squad_v2_fi.py

import json

import datasets
from datasets.tasks import QuestionAnsweringExtractive

_DESCRIPTION = """\
combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
 to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
 also determine when no answer is supported by the paragraph and abstain from answering.
"""

_URLS = {
    "train": "https://huggingface.co/datasets/TurkuNLP/squad_v2_fi/resolve/main/train-v2.0.json.gz",
    "dev": "https://huggingface.co/datasets/TurkuNLP/squad_v2_fi/resolve/main/dev-v2.0.json.gz",
}


class SquadV2Config(datasets.BuilderConfig):
    """BuilderConfig for SQUAD."""

    def __init__(self, **kwargs):
        """BuilderConfig for SQUADV2.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(SquadV2Config, self).__init__(**kwargs)


class SquadV2(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        SquadV2Config(name="squad_v2_fi", version=datasets.Version(
            "1.0.0"), description="Finnish SQuAD v2.0"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answers": datasets.features.Sequence(
                        {
                            "text": datasets.Value("string"),
                            "answer_start": datasets.Value("int32"),
                        }
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://turkunlp.org/",
            task_templates=[
                QuestionAnsweringExtractive(
                    question_column="question", context_column="context", answers_column="answers"
                )
            ],
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        urls_to_download = _URLS
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
                                    "filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={
                                    "filepath": downloaded_files["dev"]}),
        ]


    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            squad = json.load(f)
            for example in squad["data"]:
                title = example.get("title", "")
                for paragraph in example["paragraphs"]:
                    context = paragraph["context"]
                    for qa in paragraph["qas"]:
                        question = qa["question"]
                        id_ = qa["id"]

                        answer_starts = [answer["answer_start"]
                                         for answer in qa["answers"]]
                        answers = [answer["text"].strip(
                            ' .,-:') for answer in qa["answers"]]

                        yield id_, {
                            "title": title,
                            "context": context,
                            "question": question,
                            "id": id_,
                            "answers": {
                                "answer_start": answer_starts,
                                "text": answers,
                            },
                        }