This article explains how to use the Sentence Transformers library to finetune and train embedding models for a variety of applications, such as retrieval augmented generation, semantic search, and semantic textual similarity. It covers the training components, dataset format, loss function, training arguments, evaluators, and trainer.
A surprising experiment to show that the devil is in the details
Image Similarity Search
Reverse Image Search
Object Similarity Search
Robust OCR Document Search
Semantic Search
Cross-modal Retrieval
Probing Perceptual Similarity
Comparing Model Representations
Concept Interpolation
Concept Space Traversal
Image Similarity Search