gte-Qwen2-7B-instruct

A state-of-the-art multilingual text embedding model from Alibaba's GTE (General Text Embedding) series, built on the Qwen2-1.5B LLM. The model supports up to 8192 tokens and incorporates bidirectional attention mechanisms for enhanced contextual understanding across diverse domains.

A versatile multilingual text embedding model from BAAI that supports 100+ languages and can handle inputs up to 8192 tokens. BGE-M3 is unique in supporting three retrieval methods simultaneously: dense retrieval, multi-vector retrieval, and sparse retrieval.

A task-specific text embedding model that generates customized embeddings based on natural language instructions. INSTRUCTOR achieves state-of-the-art performance on 70 diverse embedding tasks by allowing users to specify the task objective and domain.

Suite of high-quality multilingual text embedding models optimized for retrieval performance, developed by Snowflake and available as open-source for commercial use.