# 384-dimensional vector

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

This is a dense single-vector dual-encoder model for French, suitable for semantic search. The model maps queries and passages to 384-dimensional dense vectors and calculates relevance through cosine similarity.

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

This is a sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space and is suitable for semantic search and clustering tasks.

A lightweight sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

This is a model based on sentence-transformers that can map sentences and paragraphs to a 384-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.