# 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'] }