Characteristics of Sentence Transformer (a.k.a bi-encoder) models:

Calculates a fixed-size vector representation (embedding) given texts, images, audio, or video.

Embedding calculation is often efficient, embedding similarity calculation is very fast.

Applicable for a wide range of tasks, such as semantic textual similarity, semantic search, clustering, classification, paraphrase mining, and more.

Often used as a first step in a two-step retrieval process, where a Cross-Encoder (a.k.a. reranker) model is used to re-rank the top-k results from the bi-encoder.