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Tevatron
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wikipedia-wq-corpus

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wikipedia-wq-corpus / wikipedia-wq-corpus.py

Xueguang Ma

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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.
	# Lint as: python3
	"""Wikipedia WQ dataset."""
	import json
	import datasets

	_CITATION = """
	@inproceedings{karpukhin-etal-2020-dense,
	title = "Dense Passage Retrieval for Open-Domain Question Answering",
	author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
	month = nov,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
	doi = "10.18653/v1/2020.emnlp-main.550",
	pages = "6769--6781",
	}
	"""
	_DESCRIPTION = "dataset load script for Wikipedia WQ Corpus"
	_DATASET_URLS = {
	'train': "https://huggingface.co/datasets/Tevatron/wikipedia-nq-corpus/resolve/main/corpus.jsonl.gz",
	}


	class WikipediaWqCorpus(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("0.0.1")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(version=VERSION,
	description="Wikipedia WQ corpus"),
	]

	def _info(self):
	features = datasets.Features(
	{'docid': datasets.Value('string'), 'text': datasets.Value('string'),
	'title': datasets.Value('string')},
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="",
	# License for the dataset if available
	license="",
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
	splits = [
	datasets.SplitGenerator(
	name="train",
	gen_kwargs={
	"filepath": downloaded_files["train"],
	},
	),
	]
	return splits

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	for line in f:
	data = json.loads(line)
	yield data['docid'], data