203 lines
5.3 KiB
Markdown
203 lines
5.3 KiB
Markdown
# NCBI
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## 概述
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NCBI 疾病语料库是一个人工标注的 PubMed 摘要资源，专为疾病名称识别与标准化而设计。它为评估疾病命名实体识别（NER）系统提供了黄金标准。
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## 任务描述
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- **任务类型**：疾病命名实体识别（Disease Named Entity Recognition, NER）
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- **输入**：PubMed 摘要文本
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- **输出**：识别出的疾病实体范围（spans）
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- **领域**：医学信息学、临床自然语言处理（Clinical NLP）
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## 主要特性
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- 人工标注的疾病提及
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- 基于 PubMed 摘要的语料库
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- 支持疾病名称标准化
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- 疾病 NER 评估的黄金标准
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- 高质量的专家标注
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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：DISEASE（包括疾病、障碍、综合征等）
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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** | `ncbi` |
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| **数据集 ID** | [extraordinarylab/ncbi](https://modelscope.cn/datasets/extraordinarylab/ncbi/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 | 940 |
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| 提示词长度（平均） | 2646.25 字符 |
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| 提示词长度（最小/最大） | 2502 / 2988 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": "e87ce89c",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nIdentification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor .\n\nOutput:\n<response>Identification of APC2 , a homologue of the <disease>adenomatous polypos ... [TRUNCATED] ... If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nClustering of missense mutations in the ataxia - telangiectasia gene in a sporadic T - cell leukaemia .\n"
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}
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],
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"target": "<response>Clustering of missense mutations in the <disease>ataxia - telangiectasia</disease> gene in a <disease>sporadic T - cell leukaemia</disease> .</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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"Clustering",
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"of",
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"missense",
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"mutations",
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"in",
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"the",
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"ataxia",
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"-",
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"telangiectasia",
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"gene",
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"in",
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"a",
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"sporadic",
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"T",
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"-",
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"cell",
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"leukaemia",
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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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"B-DISEASE",
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"I-DISEASE",
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"I-DISEASE",
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"O",
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"O",
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"O",
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"B-DISEASE",
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"I-DISEASE",
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"I-DISEASE",
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"I-DISEASE",
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"I-DISEASE",
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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 ncbi \
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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=['ncbi'],
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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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``` |