Hello
Upon investigatoing into your examples for the miltilingual-e5-base, I see you are using the dense vector type as the passage_embedding. Is it possible to use sparse vector types as the passage_embedding? And how can we extend it to support different dimensions in the sparse_vector. Unless there is a default dimension size for the sparse vector type.
see here:|Google Colab