This code repository contains the implementations of the method BiLSTM+CRF (Arxiv 2015).
If you want to train the model from scratch, please following these steps:
1.Firstly, because we adopt the vocab in transformers, so you need prepare the pretrained models that containd the vocab.txt:
2.Secondly, modify the paths in model config (demo/ner/bilstm_crf/bilstm_crf.py), including the pretrained models paths, datasets paths, work space, etc.
3.Thirdly, direct run demo/ner/bilstm_crf/dist_train.sh.
Given the trained model, direct run demo/ner/bilstm_crf/test.sh to test model.
The results in paper used some tricks during training phase. So, the results might be slightly different from reported results.
Results on various datasets and trained models can be download as follows:
| F1-score | CoNLL2003 | Resume | Links |
|---|---|---|---|
| BiLSTM+CRF(paper) | 84.3 | - | - |
| BiLSTM+CRF | 77.4 | 93.6 | config, pth (Access Code：0Os5) |
If you find this repository is helpful to your research, please feel free to cite us:
@article{DBLP:journals/corr/HuangXY15,
author = {Zhiheng Huang and
Wei Xu and
Kai Yu},
title = {Bidirectional {LSTM-CRF} Models for Sequence Tagging},
journal = {CoRR},
volume = {abs/1508.01991},
year = {2015},
}This project is released under the Apache 2.0 license
If there is any suggestion and problem, please feel free to contact the author with qiaoliang6@hikvision.com.