Update README.md

README.md

@@ -9,7 +9,9 @@ widget:
 
 ## Model description
 
-The model is used for named entity recognition. You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from the link [roberta-base-finetuned-cluener2020-chinese](https://huggingface.co/uer/roberta-base-finetuned-cluener2020-chinese).
+The model is used for named entity recognition. It is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be fine-tuned by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.
+
+You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the link [roberta-base-finetuned-cluener2020-chinese](https://huggingface.co/uer/roberta-base-finetuned-cluener2020-chinese).
 
 ## How to use
 
@@ -59,13 +61,6 @@ python3 scripts/convert_bert_token_classification_from_uer_to_huggingface.py --i
 ### BibTeX entry and citation info
 
 ```
-@article{devlin2018bert,
-  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
-  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
-  journal={arXiv preprint arXiv:1810.04805},
-  year={2018}
-}
-
 @article{liu2019roberta,
   title={Roberta: A robustly optimized bert pretraining approach},
   author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
@@ -87,4 +82,11 @@ python3 scripts/convert_bert_token_classification_from_uer_to_huggingface.py --i
   pages={241},
   year={2019}
 }
+
+@article{zhao2023tencentpretrain,
+  title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
+  author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
+  journal={ACL 2023},
+  pages={217},
+  year={2023}
 ```