(self, instruction, label_column=None, preprocess=True,
                            drop=None,
                            epochs=5,
                            batch_size=32,
                            learning_rate=3e-5,
                            max_text_length=512,
                            test_size=0.2,
                            gpu=False,
                            random_state=49,
                            generate_plots=True,
                            save_model=False,
                            save_path=os.getcwd())
Calls the body of the summarization  query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param preprocess: Preprocess the data (bool).
        :param drop: A list of the dataset's columns to drop.
        :param epochs: Number of epochs (int).
        :param batch_size: The batch size for the dataset (int).
        :param learning_rate: The learning rate of the model (float).
        :param max_text_length: The maximum length of the string of text (int).
        :param test_size: Size of the testing set (float).
        :param gpu: Use gpu for accelerated training (bool).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param generate_plots: Generate plots for the model (bool).
        :param save_model: Save the model (bool).
        :param save_path: Filepath of where to save the model (str).


        :return: an updated model and history stored in the models dictionary
Calls the body of the summarization  query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param preprocess: Preprocess the data (bool).
        :param drop: A list of the dataset's columns to drop.
        :param epochs: Number of epochs (int).
        :param batch_size: The batch size for the dataset (int).
        :param learning_rate: The learning rate of the model (float).
        :param max_text_length: The maximum length of the string of text (int).
        :param test_size: Size of the testing set (float).
        :param gpu: Use gpu for accelerated training (bool).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param generate_plots: Generate plots for the model (bool).
        :param save_model: Save the model (bool).
        :param save_path: Filepath of where to save the model (str).
Calls the body of the summarization query which is located in the nlp_queries . py file : param instruction : The objective that you want to model ( str ) . : param preprocess : Preprocess the data ( bool ) . : param drop : A list of the dataset s columns to drop . : param epochs : Number of epochs ( int ) . : param batch_size : The batch size for the dataset ( int ) . : param learning_rate : The learning rate of the model ( float ) . : param max_text_length : The maximum length of the string of text ( int ) . : param test_size : Size of the testing set ( float ) . : param gpu : Use gpu for accelerated training ( bool ) . : param random_state : Initialize a pseudo - random number generator ( int ) . : param generate_plots : Generate plots for the model ( bool ) . : param save_model : Save the model ( bool ) . : param save_path : Filepath of where to save the model ( str ) .
def summarization_query(self, instruction, label_column=None, preprocess=True,
                            drop=None,
                            epochs=5,
                            batch_size=32,
                            learning_rate=3e-5,
                            max_text_length=512,
                            test_size=0.2,
                            gpu=False,
                            random_state=49,
                            generate_plots=True,
                            save_model=False,
                            save_path=os.getcwd()):
        '''
        Calls the body of the summarization  query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param preprocess: Preprocess the data (bool).
        :param drop: A list of the dataset's columns to drop.
        :param epochs: Number of epochs (int).
        :param batch_size: The batch size for the dataset (int).
        :param learning_rate: The learning rate of the model (float).
        :param max_text_length: The maximum length of the string of text (int).
        :param test_size: Size of the testing set (float).
        :param gpu: Use gpu for accelerated training (bool).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param generate_plots: Generate plots for the model (bool).
        :param save_model: Save the model (bool).
        :param save_path: Filepath of where to save the model (str).


        :return: an updated model and history stored in the models dictionary
        '''

        self.models["summarization"] = summarization_query(
            self=self, instruction=instruction, preprocess=preprocess, label_column=label_column,
            drop=drop,
            epochs=epochs,
            batch_size=batch_size,
            learning_rate=learning_rate,
            max_text_length=max_text_length,
            test_size=test_size,
            gpu=gpu,
            random_state=random_state,
            generate_plots=generate_plots,
            save_model=save_model,
            save_path=save_path)

        self.latest_model = 'summarization'
        clearLog()
def summarization_query ( self , instruction , label_column = None , preprocess = True , drop = None , epochs = 5 , batch_size = 32 , learning_rate = 3e-5 , max_text_length = 512 , test_size = 0.2 , gpu = False , random_state = 49 , generate_plots = True , save_model = False , save_path = os . getcwd ( ) ) : self . models [ "summarization" ] = summarization_query ( self = self , instruction = instruction , preprocess = preprocess , label_column = label_column , drop = drop , epochs = epochs , batch_size = batch_size , learning_rate = learning_rate , max_text_length = max_text_length , test_size = test_size , gpu = gpu , random_state = random_state , generate_plots = generate_plots , save_model = save_model , save_path = save_path ) self . latest_model = 'summarization' clearLog ( )