Source code for torchtext.datasets.ag_news

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": "",

MD5 = {
    "train": "b1a00f826fdfbd249f79597b59e1dc12",
    "test": "d52ea96a97a2d943681189a97654912d",

    "train": 120000,
    "test": 7600,


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "test")) def AG_NEWS(root: str, split: Union[Tuple[str], str]): """AG_NEWS Dataset For additional details refer to Number of lines per split: - train: 120000 - test: 7600 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 4) and text :rtype: (int, 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, split + ".csv"), hash_dict={os.path.join(root, split + ".csv"): MD5[split]}, hash_type="md5", ) cache_dp = HttpReader(cache_dp) cache_dp = 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") return data_dp.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))


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