frenchQA.py

"""FrenchQA: One French QA Dataset to rule them all"""


import csv

import datasets
from datasets.tasks import QuestionAnsweringExtractive


# TODO(squad_v2): BibTeX citation
_CITATION = """\
"""

_DESCRIPTION = """\
One French QA Dataset to rule them all, One French QA Dataset to find them, One French QA Dataset to bring them all, and in the darkness bind them.
"""

_URLS = {
    "train": "train.csv",
    "dev": "valid.csv",
    "test": "test.csv"
}


class FrenchQAConfig(datasets.BuilderConfig):
    """BuilderConfig for frenchQA."""

    def __init__(self, **kwargs):
        """BuilderConfig for FrenchQA.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(FrenchQAConfig, self).__init__(**kwargs)


class FrenchQA(datasets.GeneratorBasedBuilder):
    """TODO(squad_v2): Short description of my dataset."""

    # TODO(squad_v2): Set up version.
    BUILDER_CONFIGS = [
        FrenchQAConfig(name="frenchQA", version=datasets.Version("1.0.0"), description="frenchQA"),
    ]

    def _info(self):
        # TODO(squad_v2): Specifies the datasets.DatasetInfo object
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # datasets.features.FeatureConnectors
            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"),
                        }
                    ),
                    # These are the features of your dataset like images, labels ...
                }
            ),
            # 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="",
            citation=_CITATION,
            task_templates=[
                QuestionAnsweringExtractive(
                    question_column="question", context_column="context", answers_column="answers"
                )
            ],
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO(squad_v2): Downloads the data and defines the splits
        # dl_manager is a datasets.download.DownloadManager that can be used to
        # download and extract URLs
        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"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        # TODO(squad_v2): Yields (key, example) tuples from the dataset
        with open(filepath, encoding="utf-8") as f:
            squad = csv.DictReader(f, delimiter = ";")
            for id_, row in enumerate(squad):
                answer_start = []
                text = []
                
                if row["answer_start"] != "-1":
                    answer_start = [row["answer_start"]]
                    text = [row["answer"]]
                    
                yield id_, {
                    "title": row["dataset"],
                    "context": row["context"],
                    "question": row["question"],
                    "id": id_,
                    "answers": {
                        "answer_start": answer_start,
                        "text": text,
                    },
                }