regular.yaml

model_checkpoint:
  _target_: pytorch_lightning.callbacks.ModelCheckpoint
  monitor: "val/f1_score_weighted" # name of the logged metric which determines when model is improving
  mode: "max" # can be "max" or "min"
  save_top_k: 1 # save k best models (determined by above metric)
  save_last: False # additionaly always save model from last epoch
  verbose: False
  dirpath: "checkpoints/"
  filename: "{epoch:03d}"
  auto_insert_metric_name: False

early_stopping:
  _target_: pytorch_lightning.callbacks.EarlyStopping
  monitor: "val/f1_score_weighted" # name of the logged metric which determines when model is improving
  mode: "max" # can be "max" or "min"
  patience: 100 # how many epochs of not improving until training stops
  min_delta: 0 # minimum change in the monitored metric needed to qualify as an improvement