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.

Multilingual embedding model supporting over 100 languages and ranking #1 on MTEB multilingual leaderboard. Offers flexible model sizes from 0.6B to 8B parameters with user-defined instructions.

Massive Text Embedding Benchmark (MTEB) - a comprehensive benchmark for evaluating text embedding models across 8 embedding tasks and 58 datasets in 112 languages. Provides a standardized leaderboard for comparing embedding quality across classification, clustering, retrieval, reranking, semantic textual similarity, and summarization tasks.

High-performance multilingual embedding model from Cohere supporting 100+ languages with 1024 dimensions, optimized for semantic search, RAG, and cross-lingual retrieval tasks.

State-of-the-art embedding model from Mistral AI that generates 1024-dimensional vectors for text, supporting semantic search, clustering, and retrieval-augmented generation applications.

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.