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run_zh.sh
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run_zh.sh
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# ARG
# python main.py \
# --seed 3759 \
# --gpu 2 \
# --lr 2e-5 \
# --model_name ARG \
# --language ch \
# --root_path /home/tzh/ARG/data/zh \
# --bert_path /home/tzh/model/chinese-bert-wwm-ext \
# --data_name zh-arg \
# --data_type rationale \
# --rationale_usefulness_evaluator_weight 2.2 \
# --llm_judgment_predictor_weight 1.8
python main.py \
--seed 0 \
--gpu 2 \
--lr 2e-5 \
--model_name ARG \
--language ch \
--root_path /home/tzh/ARG/data/zh \
--bert_path /home/tzh/model/chinese-bert-wwm-ext \
--data_name 测试种子 \
--data_type rationale \
--rationale_usefulness_evaluator_weight 2.2 \
--llm_judgment_predictor_weight 1.8
# # ARG-D
# python main.py \
# --seed 3759 \
# --gpu 1 \
# --lr 5e-4 \
# --model_name ARG-D \
# --language ch \
# --root_path /home/tzh/ARG/data/zh \
# --bert_path /home/tzh/model/chinese-bert-wwm-ext \
# --data_name zh-argd \
# --data_type rationale \
# --rationale_usefulness_evaluator_weight 2.2 \
# --llm_judgment_predictor_weight 1.8 \
# --kd_loss_weight 15 \
# --teacher_path ./param_model/ARG_zh-arg/1/parameter_bert.pkl
# python main.py \
# --seed 3759 \
# --gpu 1 \
# --lr 5e-4 \
# --model_name SLM \
# --language ch \
# --root_path /home/tzh/ARG/data/zh \
# --bert_path /home/tzh/model/chinese-bert-wwm-ext \
# --data_name zh-SLM \
# --data_type rationale \
# --rationale_usefulness_evaluator_weight 2.2 \
# --llm_judgment_predictor_weight 1.8 \
# 直接test 不train 为了尝试特征融合corrector写的slm纯test代码
# python main.py \
# --seed 3759 \
# --gpu 1 \
# --lr 5e-4 \
# --model_name SLM \
# --language ch \
# --root_path /home/tzh/ARG/data/zh \
# --bert_path /home/tzh/model/chinese-bert-wwm-ext \
# --data_name zh-SLM \
# --data_type rationale \
# --rationale_usefulness_evaluator_weight 2.2 \
# --llm_judgment_predictor_weight 1.8 \
# --eval_mode 1 \
# --eval_model_path /home/tzh/ARG/param_model/SLM_zh-SLM/1/parameter_bert.pkl