Training and Finetuning Embedding Models with Sentence Transformers
Sentence Transformers is a Python library for using and training embedding models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. In this blogpost, I’ll show you how to use it to finetune Sentence Transformer models to improve their performance on specific tasks. You can also use this method to train new Sentence Transformer models from scratch.
Finetuning Sentence Transformers involves several components, including datasets, loss functions, training arguments, evaluators, and the trainer itself. I’ll go through each of these components in detail and provide examples of how to use them to train effective models.
Table of Contents
Why Finetune?
Finetuning Sentence Transformer models can significantly enhance their performance