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https://github.com/kamalkraj/bert-ner-tf
Named Entity Recognition with BERT using TensorFlow 2.0
https://github.com/kamalkraj/bert-ner-tf
bert bert-ner curl inference named-entity-recognition postman pretrained-models tensorflow tensorflow-2 tensorflow-2-example
Last synced: 6 days ago
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Named Entity Recognition with BERT using TensorFlow 2.0
- Host: GitHub
- URL: https://github.com/kamalkraj/bert-ner-tf
- Owner: kamalkraj
- License: apache-2.0
- Created: 2019-10-17T15:34:29.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-21T21:15:45.000Z (about 2 years ago)
- Last Synced: 2025-01-09T05:13:53.924Z (13 days ago)
- Topics: bert, bert-ner, curl, inference, named-entity-recognition, postman, pretrained-models, tensorflow, tensorflow-2, tensorflow-2-example
- Language: Python
- Homepage:
- Size: 1.16 MB
- Stars: 212
- Watchers: 7
- Forks: 71
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# BERT NER
Use google BERT to do CoNLL-2003 NER !
Train model using Python and TensorFlow 2.0
[ALBERT-TF2.0](https://github.com/kamalkraj/ALBERT-TF2.0)
[BERT-SQuAD](https://github.com/kamalkraj/BERT-SQuAD)
[BERT-NER-Pytorch](https://github.com/kamalkraj/BERT-NER)
# Requirements
- `python3`
- `pip3 install -r requirements.txt`
### Download Pretrained Models from Tensorflow offical models
- [bert-base-cased](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/tf_20/cased_L-12_H-768_A-12.tar.gz) unzip into `bert-base-cased`
- [bert-large-cased](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/tf_20/cased_L-24_H-1024_A-16.tar.gz) unzip into `bert-large-cased`
code for pre-trained bert from [tensorflow-offical-models](https://github.com/tensorflow/models/tree/master/official/nlp)
# Run
## Single GPU
`python run_ner.py --data_dir=data/ --bert_model=bert-base-cased --output_dir=out_base --max_seq_length=128 --do_train --num_train_epochs 3 --do_eval --eval_on dev`
## Multi GPU
`python run_ner.py --data_dir=data/ --bert_model=bert-large-cased --output_dir=out_large --max_seq_length=128 --do_train --num_train_epochs 3 --multi_gpu --gpus 0,1,2,3 --do_eval --eval_on test`
# Result
## BERT-BASE
### Validation Data
```
precision recall f1-score support
PER 0.9677 0.9756 0.9716 1842
LOC 0.9671 0.9592 0.9631 1837
MISC 0.8872 0.9132 0.9001 922
ORG 0.9191 0.9314 0.9252 1341
avg / total 0.9440 0.9509 0.9474 5942
```
### Test Data
```
precision recall f1-score support
ORG 0.8773 0.9037 0.8903 1661
PER 0.9646 0.9592 0.9619 1617
MISC 0.7691 0.8305 0.7986 702
LOC 0.9333 0.9305 0.9319 1668
avg / total 0.9053 0.9184 0.9117 5648
```
## Pretrained model download from [here](https://drive.google.com/file/d/1ZlQimY5xbkpS_1baO-ZtCZZef4MvG9__/view?usp=sharing)
## BERT-LARGE
### Validation Data
```
precision recall f1-score support
ORG 0.9290 0.9374 0.9332 1341
MISC 0.8967 0.9230 0.9097 922
PER 0.9713 0.9734 0.9723 1842
LOC 0.9748 0.9701 0.9724 1837
avg / total 0.9513 0.9564 0.9538 5942
```
### Test Data
```
precision recall f1-score support
LOC 0.9256 0.9329 0.9292 1668
MISC 0.7891 0.8419 0.8146 702
PER 0.9647 0.9623 0.9635 1617
ORG 0.8903 0.9133 0.9016 1661
avg / total 0.9094 0.9242 0.9167 5648
```
## Pretrained model download from [here](https://drive.google.com/file/d/1BZCKj_e_SXxlvg4rKUC0EI4BJXYssTVL/view?usp=sharing)
# Inference
```python
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"
}
]
'''
```
# Deploy REST-API
BERT NER model deployed as rest api
```bash
python api.py
```
API will be live at `0.0.0.0:8000` endpoint `predict`
#### cURL request
` curl -X POST http://0.0.0.0:8000/predict -H 'Content-Type: application/json' -d '{ "text": "Steve went to Paris" }'`
Output
```json
{
"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"
}
]
}
```
#### cURL
#### Postman
### Pytorch version
- https://github.com/kamalkraj/BERT-NER