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README.md

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@@ -323,6 +323,17 @@ PaddleNLP提供了多粒度、多场景的NLP应用示例,面向动态图模
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更多版本更新说明请查看[ChangeLog](./docs/changelog.md)
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## Citation
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如果PaddleNLP对您的研究有帮助,欢迎引用
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```
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@misc{=paddlenlp,
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title={An Easy-to-use and High Performance NLP Library based on PaddlePaddle},
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author={PaddleNLP Contributors},
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howpublished = {\url{https://github.com/PaddlePaddle/PaddleNLP}},
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year={2021}
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}
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```
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## Acknowledge
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我们借鉴了Hugging Face的[Transformers](https://github.com/huggingface/transformers)🤗关于预训练模型使用的优秀设计,在此对Hugging Face作者及其开源社区表示感谢。

README_en.md

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## News <img src="./docs/imgs/news_icon.png" width="40"/>
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* [2021-12-12] PaddleNLP 2.2 has been officially relealsed! :tada: For more information please refer to [Release Note](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.2.0).
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* [2021-10-12] PaddleNLP 2.1 has been officially relealsed! :tada: For more information please refer to [Release Note](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.1.0).
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* [2021-12-12] PaddleNLP v2.2 has been officially relealsed! :tada: For more information please refer to [Release Note](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.2.0).
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* [2021-12-12] *End-to-end Question Answering Toolkit**🚀[RocketQA](https://github.com/PaddlePaddle/RocketQA) has been released!:tada:
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## Introduction
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**PaddleNLP** is a powerful NLP library with **Awesome** pre-trained Transformer models and easy-to-use interface, supporting wide-range of NLP tasks from research to industrial applications.
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**PaddleNLP** is an **easy-to-use** and **high performance** NLP library with **awesome** pre-trained Transformer models, supporting wide-range of NLP tasks from research to industrial applications.
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* **Easy-to-Use API**
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For more details about our release, please refer to [ChangeLog](./docs/changelog.md)
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## Citation
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If you find PaddleNLP useful in your research, please consider cite
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```
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@misc{=paddlenlp,
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title={An Easy-to-use and High Performance NLP Library based on PaddlePaddle},
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author={PaddleNLP Contributors},
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howpublished = {\url{https://github.com/PaddlePaddle/PaddleNLP}},
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year={2021}
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}
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```
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## Acknowledge
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We have borrowed from Hugging Face's [Transformer](https://github.com/huggingface/transformers)🤗 excellent design on pretrained models usage, and we would like to express our gratitude to the authors of Hugging Face and its open source community.

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