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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)"""
import csv
import datasets
_DESCRIPTION = """\
Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
"""
_CITATION = """\
@misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DOWNLOAD_URL = "https://storage.googleapis.com/gresearch/kelm-corpus/quadruples-{}.tsv"
_WEBPAGE = "https://github.com/google-research-datasets/KELM-corpus"
class KELM(datasets.GeneratorBasedBuilder):
"""Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"triple": datasets.Value("string"),
"sentence": datasets.Value("string"),
}
),
homepage=_WEBPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_DOWNLOAD_URL.format("train"))
validation_path = dl_manager.download_and_extract(_DOWNLOAD_URL.format("validation"))
test_path = dl_manager.download_and_extract(_DOWNLOAD_URL.format("test"))
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file, delimiter="\t", fieldnames=["triple", "sentence"])
for irow, row in enumerate(csv_reader):
yield irow, row
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