# Large-scale Retrieval Optimization

A 1.5B-parameter general text embedding model based on the Qwen2 architecture, supporting both Chinese and English, focusing on sentence similarity computation and text retrieval tasks.

This is a merged model composed of multiple foundation models including bert-large-uncased, WhereIsAI/UAE-Large-V1, BAAI/bge-large-en-v1.5, mixedbread-ai/mxbai-embed-large-v1, and avsolatorio/GIST-large-Embedding-v0. It is primarily used for text classification, retrieval, and clustering tasks.

A 7B-parameter sentence embedding model based on the Qwen1.5 architecture, focusing on sentence similarity calculation and multi-task evaluation

GTE-Large is a powerful sentence transformer model focused on sentence similarity and text embedding tasks, excelling in multiple benchmark tests.