Embedding

Models & Architectures

Numerical vector representation of tokens or objects.

Embeddings map words, sentences, images, or items into dense vector spaces where semantically similar entities are close.

Uses: Search/retrieval, clustering, recommendation systems, model features.
Training: Self-supervised (e.g., contrastive learning) or labeled.
Practice: Dimension selection, distance metric, normalization, drift monitoring.