# Specialized for semantic retrieval

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

A RoBERTa-large based sentence embedding model trained on 1 billion sentence pairs through self-supervised contrastive learning for generating semantically rich sentence vector representations

A sentence embedding model based on mpnet-base, trained on 1 billion sentence pairs through self-supervised contrastive learning, capable of generating high-quality semantic vector representations of sentences