Source code for torchtext.datasets.penntreebank

import os
from typing import Union, Tuple

from torchtext._internal.module_utils import is_module_available
from import (

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

URL = {
    "train": "",
    "test": "",
    "valid": "",

MD5 = {
    "train": "f26c4b92c5fdc7b3f8c7cdcb991d8420",
    "valid": "aa0affc06ff7c36e977d7cd49e3839bf",
    "test": "8b80168b89c18661a38ef683c0dc3721",

    "train": 42068,
    "valid": 3370,
    "test": 3761,

DATASET_NAME = "PennTreebank"

[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "valid", "test")) def PennTreebank(root, split: Union[Tuple[str], str]): """PennTreebank Dataset For additional details refer to Number of lines per split: - train: 42068 - valid: 3370 - test: 3761 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`, `valid`, `test`) :returns: DataPipe that yields text from the Treebank corpus :rtype: str """ if not is_module_available("torchdata"): raise ModuleNotFoundError( "Package `torchdata` not found. Please install following instructions at ``" ) url_dp = IterableWrapper([URL[split]]) 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) # TODO: read in text mode with utf-8 encoding, see: data_dp = FileOpener(cache_dp, mode="b") # remove single leading and trailing space from the dataset return data_dp.readlines(return_path=False, decode=True).map(lambda t: t.strip())


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