config_finetune.yaml

batch_size: 32                  # batch size
epochs: 100                     # total number of epochs
eval_every_n_epochs: 1          # validation frequency
fine_tune_from: pretrained      # directory of pre-trained model
log_every_n_steps: 50           # print training log frequency
gpu: cuda:0                     # training GPU
task_name: BBBP                 # name of fine-tuning benchmark, inlcuding
                                # classifications: BBBP/BACE/ClinTox/Tox21/HIV/SIDER/MUV
                                # regressions: FreeSolv/ESOL/Lipo/qm7/qm8

optim:
  lr: 0.0005                    # initial learning rate for the prediction head
  weight_decay: 0.000001        # weight decay of Adam
  base_ratio: 0.4               # ratio of learning rate for the base GNN encoder

model:                          # notice that other 'model' variables are defined from the config of pretrained model
  drop_ratio: 0.3               # dropout ratio
  pool: mean                    # readout pooling (i.e., mean/max/add)

dataset:
  num_workers: 4                # dataloader number of workers
  valid_size: 0.1               # ratio of validation data
  test_size: 0.1                # ratio of test data