Source code for torchtext.datasets.conll2000chunking

import os
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, HttpReader, IterableWrapper


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"


@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "test"))
def CoNLL2000Chunking(root: str, split: Union[Tuple[str], str]):
    """CoNLL2000Chunking Dataset

    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=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="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=lambda x: os.path.join(root, _EXTRACTED_FILES[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, mode="b")
    return data_dp.readlines(decode=True).read_iob(sep=" ")