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