class_docstr: Dataset based on two multilingual collections of short excerpts of journalistic texts,
  focused on language groups that are very similar and thus more difficult
  to correctly identify.
  
  Source: http://ttg.uni-saarland.de/resources/DSLCC
  
  References:
      Liling Tan, Marcos Zampieri, Nikola Ljubešić, Jörg Tiedemann (2014)
      Merging Comparable Data Sources for the Discrimination of Similar Languages:
      The DSL Corpus Collection. Proceedings of the 7th Workshop on Building
      and Using Comparable Corpora (BUCC). pp. 6-10. Reykjavik, Iceland.
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docstr: Args:
      force: If True, always download a new copy of the dataset; otherwise,
          only download dataset if it doesn't already exist on disk.
docstr_tok: ["Args",":","force",":","If","True",",","always","download","a","new","copy","of","the","dataset",";","otherwise",",","only","download","dataset","if","it","does","n't","already","exist","on","disk","."]
code: def download(self, force: bool = False):
      """
      Args:
          force: If True, always download a new copy of the dataset; otherwise,
              only download dataset if it doesn't already exist on disk.
      """
      for version in [3, 4]:
          name = f"dslcc{version}"
          url = f"http://scholar.harvard.edu/files/malmasi/files/{name}.zip"
          fpath = tio.download_file(
              url, dirpath=self.data_dir, force=force
          )
          if fpath:
              tio.unpack_archive(
                  fpath, extract_dir=self.data_dir.joinpath(name)
              )
  
imports: from __future__ import annotations
  import logging
  import operator
  import pathlib
  import random
  import re
  from typing import Dict, Iterable, List, Optional, Set, Tuple
  from cytoolz import itertoolz
  import textacy
  import textacy.utils
  from textacy import io
label_desc: Use image node_id 2 for calling the DSLCCDataset obj's underlying member method code with example usage: obj.download(force) without return types