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Script-B_finetuning_and_prediction-PMI-batchsize-10.sh
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Script-B_finetuning_and_prediction-PMI-batchsize-10.sh
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# 10-shot
export KMER=6
export MODEL_PATH=./models/output$KMER-PMImasking-warmup-correction-batch-10
export DATA_PATH=./sample_data/ft-fewshot/10/prom300-both
export OUTPUT_PATH=.sample_data/ft-fewshot/10/prom300-both/pmi10000-full-warmup
CUDA_VISIBLE_DEVICES=1 python run_finetune.py \
--model_type dna \
--tokenizer_name=dna$KMER \
--model_name_or_path $MODEL_PATH \
--task_name dnaprom \
--do_train \
--do_eval \
--data_dir $DATA_PATH \
--max_seq_length 310 \
--per_gpu_eval_batch_size=9 \
--per_gpu_train_batch_size=5 \
--learning_rate 1e-4 \
--num_train_epochs 20.0 \
--output_dir $OUTPUT_PATH \
--logging_steps 100 \
--save_steps 4000 \
--warmup_percent 0.1 \
--hidden_dropout_prob 0.1 \
--overwrite_output_dir \
--weight_decay 0.01 \
--n_process 8
# 1000-shot
export KMER=6
export MODEL_PATH=./models/output$KMER-PMImasking-warmup-correction-batch-10
export DATA_PATH=./sample_data/ft-fewshot/1000/prom300-both
export OUTPUT_PATH=./sample_data/ft-fewshot/1000/prom300-both/pmi10000-full-warmup
CUDA_VISIBLE_DEVICES=0 python run_finetune.py \
--model_type dna \
--tokenizer_name=dna$KMER \
--model_name_or_path $MODEL_PATH \
--task_name dnaprom \
--do_train \
--do_eval \
--data_dir $DATA_PATH \
--max_seq_length 310 \
--per_gpu_eval_batch_size=9 \
--per_gpu_train_batch_size=15 \
--learning_rate 5e-5 \
--num_train_epochs 5.0 \
--output_dir $OUTPUT_PATH \
--logging_steps 100 \
--save_steps 4000 \
--warmup_percent 0.1 \
--hidden_dropout_prob 0.1 \
--overwrite_output_dir \
--weight_decay 0.01 \
--n_process 8