readme: ---
  language: zh
  pipeline_tag: fill-mask
  widget:
  - text: "巴黎是[MASK]国的首都。"
  - text: "生活的真谛是[MASK]。"
  tags:
  - bert
  license: apache-2.0
  ---
  ## Chinese Kowledge-enhanced BERT (CKBERT) 
  
  Knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community to support various language understanding applications.
  
  For Chinese natural language processing, we provide three **Chinese Kowledge-enhanced BERT (CKBERT)** models named **pai-ckbert-bert-zh**, **pai-ckbert-large-zh** and **pai-ckbert-huge-zh**, from our **EMNLP 2022** paper named **Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training**.
  
  This repository is developed based on the EasyNLP framework: [https://github.com/alibaba/EasyNLP](https://github.com/alibaba/EasyNLP ) 
  
  ## Citation
  If you find the resource is useful, please cite the following papers in your work.
  
  - For the EasyNLP framework:
  ```
  @article{easynlp, 
  title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing},   		
    author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei}, 
    publisher = {arXiv}, 
    url = {https://arxiv.org/abs/2205.00258}, 
    year = {2022} 
  } 
  ```
  - For CKBERT:
  ```
  @article{ckbert, 
    title = {Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training}, 
    author = {Zhang, Taolin and Dong, Junwei and Wang, Jianing and Wang, Chengyu and Wang, An and Liu, Yinghui and Huang, Jun and Li, Yong and He, Xiaofeng}, 	
    publisher = {EMNLP}, 
    url = {https://arxiv.org/abs/2210.05287}, 
    year = {2022} 
  } 
  ```