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# 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
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