conala / conala.py
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""CoNaLa dataset."""
import json
import datasets
_CITATION = """\
@inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
pages={476--486},
year={2018},
organization={IEEE}
}
"""
_DESCRIPTION = """\
CoNaLa is a dataset of code and natural language pairs crawled from Stack Overflow, for more details please refer to this paper: https://arxiv.org/pdf/1805.08949.pdf or the dataset page https://conala-corpus.github.io/.
"""
_HOMEPAGE = "https://conala-corpus.github.io/"
_URLs = {
"mined": "data/conala-mined.json",
"curated": {"train": "data/conala-paired-train.json", "test": "data/conala-paired-test.json" },
}
class Conala(datasets.GeneratorBasedBuilder):
"""CoNaLa Code dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="curated",
version=datasets.Version("1.1.0"),
description=_DESCRIPTION,
),
datasets.BuilderConfig(name="mined", version=datasets.Version("1.1.0"), description=_DESCRIPTION),
]
DEFAULT_CONFIG_NAME = "curated"
def _info(self):
if self.config.name == "curated":
features=datasets.Features({"question_id": datasets.Value("int64"),
"intent": datasets.Value("string"),
"rewritten_intent": datasets.Value("string"),
"snippet": datasets.Value("string"),
})
else:
features=datasets.Features({"question_id": datasets.Value("int64"),
"parent_answer_post_id": datasets.Value("int64"),
"prob": datasets.Value("float64"),
"snippet": datasets.Value("string"),
"intent": datasets.Value("string"),
"id": datasets.Value("string"),
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
citation=_CITATION,
homepage=_HOMEPAGE)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
config_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(config_urls)
if self.config.name == "curated":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_dir["train"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_dir["test"], "split": "test"},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_dir, "split": "train"},
),
]
def _generate_examples(self, filepath, split):
key = 0
for line in open(filepath, encoding="utf-8"):
line = json.loads(line)
yield key, line
key += 1