Join the Webinar

About the Session

In this tutorial and presentation, we’ll dive into transformer-based embeddings for long-form text, highlighting some of the theory around why BERT-esque models perform much better than recurrent neural networks (RNNs). From there, we’ll go into some examples utilizing the “sentence-transformers” library, showcasing its use in a variety of tasks such as sentiment classification, clustering, and, RAG.

What you’ll learn:

• The theory behind long-form text embeddings
• Using sentence-transformers in an actual application

Meet the Speaker

Frank Liu

Director of Operations & ML Architect at Zilliz

Frank Liu is the Director of Operations & ML Architect at Zilliz, where he serves as a maintainer for the Towhee open-source project. Prior to Zilliz, Frank co-founded Orion Innovations, an ML-powered indoor positioning startup based in Shanghai and worked as an ML engineer at Yahoo in San Francisco. In his free time, Frank enjoys playing chess, swimming, and powerlifting. Frank holds MS and BS degrees in Electrical Engineering from Stanford University.