@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(('train', 'test'))
def AmazonReviewPolarity(root: str, split: Union[(Tuple[str], str)]):
    "AmazonReviewPolarity Dataset\n\n    For additional details refer to https://arxiv.org/abs/1509.01626\n\n    Number of lines per split:\n        - train: 3600000\n        - test: 400000\n\n    Args:\n        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')\n        split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `test`)\n\n    :returns: DataPipe that yields tuple of label (1 to 2) and text containing the review title and text\n    :rtype: (int, str)\n    "
    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])
    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').read_from_tar().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, encoding='utf-8')
    return data_dp.parse_csv().map(fn=(lambda t: (int(t[0]), ' '.join(t[1:]))))
AmazonReviewPolarity Dataset

For additional details refer to https://arxiv.org/abs/1509.01626

Number of lines per split:
    - train: 3600000
    - test: 400000

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 2) and text containing the review title and text
:rtype: (int, str)
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(('train', 'test'))
def AmazonReviewPolarity(root: str, split: Union[(Tuple[str], str)]):
    "AmazonReviewPolarity Dataset\n\n    For additional details refer to https://arxiv.org/abs/1509.01626\n\n    Number of lines per split:\n        - train: 3600000\n        - test: 400000\n\n    Args:\n        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')\n        split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `test`)\n\n    :returns: DataPipe that yields tuple of label (1 to 2) and text containing the review title and text\n    :rtype: (int, str)\n    "
    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])
    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').read_from_tar().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, encoding='utf-8')
    return data_dp.parse_csv().map(fn=(lambda t: (int(t[0]), ' '.join(t[1:]))))