Sentence Transformers are used in semantic search engines and information retrieval systems to convert text into numerical representations (embeddings) that capture semantic meaning. These embeddings enable systems to compare the similarity of sentences or documents based on their content rather than relying solely on keyword matching. For example, in a search engine, when a user submits a query like "How to fix a leaking pipe," Sentence Transformers encode both the query and a database of support articles into dense vectors. The system then calculates cosine similarity between the query embedding and each document embedding to rank results by relevance. This approach retrieves articles about "repairing water leaks" even if they don’t contain the exact words "fix" or "leaking," addressing limitations of traditional keyword-based methods.
To optimize for semantic search, Sentence Transformers are trained using techniques like contrastive learning. Models are fine-tuned on datasets where semantically similar text pairs (e.g., questions and answers) are pushed closer in the embedding space, while unrelated pairs are pushed apart. For instance, a model might be trained on Wikipedia sections and their headings, learning to associate content with its context. Pre-trained models like all-mpnet-base-v2 or multi-qa-MiniLM-L6-cos-v1 are often used as starting points and fine-tuned on domain-specific data (e.g., medical journals or legal documents) to improve accuracy in specialized applications. This training ensures that embeddings reflect nuanced relationships, such as distinguishing between "Java" the programming language and "Java" the island in a tech-focused search system.
In practice, semantic search systems combine Sentence Transformers with efficient vector search tools. After generating embeddings for documents, libraries like FAISS or Annoy index them to enable fast approximate nearest neighbor searches. For example, an e-commerce platform might encode product descriptions and user queries into embeddings, then use these indexes to instantly retrieve products matching the intent of a search like "durable winter boots." This pipeline scales to millions of documents while maintaining low latency. Unlike traditional keyword systems, which struggle with synonyms or ambiguous terms, Sentence Transformers handle paraphrasing and context shifts, improving recall and precision in applications like chatbots, recommendation engines, or enterprise document retrieval.