# Multi-dimensional Embedding

A legal domain embedding model optimized based on the ModernBERT architecture, supporting multi-dimensional feature extraction and sentence similarity calculation

This is a sentence transformer model fine-tuned based on BAAI/bge-base-en-v1.5, specifically optimized for Q&A systems related to 3GPP technical documents, capable of mapping text to a 768-dimensional vector space.

An optimized sentence transformer model based on nomic-embed-text-v2-moe, specializing in sentence similarity calculation and feature extraction for Russian legal texts

A financial domain sentence embedding model fine-tuned based on BAAI/bge-m3, supporting 1024-dimensional vector representation, suitable for semantic similarity and information retrieval tasks