Source code for torchtext.datasets.squad2

import os
from typing import Union, Tuple

from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
    _wrap_split_argument,
    _create_dataset_directory,
)

if is_module_available("torchdata"):
    from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper


URL = {
    "train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json",
    "dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json",
}

MD5 = {
    "train": "62108c273c268d70893182d5cf8df740",
    "dev": "246adae8b7002f8679c027697b0b7cf8",
}

NUM_LINES = {
    "train": 130319,
    "dev": 11873,
}


DATASET_NAME = "SQuAD2"


@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "dev"))
def SQuAD2(root: str, split: Union[Tuple[str], str]):
    """SQuAD2 Dataset

    For additional details refer to https://rajpurkar.github.io/SQuAD-explorer/

    Number of lines per split:
        - train: 130319
        - dev: 11873


    Args:
        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
        split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `dev`)

    :returns: DataPipe that yields data points from SQuaAD1 dataset which consist of context, question, list of answers and corresponding index in context
    :rtype: (str, str, list(str), list(int))
    """
    if not is_module_available("torchdata"):
        raise ModuleNotFoundError(
            "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
        )

    url_dp = IterableWrapper([URL[split]])
    # cache data on-disk with sanity check
    cache_dp = url_dp.on_disk_cache(
        filepath_fn=lambda x: os.path.join(root, os.path.basename(x)),
        hash_dict={os.path.join(root, os.path.basename(URL[split])): MD5[split]},
        hash_type="md5",
    )
    cache_dp = HttpReader(cache_dp).end_caching(mode="wb", same_filepath_fn=True)
    cache_dp = FileOpener(cache_dp, mode="b")
    return cache_dp.parse_json_files().read_squad()