code: def splits(cls, fields, root=".data", train="train.txt",
                 test="test.txt", validation_frac=0.1, **kwargs):
          
  
          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
docs: 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