97 lines
3.4 KiB
Bash
97 lines
3.4 KiB
Bash
#!/usr/bin/env bash
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# -*- coding:utf-8 -*-
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export batch_size="16"
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export model_name=uie-base-en
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export data_name=absa/14lap
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export task_name="meta"
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export decoding_format='spotasoc'
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source scripts/function_code.bash
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for index in $(seq 1 ${run_time}); do
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output_dir=${model_folder}_run${index}
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if [[ ${verbose} == true ]]
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then
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stdout_file=/dev/stdout
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stderr_file=/dev/stderr
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disable_tqdm=False
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else
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stdout_file=${output_dir}.log
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stderr_file=${output_dir}.err
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disable_tqdm=True
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fi
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ratio_data_folder=${data_folder}_ratio/seed${index}
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for ratio in $(ls ${ratio_data_folder})
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do
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run_data_folder=${ratio_data_folder}/${ratio}
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run_output_folder=${output_dir}_${ratio}
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if [[ ${max_prefix_length} == 0 ]]
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then
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run_output_folder=${run_output_folder}_noprefix
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fi
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eval_steps=$(python scripts/get_eval_batch_num.py ${run_data_folder}/train.json ${batch_size} 20)
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echo Eval each ${eval_steps} batch
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CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} ${run_command} run_seq2seq.py \
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--do_train --do_eval --do_predict ${constraint_decoding} ${fp16} \
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--trainer_type=${trainer_type} \
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--load_config_only=False \
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--use_fast_tokenizer=True \
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--ddp_find_unused_parameters=False \
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--predict_with_generate \
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--evaluation_strategy=steps \
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--save_strategy=steps \
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--load_best_model_at_end \
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--metric_for_best_model eval_overall-F1 \
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--save_total_limit 1 \
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--max_source_length=${max_source_length:-"256"} \
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--max_prefix_length=${max_prefix_length:-"-1"} \
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--max_target_length=${max_target_length:-"192"} \
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--num_train_epochs=${epoch} \
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--task=${task_name} \
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--train_file=${run_data_folder}/train.json \
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--validation_file=${run_data_folder}/val.json \
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--test_file=${run_data_folder}/test.json \
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--record_schema=${run_data_folder}/record.schema \
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--per_device_train_batch_size=${batch_size} \
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--per_device_eval_batch_size=$((batch_size * 4)) \
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--output_dir=${run_output_folder} \
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--logging_dir=${run_output_folder}_log \
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--model_name_or_path=${model_name} \
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--learning_rate=${lr} \
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--source_prefix="${task_name}: " \
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--lr_scheduler_type=${lr_scheduler} \
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--label_smoothing_factor=${label_smoothing} \
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--eval_steps ${eval_steps} \
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--decoding_format ${decoding_format} \
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--warmup_ratio ${warmup_ratio} \
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--preprocessing_num_workers=4 \
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--dataloader_num_workers=0 \
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--meta_negative=${negative} \
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--meta_positive_rate=${positive} \
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--skip_memory_metrics \
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--no_remove_unused_columns \
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--ordered_prompt=${ordered_prompt} \
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--save_better_checkpoint=True \
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--spot_noise=${spot_noise} \
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--asoc_noise=${asoc_noise} \
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--seed=${seed} --disable_tqdm=${disable_tqdm} >${stdout_file} 2>${stderr_file}
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echo "Map Config" ${map_config}
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python3 scripts/sel2record.py -p ${run_output_folder} -g ${run_data_folder} -v -d ${decoding_format} -c ${map_config}
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python3 scripts/eval_extraction.py -p ${run_output_folder} -g ${run_data_folder} -w -m ${eval_match_mode:-"normal"}
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# delete all pytorch_model.bin of checkpoints in low-resource exps for saving disk
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# find ${run_output_folder}/ | grep -P "checkpoint-\d+/pytorch_model.bin" | xargs rm -rf
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# delete all optimizer.pt in low-resource exps for saving disk
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# find ${run_output_folder}/ | grep -P "optimizer.pt" | xargs rm -rf
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done
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done
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