Datasets:

neulab
/

tldr

	# 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 = """\
	@article{zhou2022doccoder,
	title={DocCoder: Generating Code by Retrieving and Reading Docs},
	author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and JIang, Zhengbao and Neubig, Graham},
	journal={arXiv preprint arXiv:2207.05987},
	year={2022}
	}
	"""

	_DESCRIPTION = """This is the re-split of CoNaLa dataset. For each code snippet in the dev and test set, at least one function is held out from the training set. This split aims at testing a code generation model's capacity in generating unseen functions.
	We further make sure that examples from the same StackOverflow post (same question_id before -) are in the same split."""

	_HOMEPAGE = "https://github.com/shuyanzhou/docprompting"
	_URLs = {
	"docs": "tldr-docs.jsonl",
	"data": {"train": "tldr-train.jsonl", "validation": "tldr-dev.jsonl", "test": "tldr-test.jsonl" },
	}

	class DocPromptingConala(datasets.GeneratorBasedBuilder):
	"""TLDR natural language to bash generation dataset."""

	VERSION = datasets.Version("1.1.0")


	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="data",
	version=datasets.Version("1.1.0"),
	description=_DESCRIPTION,
	),
	datasets.BuilderConfig(name="docs", version=datasets.Version("1.1.0"), description=_DESCRIPTION),
	]

	DEFAULT_CONFIG_NAME = "data"


	def _info(self):
	if self.config.name == "data":
	features=datasets.Features({"question_id": datasets.Value("string"),
	"nl": datasets.Value("string"),
	"cmd": datasets.Value("string"),
	"oracle_man": datasets.Sequence(feature=datasets.Value("string")),
	"cmd_name": datasets.Value("string"),
	"tldr_cmd_name": datasets.Value("string"),
	"manual_exist": datasets.Value("bool"),
	"matching_info": datasets.Sequence(
	{
	'token': datasets.Value("string"),
	'oracle_man': datasets.Sequence(feature=datasets.Value("string"))
	}
	)
	})
	else:
	features=datasets.Features({"doc_id": datasets.Value("string"),
	"doc_content": 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 == "data":
	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"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": data_dir["validation"], "split": "validation"},
	),
	]
	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