This is a Pytorch implementation of BiLSTM-CRF for Named Entity Recognition, which is described in Bidirectional LSTM-CRF Models for Sequence Tagging
The corpus in the data folder is MSRA Chinese NER corpus. Since there are no development data, we split the data in train.txt into training and development part when traing the model.
For training the model, you can use the following command:
sh run.sh train
For those who are not able to use GPU, use the following command to train:
sh run.sh train-without-cuda
For testing, you can use the following command:
sh run.sh test
Also, if you have no GPU, you can use the following command(this procedure won't take long time when using CPU):
sh run.sh test-without-cuda
There is already a trained model in the model folder, so you can execute the testing command directly without training.
If you want to change some hyper-parameters, use the following command to refer to the options.
python run.py --help
We use conlleval.pl to evaluate the model's performance on test data, and
the experiment result on testing data of the trained model is as follows:
processed 172601 tokens with 6192 phrases; found: 5660 phrases; correct: 4820.
accuracy: 97.70%; precision: 85.16%; recall: 77.84%; FB1: 81.34
LOC: precision: 90.45%; recall: 82.31%; FB1: 86.19 2618
ORG: precision: 78.18%; recall: 75.66%; FB1: 76.90 1288
PER: precision: 82.38%; recall: 72.83%; FB1: 77.31 1754