Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

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Finetuning multi-vector models involves several components: the model itself, datasets, loss functions, training arguments, evaluators, and the trainer class. This blogpost is about training multi-vector models. Dataset Data on the Hugging Face Hub Local Data Dataset Format Loss Function Training Arguments Evaluator Trainer Callbacks Multi-Dataset Training Evaluation Optimizing the index Acknowledgements Additional Resources Training Examples Documentation What are Multi-Vector models? Training Components Training MultiVectorEncoder models involves the following components: Model: The model to finetune or the architecture to build fresh. Dataset: The data used for training and evaluation.

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