截断。*
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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 cross_ner \
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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=['cross_ner'],
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dataset_args={
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'cross_ner': {
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# subset_list: ['ai', 'literature', 'music'] # 可选，用于评估特定子集
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}
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},
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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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``` |