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Source code for torchtext.datasets.enwik9

import os
from functools import partial

from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import _create_dataset_directory

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

URL = "http://mattmahoney.net/dc/enwik9.zip"

MD5 = "3e773f8a1577fda2e27f871ca17f31fd"

_PATH = "enwik9.zip"

NUM_LINES = {"train": 13147026}

DATASET_NAME = "EnWik9"


def _filepath_fn(root, _=None):
    return os.path.join(root, _PATH)


def _extracted_filepath_fn(root, _=None):
    return os.path.join(root, os.path.splitext(_PATH)[0])


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) def EnWik9(root: str): """EnWik9 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 http://mattmahoney.net/dc/textdata.html Number of lines in dataset: 13147026 Args: root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache') :returns: DataPipe that yields raw text rows from WnWik9 dataset :rtype: str """ 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]) cache_compressed_dp = url_dp.on_disk_cache( filepath_fn=partial(_filepath_fn, root), hash_dict={_filepath_fn(root): MD5}, hash_type="md5", ) cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True) cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root)) cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_zip() cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True) data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8") return data_dp.readlines(return_path=False).shuffle().set_shuffle(False).sharding_filter()

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