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Llama-3.2-NV-EmbedQA-1B-v2

Multilingual text question-answering retrieval, transforming textual information into dense vector representations.

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About

The NVIDIA NeMo Retriever Llama3.2 embedding model is optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 8192 tokens) and dynamic embedding size (Matryoshka Embeddings). This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.

In addition to enabling multilingual and cross-lingual question-answering retrieval, this model reduces the data storage footprint by 35x through dynamic embedding sizing and support for longer token length, making it feasible to handle large-scale datasets efficiently.

For additional information please contact NVIDIA: https://www.nvidia.com/en-us/data-center/lp/aws-marketplace-offer

This model is ready for commercial use.

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