Chinese NER

• In NER task, recently, BiLSTM-CRF Neural Networks are often used, and get the best performance. but I use a simple Neural Networks(BiLSTM) and context feature to train data, and get a good performance close to the BiLSTM-CRF.

Requirement

pyorch: 0.3.1
python: 3.6.1
cuda: 8.0 (support cuda speed up, can chose)

Usage

modify the config file, detail see the Config directory
(1)	sh run.sh
(2)	python -u main_hyperparams.py --config_file ./Config/config.cfg 

Model

• BiLSTM + context feature
• BiLSTM-CRF
• Now, only support BiLSTM + context feature, BiLSTM-CRF will be support later.

Data

• The number of sentences in the two data is calculated as follows:

Time

• A simple test of the training speed and decoding time on the CPU and GPU，requires only 4 seconds for the decoding time on the GPU. why so fast ? In terms of decoding, batch calculation is performed in some places, so the decoding time is much faster than one sentence.
  

Performance

• The following results are based on the neural network model of BiLSTM + context feature.
  

Reference

• updating......

Question

• if you have any question, you can open a issue or email bamtercelboo@{gmail.com, 163.com}.

• if you have any good suggestions, you can PR or email me.