56 lines
1.3 KiB
Bash
56 lines
1.3 KiB
Bash
function fulldata_hfdata() {
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SAVING_PATH=$1
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PRETRAIN_MODEL_PATH=$2
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TRAIN_BATCH_SIZE=$3
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EVAL_BATCH_SIZE=$4
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TRAIN_EPOCH=$5
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GRAD_ACCU_STEPS=$6
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LOGGING_STEPS=${7}
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LR=${8}
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mkdir -p ${SAVING_PATH}
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python -m torch.distributed.launch --nproc_per_node=4 ./ablationknowledge.py \
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--model_name_or_path t5-base \
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--output_dir ${SAVING_PATH} \
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--dataset_name squad \
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--do_train \
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--do_eval \
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--per_device_train_batch_size ${TRAIN_BATCH_SIZE} \
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--per_device_eval_batch_size ${EVAL_BATCH_SIZE} \
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--overwrite_output_dir \
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--gradient_accumulation_steps ${GRAD_ACCU_STEPS} \
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--num_train_epochs ${TRAIN_EPOCH} \
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--warmup_ratio 0.1 \
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--logging_steps ${LOGGING_STEPS} \
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--learning_rate ${LR} \
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--predict_with_generate \
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--num_beams 4 \
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--save_strategy no \
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--evaluation_strategy no \
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--weight_decay 1e-2 \
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--max_source_length 512 \
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--label_smoothing_factor 0.1 \
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--do_lowercase True \
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--load_best_model_at_end True \
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--greater_is_better True \
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--save_total_limit 10 \
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--ddp_find_unused_parameters False 2>&1 | tee ${SAVING_PATH}/log
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}
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SAVING_PATH=./ll
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TRAIN_BATCH_SIZE=$1
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EVAL_BATCH_SIZE=$2
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TRAIN_EPOCH=$3
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GRAD_ACCU_STEPS=2
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LOGGING_STEPS=5
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LR=1e-4
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MODE=try
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SAVING_PATH=${SAVING_PATH}/lifelong/${MODE}
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fulldata_hfdata ./ll t5-base ${TRAIN_BATCH_SIZE} ${EVAL_BATCH_SIZE} ${TRAIN_EPOCH} 2 5 1e-4
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