Method used to instantiate a LanguageModelData object that can be used for a
            supported nlp task.

        Args:
            path (str): the absolute path in which temporary model data will be saved
            field (Field): torchtext field
            train (str): file location of the training data
            validation (str): file location of the validation data
            test (str): file location of the testing data
            bs (int): batch size to use
            bptt (int): back propagation through time hyper-parameter
            kwargs: other arguments

        Returns:
            a LanguageModelData instance, which most importantly, provides us the datasets for training,
                validation, and testing

        Note:
            The train, validation, and test path can be pointed to any file (or folder) that contains a valid
                text corpus.

def from_text_files(cls, path, field, train, validation, test=None, bs=64, bptt=70, **kwargs):
        """ Method used to instantiate a LanguageModelData object that can be used for a
            supported nlp task.

        Args:
            path (str): the absolute path in which temporary model data will be saved
            field (Field): torchtext field
            train (str): file location of the training data
            validation (str): file location of the validation data
            test (str): file location of the testing data
            bs (int): batch size to use
            bptt (int): back propagation through time hyper-parameter
            kwargs: other arguments

        Returns:
            a LanguageModelData instance, which most importantly, provides us the datasets for training,
                validation, and testing

        Note:
            The train, validation, and test path can be pointed to any file (or folder) that contains a valid
                text corpus.

        """
        trn_ds, val_ds, test_ds = ConcatTextDataset.splits(
            path, text_field=field, train=train, validation=validation, test=test)
        return cls(path, field, trn_ds, val_ds, test_ds, bs, bptt, **kwargs)