Training Multi-Vector Embedding Models with Sentence Transformers: A Practical Guide for MENA Teams

They are models that produce multiple vector representations per document or query, improving retrieval accuracy compared to single-vector models, especially in tasks requiring deep contextual understanding.

Teams can use the guide to fine-tune models like ColBERT on Arabic texts, enhancing search and retrieval performance in local applications such as legal or healthcare assistants.

Training can be done on mid-range GPUs, with techniques like quantization available to reduce requirements, making it accessible to emerging teams.