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    3. Qwen3 Embedding

    Qwen3 Embedding

    Multilingual embedding model supporting over 100 languages and ranking #1 on MTEB multilingual leaderboard. Offers flexible model sizes from 0.6B to 8B parameters with user-defined instructions.

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    About this tool

    Overview

    The Qwen3 Embedding series represents a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models.

    Key Features

    • Support for over 100 languages including various programming languages
    • Robust multilingual, cross-lingual, and code retrieval capabilities
    • Full spectrum of sizes from 0.6B to 8B for both embedding and reranking models
    • Flexible vector definitions across all dimensions
    • User-defined instructions to enhance performance for specific tasks, languages, or scenarios

    Performance

    • 8B size embedding model ranks #1 in MTEB multilingual leaderboard (as of June 5, 2025, score 70.58)
    • Reranking model excels in various text retrieval scenarios
    • Superior performance in multilingual and cross-lingual tasks

    Model Variants

    • Qwen3-Embedding-8B (text)
    • Qwen3-VL-Embedding (multimodal - supports text, images, screenshots, and video)
    • Multiple size options for different efficiency/effectiveness trade-offs

    License

    Open-sourced under Apache 2.0 license

    Availability

    Available on Hugging Face, ModelScope, and GitHub with published technical report and code.

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    Information

    Websitegithub.com
    PublishedMar 8, 2026

    Categories

    1 Item
    Machine Learning Models

    Tags

    3 Items
    #Multilingual#Open Source#Embeddings

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