Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Multilinguality:
monolingual
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 6,867 Bytes
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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.
"""Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs"""
import json
from itertools import chain
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Neural Code Search Evaluation Dataset},
authors = {Hongyu Li, Seohyun Kim and Satish Chandra},
journal = {arXiv e-prints},
year = 2018,
eid = {arXiv:1908.09804 [cs.SE]},
pages = {arXiv:1908.09804 [cs.SE]},
archivePrefix = {arXiv},
eprint = {1908.09804},
}
"""
_DESCRIPTION = """\
Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset \
consisting of natural language query and code snippet pairs and a search corpus \
consisting of code snippets collected from the most popular Android repositories \
on GitHub.
"""
_HOMEPAGE = "https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data"
_LICENSE = "CC-BY-NC 4.0 (Attr Non-Commercial Inter.)"
_BASE_URL = "https://raw.githubusercontent.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/master/data/"
_URLs = {
"evaluation_dataset": _BASE_URL + "287_android_questions.json",
"search_corpus_1": _BASE_URL + "search_corpus_1.tar.gz",
"search_corpus_2": _BASE_URL + "search_corpus_2.tar.gz",
}
class NeuralCodeSearch(datasets.GeneratorBasedBuilder):
"""Neural Code Search Evaluation Dataset"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="evaluation_dataset",
version=VERSION,
description="The evaluation dataset is composed of \
287 Stack Overflow question and answer pairs",
),
datasets.BuilderConfig(
name="search_corpus",
version=VERSION,
description="The search corpus is indexed using all \
method bodies parsed from the 24,549 GitHub repositories.",
),
]
FILENAME_MAP = {
"evaluation_dataset": "287_android_questions.json",
"search_corpus": "search_corpus_1.jsonl",
}
def _info(self):
if self.config.name == "evaluation_dataset":
features = datasets.Features(
{
"stackoverflow_id": datasets.Value("int32"),
"question": datasets.Value("string"),
"question_url": datasets.Value("string"),
"question_author": datasets.Value("string"),
"question_author_url": datasets.Value("string"),
"answer": datasets.Value("string"),
"answer_url": datasets.Value("string"),
"answer_author": datasets.Value("string"),
"answer_author_url": datasets.Value("string"),
"examples": datasets.features.Sequence(datasets.Value("int32")),
"examples_url": datasets.features.Sequence(datasets.Value("string")),
}
)
else:
features = datasets.Features(
{
"id": datasets.Value("int32"),
"filepath": datasets.Value("string"),
"method_name": datasets.Value("string"),
"start_line": datasets.Value("int32"),
"end_line": datasets.Value("int32"),
"url": 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."""
if self.config.name == "evaluation_dataset":
filepath = dl_manager.download_and_extract(_URLs[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": filepath},
),
]
else:
my_urls = [url for config, url in _URLs.items() if config.startswith(self.config.name)]
archives = dl_manager.download(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": chain(*(dl_manager.iter_archive(archive) for archive in archives)),
},
),
]
def _generate_examples(self, filepath=None, files=None):
"""Yields examples."""
id_ = 0
if self.config.name == "evaluation_dataset":
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for row in data:
yield id_, {
"stackoverflow_id": row["stackoverflow_id"],
"question": row["question"],
"question_url": row["question_url"],
"question_author": row["question_author"],
"question_author_url": row["question_author_url"],
"answer": row["answer"],
"answer_url": row["answer_url"],
"answer_author": row["answer_author"],
"answer_author_url": row["answer_author_url"],
"examples": row["examples"],
"examples_url": row["examples_url"],
}
id_ += 1
else:
for _, f in files:
for row in f:
data_dict = json.loads(row.decode("utf-8"))
yield id_, {
"id": data_dict["id"],
"filepath": data_dict["filepath"],
"method_name": data_dict["method_name"],
"start_line": data_dict["start_line"],
"end_line": data_dict["end_line"],
"url": data_dict["url"],
}
id_ += 1
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