| # 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'] | |
| } | |