Use google BERT to do CoNLL-2003 NER !
python3pip3 install -r requirements.txt
python run_ner.py --data_dir=data/ --bert_model=bert-base-cased --task_name=ner --output_dir=out_base --max_seq_length=128 --do_train --num_train_epochs 5 --do_eval --warmup_proportion=0.1
precision recall f1-score support
PER 0.9677 0.9745 0.9711 1842
LOC 0.9654 0.9711 0.9682 1837
MISC 0.8851 0.9111 0.8979 922
ORG 0.9299 0.9292 0.9295 1341
avg / total 0.9456 0.9534 0.9495 5942
precision recall f1-score support
PER 0.9635 0.9629 0.9632 1617
ORG 0.8883 0.9097 0.8989 1661
LOC 0.9272 0.9317 0.9294 1668
MISC 0.7689 0.8248 0.7959 702
avg / total 0.9065 0.9209 0.9135 5648
Pretrained model download from here
precision recall f1-score support
ORG 0.9288 0.9441 0.9364 1341
LOC 0.9754 0.9728 0.9741 1837
MISC 0.8976 0.9219 0.9096 922
PER 0.9762 0.9799 0.9781 1842
avg / total 0.9531 0.9606 0.9568 5942
precision recall f1-score support
LOC 0.9366 0.9293 0.9329 1668
ORG 0.8881 0.9175 0.9026 1661
PER 0.9695 0.9623 0.9659 1617
MISC 0.7787 0.8319 0.8044 702
avg / total 0.9121 0.9232 0.9174 5648
Pretrained model download from here
from bert import Ner
model = Ner("out_base/")
output = model.predict("Steve went to Paris")
print(output)
'''
[
{
"confidence": 0.9981840252876282,
"tag": "B-PER",
"word": "Steve"
},
{
"confidence": 0.9998939037322998,
"tag": "O",
"word": "went"
},
{
"confidence": 0.999891996383667,
"tag": "O",
"word": "to"
},
{
"confidence": 0.9991968274116516,
"tag": "B-LOC",
"word": "Paris"
}
]
'''Pretrained and converted bert-base model download from here
Download libtorch from here
-
install
cmake, tested withcmakeversion3.10.2 -
unzip downloaded model and
libtorchinBERT-NER -
Compile C++ App
cd cpp-app/ cmake -DCMAKE_PREFIX_PATH=../libtorch
make
-
Runing APP
./app ../base
NB: Bert-Base C++ model is split in to two parts.
- Bert Feature extractor and NER classifier.
- This is done because
jit tracedon't supportinputdependedforloop orifconditions insideforwordfunction ofmodel.
BERT NER model deployed as rest api
python api.pyAPI will be live at 0.0.0.0:8000 endpoint predict
curl -X POST http://0.0.0.0:8000/predict -H 'Content-Type: application/json' -d '{ "text": "Steve went to Paris" }'
Output
{
"result": [
{
"confidence": 0.9981840252876282,
"tag": "B-PER",
"word": "Steve"
},
{
"confidence": 0.9998939037322998,
"tag": "O",
"word": "went"
},
{
"confidence": 0.999891996383667,
"tag": "O",
"word": "to"
},
{
"confidence": 0.9991968274116516,
"tag": "B-LOC",
"word": "Paris"
}
]
}
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