Nemotron-3-Embed-1B-BF16

NVIDIA Nemotron 3 Embed**     Model Overview Description: Nemotron-3-Embed-1B-BF16** is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual retrieval capabilities and is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese. The model generates dense vector embeddings from multilingual text inputs, enabling retrieval, semantic search, and (agentic) RAG workflows. As a core component of text retrieval systems, an embedding model transforms text, such as questions or passages, into dense vector representations. These models are typically transformer encoders that process input tokens and produce embeddings suitable for efficient similarity matching. Among models of comparable size, **Nemotron-3-Embed-1B-BF16** achieves state-of-the-art performance across multiple multilingual retrieval benchmarks. Read more details in our Blog Post. This model is ready for commercial use. License/Terms of Use: This model and its associated configuration files are licensed under the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Additional Information: Built with Ministral-3-3B-Instruct-2512 which is released under Apache 2.0. This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use. Deployment Geography: Global Use Case: Nemotron-3-Embed-1B-BF16** is most suitable for users who want to build a multilingual question-and-answer application over a large text corpus, lever