# Multilingual text embedding

Qwen3-Embedding-4B is a text embedding model built on the Qwen3 series, designed specifically for text embedding and ranking tasks, and performs excellently in multilingual text processing and code retrieval.

The Qwen3 Embedding model series is the latest self-developed model of the Tongyi family, designed specifically for text embedding and ranking tasks, supporting more than 100 languages, and ranking first on the MTEB multilingual leaderboard.

Armenian text embedding model optimized based on multilingual-e5-base, supporting semantic search and cross-lingual understanding

A distilled version based on jinaai/jina-embeddings-v3, designed for scenarios with limited computing resources or high real-time performance requirements, supporting multilingual text embedding computation.

multilingual - e5 - small is a model that performs excellently in multilingual text processing tasks, supporting various tasks such as classification, retrieval, clustering, re - ranking, and semantic text similarity.

This is a sentence-transformers model fine-tuned on Spanish semantic similarity datasets, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.