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run_bert_ner.sh
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run_bert_ner.sh
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#!/usr/bin/env bash
# # source domain
python bert_ner.py
--do_lower_case=False \
--do_train=True \
--do_eval=True \
--do_test=True \
--data_dir=./data/MSRA/ \
--vocab_file=data/vocab.txt \
--bert_config_file=chinese_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=chinese_L-12_H-768_A-12/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=10.0 \
--dropout_rate=0.5 \
--output_dir=./output/MSRA/bert_bilstm_crf/ \
--bilstm=True \
--crf=True
# target domain
# using bert
# python bert_ner.py\
# --do_lower_case=False \
# --do_train=False \
# --do_eval=False \
# --do_test=True \
# --data_dir=./data/ywevents \
# --vocab_file=./chinese_L-12_H-768_A-12/vocab_update.txt \
# --bert_config_file=./chinese_L-12_H-768_A-12/bert_config.json \
# --init_checkpoint=./output/MSRA/bert/model.ckpt-4749 \
# --max_seq_length=128 \
# --train_batch_size=32 \
# --learning_rate=2e-5 \
# --num_train_epochs=3.0 \
# --output_dir=./output/ywevents/bert/ \
# --bilstm=False \
# --crf=False \
# using bert-crf
# python bert_ner.py\
# --do_lower_case=False \
# --do_train=False \
# --do_eval=False \
# --do_test=True \
# --data_dir=./data/ywevents \
# --vocab_file=./chinese_L-12_H-768_A-12/vocab_update.txt \
# --bert_config_file=./chinese_L-12_H-768_A-12/bert_config.json \
# --init_checkpoint=./output/MSRA/bert_crf/model.ckpt-6332 \
# --max_seq_length=128 \
# --train_batch_size=32 \
# --learning_rate=2e-5 \
# --num_train_epochs=4.0 \
# --output_dir=./output/ywevents/bert_crf/ \
# --bilstm=False \
# --crf=True \
# using bert-bi-lstm
epoch=1
while [ $epoch -le 10 ]
do
let checkpoint=`expr $epoch \* 1583`
echo "Looping to checkpoint=$checkpoint"
python bert_ner.py\
--do_lower_case=False \
--do_train=False \
--do_eval=True \
--do_test=True \
--data_dir=./data/ywevents \
--vocab_file=./chinese_L-12_H-768_A-12/vocab_update.txt \
--bert_config_file=./chinese_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=./output/MSRA/bert_bilstm/model.ckpt-$checkpoint \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=$epoch \
--dropout_rate=0.5 \
--output_dir=./output/ywevents/bert_bilstm/ \
--bilstm=True \
--crf=False
((epoch++))
done
# using bert-bi-lstm-crf
# epoch=1
# while [ $epoch -le 10 ]
# do
# let checkpoint=`expr $epoch \* 1583`
# echo "Looping to checkpoint=$checkpoint"
# python bert_ner.py\
# --do_lower_case=False \
# --do_train=False \
# --do_eval=True \
# --do_test=True \
# --data_dir=./data/ywevents \
# --vocab_file=./chinese_L-12_H-768_A-12/vocab_update.txt \
# --bert_config_file=./chinese_L-12_H-768_A-12/bert_config.json \
# --init_checkpoint=./output/MSRA/bert_bilstm_crf/model.ckpt-$checkpoint \
# --max_seq_length=128 \
# --train_batch_size=32 \
# --learning_rate=2e-5 \
# --num_train_epochs=$epoch \
# --dropout_rate=0.5 \
# --output_dir=./output/ywevents/bert_bilstm_crf/ \
# --bilstm=True \
# --crf=True
# ((epoch++))
# done