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# RACE
## 概述
RACEReAding Comprehension from Examinations是一个大规模阅读理解基准数据集收集自中国初中和高中英语考试题目。该数据集用于测试综合阅读理解能力。
## 任务描述
- **任务类型**:阅读理解(多项选择题)
- **输入**文章段落、问题及4个选项
- **输出**正确答案字母A、B、C 或 D
- **难度级别**:初中和高中
## 主要特点
- 包含28,000+篇文章和100,000道问题
- 使用真实考试题目,确保难度的真实性
- 包含两个子集:`middle`(较简单)和 `high`(较难)
- 考察多种阅读理解技能(推理、词汇、主旨等)
- 文章主题和题型丰富多样
## 评估说明
- 默认配置使用 **3-shot** 示例
- 最大 few-shot 数量为3受上下文长度限制
- 使用思维链Chain-of-Thought, CoT提示方法
- 提供两个子集:`high``middle`
- 在测试集test split上进行评估
## 属性
| 属性 | 值 |
|----------|-------|
| **基准测试名称** | `race` |
| **数据集ID** | [evalscope/race](https://modelscope.cn/datasets/evalscope/race/summary) |
| **论文** | N/A |
| **标签** | `MCQ`, `Reasoning` |
| **指标** | `acc` |
| **默认示例数** | 3-shot |
| **评估划分** | `test` |
| **训练划分** | `train` |
## 数据统计
| 指标 | 值 |
|--------|-------|
| 总样本数 | 4,934 |
| 提示词长度(平均) | 7217.34 字符 |
| 提示词长度(最小/最大) | 3685 / 11131 字符 |
**各子集统计数据:**
| 子集 | 样本数 | 提示平均长度 | 提示最小长度 | 提示最大长度 |
|--------|---------|-------------|------------|------------|
| `high` | 3,498 | 8279.47 | 6585 | 11131 |
| `middle` | 1,436 | 4630.07 | 3685 | 6032 |
## 样例示例
**子集**: `high`
```json
{
"input": [
{
"id": "706382e9",
"content": "Here are some examples of how to answer similar questions:\n\nArticle:\nLast week I talked with some of my students about what they wanted to do after they graduated, and what kind of job prospects they thought they had.\nGiven that I teach stud ... [TRUNCATED] ... I owe my life to her,\" said Nancy with tears.\nQuestion:\nWhat did Nancy try to do before she fell over?\n\nA) Measure the depth of the river\nB) Look for a fallen tree trunk\nC) Protect her cows from being drowned\nD) Run away from the flooded farm"
}
],
"choices": [
"Measure the depth of the river",
"Look for a fallen tree trunk",
"Protect her cows from being drowned",
"Run away from the flooded farm"
],
"target": "C",
"id": 0,
"group_id": 0,
"metadata": {
"example_id": "high19432.txt"
}
}
```
*注:部分内容因展示需要已被截断。*
## 提示模板
**提示模板:**
```text
Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.
{question}
{choices}
```
## 使用方法
### 使用命令行CLI
```bash
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets race \
--limit 10 # 正式评估时请删除此行
```
### 使用 Python
```python
from evalscope import run_task
from evalscope.config import TaskConfig
task_cfg = TaskConfig(
model='YOUR_MODEL',
api_url='OPENAI_API_COMPAT_URL',
api_key='EMPTY_TOKEN',
datasets=['race'],
dataset_args={
'race': {
# subset_list: ['high', 'middle'] # 可选,用于指定评估特定子集
}
},
limit=10, # 正式评估时请删除此行
)
run_task(task_cfg=task_cfg)
```