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
dataset_info:
- config_name: cpp
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: declaration
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: example_test
dtype: string
splits:
- name: test
num_bytes: 276141
num_examples: 164
download_size: 101881
dataset_size: 276141
- config_name: go
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: declaration
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: example_test
dtype: string
splits:
- name: test
num_bytes: 250979
num_examples: 164
download_size: 102355
dataset_size: 250979
- config_name: java
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: declaration
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: example_test
dtype: string
splits:
- name: test
num_bytes: 376189
num_examples: 164
download_size: 111925
dataset_size: 376189
- config_name: js
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: declaration
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: example_test
dtype: string
splits:
- name: test
num_bytes: 249916
num_examples: 164
download_size: 102493
dataset_size: 249916
- config_name: python
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: declaration
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: example_test
dtype: string
splits:
- name: test
num_bytes: 249240
num_examples: 164
download_size: 101470
dataset_size: 249240
configs:
- config_name: cpp
data_files:
- split: test
path: cpp/test-*
- config_name: go
data_files:
- split: test
path: go/test-*
- config_name: java
data_files:
- split: test
path: java/test-*
- config_name: js
data_files:
- split: test
path: js/test-*
- config_name: python
data_files:
- split: test
path: python/test-*
default: true
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.