# 384-Dimensional Embedding

A sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and text similarity calculation

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a sentence embedding model based on the MiniLM architecture, capable of mapping text to a 384-dimensional vector space, suitable for semantic search and text similarity tasks.

This is a sentence similarity model based on sentence-transformers, capable of mapping text to a 384-dimensional vector space, suitable for clustering and semantic search tasks.

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

This is an ONNX model based on sentence-transformers, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a sentence transformer model that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.