| Output: | |
| res_dir: "tasks/results/optimal_ph" | |
| ex_name: "optimal_ph_adapter" # ${oc.env:EXP_NAME, 'default'} | |
| offline: ${eval:"int(${oc.env:OFFLINE, '1'})"} | |
| Data: | |
| train_data_path: "/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/optimal_ph/mmseq_outdir/train.csv" | |
| val_data_path: "/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/optimal_ph/mmseq_outdir/val.csv" | |
| test_data_path: "/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/optimal_ph/mmseq_outdir/test.csv" | |
| Training: | |
| epoch: 50 # end epoch | |
| lr: 1e-4 # Learning rate | |
| lr_scheduler: "cosine" | |
| check_val_every_n_epoch: 1 | |
| seed: 2024 | |
| batch_size: 8 | |
| num_workers: 4 | |
| seq_len: 1024 | |
| Model: | |
| pretrain_model_name: 'protrek' | |
| finetune_type: 'adapter' | |
| lora_r: 8 # 低秩矩阵的秩 | |
| lora_alpha: 32 # LoRA 的 alpha 参数 | |
| lora_dropout: 0.1 # Dropout 防止过拟合 | |
| Task: | |
| task_name: "optimal_ph" | |
| task_type: "regression" | |
| num_classes: 1 | |
| # metric: "valid_spearman" | |
| # direction: "max" | |
| hydra: | |
| output_subdir: null | |