Create dataset objects for splits of the SST dataset.

        Arguments:
            text_field: The field that will be used for the sentence.
            label_field: The field that will be used for label data.
            root: The root directory that the dataset's zip archive will be
                expanded into; therefore the directory in whose trees
                subdirectory the data files will be stored.
            train: The filename of the train data. Default: 'train.txt'.
            validation: The filename of the validation data, or None to not
                load the validation set. Default: 'dev.txt'.
            test: The filename of the test data, or None to not load the test
                set. Default: 'test.txt'.
            train_subtrees: Whether to use all subtrees in the training set.
                Default: False.
            Remaining keyword arguments: Passed to the splits method of
                Dataset.

def splits(cls, text_field, label_field, root='.data',
               train='train.txt', validation='dev.txt', test='test.txt',
               train_subtrees=False, **kwargs):
        """Create dataset objects for splits of the SST dataset.

        Arguments:
            text_field: The field that will be used for the sentence.
            label_field: The field that will be used for label data.
            root: The root directory that the dataset's zip archive will be
                expanded into; therefore the directory in whose trees
                subdirectory the data files will be stored.
            train: The filename of the train data. Default: 'train.txt'.
            validation: The filename of the validation data, or None to not
                load the validation set. Default: 'dev.txt'.
            test: The filename of the test data, or None to not load the test
                set. Default: 'test.txt'.
            train_subtrees: Whether to use all subtrees in the training set.
                Default: False.
            Remaining keyword arguments: Passed to the splits method of
                Dataset.
        """
        path = cls.download(root)

        train_data = None if train is None else cls(
            os.path.join(path, train), text_field, label_field, subtrees=train_subtrees,
            **kwargs)
        val_data = None if validation is None else cls(
            os.path.join(path, validation), text_field, label_field, **kwargs)
        test_data = None if test is None else cls(
            os.path.join(path, test), text_field, label_field, **kwargs)
        return tuple(d for d in (train_data, val_data, test_data)
                     if d is not None)