By Minghui Qiu, Peng Li, Hanjie Pan, Chengyu Wang, Ang Wang, Cen Chen, Yaliang Li, Dehong Gao, Jun Huang, Yong Li, Jun Yang, Deng Cai, and Wei Lin, from Alibaba Group and Zhejiang University, China
To read the full paper on EasyTransfer, please visit this page
Transfer Learning (TL) is a rapidly growing field of machine learning that aims to improve the learning of a data-deficient task by transferring knowledge from related data-sufficient tasks. Witnessing the great representation learning abilities of deep neural networks, neural architectures based TL methods, i.e., deep transfer learning, have gained increasing popularity and are shown to be effective for a wide variety of applications.
Several TL toolkits have also been developed to make it easy to apply TL algorithms. Notable projects include:
However, when it comes to industrial-scale real-world applications, the above mentioned toolkits might be less ideal. The reasons are threefold.
To bridge this gap, we developed the EasyTransfer toolkit and release it to the open-source community. EasyTransfer is built with highly scalable distributed training strategies, which make it easy to facilitate large-scale model training. It supports a comprehensive suite of TL algorithms that can be used in various NLP task, providing a unified pipeline of model training, inference and deployment for real-world applications.
The toolkit is open-sourced in Github. Currently, we have integrated EasyTransfer into a number of deep learning products in Alibaba and observed notable performance gains.
The EasyTransfer toolkit has also been deployed at Alibaba to support a variety of business scenarios, including item recommendation, personalized search and conversational question answering. EasyTransfer is released under the Apache 2.0 License and has been open sourced at GitHub. The detailed documentation and tutorials are available on this link https://www.yuque.com/easytransfer/cn
To read the full paper on EasyTransfer, please visit https://arxiv.org/abs/2011.09463
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