Sentence-BERT (SBERT) was developed to address specific limitations of BERT in generating effective sentence-level embeddings for tasks requiring semantic similarity comparisons. While BERT excels at context-aware token-level understanding (e.g., named entity recognition or question answering), it struggles to produce meaningful embeddings for entire sentences. For instance, averaging BERT’s token embeddings or using the [CLS] token often results in poor performance for tasks like semantic search or clustering. SBERT solves this by fine-tuning BERT with a siamese network architecture and a triplet loss function, explicitly training the model to map semantically similar sentences closer in the embedding space.
A key issue with BERT was computational inefficiency in similarity tasks. To compare two sentences using vanilla BERT, both must be processed together in a single forward pass, leading to O(n²) complexity when comparing large datasets (e.g., 10,000 sentences require ~50 million comparisons). SBERT reduces this to O(n) by precomputing sentence embeddings once, then using fast cosine similarity calculations. This makes real-time applications like recommendation systems or large-scale semantic search feasible. For example, SBERT embeddings can precompute representations for millions of support articles, enabling instant similarity checks, while BERT’s pairwise approach would be impractical.
Additionally, BERT’s embeddings lack optimization for sentence-level tasks. Without fine-tuning, BERT’s embeddings cluster sentences based on superficial features like topic rather than semantic equivalence. SBERT addresses this with training strategies like contrastive learning, where the model learns to distinguish between similar and dissimilar sentence pairs. For instance, in the STS-B benchmark, SBERT outperforms BERT by aligning embeddings with human-judged similarity scores. This makes SBERT indispensable for use cases like paraphrase detection, where nuanced semantic alignment matters more than keyword overlap. By refining BERT’s architecture and training objectives, SBERT bridges the gap between token-level understanding and practical sentence-level applications.