Sentence Similarity
sentence-transformers
Safetensors
Transformers
ministral3
feature-extraction
text
text-embeddings
retrieval
semantic-search
rag
vllm
Instructions to use nvidia/Nemotron-3-Embed-1B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/Nemotron-3-Embed-1B-BF16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/Nemotron-3-Embed-1B-BF16") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use nvidia/Nemotron-3-Embed-1B-BF16 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-3-Embed-1B-BF16") model = AutoModel.from_pretrained("nvidia/Nemotron-3-Embed-1B-BF16", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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### License/Terms of Use:
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This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/).
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ADDITIONAL INFORMATION: [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
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| Verified to have met prescribed NVIDIA quality standards: | Yes |
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| Performance Metrics: | Accuracy, Throughput, and Latency |
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| Potential Known Risks: | This model does not always guarantee to retrieve the correct passage(s) for a given query. |
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| Licensing: | This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/).
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## Privacy
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| Model Application(s): | Text Embedding for Retrieval |
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| Describe the physical safety impact (if present). | Not Applicable |
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| Use Case Restrictions: | This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/).
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| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
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### License/Terms of Use:
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This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/).
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ADDITIONAL INFORMATION: [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
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| Verified to have met prescribed NVIDIA quality standards: | Yes |
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| Performance Metrics: | Accuracy, Throughput, and Latency |
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| 712 |
| Potential Known Risks: | This model does not always guarantee to retrieve the correct passage(s) for a given query. |
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| Licensing: | This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/). ADDITIONAL INFORMATION: [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
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## Privacy
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| ----- | ----- |
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| Model Application(s): | Text Embedding for Retrieval |
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| Describe the physical safety impact (if present). | Not Applicable |
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| Use Case Restrictions: | This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/). ADDITIONAL INFORMATION: [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
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| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
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