README.md

metadata

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

Dataset Card for HumanEvalPack

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
• Additional Information
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Repository: https://github.com/bigcode-project/octopack
• Paper: WIP
• Point of Contact: Niklas Muennighoff

Dataset Summary

HumanEvalPack is ...

• Languages: Python, JavaScript, Java, Go, C++, Rust
• OctoPack🐙🎒:

Data	CommitPack	4TB of GitHub commits across 350 programming languages
Data	CommitPackFT	Filtered version of CommitPack for high-quality commit messages that resemble instructions
Model	OctoCoder	StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST
Evaluation	HumanEvalPack	Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages

Dataset Structure

Data Instances

An example looks as follows:

{
  "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