Datasets:

neulab
/

conala

	# 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