class_docstr: The Penn Treebank dataset.
  A relatively small dataset originally created for POS tagging.
  
  References:
  Marcus, Mitchell P., Marcinkiewicz, Mary Ann & Santorini, Beatrice (1993).
  Building a Large Annotated Corpus of English: The Penn Treebank
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docstr: Create dataset objects for splits of the Penn Treebank dataset.
  
  Args:
      text_field: The field that will be used for text data.
      root: The root directory where the data files will be stored.
      train: The filename of the train data. Default: 'ptb.train.txt'.
      validation: The filename of the validation data, or None to not
          load the validation set. Default: 'ptb.valid.txt'.
      test: The filename of the test data, or None to not load the test
          set. Default: 'ptb.test.txt'.
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code: def splits(
      cls,
      text_field,
      root=".data",
      train="ptb.train.txt",
      validation="ptb.valid.txt",
      test="ptb.test.txt",
      **kwargs,
  ):
      """Create dataset objects for splits of the Penn Treebank dataset.
  
      Args:
          text_field: The field that will be used for text data.
          root: The root directory where the data files will be stored.
          train: The filename of the train data. Default: 'ptb.train.txt'.
          validation: The filename of the validation data, or None to not
              load the validation set. Default: 'ptb.valid.txt'.
          test: The filename of the test data, or None to not load the test
              set. Default: 'ptb.test.txt'.
      """
      return super(PennTreebank, cls).splits(
          root=root,
          train=train,
          validation=validation,
          test=test,
          text_field=text_field,
          **kwargs,
      )
  
file_path: pytext/pytext/legacy/datasets/language_modeling.py
imports: import io
  from ..None import data
label_desc: Use image node_id 1 for calling the PennTreebank obj's underlying member method code with example usage: obj.splits(cls, text_field, root, train, validation, test) and returns: super