# Billion-scale sentence pair training

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

A lightweight sentence embedding model based on MiniLM architecture, fine-tuned with contrastive learning on a 1-billion sentence pair dataset, suitable for semantic similarity calculation and information retrieval tasks