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：本文介绍HugNLP，这是一个基于HuggingFace Transformers主流后端的统一且全面的自然语言处理（NLP）库，旨在帮助NLP研究人员轻松利用现成算法，并在实际场景中通过用户自定义模型和任务开发新方法。HugNLP采用包含模型、处理器和应用程序的分层结构，统一了预训练语言模型（PLMs）在不同NLP任务上的学习过程。此外，我们展示了一些特色NLP应用以证明HugNLP的有效性，例如知识增强型PLMs、通用信息抽取、低资源挖掘以及代码理解与生成等。源代码将在GitHub上发布（https://github.com/wjn1996/HugNLP）。