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
Third download bert snapshot, extract and rename folder checkpoint
BERT
|____ bert
|____ BERT_NER.py
|____ checkpoint
|____ output
Third run:
python BERT_NER.py \
--task_name="NER" \
--do_train=True \
--do_eval=True \
--do_predict=True
--data_dir=NERdata \
--vocab_file=checkpoint/vocab.txt \
--bert_config_file=checkpoint/bert_config.json \
--init_checkpoint=checkpoint/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3.0 \
--output_dir=./output/result_dir/
result:
The predicted result is placed in folder ./output/result_dir/. It contain two files, token_test.txt is the tokens and label_test.txt is the labels for each token. If you want a more accurate evaluation result you can use script conlleval.pl for evaluation.
The following evaluation results differ from the evaluation results specified by conll2003.
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:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent