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.
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