Datasets:
lm1b

	# 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
	"""The Language Model 1 Billion dataset."""


	import os
	from fnmatch import fnmatch

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{DBLP:journals/corr/ChelbaMSGBK13,
	author = {Ciprian Chelba and
	Tomas Mikolov and
	Mike Schuster and
	Qi Ge and
	Thorsten Brants and
	Phillipp Koehn},
	title = {One Billion Word Benchmark for Measuring Progress in Statistical Language
	Modeling},
	journal = {CoRR},
	volume = {abs/1312.3005},
	year = {2013},
	url = {http://arxiv.org/abs/1312.3005},
	archivePrefix = {arXiv},
	eprint = {1312.3005},
	timestamp = {Mon, 13 Aug 2018 16:46:16 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/ChelbaMSGBK13},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """\
	A benchmark corpus to be used for measuring progress in statistical language \
	modeling. This has almost one billion words in the training data.
	"""

	_DOWNLOAD_URL = "http://www.statmt.org/lm-benchmark/" "1-billion-word-language-modeling-benchmark-r13output.tar.gz"
	_TOP_LEVEL_DIR = "1-billion-word-language-modeling-benchmark-r13output"
	_TRAIN_FILE_FORMAT = "/".join([_TOP_LEVEL_DIR, "training-monolingual.tokenized.shuffled", "news.en-*"])
	_HELDOUT_FILE_FORMAT = "/".join([_TOP_LEVEL_DIR, "heldout-monolingual.tokenized.shuffled", "news.en.heldout-*"])


	class Lm1bConfig(datasets.BuilderConfig):
	"""BuilderConfig for Lm1b."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Lm1b.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(Lm1bConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class Lm1b(datasets.GeneratorBasedBuilder):
	"""1 Billion Word Language Model Benchmark dataset."""

	BUILDER_CONFIGS = [
	Lm1bConfig(
	name="plain_text",
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({"text": datasets.Value("string")}),
	supervised_keys=("text", "text"),
	homepage="http://www.statmt.org/lm-benchmark/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	archive = dl_manager.download(_DOWNLOAD_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files": dl_manager.iter_archive(archive), "pattern": _TRAIN_FILE_FORMAT},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"files": dl_manager.iter_archive(archive), "pattern": _HELDOUT_FILE_FORMAT},
	),
	]

	def _generate_examples(self, files, pattern):
	for path, f in files:
	if fnmatch(path, pattern):
	for idx, line in enumerate(f):
	yield "%s_%d" % (os.path.basename(path), idx), {
	"text": line.decode("utf-8").strip(),
	}