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).
@article{arxiv.2302.14286,
title = {HugNLP: A Unified and Comprehensive Library for Natural Language Processing},
author = {Jianing Wang and Nuo Chen and Qiushi Sun and Wenkang Huang and Chengyu Wang and Ming Gao},
journal= {arXiv preprint arXiv:2302.14286},
year = {2023}
}