defaults:
  - paths: default
  - hydra: default  # ← tells Hydra to use the logging/output config
  - data_module: pair
  - model: classifier
  - trainer: gpu
  - extras: default
  - logger: wandb
  - callbacks: default
  - _self_

  # experiment configs allow for version control of specific hyperparameters
  # e.g. best hyperparameters for given model and datamodule
  - experiment: null

  # config for hyperparameter optimization
  - hparams_search: null

  # debugging config (enable through command line, e.g. `python train.py debug=default)
  - debug: null

task_name: eval/${model}

# tags to help you identify your experiments
# you can overwrite this in experiment configs
# overwrite from command line with `python train.py tags="[first_tag, second_tag]"`
tags: ["dev"]

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

# simply provide checkpoint path to resume training
ckpt_path: /home/a03-svincoff/DPACMAN/logs/train/classifier/runs/2025-08-25_18-08-13/checkpoints/epoch_009.ckpt

# seed for random number generators in pytorch, numpy and python.random
seed: 42

data_module:
  train_file: null
  val_file: null
  test_file: data_files/processed/splits/by_dna/test.csv