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import json |
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import datasets |
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from pathlib import Path |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@misc{multipl-e, |
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doi = {10.48550/ARXIV.2208.08227}, |
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url = {https://arxiv.org/abs/2208.08227}, |
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author = {Cassano, Federico and Gouwar, John and Nguyen, Daniel and |
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Nguyen, Sydney and Phipps-Costin, Luna and Pinckney, Donald and |
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Yee, Ming-Ho and Zi, Yangtian and Anderson, Carolyn Jane and |
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Feldman, Molly Q and Guha, Arjun and |
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Greenberg, Michael and Jangda, Abhinav}, |
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title = {A Scalable and Extensible Approach to Benchmarking NL2Code for 18 |
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Programming Languages}, |
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publisher = {arXiv}, |
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year = {2022}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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MultiPL-E is a dataset for evaluating large language models for code \ |
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generation that supports 18 programming languages. It takes the OpenAI \ |
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"HumanEval" and the MBPP Python benchmarks and uses little compilers to \ |
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translate them to other languages. It is easy to add support for new languages \ |
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and benchmarks. |
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""" |
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_SRCDATA = [ "humaneval", "mbpp" ] |
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_LANGUAGES = [ |
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"cpp", "cs", "d", "go", "java", "jl", "js", "lua", "php", "pl", "py", "r", |
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"rb", "rkt", "rs", "scala", "sh", "swift", "ts" |
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] |
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_VARIATIONS = [ "keep", "transform", "reworded", "remove" ] |
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class MultiPLEBuilderConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MultiPLEBuilderConfig.""" |
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def __init__( |
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self, |
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srcdata, |
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language, |
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variation, |
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**kwargs, |
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): |
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self.language = language |
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self.variation = variation |
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self.srcdata = srcdata |
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name = f"{srcdata}-{language}" |
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if variation != "reworded": |
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name = f"{name}-{variation}" |
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kwargs["name"] = name |
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super(MultiPLEBuilderConfig, self).__init__(**kwargs) |
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def _is_interesting(srcdata: str, variation: str): |
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if srcdata == "humaneval": |
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return True |
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if srcdata == "mbpp": |
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return variation in [ "keep", "reworded" ] |
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class MultiPLE(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIG_CLASS = MultiPLEBuilderConfig |
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BUILDER_CONFIGS = [ |
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MultiPLEBuilderConfig( |
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srcdata=srcdata, |
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language=language, |
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variation=variation, |
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version=datasets.Version("2.0.0")) |
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for srcdata in _SRCDATA |
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for language in _LANGUAGES |
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for variation in _VARIATIONS |
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if _is_interesting(srcdata, variation) |
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] |
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DEFAULT_CONFIG_NAME = "humaneval-cpp" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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license="MIT", |
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features=datasets.Features({ |
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"name": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"prompt": datasets.Value("string"), |
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"doctests": datasets.Value("string"), |
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"original": datasets.Value("string"), |
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"prompt_terminology": datasets.Value("string"), |
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"tests": datasets.Value("string"), |
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"stop_tokens": datasets.features.Sequence(datasets.Value("string")), |
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}), |
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supervised_keys=None, |
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homepage="https://nuprl.github.io/MultiPL-E/", |
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citation=_CITATION, |
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task_templates=[] |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager): |
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files = dl_manager.download( |
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f"https://raw.githubusercontent.com/nuprl/MultiPL-E/8b88bd55ca97b48fe999e2eca49c2bbd0000daf8/prompts/{self.config.srcdata}-{self.config.language}-{self.config.variation}.json" |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": files, |
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} |
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) |
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] |
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def _generate_examples(self, filepath): |
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with open(filepath, encoding="utf-8") as f: |
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data = json.load(f) |
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for id_, row in enumerate(data): |
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yield id_, row |
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