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

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