The provided text introduces **Sentence-BERT (SBERT)**, a modification of the popular **BERT** and **RoBERTa** language models, designed to efficiently generate **semantically meaningful sentence embeddings**. The authors address the significant **computational overhead** of using standard BERT for tasks requiring sentence-pair comparisons, such as semantic similarity search and clustering, which can take hours for large datasets. SBERT utilizes **siamese and triplet network structures** to create fixed-size sentence vectors that can be quickly compared using metrics like **cosine-similarity**, drastically reducing the computation time from hours to seconds while **maintaining or exceeding accuracy**. Evaluation results demonstrate that SBERT significantly **outperforms other state-of-the-art sentence embedding methods** on various Semantic Textual Similarity (STS) and transfer learning tasks. Ultimately, SBERT makes **BERT usable for large-scale applications** where the original architecture was too slow.

Source:

https://arxiv.org/pdf/1908.10084