Text Embedding Models

Text embedding models turn language into vectors so systems can compare texts, retrieve relevant documents, group content, or use text as features in other tasks. Common approaches include contextual transformer encoders trained with contrastive learning, hard negatives, synthetic query–document pairs, and distillation; newer methods also adapt large language models through prompting, reasoning objectives, or test-time refinement. Work spans multilingual and domain-specific retrieval, long-context representation, and efficient deployment through compression, quantization, and smaller student models. A central challenge is that benchmark scores can depend heavily on prompts, datasets, and task type, so stronger general-purpose performance does not guarantee robust results in a particular language or application.