text: class Hyperparams:
      """Hyperparameters"""
  
      # data
      source_train = "corpora/cn.txt"
      target_train = "corpora/en.txt"
      source_test = "corpora/cn.test.txt"
      target_test = "corpora/en.test.txt"
  
      # training
      batch_size = 48  # alias = N
      batch_size_valid = 32
      lr = (
          0.0001  # learning rate. In paper, learning rate is adjusted to the global step.
      )
      logdir = "logdir"  # log directory
  
      model_dir = "./models/"  # saving directory
  
      # model
      maxlen = 50  # Maximum number of words in a sentence. alias = T.
      min_cnt = 0  # words whose occurred less than min_cnt are encoded as <UNK>.
      hidden_units = 512  # alias = C
      num_blocks = 12  # number of encoder/decoder blocks
      num_epochs = 50
      num_heads = 8
      dropout_rate = 0.4
      sinusoid = False  # If True, use sinusoid. If false, positional embedding.
      eval_epoch = 1  # epoch of model for eval
      eval_script = 'scripts/validate.sh'
      check_frequency = 10  # checkpoint frequency
  