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run_clf.sh
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run_clf.sh
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# no aug
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --num_iter 5 --bsz 16
# # K2T
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file mix1_doc_aspect_avg_with_label --num_iter 5 --bsz 16
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file mix4_doc_aspect_avg_with_label --num_iter 5 --bsz 16
# # random, 4 times
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file random_re_in_sw_de_0.1_1/train_mix --num_iter 5 --bsz 16
# # # selective global, 4 times
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_global_re_in_se_de_0.05_Q2_1/train_mix --num_iter 5 --bsz 16
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_global_re_in_se_de_0.1_Q2_1/train_mix --num_iter 5 --bsz 16
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_global_re_in_se_de_0.2_Q2_1/train_mix --num_iter 5 --bsz 16
# # selective local, 4 times
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.05_Q2_1/train_mix --num_iter 5 --bsz 16
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.1_Q2_1/train_mix --num_iter 5 --bsz 16
# CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.2_Q2_1/train_mix --num_iter 5 --bsz 16
####################-------------ablation study------------------###########################
# selective augmentation
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.1_Q2_1/train_de --num_iter 5 --bsz 16 --comment de --group_head
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.1_Q2_1/train_se --num_iter 5 --bsz 16 --comment se
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.1_Q2_1/train_in --num_iter 5 --bsz 16 --comment in
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file selective_local_re_in_se_de_0.1_Q2_1/train_re --num_iter 5 --bsz 16 --comment re
# random augmentation
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file random_re_in_sw_de_0.1_1/train_de --num_iter 5 --bsz 16 --comment de --group_head
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file random_re_in_sw_de_0.1_1/train_sw --num_iter 5 --bsz 16 --comment sw
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file random_re_in_sw_de_0.1_1/train_in --num_iter 5 --bsz 16 --comment in
CUDA_VISIBLE_DEVICES=3 python clf.py --dataset bbc_50 --train_file random_re_in_sw_de_0.1_1/train_re --num_iter 5 --bsz 16 --comment re