Getting Started with Gradient Checkpointing using SentenceTransformers: Mechanism and Practical Points
Analysis
This article, part of the Uzabase Advent Calendar 2025, discusses the use of SentenceTransformers for gradient checkpointing. It highlights the development of a Speeda AI Agent and its reliance on vector search. The article mentions in-house fine-tuning of vector search models, achieving superior accuracy compared to Gemini on internal benchmarks. The focus is on the practical application of SentenceTransformers within a real-world product, emphasizing performance and stability in handling frequently updated data, such as news articles. The article sets the stage for a deeper dive into the technical aspects of gradient checkpointing.
Key Takeaways
- •The article focuses on the practical application of SentenceTransformers for vector search within a product.
- •It highlights the benefits of in-house fine-tuning for achieving superior accuracy.
- •The article emphasizes the importance of stability and performance in handling frequently updated data.
Reference
“The article is part of the Uzabase Advent Calendar 2025.”