docstr: Downloads and loads the CoNLL 2000 Chunking dataset.
  NOTE: There is only a train and test dataset so we use 10% of the train set as validation
code: def splits(
      cls,
      fields,
      root=".data",
      train="train.txt",
      test="test.txt",
      validation_frac=0.1,
      **kwargs,
  ):
      """Downloads and loads the CoNLL 2000 Chunking dataset.
      NOTE: There is only a train and test dataset so we use 10% of the train set as validation
      """
  
      train, test = super(CoNLL2000Chunking, cls).splits(
          fields=fields,
          root=root,
          train=train,
          test=test,
          separator=" ",
          **kwargs,
      )
  
      # HACK: Saving the sort key function as the split() call removes it
      sort_key = train.sort_key
  
      # Now split the train set
      # Force a random seed to make the split deterministic
      random.seed(0)
      train, val = train.split(
          1 - validation_frac, random_state=random.getstate()
      )
      # Reset the seed
      random.seed()
  
      # HACK: Set the sort key
      train.sort_key = sort_key
      val.sort_key = sort_key
  
      return train, val, test
  
file_path: pytext/pytext/legacy/datasets/sequence_tagging.py
imports: import random
  from ..None import data
label_desc: Use image node_id 1 for calling the CoNLL2000Chunking obj's underlying member method code with example usage: obj.splits(cls, fields, root, train, test, validation_frac) and returns: train,  val,  test