# 384-dimensional vector space

A Korean sentence embedding model fine-tuned from intfloat/multilingual-e5-small, supporting 384-dimensional vector representation, suitable for tasks like semantic similarity calculation

A lightweight model based on sentence-transformers, used to map sentences and paragraphs to a 384-dimensional vector space, supporting tasks such as clustering and semantic search.

A model based on sentence-transformers that maps queries to a 384-dimensional vector space for tasks like query clustering or semantic search

This is a multilingual sentence embedding model that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.