Datasets:
sent_comp

	# 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.
	"""Google Sentence Compression dataset"""


	import gzip
	import json

	import datasets


	_CITATION = """\
	@inproceedings{filippova-altun-2013-overcoming,
	title = "Overcoming the Lack of Parallel Data in Sentence Compression",
	author = "Filippova, Katja and
	Altun, Yasemin",
	booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
	month = oct,
	year = "2013",
	address = "Seattle, Washington, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/D13-1155",
	pages = "1481--1491",
	}
	"""

	_DESCRIPTION = """\
	Large corpus of uncompressed and compressed sentences from news articles.
	"""

	_HOMEPAGE = "https://github.com/google-research-datasets/sentence-compression"


	_URLs = {
	datasets.Split.VALIDATION: [
	"https://github.com/google-research-datasets/sentence-compression/raw/master/data/comp-data.eval.json.gz"
	],
	datasets.Split.TRAIN: [
	f"https://github.com/google-research-datasets/sentence-compression/raw/master/data/sent-comp.train{str(i).zfill(2)}.json.gz"
	for i in range(1, 11)
	],
	}


	class SentComp(datasets.GeneratorBasedBuilder):
	"""Google Setence Compression dataset"""

	def _info(self):
	node_features = {
	"form": datasets.Value("string"),
	"type": datasets.Value("string"),
	"mid": datasets.Value("string"),
	"word": datasets.features.Sequence(
	{
	"id": datasets.Value("int32"),
	"form": datasets.Value("string"),
	"stem": datasets.Value("string"),
	"tag": datasets.Value("string"),
	}
	),
	"gender": datasets.Value("int32"),
	"head_word_index": datasets.Value("int32"),
	}
	compression_edge_features = {
	"parent_id": datasets.Value("int32"),
	"child_id": datasets.Value("int32"),
	}
	edge_features = {**compression_edge_features, "label": datasets.Value("string")}
	entity_features = {
	"start": datasets.Value("int32"),
	"end": datasets.Value("int32"),
	"head": datasets.Value("int32"),
	"name": datasets.Value("string"),
	"type": datasets.Value("string"),
	"mid": datasets.Value("string"),
	"is_proper_name_entity": datasets.Value("bool"),
	"gender": datasets.Value("int32"),
	}
	tree_features = {
	"id": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	"node": datasets.features.Sequence(node_features),
	"edge": datasets.features.Sequence(edge_features),
	"entity_mention": datasets.features.Sequence(entity_features),
	}
	compression_features = {
	"text": datasets.Value("string"),
	"edge": datasets.features.Sequence(compression_edge_features),
	}

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"graph": tree_features,
	"compression": compression_features,
	"headline": datasets.Value("string"),
	"compression_ratio": datasets.Value("float"),
	"doc_id": datasets.Value("string"),
	"source_tree": tree_features,
	"compression_untransformed": compression_features,
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	return [
	datasets.SplitGenerator(
	name=split,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepaths": dl_manager.download(_URLs[split])},
	)
	for split in _URLs
	]

	def _generate_examples(self, filepaths):
	"""Yields examples."""
	id_ = -1
	for ix, filepath in enumerate(filepaths):
	with gzip.open(filepath, mode="rt", encoding="utf-8") as f:
	all_text = f.read()

	# in the data file, it's in the form of JSON objects, separated with '\n\n' characters
	# we'll format the file to be able to read with json package
	all_text = "[" + all_text + "]"
	all_text = all_text.replace("}\n\n{", "},\n{")

	samples = json.loads(all_text)
	for sample in samples:
	# add some default values
	for node in sample["graph"]["node"] + sample["source_tree"]["node"]:
	if "type" not in node:
	node["type"] = ""
	if "mid" not in node:
	node["mid"] = ""

	id_ += 1
	yield id_, sample