# Hebrew sentence embedding

This is a Hebrew embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a Hebrew sentence similarity calculation model based on sentence-transformers, which can map text to a 512-dimensional vector space for semantic search and clustering tasks