metadata

license: apache-2.0
task_categories:
  - text-generation
  - text2text-generation
language:
  - en
tags:
  - code
pretty_name: BabelCode MBPP
size_categories:
  - 1K<n<10K

Dataset Card for BabelCode HumanEval

Dataset Description

Repository: GitHub Repository
Paper: Measuring The Impact Of Programming Language Distribution

How To Use This Dataset

To quickly evaluate BC-HumanEval predictions, save the qid and language keys along with the postprocessed prediction code in a JSON lines file. Then follow the install instructions for BabelCode, and you can evaluate your predictions.

Dataset Summary

The BabelCode-MBPP (BC-MBPP) dataset converts the MBPP dataset released by Google to 16 programming languages.

Supported Tasks and Leaderboards

Languages

BC-MBPP supports:

C++
C#
Dart
Go
Haskell
Java
Javascript
Julia
Kotlin
Lua
PHP
Python
R
Rust
Scala
TypeScript

Dataset Structure

>>> from datasets import load_dataset
>>> load_dataset("gabeorlanski/bc-humaneval")
DatasetDict({
    test: Dataset({
        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code'],
        num_rows: 2576
    })
})

Data Fields

qid: The question ID used for running tests.
title: The title of the question.
language: The programming language of the example.
text: The description of the problem.
signature: The signature for the problem.
signature_with_docstring: The signature with the adequately formatted docstring for the given problem.
arguments: The arguments of the problem.
entry_fn_name: The function's name to use an entry point.
entry_cls_name: The class name to use an entry point.
test_code: The raw testing script used in the language. If you want to use this, replace PLACEHOLDER_FN_NAME (and PLACEHOLDER_CLS_NAME if needed) with the corresponding entry points. Next, replace PLACEHOLDER_CODE_BODY with the postprocessed prediction.

Dataset Creation

See section 2 of the BabelCode Paper to learn more about how the datasets are translated.

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

None.

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Google Research

Licensing Information

CC-BY-4.0

Citation Information

@article{orlanski2023measuring,
  title={Measuring The Impact Of Programming Language Distribution},
  author={Orlanski, Gabriel and Xiao, Kefan and Garcia, Xavier and Hui, Jeffrey and Howland, Joshua and Malmaud, Jonathan and Austin, Jacob and Singh, Rishah and Catasta, Michele},
  journal={arXiv preprint arXiv:2302.01973},
  year={2023}
}
@article{Austin2021ProgramSW,
  title={Program Synthesis with Large Language Models},
  author={Jacob Austin and Augustus Odena and Maxwell Nye and Maarten Bosma and Henryk Michalewski and David Dohan and Ellen Jiang and Carrie J. Cai and Michael Terry and Quoc V. Le and Charles Sutton},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.07732}
}