A lean, focused specialist that converts text into sparse vector representations — meaning most values in its output are zero, with only a handful of dimensions activated. This makes its embeddings highly efficient to store and search at scale. The trade-off is that sparse embeddings can miss nuanced semantic relationships that denser models capture.