Models on Hugging Face

Embedding models are used to convert raw data like text, code, or images into high-dimensional vectors that capture semantic meaning. These vector representations allow AI systems to compare, cluster, and retrieve information based on similarity rather than exact matches. Hugging Face provides a wide range of pretrained embedding models, which are commonly used for tasks like semantic search, recommendation systems, duplicate detection, and retrieval-augmented generation (RAG). These models can be accessed through libraries like transformers or sentence-transformers, making it easy to generate high-quality embeddings for both general-purpose and task-specific applications.