Datasets:
Tasks:
Text Classification
Sub-tasks:
text-scoring
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
File size: 3,239 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.
"""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]}
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