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Source code for torchtext.datasets.ag_news

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,
)

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

URL = {
    "train": "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv",
    "test": "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv",
}

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

NUM_LINES = {
    "train": 120000,
    "test": 7600,
}

DATASET_NAME = "AG_NEWS"


def _filepath_fn(root, split, _=None):
    return os.path.join(root, split + ".csv")


def _modify_res(t):
    return int(t[0]), " ".join(t[1:])


[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 .. 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://paperswithcode.com/dataset/ag-news 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 https://github.com/pytorch/data" ) url_dp = IterableWrapper([URL[split]]) 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) cache_dp = cache_dp.end_caching(mode="wb", same_filepath_fn=True) data_dp = FileOpener(cache_dp, encoding="utf-8") return data_dp.parse_csv().map(fn=_modify_res).shuffle().set_shuffle(False).sharding_filter()

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