# High-precision retrieval

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.

Qwen3-Reranker-4B is the latest text re-ranking model with a 4B parameter scale launched by the Tongyi family. It supports over 100 languages and performs excellently in text retrieval tasks.

Snowflake Arctic Embed M v1.5 is an efficient sentence embedding model, focusing on sentence similarity calculation and feature extraction tasks.

A sentence-transformers model fine-tuned on the MS-MARCO Russian passage ranking dataset, based on the DeepPavlov/rubert-base-cased pre-trained model, designed for asymmetric semantic search in Russian.

Dmeta-embedding-zh-small is a model that performs excellently in multiple natural language processing tasks and is particularly suitable for Chinese text processing.