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()