BGE-M3

BGE-M3 is a text embedding model from BAAI, described in a paper published in February 2024, and the name refers to three things it does at once: multi-linguality across more than 100 languages, multi-granularity handling of inputs up to 8192 tokens, and multi-functionality in that it produces dense vectors, sparse lexical weights, and ColBERT-style token vectors from a single forward pass. It has 568 million parameters, which is modest next to large LLMs but is trained specifically for retrieval rather than generation. That combination let it post state-of-the-art results at the time on the multilingual MIRACL benchmark and the cross-lingual MKQA benchmark, since a single model could substitute for what previously required separate dense and sparse retrieval systems. BAAI released it under the MIT license on Hugging Face, and it’s widely used in retrieval-augmented generation pipelines that need to search across documents in multiple languages or documents longer than the 512-token limit typical of earlier BERT-based embedding models.