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res_dir: "tasks/results/deep_loc_binary"
ex_name: "deep_loc_binary_adapter"
offline: ${eval:"int(${oc.env:OFFLINE, '1'})"}
Data:
train_data_path: "/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/DeepLocBinary/mmseq_outdir/train.csv"
val_data_path: "/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/DeepLocBinary/mmseq_outdir/val.csv"
test_data_path: "/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/DeepLocBinary/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: 64
num_workers: 4
seq_len: 1024
Model:
pretrain_model_name: 'saport'
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: "deep_loc_binary"
task_type: "binary_classification"
num_classes: 1
# metric: "valid_auroc"
# direction: "max"
hydra:
output_subdir: null
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