aprilspring/bi-lstm-crf-ner-tf2.0

Named Entity Recognition (NER) task using Bi-LSTM-CRF model implemented in Tensorflow 2.0(tensorflow2.0 +)

bi-lstm-crf-ner-tf2.0

Named Entity Recognition (NER) task using Bi-LSTM-CRF model implemented in Tensorflow2.0.

Requirements

• python >3.6
• tensorflow==2.0.0
• tensorflow-addons==0.6.0

data

data example

１	B-TIME
９	I-TIME
９	I-TIME
７	I-TIME
年	E-TIME
，	O
是	O
中	B-LOC
国	E-LOC
发	O
展	O
历	O
史	O
上	O
非	O
常	O
重	O
要	O
的	O
很	O
不	O
平	O
凡	O
的	O
一	O
年	O
。	O
end

Usage

train

$ # pip install requirement.txt
$ python3 train.py

...
[-INFO-] 2019-12-05 21:11:15,037 24300 epoch   1, step 575, loss  5.0533 , accuracy --
[-INFO-] 2019-12-05 21:11:34,002 24300 epoch   1, step 576, loss  6.2023 , accuracy --
[-INFO-] 2019-12-05 21:11:52,543 24300 epoch   1, step 577, loss  4.3899 , accuracy --
[-INFO-] 2019-12-05 21:12:11,175 24300 epoch   1, step 578, loss  3.1313 , accuracy --
[-INFO-] 2019-12-05 21:12:29,661 24300 epoch   1, step 579, loss  6.4625 , accuracy --
[-INFO-] 2019-12-05 21:12:48,233 24300 epoch   1, step 580, loss  5.5159 , accuracy --
[-INFO-] 2019-12-05 21:12:48,325 24300 model saved
...

predict

$ python3 predict.py


input: ****总书记、国家主席***发表１９９８年新年讲话

[
    {
        "end": 4,
        "words": "****",
        "type": "ORG",
        "begin": 1
    },
    {
        "end": 15,
        "words": "***",
        "type": "PER",
        "begin": 13
    },
    {
        "end": 22,
        "words": "１９９８年",
        "type": "TIME",
        "begin": 18
    }
]

参考

https://zhuanlan.zhihu.com/p/88748460