# 512-dimensional vector space

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

This is a multilingual sentence embedding model that maps text to a 512-dimensional vector space, suitable for semantic search and clustering tasks.

A sentence transformer model based on ult5-pt-small that maps sentences and paragraphs into 512-dimensional vectors, suitable for tasks like text clustering, similarity calculation, and semantic search.

This is a multilingual sentence transformer model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering or semantic search.