116 lines
3.4 KiB
Markdown
116 lines
3.4 KiB
Markdown
# Med-MCQA
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## 概述
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MedMCQA 是一个大规模的多项选择题问答数据集，旨在解决真实世界中的医学入学考试题目。该数据集包含超过 19.4 万道题目，涵盖印度医学入学考试（AIIMS、NEET-PG）中的各类医学主题。
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## 任务描述
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- **任务类型**：医学知识多项选择题问答（MCQA）
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- **输入**：一道包含 4 个选项的医学问题
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- **输出**：正确答案对应的字母
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- **领域**：临床医学、基础科学、医疗保健
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## 主要特点
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- 超过 194,000 道医学考试题目
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- 来自 AIIMS 和 NEET-PG 考试的真实题目
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- 覆盖 21 个医学科目（解剖学、药理学、病理学等）
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- 专家验证的正确答案及解析
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- 考察医学知识理解与临床推理能力
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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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- 在验证集（validation split）上进行评估
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- 使用简单准确率（accuracy）作为评估指标
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- 提供训练集（train split），可用于少样本（few-shot）学习
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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** | `med_mcqa` |
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| **数据集 ID** | [extraordinarylab/medmcqa](https://modelscope.cn/datasets/extraordinarylab/medmcqa/summary) |
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| **论文** | N/A |
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| **标签** | `Knowledge`, `MCQ` |
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| **指标** | `acc` |
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| **默认样本数** | 0-shot |
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| **评估集** | `validation` |
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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 | 4,183 |
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| 提示词长度（平均） | 374.31 字符 |
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| 提示词长度（最小/最大） | 232 / 1004 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": "9bf68985",
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"content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nWhich of the following is not true for myelinated ner ... [TRUNCATED] ... ugh myelinated fibers is slower than non-myelinated fibers\nB) Membrane currents are generated at nodes of Ranvier\nC) Saltatory conduction of impulses is seen\nD) Local anesthesia is effective only when the nerve is not covered by myelin sheath"
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}
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],
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"choices": [
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"Impulse through myelinated fibers is slower than non-myelinated fibers",
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"Membrane currents are generated at nodes of Ranvier",
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"Saltatory conduction of impulses is seen",
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"Local anesthesia is effective only when the nerve is not covered by myelin sheath"
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],
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"target": "A",
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"id": 0,
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"group_id": 0,
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"metadata": {}
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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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Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.
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{question}
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{choices}
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```
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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 med_mcqa \
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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=['med_mcqa'],
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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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``` |