config.yaml

# @package _global_

# specify here default training configuration
defaults: 
  - _self_
  - trainer: default.yaml
  - model: debiaser.yaml
  - datamodule: default.yaml
  - callbacks: default.yaml
  - logger: tensorboard.yaml
  - experiment: null
  - hydra: default.yaml

  - override hydra/job_logging: colorlog
  - override hydra/hydra_logging: colorlog

seed: 42

# path to original working directory
# hydra hijacks working directory by changing it to the current log directory,
# so it's useful to have this path as a special variable
# learn more here: https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory
work_dir: ${hydra:runtime.cwd}

# path to folder with data
data_dir: ${work_dir}/data

exp_name: ${model.model_name}/${model.embedding_layer}-layer/${model.debias_mode}-debias

# use `python run.py debug=true` for easy debugging!
# this will run 1 train, val and test loop with only 1 batch
# equivalent to running `python run.py trainer.fast_dev_run=true`
# (this is placed here just for easier access from command line)
debug: False

# pretty print config at the start of the run using Rich library
print_config: True

# disable python warnings if they annoy you
ignore_warnings: True

# check performance on test set, using the best model achieved during training
# lightning chooses best model based on metric specified in checkpoint callback
test_after_training: True