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Source code for torchtext.datasets.conll2000chunking

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://www.clips.uantwerpen.be/conll2000/chunking/train.txt.gz",
    "test": "https://www.clips.uantwerpen.be/conll2000/chunking/test.txt.gz",
}

MD5 = {
    "train": "6969c2903a1f19a83569db643e43dcc8",
    "test": "a916e1c2d83eb3004b38fc6fcd628939",
}

NUM_LINES = {
    "train": 8936,
    "test": 2012,
}

_EXTRACTED_FILES = {"train": "train.txt", "test": "test.txt"}

DATASET_NAME = "CoNLL2000Chunking"


def _filepath_fn(root, split, _=None):
    return os.path.join(root, os.path.basename(URL[split]))


def _extracted_filepath_fn(root, split, _=None):
    return os.path.join(root, _EXTRACTED_FILES[split])


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "test")) def CoNLL2000Chunking(root: str, split: Union[Tuple[str], str]): """CoNLL2000Chunking 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://www.clips.uantwerpen.be/conll2000/chunking/ Number of lines per split: - train: 8936 - test: 2012 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 list of words along with corresponding Parts-of-speech tag and chunk tag :rtype: [list(str), list(str), list(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 and check HTTP response cache_compressed_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_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True) # Cache and check the gzip extraction for relevant split cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split)) cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").extract(file_type="gzip") 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.readlines().read_iob(sep=" ").shuffle().set_shuffle(False).sharding_filter()

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