134 lines
4.8 KiB
Markdown
134 lines
4.8 KiB
Markdown
# HumanEval
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## Overview
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HumanEval is a benchmark for evaluating the code generation capabilities of language models. It consists of 164 hand-written Python programming problems with function signatures, docstrings, and comprehensive test cases.
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## Task Description
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- **Task Type**: Code Generation (Python)
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- **Input**: Function signature with docstring describing the expected behavior
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- **Output**: Complete Python function implementation
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- **Languages**: Python only
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## Key Features
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- 164 hand-crafted programming problems
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- Each problem includes a function signature, docstring, and test cases
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- Problems range from simple string manipulation to complex algorithms
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- Canonical solutions provided for reference
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- Automatic correctness verification through test execution
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## Evaluation Notes
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- **Security Warning**: By default, code is executed in the local environment. We strongly recommend using sandbox execution for safety. See the [sandbox documentation](https://evalscope.readthedocs.io/en/latest/user_guides/sandbox.html) for details.
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- Supports `pass@k` metric calculation for measuring generation quality
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- Default timeout is 4 seconds per problem
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- Code is extracted from markdown code blocks if present
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `humaneval` |
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| **Dataset ID** | [opencompass/humaneval](https://modelscope.cn/datasets/opencompass/humaneval/summary) |
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| **Paper** | N/A |
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| **Tags** | `Coding` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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| **Aggregation** | `mean_and_pass_at_k` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 164 |
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| Prompt Length (Mean) | 609.6 chars |
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| Prompt Length (Min/Max) | 274 / 1519 chars |
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## Sample Example
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**Subset**: `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',\n '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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*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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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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## Sandbox Configuration
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This benchmark requires a sandbox environment for code execution.
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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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## 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 humaneval \
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--sandbox '{"enabled": true}' \
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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=['humaneval'],
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sandbox={'enabled': True},
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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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