sentence-bert-siamese-triplet-loss-architecture

IN premise — summaries/2026/08/24/wiki-Word_embedding-chunk-2.md

Created 2026-08-24T17:11:28+00:00

Sentence-BERT (Reimers & Gurevych, 2019) uses a siamese BERT architecture trained with triplet loss to produce fixed-size sentence vectors suitable for semantic similarity and natural language inference

Summary

Sentence-BERT converts each sentence into a single fixed-length numerical vector by training two shared-weight BERT copies to pull semantically matching sentences closer together and push mismatched ones apart. This gives the system a fast, reliable way to measure how similar two sentences are or whether one logically follows from another, without doing slow word-by-word comparison.