readme: ---
  license: openrail
  ---
  pip install transformers
  from transformers import Trainer, TrainingArguments
  
  # Load the training and validation data
  train_data = ...
  validation_data = ...
  
  # Define the model architecture and hyperparameters
  model_name = "bert-base-cased"
  num_labels = 2
  
  # Define the training arguments
  training_args = TrainingArguments(
      output_dir="./output",  # directory to save the trained model
      num_train_epochs=3,  # number of training epochs
      per_device_train_batch_size=32,  # batch size
      per_device_eval_batch_size=64,  # batch size for evaluation
      warmup_steps=500,  # number of warmup steps
      weight_decay=0.01,  # L2 regularization coefficient
      learning_rate=3e-5,  # learning rate
      adam_epsilon=1e-8,  # epsilon for Adam optimizer
      max_grad_norm=1.0,  # maximum gradient norm for gradient clipping
      save_steps=1000,  # number of steps after which to save the model
      save_total_limit=2,  # maximum number of models to save
  )
  
  # Initialize the trainer
  trainer = Trainer(
      model_name=model_name,
      num_labels=num_labels,
      data_collator=data_collator,  # data collator for the training and validation data
      args=training_args,
  )
  
  # Train the model
  trainer.train(train_data, validation_data)
  