sentence-transformers

To generate embeddings for semantic search, you need pre-trained Sentence Transformers models that convert text into normalized dense vectors for efficient similarity computation. This simplifies ranking and retrieval without manual feature engineering.

Text embeddings are dense numerical vector representations of sentences used to group similar items. You need them for clustering when organizing unstructured text data by semantic similarity rather than exact keyword matching.

Yes, you can use multilingual Sentence Transformers models for retrieval augmented generation tasks. They support cross-lingual semantic matching by mapping different languages into a shared vector space for consistent retrieval.

The best way to compute similarity for a large text corpus is generating normalized embeddings via batch encoding, then applying cosine similarity. This approach scales efficiently for semantic search and clustering workflows.

You do not always need to fine-tune models for specialized domain classification, as pre-trained legal, scientific, and code models exist. However, fine-tuning provides reliable downstream NLP pipeline performance for highly specific tasks.