# Dense Vectors

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

This is a sentence transformer model adapted from facebook/dragon-plus-context-encoder, designed to map sentences and paragraphs into a 768-dimensional vector space, suitable for tasks such as clustering and semantic search.

NegMPNet is a negation-aware version based on all-mpnet-base-v2, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, particularly adept at handling negation semantics.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 16-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A model based on sentence-transformers that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A sentence embedding model based on ConGen, capable of mapping sentences to a 312-dimensional vector space, suitable for tasks like semantic search.

This is a sentence-transformers-based model that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.