/
run_seq2seq.bash
executable file
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/
run_seq2seq.bash
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#!/usr/bin/env bash
# -*- coding:utf-8 -*-
EXP_ID=$(date +%F-%H-%M-$RANDOM)
export CUDA_VISIBLE_DEVICES="0"
export batch_size="16"
export model_name=t5-base
export data_name=one_ie_ace2005_subtype
export lr=5e-5
export task_name="event"
export seed="421"
export lr_scheduler=constant_with_warmup
export label_smoothing="0"
export epoch=30
export decoding_format='tree'
export eval_steps=500
export warmup_steps=0
export constraint_decoding='--constraint_decoding'
OPTS=$(getopt -o b:d:m:i:t:s:l:f: --long batch:,device:,model:,data:,task:,seed:,lr:,lr_scheduler:,label_smoothing:,epoch:,format:,eval_steps:,warmup_steps:,wo_constraint_decoding -n 'parse-options' -- "$@")
if [ $? != 0 ]; then
echo "Failed parsing options." >&2
exit 1
fi
eval set -- "$OPTS"
while true; do
case "$1" in
-b | --batch)
batch_size="$2"
shift
shift
;;
-d | --device)
CUDA_VISIBLE_DEVICES="$2"
shift
shift
;;
-m | --model)
model_name="$2"
shift
shift
;;
-i | --data)
data_name="$2"
shift
shift
;;
-t | --task)
task_name="$2"
shift
shift
;;
-s | --seed)
seed="$2"
shift
shift
;;
-l | --lr)
lr="$2"
shift
shift
;;
-f | --format)
decoding_format="$2"
shift
shift
;;
--lr_scheduler)
lr_scheduler="$2"
shift
shift
;;
--label_smoothing)
label_smoothing="$2"
shift
shift
;;
--epoch)
epoch="$2"
shift
shift
;;
--eval_steps)
eval_steps="$2"
shift
shift
;;
--warmup_steps)
warmup_steps="$2"
shift
shift
;;
--wo_constraint_decoding)
constraint_decoding=""
shift
;;
--)
shift
break
;;
*)
echo "$1" not recognize.
exit
;;
esac
done
# google/mt5-base -> google_mt5-base
model_name_log=$(echo ${model_name} | sed -s "s/\//_/g")
model_folder=models/CF_${EXP_ID}_${model_name_log}_${decoding_format}_${data_name}_${lr_scheduler}_lr${lr}_ls${label_smoothing}_${batch_size}_wu${warmup_steps}
data_folder=data/text2${decoding_format}/${data_name}
export TOKENIZERS_PARALLELISM=false
output_dir=${model_folder}
CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} python run_seq2seq.py \
--do_train --do_eval --do_predict ${constraint_decoding} \
--label_smoothing_sum=False \
--use_fast_tokenizer=False \
--evaluation_strategy steps \
--predict_with_generate \
--metric_for_best_model eval_role-F1 \
--save_total_limit 1 \
--load_best_model_at_end \
--max_source_length=256 \
--max_target_length=128 \
--num_train_epochs=${epoch} \
--task=${task_name} \
--train_file=${data_folder}/train.json \
--validation_file=${data_folder}/val.json \
--test_file=${data_folder}/test.json \
--event_schema=${data_folder}/event.schema \
--per_device_train_batch_size=${batch_size} \
--per_device_eval_batch_size=$((batch_size * 4)) \
--output_dir=${output_dir} \
--logging_dir=${output_dir}_log \
--model_name_or_path=${model_name} \
--learning_rate=${lr} \
--lr_scheduler_type=${lr_scheduler} \
--label_smoothing_factor=${label_smoothing} \
--eval_steps ${eval_steps} \
--decoding_format ${decoding_format} \
--warmup_steps ${warmup_steps} \
--source_prefix="${task_name}: " \
--seed=${seed} --disable_tqdm True >${output_dir}.log 2>${output_dir}.log