# 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. """OneStopQA - a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme""" import json import os import datasets # from datasets.tasks import QuestionAnsweringExtractive logger = datasets.logging.get_logger(__name__) # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{starc2020, author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger}, title = {STARC: Structured Annotations for Reading Comprehension}, booktitle = {ACL}, year = {2020}, publisher = {Association for Computational Linguistics} } """ _DESCRIPTION = """\ OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC \ (Structured Annotations for Reading Comprehension) scheme. \ The reading materials are Guardian articles taken from the \ [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). \ Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. \ Each paragraph is annotated with three multiple choice reading comprehension questions. \ The reading comprehension questions can be answered based on any of the three paragraph levels. """ _HOMEPAGE = "https://github.com/berzak/onestop-qa" _LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License" # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URL = "https://github.com/berzak/onestop-qa/raw/master/annotations/onestop_qa.zip" class OneStopQA(datasets.GeneratorBasedBuilder): """OneStopQA - a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme""" 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') # data = datasets.load_dataset('my_dataset', 'second_domain') def _info(self): features = datasets.Features( { "title": datasets.Value("string"), "paragraph": datasets.Value("string"), "level": datasets.ClassLabel(names=["Adv", "Int", "Ele"]), "question": datasets.Value("string"), "paragraph_index": datasets.Value("int32"), "answers": datasets.features.Sequence(datasets.Value("string"), length=4), "a_span": datasets.features.Sequence(datasets.Value("int32")), "d_span": datasets.features.Sequence(datasets.Value("int32")), } ) 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, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, task_templates=[] # QuestionAnsweringExtractive( # question_column="question", context_column="context", answers_column="answers" # ) # ], # When issue #2434 is resolved uncomment task_templates and the QuestionAnsweringExtractive (or similar) ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive data_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "onestop_qa.json"), "split": "train", }, ), ] def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """Yields examples as (key, example) tuples.""" # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. # Based on the squad dataset logger.info("generating examples from = %s", filepath) key = 0 with open(filepath, encoding="utf-8") as f: onestop_qa = json.load(f) for article in onestop_qa["data"]: title = article.get("title", "") for paragraph_index, paragraph in enumerate(article["paragraphs"]): for level in ["Adv", "Int", "Ele"]: paragraph_context_and_spans = paragraph[level] paragraph_context = paragraph_context_and_spans["context"] a_spans = paragraph_context_and_spans["a_spans"] d_spans = paragraph_context_and_spans["d_spans"] qas = paragraph["qas"] for qa, a_span, d_span in zip(qas, a_spans, d_spans): yield key, { "title": title, "paragraph": paragraph_context, "question": qa["question"], "paragraph_index": paragraph_index, "answers": qa["answers"], "level": level, "a_span": a_span, "d_span": d_span, }, key += 1