def __init__(self, model: Optional[str]='bert-large-uncased', custom_model: PreTrainedModel=None, custom_tokenizer: PreTrainedTokenizer=None, hidden: Union[(List[int], int)]=(- 2), reduce_option: str='mean', sentence_handler: SentenceHandler=SentenceHandler(), random_state: int=12345, hidden_concat: bool=False, gpu_id: int=0):
    '\n        This is the parent Bert Summarizer model. New methods should implement this class.\n\n        :param model: This parameter is associated with the inherit string parameters from the transformers library.\n        :param custom_model: If you have a pre-trained model, you can add the model class here.\n        :param custom_tokenizer: If you have a custom tokenizer, you can add the tokenizer here.\n        :param hidden: This signifies which layer(s) of the BERT model you would like to use as embeddings.\n        :param reduce_option: Given the output of the bert model, this param determines how you want to reduce results.\n        :param sentence_handler: The handler to process sentences. If want to use coreference, instantiate and pass.\n        CoreferenceHandler instance\n        :param random_state: The random state to reproduce summarizations.\n        :param hidden_concat: Whether or not to concat multiple hidden layers.\n        :param gpu_id: GPU device index if CUDA is available.\n        '
    model = BertEmbedding(model, custom_model, custom_tokenizer, gpu_id)
    model_func = partial(model, hidden=hidden, reduce_option=reduce_option, hidden_concat=hidden_concat)
    super().__init__(model_func, sentence_handler, random_state)
This is the parent Bert Summarizer model. New methods should implement this class.

:param model: This parameter is associated with the inherit string parameters from the transformers library.
:param custom_model: If you have a pre-trained model, you can add the model class here.
:param custom_tokenizer: If you have a custom tokenizer, you can add the tokenizer here.
:param hidden: This signifies which layer(s) of the BERT model you would like to use as embeddings.
:param reduce_option: Given the output of the bert model, this param determines how you want to reduce results.
:param sentence_handler: The handler to process sentences. If want to use coreference, instantiate and pass.
CoreferenceHandler instance
:param random_state: The random state to reproduce summarizations.
:param hidden_concat: Whether or not to concat multiple hidden layers.
:param gpu_id: GPU device index if CUDA is available.
def __init__(self, model: Optional[str]='bert-large-uncased', custom_model: PreTrainedModel=None, custom_tokenizer: PreTrainedTokenizer=None, hidden: Union[(List[int], int)]=(- 2), reduce_option: str='mean', sentence_handler: SentenceHandler=SentenceHandler(), random_state: int=12345, hidden_concat: bool=False, gpu_id: int=0):
    '\n        This is the parent Bert Summarizer model. New methods should implement this class.\n\n        :param model: This parameter is associated with the inherit string parameters from the transformers library.\n        :param custom_model: If you have a pre-trained model, you can add the model class here.\n        :param custom_tokenizer: If you have a custom tokenizer, you can add the tokenizer here.\n        :param hidden: This signifies which layer(s) of the BERT model you would like to use as embeddings.\n        :param reduce_option: Given the output of the bert model, this param determines how you want to reduce results.\n        :param sentence_handler: The handler to process sentences. If want to use coreference, instantiate and pass.\n        CoreferenceHandler instance\n        :param random_state: The random state to reproduce summarizations.\n        :param hidden_concat: Whether or not to concat multiple hidden layers.\n        :param gpu_id: GPU device index if CUDA is available.\n        '
    model = BertEmbedding(model, custom_model, custom_tokenizer, gpu_id)
    model_func = partial(model, hidden=hidden, reduce_option=reduce_option, hidden_concat=hidden_concat)
    super().__init__(model_func, sentence_handler, random_state)