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tencent
/
R3-rerank-0.6b

Text Ranking
sentence-transformers
Safetensors
qwen3
cross-encoder
agent-skill-retrieval
Model card Files Files and versions
xet
Community
1

Instructions to use tencent/R3-rerank-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use tencent/R3-rerank-0.6b with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("tencent/R3-rerank-0.6b")
    
    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)
  • Notebooks
  • Google Colab
  • Kaggle
R3-rerank-0.6b
2.4 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 5 commits
xiaoxiaoshadiao's picture
xiaoxiaoshadiao
Expand code snippet for easier use + add missing tag (#1)
6d0c3e4 18 days ago
  • 1_LogitScore
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  • .gitattributes
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  • README.md
    1.69 kB
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  • added_tokens.json
    707 Bytes
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  • chat_template.jinja
    741 Bytes
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  • config.json
    1.36 kB
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  • config_sentence_transformers.json
    342 Bytes
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  • generation_config.json
    214 Bytes
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  • merges.txt
    1.67 MB
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  • model.safetensors
    2.38 GB
    xet
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  • modules.json
    280 Bytes
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  • sentence_bert_config.json
    362 Bytes
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  • special_tokens_map.json
    613 Bytes
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  • tokenizer.json
    11.4 MB
    xet
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  • tokenizer_config.json
    5.4 kB
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  • vocab.json
    2.78 MB
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