# Semantic vector encoding

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

A sentence embedding model fine-tuned on over 1 billion sentence pairs through self-supervised contrastive learning based on MiniLM-L12, capable of generating high-quality semantic vector representations

A sentence embedding model based on MiniLM-L12 architecture, trained on over 1 billion sentence pairs through contrastive learning, capable of generating high-quality semantic vector representations