Text Embedding Model

A neural network model designed to map text into high-dimensional floating-point vectors for semantic similarity computation and retrieval. Common examples include OpenAI text-embedding-3, Qwen text-embedding-v3, and the BGE series. Evaluation metrics include accuracy on the MTEB benchmark, maximum input length, and inference cost.