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

Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin

Abstract
Pre-Trained Models (PTMs) have reshaped the development of Natural Language Processing (NLP) and achieved significant improvement in various benchmarks. Yet, it is not easy for industrial practitioners to obtain high-performing PTM-based models without a large amount of labeled training data and deploy them online with fast inference speed. 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, and provides a unified framework of model training, inference and deployment for real-world applications. 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 EasyNLP is released at GitHub (https://github.com/alibaba/EasyNLP).
Anthology ID:
2022.emnlp-demos.3
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Wanxiang Che, Ekaterina Shutova
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–29
Language:
URL:
https://aclanthology.org/2022.emnlp-demos.3
DOI:
10.18653/v1/2022.emnlp-demos.3
Bibkey:
Cite (ACL):
Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, and Wei Lin. 2022. EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 22–29, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (Wang et al., EMNLP 2022)
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