code: import pandas as pd
  import numpy as np
  import torch
  
  from sklearn.model_selection import train_test_split
  from backend.services.toxic_comment_jigsaw.application.ai.model import BERTClassifier
  from backend.services.toxic_comment_jigsaw.application.ai.training.src.dataset import BERTDataset
  from backend.services.toxic_comment_jigsaw.application.ai.training.src.preprocess import Preprocess
  from backend.services.toxic_comment_jigsaw.application.ai.training.src.engine import Engine
  from backend.services.toxic_comment_jigsaw.application.ai.settings import Settings
  
  from transformers import AdamW, get_linear_schedule_with_warmup
  from torch.utils.data import DataLoader
  
  
  class Train:
      def __init__(self):
          # initialize required class
          self.settings = Settings
          self.engine = Engine()
          self.preprocess = Preprocess()
  
          # initialize required variables
          self.bert_classifier = None
          self.optimizer = None
          self.scheduler = None
          self.train_data_loader = None
          self.val_data_loader = None
          self.total_steps = None
          self.best_accuracy = 0
  
      def __initialize(self):
          # Instantiate Bert Classifier
          self.bert_classifier = BERTClassifier(freeze_bert=False)
          self.bert_classifier.to(self.settings.DEVICE)
  
          # Create the optimizer
          self.optimizer = AdamW(self.bert_classifier.parameters(),
                                 lr=5e-5,  # Default learning rate
                                 eps=1e-8  # Default epsilon value
                                 )
          # Set up the learning rate scheduler
          self.scheduler = get_linear_schedule_with_warmup(self.optimizer,
                                                           num_warmup_steps=0,  # Default value
                                                           num_training_steps=self.total_steps)
  
      def crete_data_loaders(self, dataset):
          pass
  
      def load_data(self):
          train_df = pd.read_csv(self.settings.TRAIN_DATA).fillna("none")
          train_df['comment_text'] = train_df['comment_text'].apply(lambda x: self.preprocess.clean_text(x))
          X = list(train_df['comment_text'])
          y = np.array(train_df.loc[:, 'toxic':])
  
          X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=self.settings.RANDOM_STATE)
  
          # training dataset
          train_dataset = BERTDataset(X_train, y_train)
  
          # validation dataset
          val_dataset = BERTDataset(X_val, y_val)
  
          self.train_data_loader = DataLoader(train_dataset,
                                              batch_size=self.settings.TRAIN_BATCH_SIZE,
                                              shuffle=True,
                                              num_workers=self.settings.TRAIN_NUM_WORKERS)
  
          self.val_data_loader = DataLoader(val_dataset,
                                            batch_size=self.settings.VALID_BATCH_SIZE,
                                            shuffle=True,
                                            num_workers=self.settings.VAL_NUM_WORKERS)
  
          self.total_steps = int(len(X_train) / self.settings.TRAIN_BATCH_SIZE * self.settings.EPOCHS)
  
      def train(self):
          for epochs in range(self.settings.EPOCHS):
  
              # calling the training function in engine.py file
              self.engine.train_fn(data_loader=self.train_data_loader,
                                   model=self.bert_classifier,
                                   optimizer=self.optimizer,
                                   device=self.settings.DEVICE,
                                   schedular=self.scheduler)
  
              # calling the evaluation function from the engine.py file to compute evaluation
              val_loss, val_accuracy = self.engine.eval_fn(data_loader=self.val_data_loader,
                                                           model=self.bert_classifier,
                                                           device=self.settings.DEVICE)
  
              # updating the accuracy
              if val_accuracy > self.best_accuracy:
                  torch.save(self.bert_classifier.state_dict(), self.settings.MODEL_PATH)
                  self.best_accuracy = val_accuracy
  
      def run(self):
          try:
              print("Loading and Preparing the Dataset-----!! ")
              self.load_data()
              print("Dataset Successfully Loaded and Prepared-----!! ")
              print()
              print("-" * 70)
              print("Loading and Initializing the Bert Model -----!! ")
              self.__initialize()
              print("Model Successfully Loaded and Initialized-----!! ")
              print()
              print("-" * 70)
              print("------------------Starting Training-----------!!")
              self.engine.set_seed()
              self.train()
              print("Training complete-----!!!")
  
          except BaseException as ex:
              print("Following Exception Occurred---!! ", str(ex))
  
  
apis: numpy.array torch.utils.data.DataLoader sklearn.model_selection.train_test_split pandas.read_csv