In this article, we examine the training and fine-tuning methods for multi-vector embedding models implemented with the Sentence Transformers library. By generating multiple vectors for each text, these models enable the extraction of richer information and improve performance in tasks such as information retrieval, text classification, and semantic similarity detection.
Using fine-tuning techniques, pre-trained models can be adapted to domain-specific data, significantly increasing accuracy. This approach serves as an efficient and scalable option for AI developers and researchers seeking to enhance natural language processing systems.