task:
  hub_module_url: ''
  model:
    num_classes: 3
  init_checkpoint: ''
  metric_type: 'accuracy'
  train_data:
    drop_remainder: true
    global_batch_size: 32
    input_path: ''
    is_training: true
    seq_length: 128
    label_type: 'int'
  validation_data:
    drop_remainder: false
    global_batch_size: 32
    input_path: ''
    is_training: false
    seq_length: 128
    label_type: 'int'
trainer:
  checkpoint_interval: 3000
  optimizer_config:
    learning_rate:
      polynomial:
        # 100% of train_steps.
        decay_steps: 36813
        end_learning_rate: 0.0
        initial_learning_rate: 3.0e-05
        power: 1.0
      type: polynomial
    optimizer:
      type: adamw
    warmup:
      polynomial:
        power: 1
        # ~10% of train_steps.
        warmup_steps: 3681
      type: polynomial
  steps_per_loop: 1000
  summary_interval: 1000
  # Training data size 392,702 examples, 3 epochs.
  train_steps: 36813
  validation_interval: 6135
  # Eval data size = 9815 examples.
  validation_steps: 307
  best_checkpoint_export_subdir: 'best_ckpt'
  best_checkpoint_eval_metric: 'cls_accuracy'
  best_checkpoint_metric_comp: 'higher'