130 lines
4.7 KiB
Markdown
130 lines
4.7 KiB
Markdown
# HumanEval
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## 概述
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HumanEval 是一个用于评估语言模型代码生成能力的基准测试。它包含 164 个手工编写的 Python 编程问题，每个问题都提供了函数签名、文档字符串（docstring）和全面的测试用例。
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## 任务描述
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- **任务类型**：代码生成（Python）
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- **输入**：带有文档字符串的函数签名，描述预期行为
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- **输出**：完整的 Python 函数实现
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- **语言**：仅限 Python
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## 主要特性
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- 164 个精心设计的编程问题
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- 每个问题均包含函数签名、文档字符串和测试用例
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- 问题难度从简单的字符串操作到复杂的算法不等
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- 提供标准参考解答
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- 通过执行测试用例自动验证正确性
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## 评估说明
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- **安全警告**：默认情况下，代码会在本地环境中执行。我们强烈建议使用沙箱（sandbox）执行以确保安全。详情请参阅 [沙箱文档](https://evalscope.readthedocs.io/zh-cn/latest/user_guides/sandbox.html)。
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- 支持 `pass@k` 指标计算，用于衡量生成质量
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- 每个问题默认超时时间为 4 秒
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- 若存在 Markdown 代码块，则从中提取代码
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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** | `humaneval` |
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| **数据集ID** | [opencompass/humaneval](https://modelscope.cn/datasets/opencompass/humaneval/summary) |
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| **论文** | N/A |
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| **标签** | `Coding` |
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| **指标** | `acc` |
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| **默认示例数（Shots）** | 0-shot |
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| **评估划分** | `test` |
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| **聚合方式** | `mean_and_pass_at_k` |
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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 | 164 |
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| 提示词长度（平均） | 609.6 字符 |
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| 提示词长度（最小/最大） | 274 / 1519 字符 |
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## 样例示例
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**子集**: `openai_humaneval`
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```json
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{
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"input": [
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{
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"id": "5f652252",
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"content": "Read the following function signature and docstring, and fully implement the function described. Your response should only contain the code for this function.\nfrom typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: f ... [TRUNCATED] ... Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n"
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}
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],
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"target": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"task_id": "HumanEval/0",
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"entry_point": "has_close_elements",
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"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
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"test": "\n\nMETADATA = {\n 'author': 'jt',
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'dataset': 'test'\n}\n\n\ndef check(candidate):\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert candidate([1.0 ... [TRUNCATED] ... candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n"
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}
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}
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```
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*注：部分内容因显示需要已被截断。*
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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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Read the following function signature and docstring, and fully implement the function described. Your response should only contain the code for this function.
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{question}
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```
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## 沙箱配置
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此基准测试需要沙箱环境来执行代码。
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```json
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{
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"image": "python:3.11-slim",
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"tools_config": {
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"shell_executor": {},
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"python_executor": {}
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}
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}
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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 humaneval \
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--sandbox '{"enabled": true}' \
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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=['humaneval'],
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sandbox={'enabled': True},
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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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``` |