EasyTransfer – A Simple and Scalable Deep Transfer Learning Platform for NLP Applications

by   Minghui Qiu, et al.

The literature has witnessed the success of applying deep Transfer Learning (TL) algorithms to many NLP applications, yet it is not easy to build a simple and scalable TL toolkit for this purpose. To bridge this gap, the EasyTransfer platform is designed to make it easy to develop deep TL algorithms for NLP applications. It is built with rich API abstractions, a scalable architecture and comprehensive deep TL algorithms, to make the development of NLP applications easier. To be specific, the build-in data and model parallelism strategy shows to be 4x faster than the default distribution strategy of Tensorflow. EasyTransfer supports the mainstream pre-trained ModelZoo, including Pre-trained Language Models (PLMs) and multi-modality models. It also integrates various SOTA models for mainstream NLP applications in AppZoo, and supports mainstream TL algorithms as well. The toolkit is convenient for users to quickly start model training, evaluation, offline prediction, and online deployment. This system is currently deployed at Alibaba to support a variety of business scenarios, including item recommendation, personalized search, and conversational question answering. Extensive experiments on real-world datasets show that EasyTransfer is suitable for online production with cutting-edge performance. The source code of EasyTransfer is released at Github (https://github.com/alibaba/EasyTransfer).



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Code Repositories


EasyTransfer is designed to make the development of transfer learning in NLP applications easier.

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