# Cross-language Retrieval

Multilingual E5 large instruction model, supporting text embedding and classification tasks in multiple languages with strong cross-language capabilities.

This is a multilingual sentence embedding model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

This is a GGUF format text ranking model converted from the BAAI/bge-reranker-v2-m3 model, supporting multilingual text embedding inference.

Snowflake Arctic Embed v2.0 is a multilingual sentence embedding model that supports text feature extraction and sentence similarity calculation for over 100 languages.

Snowflake Arctic Embed M v2.0 is a multilingual sentence embedding model that supports over 50 languages, focusing on sentence similarity and feature extraction tasks.

A multilingual passage reranking model supporting over 100 languages, designed to improve the relevance ranking of search engine results