137 lines
3.7 KiB
Markdown
137 lines
3.7 KiB
Markdown
# RACE
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## Overview
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RACE (ReAding Comprehension from Examinations) is a large-scale reading comprehension benchmark collected from Chinese middle school and high school English examinations. It tests comprehensive reading comprehension abilities.
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## Task Description
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- **Task Type**: Reading Comprehension (Multiple-Choice)
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- **Input**: Article passage with question and 4 answer choices
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- **Output**: Correct answer letter (A, B, C, or D)
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- **Difficulty Levels**: Middle school and High school
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## Key Features
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- 28,000+ passages with 100,000 questions
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- Real examination questions for authentic difficulty
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- Two subsets: middle (easier) and high (harder)
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- Tests various comprehension skills (inference, vocabulary, main idea, etc.)
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- Diverse article topics and question types
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## Evaluation Notes
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- Default configuration uses **3-shot** examples
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- Maximum few-shot number is 3 (context length consideration)
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- Uses Chain-of-Thought (CoT) prompting
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- Two subsets available: `high` and `middle`
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- Evaluates on test split
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `race` |
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| **Dataset ID** | [evalscope/race](https://modelscope.cn/datasets/evalscope/race/summary) |
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| **Paper** | N/A |
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| **Tags** | `MCQ`, `Reasoning` |
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| **Metrics** | `acc` |
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| **Default Shots** | 3-shot |
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| **Evaluation Split** | `test` |
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| **Train Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 4,934 |
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| Prompt Length (Mean) | 7217.34 chars |
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| Prompt Length (Min/Max) | 3685 / 11131 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `high` | 3,498 | 8279.47 | 6585 | 11131 |
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| `middle` | 1,436 | 4630.07 | 3685 | 6032 |
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## Sample Example
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**Subset**: `high`
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```json
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{
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"input": [
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{
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"id": "706382e9",
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"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"
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}
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],
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"choices": [
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"Measure the depth of the river",
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"Look for a fallen tree trunk",
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"Protect her cows from being drowned",
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"Run away from the flooded farm"
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],
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"target": "C",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"example_id": "high19432.txt"
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}
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}
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```
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*Note: Some content was truncated for display.*
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## Prompt Template
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**Prompt Template:**
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```text
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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.
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{question}
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{choices}
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```
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## Usage
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### Using 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 race \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using 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=['race'],
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dataset_args={
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'race': {
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# subset_list: ['high', 'middle'] # optional, evaluate specific subsets
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}
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},
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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