Boost Your RAG Pipeline Performance with Fine-Tuning Your Embedding Model

Boost Your RAG Pipeline Performance with Fine-Tuning Your Embedding Model

🚀 Boost Your RAG Pipeline Performance with Fine-Tuning Your Embedding Model

This video walks you through how to fine-tune any Hugging Face embedding model on your own data using contrastive learning with MultiNegativeRankingLoss.

🏆Advantage: No handcrafted negatives required. Instead, MNR-Loss leverages in-batch negatives, treating all other samples in the batch as incorrect contexts. This trains the model to pull relevant context closer to the query while pushing irrelevant ones further away.

Fine-tuning an off-the-shelf embedding model can significantly boost your RAG pipeline's retrieval quality in production. In this tutorial, we improve accuracy@1 by 8% 🔍💥

Resources and Further Reading

This article accompanies the LinkedIn video about fine-tuning embedding models for RAG pipelines.