onestop_qa.py

# 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