Datasets:
Tasks:
Text Classification
Sub-tasks:
text-scoring
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
# 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. | |
"""Google Wellformed Query Dataset""" | |
import datasets | |
_CITATION = """\ | |
@misc{faruqui2018identifying, | |
title={Identifying Well-formed Natural Language Questions}, | |
author={Manaal Faruqui and Dipanjan Das}, | |
year={2018}, | |
eprint={1808.09419}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. | |
""" | |
_URL = "https://raw.githubusercontent.com/google-research-datasets/query-wellformedness/master/{}.tsv" | |
class GoogleWellformedQuery(datasets.GeneratorBasedBuilder): | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features({"rating": datasets.Value("float"), "content": datasets.Value("string")}), | |
supervised_keys=None, | |
homepage="https://github.com/google-research-datasets/query-wellformedness", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
tr_file = dl_manager.download_and_extract(_URL.format("train")) | |
tst_file = dl_manager.download_and_extract(_URL.format("test")) | |
dev_file = dl_manager.download_and_extract(_URL.format("dev")) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": tr_file, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": tst_file, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": dev_file, | |
}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""Yields examples.""" | |
with open(filepath, "r", encoding="utf-8") as file: | |
reader = file.read().split("\n") | |
for idx, row in enumerate(reader): | |
row = row.split("\t") | |
if len(row) == 1: | |
continue | |
yield idx, {"rating": row[1], "content": row[0]} | |