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exp.sh
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exp.sh
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tagging=IO
k=5shots
task=ner
dataset=conll2003
for mode in standard standard_wrong standard_no_l random_totally random_support
do
for id in 1 2 3 4 5
do
for seed in 11 42 55
do
python3 train.py --gpu 0 \
--task ${task} \
--dataset ${dataset} \
--tagging ${tagging} \
--mode ${mode} \
--do_train \
--do_eval \
--do_predict \
--evaluate_during_training \
--evaluate_period 2 \
--batch_size 4 \
--learning_rate 2e-5 \
--num_train_epochs 50 \
--max_seq_length 300 \
--output_dir out/${task}-${k}/${dataset}-${tagging}/${mode}/${id}_${seed} \
--few_shot_dir data/${task}/${dataset}/${k}/${id} \
--seed $seed
done
done
done
for mode in no
do
for id in 1 2 3 4 5
do
for seed in 11 42 55
do
python3 train.py --gpu 0 \
--task ${task} \
--dataset ${dataset} \
--tagging ${tagging} \
--mode ${mode} \
--do_train \
--do_eval \
--do_predict \
--evaluate_during_training \
--evaluate_period 2 \
--batch_size 4 \
--learning_rate 2e-5 \
--num_train_epochs 50 \
--max_seq_length 256 \
--output_dir out/${task}-${k}/${dataset}-${tagging}/${mode}/${id}_${seed} \
--few_shot_dir data/${task}/${dataset}/${k}/${id} \
--seed $seed
done
done
done