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reddit-topics-targz / reddit-topics-targz.py

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	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the 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
	"""SQUAD: The Stanford Question Answering Dataset."""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{2016arXiv160605250R,
	author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
	Konstantin and {Liang}, Percy},
	title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
	journal = {arXiv e-prints},
	year = 2016,
	eid = {arXiv:1606.05250},
	pages = {arXiv:1606.05250},
	archivePrefix = {arXiv},
	eprint = {1606.05250},
	}
	"""

	_DESCRIPTION = """\
	Demo...
	"""

	_URL = "https://github.com/jamescalam/hf-datasets/raw/main/01_builder_script/dataset.tar.gz"


	class RedditTopicsTargz(datasets.GeneratorBasedBuilder):
	"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sub": datasets.Value("string"),
	"title": datasets.Value("string"),
	"selftext": datasets.Value("string"),
	"upvote_ratio": datasets.Value("float32"),
	"id": datasets.Value("string"),
	"created_utc": datasets.Value("float32"),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://github.com/jamescalam/hf-datasets/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	path = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": path+'/dataset.jsonl'}
	),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	idx = 0
	# open the file and read the lines
	with open(filepath, encoding="utf-8") as fp:
	for line in fp:
	# load json line
	obj = json.loads(line)
	yield idx, obj
	idx += 1