Datasets:
wiki_auto

	# 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.
	"""WikiAuto dataset for Text Simplification"""


	import json

	import datasets


	_CITATION = """\
	@inproceedings{acl/JiangMLZX20,
	author = {Chao Jiang and
	Mounica Maddela and
	Wuwei Lan and
	Yang Zhong and
	Wei Xu},
	editor = {Dan Jurafsky and
	Joyce Chai and
	Natalie Schluter and
	Joel R. Tetreault},
	title = {Neural {CRF} Model for Sentence Alignment in Text Simplification},
	booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational
	Linguistics, {ACL} 2020, Online, July 5-10, 2020},
	pages = {7943--7960},
	publisher = {Association for Computational Linguistics},
	year = {2020},
	url = {https://www.aclweb.org/anthology/2020.acl-main.709/}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia
	as a resource to train sentence simplification systems. The authors first crowd-sourced a set of manual alignments
	between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia
	(this corresponds to the `manual` config), then trained a neural CRF system to predict these alignments.
	The trained model was then applied to the other articles in Simple English Wikipedia with an English counterpart to
	create a larger corpus of aligned sentences (corresponding to the `auto`, `auto_acl`, `auto_full_no_split`, and `auto_full_with_split` configs here).
	"""

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "CC-BY-SA 3.0"

	# 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)
	_URLs = {
	"manual": {
	"train": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AACdl4UPKtu7CMMa-CJhz4G7a/wiki-manual/train.tsv?dl=1",
	"dev": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/dev.tsv",
	"test": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/test.tsv",
	},
	"auto_acl": {
	"normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/ACL2020/train.src",
	"simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/ACL2020/train.dst",
	},
	"auto_full_no_split": {
	"normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_no_split/train.src",
	"simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_no_split/train.dst",
	},
	"auto_full_with_split": {
	"normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.src",
	"simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.dst",
	},
	"auto": {
	"part_1": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AAATBDhU1zpdcT5x5WgO8DMaa/wiki-auto-all-data/wiki-auto-part-1-data.json?dl=1",
	"part_2": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AAATgPkjo_tPt9z12vZxJ3MRa/wiki-auto-all-data/wiki-auto-part-2-data.json?dl=1",
	},
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class WikiAuto(datasets.GeneratorBasedBuilder):
	"""WikiAuto dataset for sentence simplification"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="manual",
	version=VERSION,
	description="A set of 10K Wikipedia sentence pairs aligned by crowd workers.",
	),
	datasets.BuilderConfig(
	name="auto_acl",
	version=VERSION,
	description="Automatically aligned and filtered sentence pairs used to train the ACL2020 system.",
	),
	datasets.BuilderConfig(
	name="auto_full_no_split",
	version=VERSION,
	description="All automatically aligned sentence pairs without sentence splitting.",
	),
	datasets.BuilderConfig(
	name="auto_full_with_split",
	version=VERSION,
	description="All automatically aligned sentence pairs with sentence splitting.",
	),
	datasets.BuilderConfig(
	name="auto", version=VERSION, description="A large set of automatically aligned sentence pairs."
	),
	]

	DEFAULT_CONFIG_NAME = "auto"

	def _info(self):
	if self.config.name == "manual": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"alignment_label": datasets.ClassLabel(names=["notAligned", "aligned", "partialAligned"]),
	"normal_sentence_id": datasets.Value("string"),
	"simple_sentence_id": datasets.Value("string"),
	"normal_sentence": datasets.Value("string"),
	"simple_sentence": datasets.Value("string"),
	"gleu_score": datasets.Value("float32"),
	}
	)
	elif (
	self.config.name == "auto_acl"
	or self.config.name == "auto_full_no_split"
	or self.config.name == "auto_full_with_split"
	):
	features = datasets.Features(
	{
	"normal_sentence": datasets.Value("string"),
	"simple_sentence": datasets.Value("string"),
	}
	)
	else:
	features = datasets.Features(
	{
	"example_id": datasets.Value("string"),
	"normal": {
	"normal_article_id": datasets.Value("int32"),
	"normal_article_title": datasets.Value("string"),
	"normal_article_url": datasets.Value("string"),
	"normal_article_content": datasets.Sequence(
	{
	"normal_sentence_id": datasets.Value("string"),
	"normal_sentence": datasets.Value("string"),
	}
	),
	},
	"simple": {
	"simple_article_id": datasets.Value("int32"),
	"simple_article_title": datasets.Value("string"),
	"simple_article_url": datasets.Value("string"),
	"simple_article_content": datasets.Sequence(
	{
	"simple_sentence_id": datasets.Value("string"),
	"simple_sentence": datasets.Value("string"),
	}
	),
	},
	"paragraph_alignment": datasets.Sequence(
	{
	"normal_paragraph_id": datasets.Value("string"),
	"simple_paragraph_id": datasets.Value("string"),
	}
	),
	"sentence_alignment": datasets.Sequence(
	{
	"normal_sentence_id": datasets.Value("string"),
	"simple_sentence_id": datasets.Value("string"),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage="https://github.com/chaojiang06/wiki-auto",
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	if self.config.name in ["manual", "auto"]:
	return [
	datasets.SplitGenerator(
	name=spl,
	gen_kwargs={
	"filepaths": data_dir,
	"split": spl,
	},
	)
	for spl in data_dir
	]
	else:
	return [
	datasets.SplitGenerator(
	name="full",
	gen_kwargs={"filepaths": data_dir, "split": "full"},
	)
	]

	def _generate_examples(self, filepaths, split):
	if self.config.name == "manual":
	keys = [
	"alignment_label",
	"simple_sentence_id",
	"normal_sentence_id",
	"simple_sentence",
	"normal_sentence",
	"gleu_score",
	]
	with open(filepaths[split], encoding="utf-8") as f:
	for id_, line in enumerate(f):
	values = line.strip().split("\t")
	assert len(values) == 6, f"Not enough fields in ---- {line} --- {values}"
	yield id_, dict(
	[(k, val) if k != "gleu_score" else (k, float(val)) for k, val in zip(keys, values)]
	)
	elif (
	self.config.name == "auto_acl"
	or self.config.name == "auto_full_no_split"
	or self.config.name == "auto_full_with_split"
	):
	with open(filepaths["normal"], encoding="utf-8") as fi:
	with open(filepaths["simple"], encoding="utf-8") as fo:
	for id_, (norm_se, simp_se) in enumerate(zip(fi, fo)):
	yield id_, {
	"normal_sentence": norm_se,
	"simple_sentence": simp_se,
	}
	else:
	dataset_dict = json.load(open(filepaths[split], encoding="utf-8"))
	for id_, (eid, example_dict) in enumerate(dataset_dict.items()):
	res = {
	"example_id": eid,
	"normal": {
	"normal_article_id": example_dict["normal"]["id"],
	"normal_article_title": example_dict["normal"]["title"],
	"normal_article_url": example_dict["normal"]["url"],
	"normal_article_content": {
	"normal_sentence_id": [
	sen_id for sen_id, sen_txt in example_dict["normal"]["content"].items()
	],
	"normal_sentence": [
	sen_txt for sen_id, sen_txt in example_dict["normal"]["content"].items()
	],
	},
	},
	"simple": {
	"simple_article_id": example_dict["simple"]["id"],
	"simple_article_title": example_dict["simple"]["title"],
	"simple_article_url": example_dict["simple"]["url"],
	"simple_article_content": {
	"simple_sentence_id": [
	sen_id for sen_id, sen_txt in example_dict["simple"]["content"].items()
	],
	"simple_sentence": [
	sen_txt for sen_id, sen_txt in example_dict["simple"]["content"].items()
	],
	},
	},
	"paragraph_alignment": {
	"normal_paragraph_id": [
	norm_id for simp_id, norm_id in example_dict.get("paragraph_alignment", [])
	],
	"simple_paragraph_id": [
	simp_id for simp_id, norm_id in example_dict.get("paragraph_alignment", [])
	],
	},
	"sentence_alignment": {
	"normal_sentence_id": [
	norm_id for simp_id, norm_id in example_dict.get("sentence_alignment", [])
	],
	"simple_sentence_id": [
	simp_id for simp_id, norm_id in example_dict.get("sentence_alignment", [])
	],
	},
	}
	yield id_, res