Datasets:

GEM
/

squality

	# 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.
	# TODO: Address all TODOs and remove all explanatory comments
	"""TODO: Add a description here."""
	""" Dataset loading script for SQuALITY, an abstractive summarization dataset that is
	* long document: 3k-6k words
	* question-focused: 5/doc
	* multi-reference 4/question
	"""

	import os
	import csv
	import json

	import datasets


	_CITATION = """\
	@article{wang2022squality,
	title={{SQ}u{ALITY}: Building a Long-Document Summarization Dataset the Hard Way},
	author={Wang, Alex and Pang, Richard Yuanzhe and Chen, Angelica and Phang, Jason and Bowman, Samuel R.},
	journal={arXiv preprint 2205.11465},
	year={2022}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
	"""

	_HOMEPAGE = "ihttps://github.com/nyu-mll/SQuALITY"

	_LICENSE = "CC BY"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	#_URLS = {
	# "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
	# "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
	#}


	class SQuALITYDataset(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="squality-v1", version=datasets.Version("1.0.0"), description="SQUALITY v1.0, containing 100 stories (2000 summaries)"),
	datasets.BuilderConfig(name="squality-v1.1", version=VERSION, description="SQuALITY version v1.1, expands on v1.0 by adding 27 stories (540 summaries)"),
	]

	DEFAULT_CONFIG_NAME = "squality-v1.1" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

	#if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above
	# features = datasets.Features(
	# {
	# "sentence": datasets.Value("string"),
	# "option1": datasets.Value("string"),
	# "answer": datasets.Value("string")
	# # These are the features of your dataset like images, labels ...
	# }
	# )

	features = datasets.Features(
	{
	"document": datasets.Value("string"),
	"question": datasets.Value("string"),
	"summary": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features,
	# If there's a common (input, target) tuple from the features,
	# uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# This method is tasked with downloading/extracting the data and
	# defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS),
	# the configuration selected by the user is in self.config.name

	if self.config.name == "squality-v1":
	data_dir = "data/v1"
	elif self.config.name == "squality-v1.1":
	data_dir = "data/v1-1"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test.jsonl"),
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "validation.jsonl"),
	"split": "dev",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	for row in enumerate(f):
	# fields
	# * metadata
	# * document
	# * questions
	story = json.loads(row)
	for question in story['questions']:
	# fields
	# * question_text
	# * question_number
	# * responses
	key = question['gem_id']

	# for the test split, yield all references at once
	# to easily compute multi-reference metrics
	if split == "test":
	yield key, {
	'document': story['document'],
	'question': question['question_text'],
	'summary': [r['response_text'] for r in question['responses']]
	}

	else:
	for response in question['responses']:
	# fields
	# * uid
	# * worker_uid
	# * response_text
	yield key, {
	'document': story['document'],
	'question': question['question_text'],
	'summary': response['response_text']
	}