def predict_zero_shot(self, sentences: Union[(List[Sentence], Sentence)], candidate_label_set: Union[(List[str], Set[str], str)], multi_label: bool=True):
    '\n        Method to make zero shot predictions from the TARS model\n        :param sentences: input sentence objects to classify\n        :param candidate_label_set: set of candidate labels\n        :param multi_label: indicates whether multi-label or single class prediction. Defaults to True.\n        '
    if ((candidate_label_set is None) or (len(candidate_label_set) == 0)):
        log.warning('Provided candidate_label_set is empty')
        return
    if isinstance(candidate_label_set, str):
        candidate_label_set = {candidate_label_set}
    label_dictionary = Dictionary(add_unk=False)
    for label in candidate_label_set:
        label_dictionary.add_item(label)
    existing_current_task = self._current_task
    self.add_and_switch_to_new_task(task_name='ZeroShot', label_dictionary=label_dictionary, label_type='-'.join(label_dictionary.get_items()), multi_label=multi_label)
    try:
        self.predict(sentences)
    finally:
        self.switch_to_task(existing_current_task)
        self._drop_task('ZeroShot')
    return
Method to make zero shot predictions from the TARS model
:param sentences: input sentence objects to classify
:param candidate_label_set: set of candidate labels
:param multi_label: indicates whether multi-label or single class prediction. Defaults to True.
def predict_zero_shot(self, sentences: Union[(List[Sentence], Sentence)], candidate_label_set: Union[(List[str], Set[str], str)], multi_label: bool=True):
    '\n        Method to make zero shot predictions from the TARS model\n        :param sentences: input sentence objects to classify\n        :param candidate_label_set: set of candidate labels\n        :param multi_label: indicates whether multi-label or single class prediction. Defaults to True.\n        '
    if ((candidate_label_set is None) or (len(candidate_label_set) == 0)):
        log.warning('Provided candidate_label_set is empty')
        return
    if isinstance(candidate_label_set, str):
        candidate_label_set = {candidate_label_set}
    label_dictionary = Dictionary(add_unk=False)
    for label in candidate_label_set:
        label_dictionary.add_item(label)
    existing_current_task = self._current_task
    self.add_and_switch_to_new_task(task_name='ZeroShot', label_dictionary=label_dictionary, label_type='-'.join(label_dictionary.get_items()), multi_label=multi_label)
    try:
        self.predict(sentences)
    finally:
        self.switch_to_task(existing_current_task)
        self._drop_task('ZeroShot')
    return