Datasets:
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import json
import datasets
from pathlib import Path
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@misc{multipl-e,
doi = {10.48550/ARXIV.2208.08227},
url = {https://arxiv.org/abs/2208.08227},
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 = {A Scalable and Extensible Approach to Benchmarking NL2Code for 18
Programming Languages},
publisher = {arXiv},
year = {2022},
}
"""
_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("2.0.0"))
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/8b88bd55ca97b48fe999e2eca49c2bbd0000daf8/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
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