my code:
return tokenizer(list(dataset['sentense']),
                          padding = True,
                          truncation = True,
                          max_length = 128 )

  training_args = TrainingArguments(
    output_dir='./results',          # output directory
    save_total_limit=5,              # number of total save model.
    save_steps=5000,                 # model saving step.
    num_train_epochs=20,              # total number of training epochs
    learning_rate=5e-5,               # learning_rate
    per_device_train_batch_size=16,  # batch size per device during training
    per_device_eval_batch_size=16,   # batch size for evaluation
...

Hello, I understand the docs like that

If I want to use dynamic padding → padding=True
or not (max_length) → padding = 'max_length

Is it right?
And I want to use smart batching,
Does per_device_train_batch_size automatically support this feature?
If not, I wonder if there is anything that make me to use smart batching.
Thanks!!