Datasets:
onestop_english

	# 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.
	"""OneStopEnglish Corpus: Dataset of texts classified into reading levels/text complexities."""


	import os

	import datasets
	from datasets.tasks import TextClassification


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{vajjala-lucic-2018-onestopenglish,
	title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification},
	author = {Sowmya Vajjala and Ivana Lučić},
	booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
	year = {2018}
	}
	"""

	_DESCRIPTION = """\
	This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file.
	Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance).
	This dataset demonstrates its usefulness for through two applica-tions - automatic readability assessment and automatic text simplification.
	The corpus consists of 189 texts, each in three versions/reading levels (567 in total).
	"""

	_HOMEPAGE = "https://github.com/nishkalavallabhi/OneStopEnglishCorpus"

	_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"

	_URL = "https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip"


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class OnestopEnglish(datasets.GeneratorBasedBuilder):
	"""OneStopEnglish Corpus: Dataset of texts classified into reading levels"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["ele", "int", "adv"])}
	),
	supervised_keys=[""],
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _vocab_text_gen(self, train_file):
	for _, ex in self._generate_examples(train_file):
	yield ex["text"]

	def _split_generators(self, dl_manager):
	"""Downloads OneStopEnglish corpus"""
	extracted_folder_path = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"split_key": "train", "data_dir": extracted_folder_path},
	)
	]

	def _get_examples_from_split(self, split_key, data_dir):
	"""Reads the downloaded and extracted files and combines the individual text files to one dataset."""

	data_dir = os.path.join(data_dir, "Texts-SeparatedByReadingLevel")

	ele_samples = []
	dir_path = os.path.join(data_dir, "Ele-Txt")
	files = os.listdir(dir_path)
	for f in sorted(files):
	try:
	with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
	text = myfile.read().strip()
	ele_samples.append(text)
	except Exception as e:
	logger.info("Error with:", os.path.join(dir_path, f), e)

	int_samples = []
	dir_path = os.path.join(data_dir, "Int-Txt")
	files = os.listdir(dir_path)
	for f in sorted(files):
	try:
	with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
	text = myfile.read().strip()
	int_samples.append(text)
	except Exception as e:
	logger.info("Error with:", os.path.join(dir_path, f), e)

	adv_samples = []
	dir_path = os.path.join(data_dir, "Adv-Txt")
	files = os.listdir(dir_path)
	for f in sorted(files):
	try:
	with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
	text = myfile.read().strip()
	adv_samples.append(text)
	except Exception as e:
	logger.info("Error with:", os.path.join(dir_path, f), e)

	train_samples = ele_samples + int_samples + adv_samples
	train_labels = (["ele"] * len(ele_samples)) + (["int"] * len(int_samples)) + (["adv"] * len(adv_samples))

	if split_key == "train":
	return (train_samples, train_labels)
	else:
	raise ValueError(f"Invalid split key {split_key}")

	def _generate_examples(self, split_key, data_dir):
	"""Yields examples for a given split of dataset."""
	split_text, split_labels = self._get_examples_from_split(split_key, data_dir)
	for id_, (text, label) in enumerate(zip(split_text, split_labels)):
	feature_dict = {"text": text, "label": label}
	yield id_, feature_dict