198 lines
5.4 KiB
Markdown
198 lines
5.4 KiB
Markdown
# JNLPBA-Rare
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## 概述
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JNLPBA-Rare 数据集是 JNLPBA 测试集的一个专门子集，用于评估模型在最不常见的实体类型（RNA 和细胞系）上的零样本（zero-shot）性能。该数据集用于测试模型识别稀有生物医学实体的能力。
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## 任务描述
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- **任务类型**：稀有生物医学命名实体识别（NER）
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- **输入**：来自 MEDLINE 摘要的生物医学文本
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- **输出**：识别出的 RNA 和细胞系实体范围
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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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- 聚焦于稀有实体类型（RNA、细胞系）
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- 作为 JNLPBA 的子集，专用于零样本评估
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- 测试模型对低频生物医学实体的处理能力
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- 是命名实体识别任务中具有挑战性的基准
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- 适用于评估模型在长尾分布上的表现
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## 评估说明
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- 默认配置使用 **0-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：RNA、CELL_LINE
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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** | `jnlpba_rare` |
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| **数据集ID** | [extraordinarylab/jnlpba-rare](https://modelscope.cn/datasets/extraordinarylab/jnlpba-rare/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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| **默认样本数** | 0-shot |
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| **评估划分** | `test` |
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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 | 465 |
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| 提示词长度（平均） | 1111.66 字符 |
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| 提示词长度（最小/最大） | 976 / 1403 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": "11496ce5",
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"content": "You are a named entity recognition system that identifies the following entity types:\nrna (Names of RNA molecules), cell_line (Names of specific, cultured cell lines)\n\nProcess the provided text and mark all named entities with XML-style tags. ... [TRUNCATED] ... rlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nOctamer-binding proteins from B or HeLa cells stimulate transcription of the immunoglobulin heavy-chain promoter in vitro .\n"
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}
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],
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"target": "<response>Octamer-binding proteins from <cell_line>B or HeLa cells</cell_line> stimulate transcription of the immunoglobulin heavy-chain promoter in vitro .</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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"Octamer-binding",
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"proteins",
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"from",
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"B",
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"or",
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"HeLa",
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"cells",
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"stimulate",
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"transcription",
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"of",
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"the",
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"immunoglobulin",
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"heavy-chain",
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"promoter",
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"in",
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"vitro",
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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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"B-CELL_LINE",
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"I-CELL_LINE",
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"I-CELL_LINE",
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"I-CELL_LINE",
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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 jnlpba_rare \
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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=['jnlpba_rare'],
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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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``` |