multilingual-e5-large

First fully reproducible open-source text embedding model with 8,192 context length. v2 introduces Mixture-of-Experts architecture for multilingual embeddings. Outperforms OpenAI models on benchmarks. This is an OSS model under Apache 2.0 license.

Frontier multilingual text embedding model with 570M parameters and 8192 token-length, featuring task-specific LoRA adapters and outperforming OpenAI and Cohere embeddings on MTEB benchmark.

Groundbreaking multilingual information retrieval model supporting 89 languages with token-level embeddings and late interaction. Features Matryoshka embeddings for flexible efficiency-precision tradeoffs and 8192 token input context.

Open-source text embedding models from Microsoft supporting 100+ languages. Features small, base, and large variants with weakly-supervised contrastive pre-training. This is an OSS model family released by Microsoft Research.

State-of-the-art foundational model for ranking with 4096 context length and multilingual support for 100+ languages. Offers exceptional performance on BEIR benchmarks and specialized domains including finance, e-commerce, and enterprise search.

A versatile embedding model from BAAI that simultaneously supports dense retrieval, sparse retrieval, and multi-vector retrieval, with multilingual support for 100+ languages and multi-granularity processing from short sentences to 8192-token documents.