In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios. HugNLP consists of a hierarchical structure including models, processors and applications that unifies the learning process of pre-trained language models (PLMs) on different NLP tasks. Additionally, we present some featured NLP applications to show the effectiveness of HugNLP, such as knowledge-enhanced PLMs, universal information extraction, low-resource mining, and code understanding and generation, etc. The source code will be released on GitHub (https://github.com/wjn1996/HugNLP).

翻译：在本文中,我们介绍HugNLP(HugNLP),这是一个与Hugging Face变形器(Hugging Face 变形器)的普遍后端统一和综合的自然语言处理图书馆(HugNLP),设计该图书馆是为了让NLP研究人员在现实世界情景中方便地利用现成的算法,并用用户定义的模式和任务开发新的方法。HugNLP包括一个等级结构,包括模型、处理器和应用,使预先训练的语言模型(PLMs)学习过程统一到不同的NLP任务。此外,我们介绍了一些突出的NLP应用程序,以显示HugNP(HugNP)的有效性,例如知识强化的PLMs、普遍的信息提取、低资源开采、代码理解和生成等。 源代码代码将在GitHub上发布(https://github.com/wjn1996/HugNLP)。</s>