Text Ranking
sentence-transformers
Safetensors
Transformers
mistral3
text-classification
reranker
cross-encoder
visual-document-retrieval
multimodal reranking
semantic-search
rag
vllm
custom_code
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
fix broken link in bias table
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README.md
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| Bias Metric (If Measured): | Not Applicable |
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| Participation considerations from adversely impacted groups [protected classes](https://calcivilrights.ca.gov/disputeresolution/protected-characteristics/) in model design and testing: | None |
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| Measures taken to mitigate against unwanted bias: | None |
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| Bias Metric (If Measured): | Not Applicable |
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