Datasets:

Skylion007
/

openwebtext

	# 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.
	"""The Open WebText Corpus"""

	from __future__ import absolute_import, division, print_function

	import os
	import re
	from itertools import chain

	import datasets


	_CITATION = """\
	@misc{Gokaslan2019OpenWeb,
	title={OpenWebText Corpus},
	author={Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, Stefanie Tellex},
	howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	An open-source replication of the WebText dataset from OpenAI.
	"""

	_URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"


	class Openwebtext(datasets.GeneratorBasedBuilder):
	"""The Open WebText dataset."""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	description="Plain text",
	version=datasets.Version("1.0.0"),
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({"text": datasets.Value("string")}),
	homepage="https://skylion007.github.io/OpenWebTextCorpus/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(_URL)
	owt_dir = os.path.join(dl_dir, "openwebtext")
	subset_xzs = [
	os.path.join(owt_dir, file_name)
	for file_name in sorted(os.listdir(owt_dir))
	if file_name.endswith("xz") # filter out ...xz.lock
	]
	ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count() * 0.75))
	nested_txt_files = [
	[
	os.path.join(ex_dir, txt_file_name)
	for txt_file_name in sorted(os.listdir(ex_dir))
	if txt_file_name.endswith("txt")
	]
	for ex_dir in ex_dirs
	]
	txt_files = chain(*nested_txt_files)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files}),
	]

	def _generate_examples(self, txt_files):
	""" Yields examples. """
	for idx, filepath in enumerate(txt_files):
	with open(filepath, encoding="utf-8") as f:
	yield idx, {"text": re.sub("\n\n\n+", "\n\n", f.read()).strip()}