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### Dataset
### Protein Language Model (PLM)
# facebook: esm2_t30_150M_UR50D esm2_t33_650M_UR50D esm2_t36_3B_UR50D
# rostLab: prot_bert prot_bert_bfd prot_t5_xl_uniref50 prot_t5_xl_bfd ankh-base ankh-large
# ESM model target_modules name: query key value
# Bert_base(prot_bert) model target_modules name: query key value
# T5_base(ankh, t5) model target_modules name: q k v
# if need to use HF mirror
export HF_ENDPOINT=https://hf-mirror.com
dataset=GO_BP
pdb_type=ESMFold
pooling_head=mean
plm_source=facebook
plm_model=esm2_t33_650M_UR50D
lr=5e-4
training_method=plm-adalora
python src/train.py \
--plm_model $plm_source/$plm_model \
--dataset_config data/$dataset/"$dataset"_"$pdb_type"_HF.json \
--learning_rate $lr \
--gradient_accumulation_steps 8 \
--num_epochs 100 \
--batch_token 12000 \
--patience 10 \
--output_dir debug/$dataset/$plm_model \
--output_model_name "$training_method"_"$pdb_type"_lr"$lr"_bt12k_ga8.pt \
--training_method $training_method \
--lora_target_modules query key value
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