# High-dimensional vectors

Qwen3-Embedding-8B is the latest proprietary model in the Qwen family, designed for text embedding and ranking tasks. It is built on the dense base model of the Qwen3 series and has excellent multilingual capabilities and long text understanding capabilities.

PLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, demonstrating outstanding performance in Japanese text embedding benchmarks

This is a sentence-transformers model fine-tuned from Alibaba NLP/gte-modernbert-base, designed for semantic text similarity computation to implement semantic caching functionality.

Vietnamese embedding model fine-tuned on BGE-M3, enhancing Vietnamese retrieval capabilities

MMLW (I Must Get Better Messages) is a Polish neural text encoder optimized for information retrieval tasks, capable of converting queries and passages into 768-dimensional vectors.

This is a sentence embedding model based on the MPNet architecture, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and sentence similarity tasks.