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Source code for torchtext.datasets.squad2

import os
from functools import partial
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,
)

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"


def _filepath_fn(root, split, _=None):
    return os.path.join(root, os.path.basename(URL[split]))


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "dev")) def SQuAD2(root: str, split: Union[Tuple[str], str]): """SQuAD2 Dataset .. warning:: using datapipes is still currently subject to a few caveats. if you wish to use this dataset with shuffling, multi-processing, or distributed learning, please see :ref:`this note <datapipes_warnings>` for further instructions. 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" ) from torchdata.datapipes.iter import FileOpener, GDriveReader, HttpReader, IterableWrapper # noqa url_dp = IterableWrapper([URL[split]]) # cache data on-disk with sanity check cache_dp = url_dp.on_disk_cache( filepath_fn=partial(_filepath_fn, root, split), hash_dict={_filepath_fn(root, split): MD5[split]}, hash_type="md5", ) cache_dp = HttpReader(cache_dp).end_caching(mode="wb", same_filepath_fn=True) cache_dp = FileOpener(cache_dp, encoding="utf-8") return cache_dp.parse_json_files().read_squad().shuffle().set_shuffle(False).sharding_filter()

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