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wikihow / wikihow.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
	"""WikiHow Datasets."""


	import csv
	import os
	import re

	import datasets


	_CITATION = """\
	@misc{koupaee2018wikihow,
	title={WikiHow: A Large Scale Text Summarization Dataset},
	author={Mahnaz Koupaee and William Yang Wang},
	year={2018},
	eprint={1810.09305},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	WikiHow is a new large-scale dataset using the online WikiHow
	(http://www.wikihow.com/) knowledge base.

	There are two features:
	- text: wikihow answers texts.
	- headline: bold lines as summary.

	There are two separate versions:
	- all: consisting of the concatenation of all paragraphs as the articles and
	the bold lines as the reference summaries.
	- sep: consisting of each paragraph and its summary.

	Download "wikihowAll.csv" and "wikihowSep.csv" from
	https://github.com/mahnazkoupaee/WikiHow-Dataset and place them in manual folder
	https://www.tensorflow.org/datasets/api_docs/python/tfds/download/DownloadConfig.
	Train/validation/test splits are provided by the authors.
	Preprocessing is applied to remove short articles
	(abstract length < 0.75 article length) and clean up extra commas.
	"""

	_DOCUMENT = "text"
	_SUMMARY = "headline"

	_URLS = {
	"train": "https://raw.githubusercontent.com/mahnazkoupaee/WikiHow-Dataset/master/all_train.txt",
	"validation": "https://raw.githubusercontent.com/mahnazkoupaee/WikiHow-Dataset/master/all_val.txt",
	"test": "https://raw.githubusercontent.com/mahnazkoupaee/WikiHow-Dataset/master/all_test.txt",
	}


	class WikihowConfig(datasets.BuilderConfig):
	"""BuilderConfig for Wikihow."""

	def __init__(self, filename=None, **kwargs):
	"""BuilderConfig for Wikihow.

	Args:
	filename: filename of different configs for the dataset.
	**kwargs: keyword arguments forwarded to super.
	"""
	# Version 1.1.0 remove empty document and summary strings.
	# Version 1.2.0 add train validation test split, add cleaning & filtering.
	super(WikihowConfig, self).__init__(version=datasets.Version("1.2.0"), **kwargs)
	self.filename = filename


	class Wikihow(datasets.GeneratorBasedBuilder):
	"""WikiHow: A Large Scale Text Summarization Dataset."""

	BUILDER_CONFIGS = [
	WikihowConfig(
	name="all",
	filename="wikihowAll.csv",
	description="Use the concatenation of all paragraphs as the articles"
	" and the bold lines as the reference summaries",
	),
	WikihowConfig(name="sep", filename="wikihowSep.csv", description="use each paragraph and its summary."),
	]

	@property
	def manual_download_instructions(self):
	return """\
	You need to manually download one of the wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset.
	You need to download one the following two data files manually, depending on the version you want:
	1) all: https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv
	2) sep: https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv

	The <path/to/folder> can e.g. be "~/manual_wikihow_data".

	Wikihow can then be loaded for example using the following command `datasets.load_dataset("wikihow", "all", data_dir="<path/to/folder>")`.
	"""

	def _info(self):
	feature_names = [_DOCUMENT, _SUMMARY, "title"]
	if self.config.name == "sep":
	feature_names.extend(["overview", "sectionLabel"])
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({k: datasets.Value("string") for k in feature_names}),
	supervised_keys=None,
	homepage="https://github.com/mahnazkoupaee/WikiHow-Dataset",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_path = dl_manager.download_and_extract(_URLS)
	titles = {k: set() for k in dl_path}
	for k, path in dl_path.items():
	with open(path, encoding="utf-8") as f:
	for line in f:
	titles[k].add(line.strip())

	path_to_manual_file = os.path.join(
	os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename
	)

	if not os.path.exists(path_to_manual_file):
	raise FileNotFoundError(
	f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('wikihow', data_dir=...)` that includes a file name {self.config.filename}. Manual download instructions: {self.manual_download_instructions})"
	)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"path": path_to_manual_file,
	"title_set": titles["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"path": path_to_manual_file,
	"title_set": titles["validation"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"path": path_to_manual_file,
	"title_set": titles["test"],
	},
	),
	]

	def _generate_examples(self, path=None, title_set=None):
	"""Yields examples."""
	with open(path, encoding="utf-8") as f:
	reader = csv.reader(f)
	headers = next(reader)
	if self.config.name == "all" and headers != ["headline", "title", "text"]:
	raise ValueError("Mismatched header in WikiAll.txt")
	if self.config.name == "sep" and headers != ["overview", "headline", "text", "sectionLabel", "title"]:
	raise ValueError("Mismatched header in WikiSep.txt")
	key2id = {key: i for i, key in enumerate(headers)}
	for i, line in enumerate(reader):
	# skip empty line or insufficient line.
	if len(line) == len(key2id):
	summary = line[key2id[_SUMMARY]].strip()
	document = line[key2id[_DOCUMENT]].strip()
	summary, document = _filter_and_clean(summary, document)
	if summary and document:
	if line[key2id["title"]].strip().replace(" ", "") in title_set:
	d = {k: line[v].strip() for k, v in key2id.items() if k not in [_SUMMARY, _DOCUMENT]}
	d[_DOCUMENT] = document
	d[_SUMMARY] = summary
	yield i, d


	# This functions follow data processing acoording to original paper at
	# https://github.com/mahnazkoupaee/WikiHow-Dataset/blob/master/process.py
	def _filter_and_clean(abstract, article):
	"""Remove short article and clean up commas in abstract and article."""
	# a threshold is used to remove short articles with long summaries
	# as well as articles with no summary
	if len(abstract) < (0.75 * len(article)):
	# remove extra commas in abstracts
	abstract = abstract.replace(".,", ".")
	# remove extra commas in articles
	article = re.sub(r"[.]+[\n]+[,]", ".\n", article)
	return abstract, article
	else:
	return "", ""