Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Source Datasets:
original
ArXiv:
License:
File size: 5,447 Bytes
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import json
import datasets
_DESCRIPTION = """\
The MBPP (Mostly Basic Python Problems) dataset consists of around 1,000 crowd-sourced Python
programming problems, designed to be solvable by entry level programmers, covering programming
fundamentals, standard library functionality, and so on. Each problem consists of a task
description, code solution and 3 automated test cases. The sanitized subset of the data has been
hand-verified by the authors.
"""
_URLs = {
"full": "https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl",
"sanitized": "https://raw.githubusercontent.com/google-research/google-research/master/mbpp/sanitized-mbpp.json",
}
_CITATION = """\
@article{austin2021program,
title={Program Synthesis with Large Language Models},
author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
journal={arXiv preprint arXiv:2108.07732},
year={2021}
}"""
_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/mbpp"
_LICENSE = "CC-BY-4.0"
class MBPP(datasets.GeneratorBasedBuilder):
"""MBPP: Mostly Basic Python Problems Dataset"""
VERSION = datasets.Version("1.0.2")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="full",
version=datasets.Version("1.0.2"),
description=_DESCRIPTION,
),
datasets.BuilderConfig(name="sanitized", version=datasets.Version("1.0.2"), description=_DESCRIPTION),
]
DEFAULT_CONFIG_NAME = "full"
def _info(self):
if self.config.name == "full":
features = datasets.Features(
{
"task_id": datasets.Value("int32"),
"text": datasets.Value("string"),
"code": datasets.Value("string"),
"test_list": datasets.Sequence(datasets.Value("string")),
"test_setup_code": datasets.Value("string"),
"challenge_test_list": datasets.Sequence(datasets.Value("string")),
}
)
elif self.config.name == "sanitized":
features = datasets.Features(
{
"source_file": datasets.Value("string"),
"task_id": datasets.Value("int32"),
"prompt": datasets.Value("string"),
"code": datasets.Value("string"),
"test_imports": datasets.Sequence(datasets.Value("string")),
"test_list": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
config_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(config_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_dir, "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_dir, "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_dir, "split": "validation"},
),
datasets.SplitGenerator(
name=datasets.Split("prompt"),
gen_kwargs={"filepath": data_dir, "split": "prompt"},
),
]
def _generate_examples(self, filepath, split):
if self.config.name == "full":
def _read_lines(fn, start, end):
data = []
with open(fn, encoding="utf-8") as f:
for line in f:
sample = json.loads(line)
if start <= sample["task_id"] <= end:
data.append(sample)
elif sample["task_id"] > end:
break
return data
if split == "test":
data = _read_lines(filepath, 11, 510)
elif split == "train":
data = _read_lines(filepath, 601, 974)
elif split == "validation":
data = _read_lines(filepath, 511, 600)
elif split == "prompt":
data = _read_lines(filepath, 1, 10)
elif self.config.name == "sanitized":
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
if split == "test":
data = [sample for sample in data if 11 <= sample["task_id"] <= 510]
elif split == "train":
data = [sample for sample in data if 601 <= sample["task_id"] <= 974]
elif split == "validation":
data = [sample for sample in data if 511 <= sample["task_id"] <= 600]
elif split == "prompt":
data = [sample for sample in data if 1 <= sample["task_id"] <= 10]
id_ = 0
for sample in data:
yield id_, sample
id_ += 1
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