text: from multiprocessing import cpu_count
  
  SEED = 777
  TEMP_DIRECTORY = "temp/data"
  RESULT_FILE = "result.tsv"
  SUBMISSION_FILE = "predictions.txt"
  RESULT_IMAGE = "result.jpg"
  GOOGLE_DRIVE = False
  DRIVE_FILE_ID = None
  MODEL_TYPE = "xlmroberta"
  MODEL_NAME = "xlm-roberta-large"
  
  monotransquest_config = {
      'output_dir': 'temp/outputs/',
      "best_model_dir": "temp/outputs/best_model",
      'cache_dir': 'temp/cache_dir/',
  
      'fp16': False,
      'fp16_opt_level': 'O1',
      'max_seq_length': 80,
      'train_batch_size': 8,
      'gradient_accumulation_steps': 1,
      'eval_batch_size': 8,
      'num_train_epochs': 3,
      'weight_decay': 0,
      'learning_rate': 1e-5,
      'adam_epsilon': 1e-8,
      'warmup_ratio': 0.06,
      'warmup_steps': 0,
      'max_grad_norm': 1.0,
      'do_lower_case': False,
  
      'logging_steps': 600,
      'save_steps': 600,
      "no_cache": False,
      'save_model_every_epoch': True,
      'n_fold': 3,
      'evaluate_during_training': True,
      'evaluate_during_training_steps': 600,
      "evaluate_during_training_verbose": True,
      'use_cached_eval_features': False,
      'save_eval_checkpoints': True,
      'tensorboard_dir': None,
  
      'regression': True,
  
      'overwrite_output_dir': True,
      'reprocess_input_data': True,
  
      'process_count': cpu_count() - 2 if cpu_count() > 2 else 1,
      'n_gpu': 1,
      'use_multiprocessing': True,
      'silent': False,
  
      'wandb_project': None,
      'wandb_kwargs': {},
  
      "use_early_stopping": True,
      "early_stopping_patience": 10,
      "early_stopping_delta": 0,
      "early_stopping_metric": "eval_loss",
      "early_stopping_metric_minimize": True,
  
      "manual_seed": 777,
  
      "encoding": None,
  }
  