Source code for torchtext.datasets.multi30k

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": r"",
    "valid": r"",
    "test": r"",

MD5 = {
    "train": "20140d013d05dd9a72dfde46478663ba05737ce983f478f960c1123c6671be5e",
    "valid": "a7aa20e9ebd5ba5adce7909498b94410996040857154dab029851af3a866da8c",
    "test": "0681be16a532912288a91ddd573594fbdd57c0fbb81486eff7c55247e35326c2",

    "train": "train",
    "valid": "val",
    "test": "test",

    "train": 29000,
    "valid": 1014,
    "test": 1000,

DATASET_NAME = "Multi30k"

[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "valid", "test")) def Multi30k( root: str, split: Union[Tuple[str], str], language_pair: Tuple[str] = ("de", "en") ): """Multi30k dataset For additional details refer to Number of lines per split: - train: 29000 - valid: 1014 - test: 1000 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', 'valid', 'test') language_pair: tuple or list containing src and tgt language. Available options are ('de','en') and ('en', 'de') :return: DataPipe that yields tuple of source and target sentences :rtype: (str, str) """ assert ( len(language_pair) == 2 ), "language_pair must contain only 2 elements: src and tgt language respectively" assert tuple(sorted(language_pair)) == ( "de", "en", ), "language_pair must be either ('de','en') or ('en', 'de')" if not is_module_available("torchdata"): raise ModuleNotFoundError( "Package `torchdata` not found. Please install following instructions at ``" ) url_dp = IterableWrapper([URL[split]]) cache_compressed_dp = url_dp.on_disk_cache( filepath_fn=lambda x: os.path.join(root, os.path.basename(URL[split])), hash_dict={os.path.join(root, os.path.basename(URL[split])): MD5[split]}, hash_type="sha256", ) cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching( mode="wb", same_filepath_fn=True ) src_cache_decompressed_dp = cache_compressed_dp.on_disk_cache( filepath_fn=lambda x: os.path.join(root, f"{_PREFIX[split]}.{language_pair[0]}") ) src_cache_decompressed_dp = ( FileOpener(src_cache_decompressed_dp, mode="b") .read_from_tar() .filter(lambda x: f"{_PREFIX[split]}.{language_pair[0]}" in x[0]) ) src_cache_decompressed_dp = src_cache_decompressed_dp.end_caching( mode="wb", same_filepath_fn=True ) tgt_cache_decompressed_dp = cache_compressed_dp.on_disk_cache( filepath_fn=lambda x: os.path.join(root, f"{_PREFIX[split]}.{language_pair[1]}") ) tgt_cache_decompressed_dp = ( FileOpener(tgt_cache_decompressed_dp, mode="b") .read_from_tar() .filter(lambda x: f"{_PREFIX[split]}.{language_pair[1]}" in x[0]) ) tgt_cache_decompressed_dp = tgt_cache_decompressed_dp.end_caching( mode="wb", same_filepath_fn=True ) src_data_dp = FileOpener(src_cache_decompressed_dp, mode="b").readlines( decode=True, return_path=False, strip_newline=True ) tgt_data_dp = FileOpener(tgt_cache_decompressed_dp, mode="b").readlines( decode=True, return_path=False, strip_newline=True ) return


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