237 lines
5.9 KiB
Markdown
237 lines
5.9 KiB
Markdown
# GeniaNER
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## 概述
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GeniaNER 是一个大规模的生物医学命名实体识别（NER）数据集，包含 2,000 篇 MEDLINE 摘要，涵盖超过 40 万个单词和近 10 万个生物学术语标注。它是生物医学实体识别领域最全面的资源之一。
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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**：来自 GENIA 语料库的 MEDLINE 摘要
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- **输出**：识别出的生物实体片段
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- **领域**：分子生物学、生物信息学
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## 主要特点
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- 2,000 篇 MEDLINE 摘要
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- 超过 40 万个单词
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- 近 10 万个生物学术语标注
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- 五种分子生物学实体类型
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- 对生物分子实体的全面覆盖
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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：CELL_LINE（细胞系）、CELL_TYPE（细胞类型）、DNA、PROTEIN（蛋白质）、RNA
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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** | `genia_ner` |
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| **数据集 ID** | [extraordinarylab/genia-ner](https://modelscope.cn/datasets/extraordinarylab/genia-ner/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 | 1,854 |
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| 提示词长度（平均） | 3921.68 字符 |
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| 提示词长度（最小/最大） | 3781 / 4433 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": "3bb926a4",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nIL-2 gene expression and NF-kappa B activation through CD28 requires reactive oxygen production by 5-lipoxygenase .\n\nOutput:\n<response><dna>IL-2 gene</dna> expression and <protein>NF ... [TRUNCATED] ... a matching closing tag.\n\nText to process:\nThere is a single methionine codon-initiated open reading frame of 1,458 nt in frame with a homeobox and a CAX repeat , and the open reading frame is predicted to encode a protein of 51,659 daltons.\n"
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}
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],
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"target": "<response>There is a single <dna>methionine codon-initiated open reading frame</dna> of 1,458 nt in frame with a <dna>homeobox</dna> and a <dna>CAX repeat</dna> , and the <dna>open reading frame</dna> is predicted to encode a protein of 51,659 daltons.</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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"There",
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"is",
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"a",
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"single",
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"methionine",
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"codon-initiated",
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"open",
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"reading",
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"frame",
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"of",
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"1,458",
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"nt",
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"in",
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"frame",
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"with",
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"a",
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"homeobox",
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"and",
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"a",
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"CAX",
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"repeat",
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",",
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"and",
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"the",
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"open",
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"reading",
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"frame",
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"is",
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"predicted",
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"to",
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"encode",
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"a",
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"protein",
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"of",
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"51,659",
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"daltons."
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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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"B-DNA",
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"I-DNA",
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"I-DNA",
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"I-DNA",
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"I-DNA",
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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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"B-DNA",
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"O",
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"O",
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"B-DNA",
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"I-DNA",
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"O",
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"O",
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"O",
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"B-DNA",
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"I-DNA",
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"I-DNA",
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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 genia_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=['genia_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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run_task(task_cfg=task_cfg)
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``` |