212 lines
5.6 KiB
Markdown
212 lines
5.6 KiB
Markdown
# BC5CDR
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## 概述
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BC5CDR 语料库是一个为 BioCreative V 挑战赛开发的手动标注资源，包含 1,500 篇 PubMed 文章，其中包含超过 4,400 个化学物质提及、5,800 个疾病提及以及 3,100 个化学-疾病相互作用。
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## 任务描述
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- **任务类型**：生物医学命名实体识别（NER）
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- **输入**：PubMed 文章文本
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- **输出**：识别出的化学物质和疾病实体范围
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- **领域**：药理学、医学信息学、毒理学
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## 主要特点
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- 1,500 篇带有专家标注的 PubMed 文章
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- 4,400+ 个化学物质提及
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- 5,800+ 个疾病提及
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- 3,100+ 个化学-疾病相互作用
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- 来自 BioCreative V 挑战赛的基准数据集
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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：CHEMICAL（化学物质）、DISEASE（疾病）
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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** | `bc5cdr` |
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| **数据集ID** | [extraordinarylab/bc5cdr](https://modelscope.cn/datasets/extraordinarylab/bc5cdr/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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| **评估集** | `test` |
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| **训练集** | `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 | 4,797 |
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| 提示词长度（平均） | 3598.18 字符 |
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| 提示词长度（最小/最大） | 3455 / 4087 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": "154a621d",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nSelegiline - induced postural hypotension in Parkinson ' s disease : a longitudinal study on the effects of drug withdrawal .\n\nOutput:\n<response><chemical>Selegiline</chemical> - ind ... [TRUNCATED] ... e.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nTorsade de pointes ventricular tachycardia during low dose intermittent dobutamine treatment in a patient with dilated cardiomyopathy and congestive heart failure .\n"
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}
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],
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"target": "<response><disease>Torsade de pointes ventricular tachycardia</disease> during low dose intermittent <chemical>dobutamine</chemical> treatment in a patient with <disease>dilated cardiomyopathy</disease> and <disease>congestive heart failure</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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"Torsade",
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"de",
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"pointes",
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"ventricular",
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"tachycardia",
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"during",
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"low",
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"dose",
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"intermittent",
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"dobutamine",
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"treatment",
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"in",
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"a",
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"patient",
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"with",
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"dilated",
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"cardiomyopathy",
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"and",
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"congestive",
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"heart",
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"failure",
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"."
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],
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"ner_tags": [
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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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"O",
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"O",
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"O",
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"B-CHEMICAL",
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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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"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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]
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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 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 bc5cdr \
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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=['bc5cdr'],
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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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``` |