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EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing

Authors :
Wang, Chengyu
Qiu, Minghui
Shi, Chen
Zhang, Taolin
Liu, Tingting
Li, Lei
Wang, Jianing
Wang, Ming
Huang, Jun
Lin, Wei
Publication Year :
2022

Abstract

The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP). Yet, it is not easy to obtain high-performing models and deploy them online for industrial practitioners. To bridge this gap, EasyNLP is designed to make it easy to build NLP applications, which supports a comprehensive suite of NLP algorithms. It further features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities for large-scale PTMs, and provides a unified framework of model training, inference and deployment for real-world applications. Currently, EasyNLP has powered over ten business units within Alibaba Group and is seamlessly integrated to the Platform of AI (PAI) products on Alibaba Cloud. The source code of our EasyNLP toolkit is released at GitHub (https://github.com/alibaba/EasyNLP).<br />Comment: 8 pages

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2205.00258
Document Type :
Working Paper