222 lines
5.6 KiB
Markdown
222 lines
5.6 KiB
Markdown
# BC4CHEMD
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## 概述
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BC4CHEMD（BioCreative IV CHEMDNER）数据集是一个包含 10,000 篇 PubMed 摘要的语料库，其中包含 84,355 个化学实体提及，均由专家手动标注，用于化学命名实体识别任务。
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## 任务描述
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- **任务类型**：化学命名实体识别（NER）
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- **输入**：来自 PubMed 摘要的科学文本
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- **输出**：识别出的化学化合物名称片段
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- **领域**：化学、药理学、药物发现
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## 主要特点
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- 10,000 篇 PubMed 摘要
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- 84,355 个化学实体提及
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- 专家手动标注
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- 来自 BioCreative IV 挑战赛的基准数据集
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- 覆盖全面的化学化合物类型
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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（化学物质和药物名称）
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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** | `bc4chemd` |
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| **数据集ID** | [extraordinarylab/bc4chemd](https://modelscope.cn/datasets/extraordinarylab/bc4chemd/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 | 26,364 |
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| 提示词长度（平均） | 3042.58 字符 |
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| 提示词长度（最小/最大） | 2879 / 3709 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": "5067e19c",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nDPP6 as a candidate gene for neuroleptic - induced tardive dyskinesia .\n\nOutput:\n<response>DPP6 as a candidate gene for neuroleptic - induced tardive dyskinesia .</response>\n\nInput:\n ... [TRUNCATED] ... ry opening tag has a matching closing tag.\n\nText to process:\nEffects of docosahexaenoic acid and methylmercury on child ' s brain development due to consumption of fish by Finnish mother during pregnancy : a probabilistic modeling approach .\n"
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}
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],
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"target": "<response>Effects of <chemical>docosahexaenoic acid</chemical> and <chemical>methylmercury</chemical> on child ' s brain development due to consumption of fish by Finnish mother during pregnancy : a probabilistic modeling approach .</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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"Effects",
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"of",
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"docosahexaenoic",
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"acid",
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"and",
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"methylmercury",
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"on",
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"child",
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"'",
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"s",
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"brain",
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"development",
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"due",
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"to",
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"consumption",
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"of",
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"fish",
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"by",
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"Finnish",
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"mother",
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"during",
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"pregnancy",
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":",
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"a",
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"probabilistic",
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"modeling",
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"approach",
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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-CHEMICAL",
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"I-CHEMICAL",
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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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"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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"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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]
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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 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 bc4chemd \
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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=['bc4chemd'],
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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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``` |