BERT NER

Use google BERT to do CoNLL-2003 NER !

Requirements

• python3
• pip3 install -r requirements.txt

Run

python run_ner.py --data_dir=data/ --bert_model=bert-base-cased --task_name=ner --output_dir=out_!x --max_seq_length=128 --do_train --num_train_epochs 5 --do_eval --warmup_proportion=0.4

Result

Validation Data

             precision    recall  f1-score   support

        PER     0.9687    0.9756    0.9721      1842
        ORG     0.9299    0.9292    0.9295      1341
       MISC     0.8878    0.9100    0.8988       922
        LOC     0.9674    0.9701    0.9687      1837

avg / total     0.9470    0.9532    0.9501      5942

Test Data

             precision    recall  f1-score   support

        ORG     0.8754    0.9055    0.8902      1661
        PER     0.9663    0.9573    0.9618      1617
       MISC     0.7803    0.8348    0.8066       702
        LOC     0.9271    0.9305    0.9288      1668

avg / total     0.9049    0.9189    0.9117      5648

Pretrained model download from here

Inference

from bert import Ner

model = Ner("out_!x/")

output = model.predict("Steve went to Paris")

print(output)
# ('Steve', {'tag': 'B-PER', 'confidence': 0.9981840252876282})
# ('went', {'tag': 'O', 'confidence': 0.9998939037322998})
# ('to', {'tag': 'O', 'confidence': 0.999891996383667})
# ('Paris', {'tag': 'B-LOC', 'confidence': 0.9991968274116516})

Tensorflow version

• https://github.com/kyzhouhzau/BERT-NER