| 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 |