What is Embedding Model?
An embedding model is a neural network trained to transform text into dense vector representations that capture semantic meaning. These models encode text of varying lengths (words, sentences, paragraphs, or documents) into fixed-length numerical vectors, positioning semantically similar text close together in the embedding space. The resulting embeddings serve as the foundation for semantic search, similarity comparison, clustering, and other downstream tasks in AI applications.
Modern embedding models are typically based on transformer architectures and trained on large text corpora using objectives that encourage semantically similar content to have similar embeddings. Training approaches include contrastive learning (where similar pairs are pulled together and dissimilar pairs pushed apart), masked language modeling, or task-specific fine-tuning on retrieval or semantic similarity datasets. The quality of an embedding model significantly impacts the performance of systems that rely on it.
Embedding models come in various sizes and specializations. General-purpose models like OpenAI's text-embedding-ada-002, Cohere's embed models, and open-source options like sentence-transformers work well across diverse tasks. Domain-specific models fine-tuned for legal, medical, or technical content often outperform general models in their specialized areas. Key considerations when selecting an embedding model include embedding dimension, maximum input length, inference speed, cost, and performance on relevant benchmark tasks.