metadata

annotations_creators: []
language_creators:
  - crowdsourced
  - expert-generated
language:
  - code
license:
  - apache-2.0
multilinguality:
  - multilingual
size_categories:
  - unknown
source_datasets: []
task_categories:
  - text-generation
task_ids:
  - language-modeling
pretty_name: HumanEval-X

HumanEval-X

Dataset Description

HumanEval-X is a benchmark for evaluating the multilingual ability of code generative models. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation.

Languages

The dataset contains coding problems in 5 programming languages: Python, C++, Java, JavaScript, and Go.

Dataset Structure

To load the dataset you need to specify a subset among the 5 exiting languages [python, cpp, go, java, js]. By default python is loaded.

from datasets import load_dataset
load_dataset("THUDM/humaneval-x", "js")

DatasetDict({
    test: Dataset({
        features: ['task_id', 'prompt', 'declaration', 'canonical_solution', 'test', 'example_test'],
        num_rows: 164
    })
})

next(iter(data["test"]))
{'task_id': 'JavaScript/0',
 'prompt': '/* Check if in given list of numbers, are any two numbers closer to each other than\n  given threshold.\n  >>> hasCloseElements([1.0, 2.0, 3.0], 0.5)\n  false\n  >>> hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n  true\n  */\nconst hasCloseElements = (numbers, threshold) => {\n',
 'declaration': '\nconst hasCloseElements = (numbers, threshold) => {\n',
 'canonical_solution': '  for (let i = 0; i < numbers.length; i++) {\n    for (let j = 0; j < numbers.length; j++) {\n      if (i != j) {\n        let distance = Math.abs(numbers[i] - numbers[j]);\n        if (distance < threshold) {\n          return true;\n        }\n      }\n    }\n  }\n  return false;\n}\n\n',
 'test': 'const testHasCloseElements = () => {\n  console.assert(hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) === true)\n  console.assert(\n    hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) === false\n  )\n  console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) === true)\n  console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) === false)\n  console.assert(hasCloseElements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) === true)\n  console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) === true)\n  console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) === false)\n}\n\ntestHasCloseElements()\n',
 'example_test': 'const testHasCloseElements = () => {\n  console.assert(hasCloseElements([1.0, 2.0, 3.0], 0.5) === false)\n  console.assert(\n    hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) === true\n  )\n}\ntestHasCloseElements()\n'}

Data Fields

task_id: indicates the target language and ID of the problem. Language is one of ["Python", "Java", "JavaScript", "CPP", "Go"].
prompt: the function declaration and docstring, used for code generation.
declaration: only the function declaration, used for code translation.
canonical_solution: human-crafted example solutions.
test: hidden test samples, used for evaluation.
example_test: public test samples (appeared in prompt), used for evaluation.

Data Splits

Each subset has one split: test.

Citation Information

Refer to https://github.com/THUDM/CodeGeeX.