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nvidia
/
Nemotron-3.5-Rerank-8B-BF16

Text Ranking
sentence-transformers
Safetensors
Transformers
mistral3
text-classification
reranker
cross-encoder
visual-document-retrieval
multimodal reranking
semantic-search
rag
vllm
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use nvidia/Nemotron-3.5-Rerank-8B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use nvidia/Nemotron-3.5-Rerank-8B-BF16 with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("nvidia/Nemotron-3.5-Rerank-8B-BF16", trust_remote_code=True)
    
    query = "Which planet is known as the Red Planet?"
    passages = [
    	"Venus is often called Earth's twin because of its similar size and proximity.",
    	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
    	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
    ]
    
    scores = model.predict([(query, passage) for passage in passages])
    print(scores)
  • Transformers

    How to use nvidia/Nemotron-3.5-Rerank-8B-BF16 with Transformers:

    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSequenceClassification
    
    processor = AutoProcessor.from_pretrained("nvidia/Nemotron-3.5-Rerank-8B-BF16", trust_remote_code=True)
    model = AutoModelForSequenceClassification.from_pretrained("nvidia/Nemotron-3.5-Rerank-8B-BF16", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Nemotron-3.5-Rerank-8B-BF16 / 2_Dense
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  • 4 contributors
History: 1 commit
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ybabakhin
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  • config.json
    204 Bytes
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  • model.safetensors
    16.5 kB
    xet
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