BAAI/bge-m3-multi
BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval), and Multi-Granularity (handling inputs from short queries to 8192-token documents). It achieves state-of-the-art retrieval performance across diverse benchmarks while maintaining a single model for multiple retrieval modes.
Try the model with a sample request using the same API shape shown in the reference page.
You can add more items with the button on the right
Colbert is a system of contextualized vectors where every token in the input is represented by its own BERT-derived embedding.
Sparse is a collection of high-dimensional vectors where each word in the input is assigned a lexical weight, with most values being zero.
Dense is a set of low-dimensional vectors where every token in the input is represented by a fully populated embedding derived from a neural model.
whether to normalize the computed embeddings
Adjust settings and run the demo to see the model output generated here in real-time.
