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sumanksaha
/
Foodmultidomain

Text Ranking
sentence-transformers
Safetensors
bert
cross-encoder
reranker
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use sumanksaha/Foodmultidomain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use sumanksaha/Foodmultidomain with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("sumanksaha/Foodmultidomain")
    
    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

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  • .gitattributes
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    initial commit 6 days ago
  • README.md
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    push legal_ce_v2_K500 (fine-tuned legal cross-encoder) 6 days ago
  • config.json
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  • config_sentence_transformers.json
    266 Bytes
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  • model.safetensors
    90.9 MB
    xet
    push legal_ce_v2_K500 (fine-tuned legal cross-encoder) 6 days ago
  • modules.json
    138 Bytes
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  • sentence_bert_config.json
    234 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
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