# 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']
}