Source code for torchtext.datasets.yahooanswers

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, GDriveReader, IterableWrapper

URL = ""

MD5 = "f3f9899b997a42beb24157e62e3eea8d"

    "train": 1400000,
    "test": 60000,

_PATH = "yahoo_answers_csv.tar.gz"

DATASET_NAME = "YahooAnswers"

    "train": os.path.join("yahoo_answers_csv", "train.csv"),
    "test": os.path.join("yahoo_answers_csv", "test.csv"),

[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "test")) def YahooAnswers(root: str, split: Union[Tuple[str], str]): """YahooAnswers Dataset For additional details refer to Number of lines per split: - train: 1400000 - test: 60000 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`, `test`) :returns: DataPipe that yields tuple of label (1 to 10) and text containing the question title, question content, and best answer :rtype: (int, str) """ if not is_module_available("torchdata"): raise ModuleNotFoundError( "Package `torchdata` not found. Please install following instructions at ``" ) url_dp = IterableWrapper([URL]) cache_compressed_dp = url_dp.on_disk_cache( filepath_fn=lambda x: os.path.join(root, _PATH), hash_dict={os.path.join(root, _PATH): MD5}, hash_type="md5", ) cache_compressed_dp = GDriveReader(cache_compressed_dp).end_caching( mode="wb", same_filepath_fn=True ) cache_decompressed_dp = cache_compressed_dp.on_disk_cache( filepath_fn=lambda x: os.path.join(root, _EXTRACTED_FILES[split]) ) cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b") cache_decompressed_dp = cache_decompressed_dp.read_from_tar() cache_decompressed_dp = cache_decompressed_dp.filter( lambda x: _EXTRACTED_FILES[split] in x[0] ) cache_decompressed_dp = cache_decompressed_dp.end_caching( mode="wb", same_filepath_fn=True ) data_dp = FileOpener(cache_decompressed_dp, mode="b") return data_dp.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))


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