Datasets:
ptb_text_only

	# 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.
	"""
	Load the Penn Treebank dataset.

	This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall
	Street Journal material.
	"""


	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{marcus-etal-1993-building,
	title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank",
	author = "Marcus, Mitchell P. and
	Santorini, Beatrice and
	Marcinkiewicz, Mary Ann",
	journal = "Computational Linguistics",
	volume = "19",
	number = "2",
	year = "1993",
	url = "https://www.aclweb.org/anthology/J93-2004",
	pages = "313--330",
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. This corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC99T42"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "LDC User Agreement for Non-Members"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URL = "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/"
	_TRAINING_FILE = "ptb.train.txt"
	_DEV_FILE = "ptb.valid.txt"
	_TEST_FILE = "ptb.test.txt"


	class PtbTextOnlyConfig(datasets.BuilderConfig):
	"""BuilderConfig for PtbTextOnly"""

	def __init__(self, **kwargs):
	"""BuilderConfig PtbTextOnly.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(PtbTextOnlyConfig, self).__init__(**kwargs)


	class PtbTextOnly(datasets.GeneratorBasedBuilder):
	"""Load the Penn Treebank dataset."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	PtbTextOnlyConfig(
	name="penn_treebank",
	version=VERSION,
	description="Load the Penn Treebank dataset",
	),
	]

	def _info(self):
	features = datasets.Features({"sentence": 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
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"dev": f"{_URL}{_DEV_FILE}",
	"test": f"{_URL}{_TEST_FILE}",
	}
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir["test"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir["dev"]}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
	# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
	# The key is not important, it's more here for legacy reason (legacy from tfds)
	with open(filepath, encoding="utf-8") as f:
	for id_, line in enumerate(f):
	line = line.strip()
	yield id_, {"sentence": line}