text: from simpletransformers.language_modeling import LanguageModelingModel
  import logging
  
  
  logging.basicConfig(level=logging.INFO)
  transformers_logger = logging.getLogger("transformers")
  transformers_logger.setLevel(logging.WARNING)
  
  train_args = {
      "reprocess_input_data": False,
      "overwrite_output_dir": True,
      "num_train_epochs": 3,
      "save_eval_checkpoints": True,
      "save_model_every_epoch": False,
      "learning_rate": 5e-4,
      "warmup_steps": 10000,
      "train_batch_size": 16,
      "eval_batch_size": 16,
      "gradient_accumulation_steps": 1,
      "block_size": 128,
      "max_seq_length": 128,
      "dataset_type": "simple",
      "wandb_project": "ELECTRA - Indonesian",
      "wandb_kwargs": {"name": "Electra-SMALL"},
      "logging_steps": 100,
      "evaluate_during_training": True,
      "evaluate_during_training_steps": 50000,
      "evaluate_during_training_verbose": True,
      "use_cached_eval_features": True,
      "sliding_window": True,
      "vocab_size": 52000,
      "generator_config": {
          "embedding_size": 128,
          "hidden_size": 256,
          "num_hidden_layers": 3,
      },
      "discriminator_config": {
          "embedding_size": 128,
          "hidden_size": 256,
      },
      "fp16": False,
  }
  
  train_file = "data/train.txt"
  test_file = "data/test.txt"
  
  model = LanguageModelingModel(
      "electra",
      None,
      args=train_args,
      train_files=train_file,
  )
  
  
  model.train_model(
      train_file, eval_file=test_file,
  )
  
  model.eval_model(test_file)
  