Sentence-transformers is a Python library that uses transformer models like BERT to convert text into dense vector embeddings, enabling semantic similarity comparisons and retrieval tasks. Developers use it to build applications that need to understand meaning and relationships between sentences or documents, such as search engines, recommendation systems, and clustering tools. It's built on PyTorch and handles the complexity of fine-tuning and deploying transformer networks for these NLP tasks.