Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Annotations Creators:
machine-generated
License:
| import json | |
| import datasets | |
| from pathlib import Path | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @article{cassano:multipl-e, | |
| author = {Cassano, Federico and Gouwar, John and Nguyen, Daniel and Nguyen, Sydney and | |
| Phipps-Costin, Luna and Pinckney, Donald and Yee, Ming-Ho and Zi, Yangtian and | |
| Anderson, Carolyn Jane and Feldman, Molly Q and Guha, Arjun and | |
| Greenberg, Michael and Jangda, Abhinav}, | |
| title = {{MultiPL-E}: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation}, | |
| journal = "{IEEE} Transactions of Software Engineering (TSE)", | |
| year = 2023 | |
| }""" | |
| _DESCRIPTION = """\ | |
| MultiPL-E is a dataset for evaluating large language models for code \ | |
| generation that supports 18 programming languages. It takes the OpenAI \ | |
| "HumanEval" and the MBPP Python benchmarks and uses little compilers to \ | |
| translate them to other languages. It is easy to add support for new languages \ | |
| and benchmarks. | |
| """ | |
| _SRCDATA = [ "humaneval", "mbpp" ] | |
| _LANGUAGES = [ | |
| "cpp", "cs", "d", "go", "java", "jl", "js", "lua", "php", "pl", "py", "r", | |
| "rb", "rkt", "rs", "scala", "sh", "swift", "ts" | |
| ] | |
| _VARIATIONS = [ "keep", "transform", "reworded", "remove" ] | |
| class MultiPLEBuilderConfig(datasets.BuilderConfig): | |
| """BuilderConfig for MultiPLEBuilderConfig.""" | |
| def __init__( | |
| self, | |
| srcdata, | |
| language, | |
| variation, | |
| **kwargs, | |
| ): | |
| self.language = language | |
| self.variation = variation | |
| self.srcdata = srcdata | |
| name = f"{srcdata}-{language}" | |
| if variation != "reworded": | |
| name = f"{name}-{variation}" | |
| kwargs["name"] = name | |
| super(MultiPLEBuilderConfig, self).__init__(**kwargs) | |
| def _is_interesting(srcdata: str, variation: str): | |
| if srcdata == "humaneval": | |
| return True | |
| if srcdata == "mbpp": | |
| # MBPP does not have doctests, so these are the only interesting | |
| # variations | |
| return variation in [ "keep", "reworded" ] | |
| class MultiPLE(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIG_CLASS = MultiPLEBuilderConfig | |
| BUILDER_CONFIGS = [ | |
| MultiPLEBuilderConfig( | |
| srcdata=srcdata, | |
| language=language, | |
| variation=variation, | |
| version=datasets.Version("2.1.0")) | |
| for srcdata in _SRCDATA | |
| for language in _LANGUAGES | |
| for variation in _VARIATIONS | |
| if _is_interesting(srcdata, variation) | |
| ] | |
| DEFAULT_CONFIG_NAME = "humaneval-cpp" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| license="MIT", | |
| features=datasets.Features({ | |
| "name": datasets.Value("string"), | |
| "language": datasets.Value("string"), | |
| "prompt": datasets.Value("string"), | |
| "doctests": datasets.Value("string"), | |
| "original": datasets.Value("string"), | |
| "prompt_terminology": datasets.Value("string"), | |
| "tests": datasets.Value("string"), | |
| "stop_tokens": datasets.features.Sequence(datasets.Value("string")), | |
| }), | |
| supervised_keys=None, | |
| homepage="https://nuprl.github.io/MultiPL-E/", | |
| citation=_CITATION, | |
| task_templates=[] | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager): | |
| files = dl_manager.download( | |
| f"https://raw.githubusercontent.com/nuprl/MultiPL-E/11b407bd2dd98c8204afea4d20043faf2145c20c/prompts/{self.config.srcdata}-{self.config.language}-{self.config.variation}.json" | |
| ) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": files, | |
| } | |
| ) | |
| ] | |
| def _generate_examples(self, filepath): | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for id_, row in enumerate(data): | |
| yield id_, row | |