---
license: apache-2.0
pretty_name: HumanEvalPack
language:
- code
---

![Octopack](https://github.com/bigcode-project/octopack/blob/31f3320f098703c7910e43492c39366eeea68d83/banner.png?raw=true)

# Dataset Card for HumanEvalPack

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Repository:** https://github.com/bigcode-project/octopack
- **Paper:** WIP
- **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com)

### Dataset Summary

> HumanEvalPack is ...
> 
- **Languages:** Python, JavaScript, Java, Go, C++, Rust
- **OctoPack🐙🎒:**

<table>
<tr>
<th>Data</t> 
<td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th>Data</t> 
<td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t> 
<td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation</t> 
<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>

## Dataset Structure


### Data Instances


An example looks as follows:

```json
{
  "task_id": "Python/0",
  "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",
  "declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
  "canonical_solution": "    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",
  "buggy_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = elem - elem2\n                if distance < threshold:\n                    return True\n\n    return False\n",
  "bug_type": "missing logic",
  "failure_symptoms": "incorrect output",
  "entry_point": "has_close_elements",
  "import": ""
  "test_setup": ""
  "test": "\n\n\n\n\ndef check(has_close_elements):\n    assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n    assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n    assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n    assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)",
  "example_test": "def check(has_close_elements):\n    assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n    assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\ncheck(has_close_elements)\n",
  "signature": "has_close_elements(numbers: List[float], threshold: float) -> bool",
  "docstring": "Check if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue",
  "instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following problem:\nCheck if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue"
}
```

### Data Fields

The data fields are the same among all splits:
- `task_id`: task id (from 0 to 163)
- `prompt`: the prompt for models relying on code continuation
- `declaration`: the declaration of the function (same as prompt but without the docstring)
- `canonical_solution`: the correct solution passing all unit tests for the problem
- `buggy_solution`: same as `canonical_solution` but with a subtle human-written bug causing the unit tests to fail
- `bug_type`: the type of the bug in `buggy_solution` (one of [`missing logic`, `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`])
- `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`])
- `entry_point`: the name of the function
- 'import': imports necessary for the solution (only present for Go)
- 'test_setup': imports necessary for the test execution (only present for Go)
- `test`: the unit tests for the problem
- `example_test`: additional unit tests different from `test` that could be e.g. provided to the model (these are not used in the paper)
- `signature`: the signature of the function
- `docstring`: the docstring describing the problem
- `instruction`: an instruction for HumanEvalSynthesize in the form `Write a {language_name} function {signature} to solve the following problem:\n{docstring}`

### Data Splits

## Additional Information

### Licensing Information

Each sample has comes from a code repository with a permissive license. The license is provided by the `license` field for each sample.

### Citation Information

```bibtex
```