(self, instruction, label_column=None, drop=None,
                                  preprocess=True,
                                  test_size=0.2,
                                  random_state=49,
                                  learning_rate=1e-2,
                                  epochs=5,
                                  monitor="val_loss",
                                  batch_size=32,
                                  max_text_length=20,
                                  generate_plots=True,
                                  save_model=False,
                                  save_path=os.getcwd())
Calls the body of the text_classification query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param drop: A list of the dataset's columns to drop.
        :param preprocess: Preprocess the data (bool).
        :param test_size: Size of the testing set (float).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param learning_rate: The learning rate of the model (float).
        :param epochs: Number of epochs (int).
        :param batch_size: The batch size for the dataset (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 text_classification query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param drop: A list of the dataset's columns to drop.
        :param preprocess: Preprocess the data (bool).
        :param test_size: Size of the testing set (float).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param learning_rate: The learning rate of the model (float).
        :param epochs: Number of epochs (int).
        :param batch_size: The batch size for the dataset (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 text_classification query which is located in the nlp_queries . py file : param instruction : The objective that you want to model ( str ) . : param drop : A list of the dataset s columns to drop . : param preprocess : Preprocess the data ( bool ) . : param test_size : Size of the testing set ( float ) . : param random_state : Initialize a pseudo - random number generator ( int ) . : param learning_rate : The learning rate of the model ( float ) . : param epochs : Number of epochs ( int ) . : param batch_size : The batch size for the dataset ( 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 text_classification_query(self, instruction, label_column=None, drop=None,
                                  preprocess=True,
                                  test_size=0.2,
                                  random_state=49,
                                  learning_rate=1e-2,
                                  epochs=5,
                                  monitor="val_loss",
                                  batch_size=32,
                                  max_text_length=20,
                                  generate_plots=True,
                                  save_model=False,
                                  save_path=os.getcwd()):
        '''
        Calls the body of the text_classification query which is located in the nlp_queries.py file
        :param instruction: The objective that you want to model (str).
        :param drop: A list of the dataset's columns to drop.
        :param preprocess: Preprocess the data (bool).
        :param test_size: Size of the testing set (float).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param learning_rate: The learning rate of the model (float).
        :param epochs: Number of epochs (int).
        :param batch_size: The batch size for the dataset (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
        '''

        # storing values the model dictionary
        self.models["text_classification"] = text_classification_query(
            self=self, instruction=instruction, label_column=label_column, drop=drop,
            preprocess=preprocess,
            test_size=test_size,
            random_state=random_state,
            learning_rate=learning_rate,
            monitor=monitor,
            epochs=epochs,
            batch_size=batch_size,
            max_text_length=max_text_length,
            generate_plots=generate_plots,
            save_model=save_model,
            save_path=save_path)
        self.latest_model = 'text_classification'
        clearLog()
def text_classification_query ( self , instruction , label_column = None , drop = None , preprocess = True , test_size = 0.2 , random_state = 49 , learning_rate = 1e-2 , epochs = 5 , monitor = "val_loss" , batch_size = 32 , max_text_length = 20 , generate_plots = True , save_model = False , save_path = os . getcwd ( ) ) : # storing values the model dictionary self . models [ "text_classification" ] = text_classification_query ( self = self , instruction = instruction , label_column = label_column , drop = drop , preprocess = preprocess , test_size = test_size , random_state = random_state , learning_rate = learning_rate , monitor = monitor , epochs = epochs , batch_size = batch_size , max_text_length = max_text_length , generate_plots = generate_plots , save_model = save_model , save_path = save_path ) self . latest_model = 'text_classification' clearLog ( )