Use google BERT to do CoNLL-2003 NER !
Try to implement NER work based on google's BERT code!
First git clone https://github.com/google-research/bert.git
Second download file in this project
BERT
|____ bert
|____ NERdata
|____ BERT_NER.py
|____ output
Third run:
python3 BERT_NER.py \
--task_name="NER" \
--do_train=True \
--do_eval=True \
--do_predict=True \
--data_dir=NERdata/en \
--column_sep=" " \
--vocab_file=../BERT-BiLSTM-CRF-NER/uncased_L-12_H-768_A-12/vocab.txt \
--bert_config_file=../BERT-BiLSTM-CRF-NER/uncased_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=../BERT-BiLSTM-CRF-NER/uncased_L-12_H-768_A-12/bert_model.ckpt \
--max_seq_length=32 \
--train_batch_size=16 \
--learning_rate=2e-5 \
--num_train_epochs=6 \
--save_checkpoints_steps=1000 \
--output_dir=./output_en/result_dir/
result:
The predicted result is placed in folder ./output/result_dir/. It is in the file predicted_results.tx.
The following evaluation results come from script conlleval.pl.
accuracy: 98.09%; precision: 90.01%; recall: 91.51%; FB1: 90.76
LOC: precision: 91.81%; recall: 93.58%; FB1: 92.69 1698
MISC: precision: 75.73%; recall: 81.34%; FB1: 78.43 754
ORG: precision: 88.59%; recall: 89.70%; FB1: 89.14 1682
PER: precision: 96.62%; recall: 95.67%; FB1: 96.14 1599
注：For the parameters of the above model, I have not made any modifications. All parameters are based on the BERT default parameters. The better parameters for this problem can be adjusted by yourselves.
The f_score evaluation codes come from:https://github.com/guillaumegenthial/tf_metrics/blob/master/tf_metrics/__init__.py
reference: