Vectara and Ofer Mendelevitch collaborate to offer a short course on enhancing search relevance using advanced embedding techniques.
Semantic search is a technology that enables search engines to understand the underlying intent behind the queries. It is quickly transforming information retrieval by delivering more relevant and accurate search results.
However, conventional semantic search often falls short in language learning model (LLM) applications, which rely on a single embedding model. This approach retrieves results that resemble the question rather than relevant answers.
To circumvent this, Vectara and Ofer Mendelevitch have partnered to offer a short course, Embedding Models: From Architecture to Implementation, for all data science enthusiasts.
The course teaches building, training, and deploying dual encoder models using separate embedding models for questions and answers. This significantly improves matching questions with appropriate answers, enhancing overall search relevance.
Read More: Building High-Quality Datasets with LLMs
The course also covers the concept of word embeddings and their evolution to BERT, where embeddings consider the surrounding context of each word. Learners will also gain hands-on experience using contrastive loss to build a dual encoder model with one encoder trained to embed questions and the other responses.
Lastly, the participants will learn to analyze the impact of dual encoders on search relevance and compare it to retrieval processes using single encoders. This course provides a valuable opportunity for anyone looking to advance their understanding of embedding models and their application in modern search systems.
The best part is that applicants can enroll in this course for free. Here’s the link to apply now!
