Sentence Transformers and contextual word embeddings serve different purposes in tasks like clustering or semantic search, primarily due to how they represent textual information. Sentence Transformers generate dense vectors for entire sentences or paragraphs, optimized for capturing overall semantic meaning. Models like Sentence-BERT are fine-tuned using objectives such as triplet loss, which explicitly trains them to differentiate between similar and dissimilar sentences. In contrast, contextual word embeddings (e.g., from BERT) produce vectors for individual words, influenced by their surrounding context. To use these for sentence-level tasks, developers must aggregate word vectors—often via averaging or using the [CLS] token—which can dilute nuanced relationships between words.
For clustering, Sentence Transformers provide a clear advantage. Their sentence-level embeddings preserve holistic meaning, making them more effective at grouping semantically similar texts. For example, in clustering product reviews, a Sentence Transformer captures sentiments like "easy to use but expensive" as a cohesive concept, while averaging word embeddings might weaken the connection between "easy" and "expensive." Similarly, in semantic search, Sentence Transformers excel at matching queries to documents by understanding the full context. A search for "budget-friendly laptops with long battery life" would align better with relevant results using sentence embeddings, whereas aggregated word vectors might overemphasize individual terms like "budget-friendly" without their contextual interplay.
Efficiency and training objectives further differentiate the two approaches. Sentence Transformers output fixed-size vectors regardless of input length, simplifying processing for variable-length texts. They’re also computationally efficient for large-scale applications, as they avoid the overhead of aggregating word vectors. Contextual word embeddings, while useful for tasks requiring word-level granularity (e.g., named entity recognition), aren’t inherently optimized for sentence similarity. Studies show models like Sentence-BERT outperform BERT with average pooling on benchmarks like STS-B, underscoring the importance of task-specific training. In summary, Sentence Transformers are better suited for clustering and semantic search, while aggregated word embeddings may suffice only when sentence-level training isn’t feasible.