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oksomu
/
role-radar-scoring

Text Classification
sentence-transformers
ONNX
English
job-matching
lightgbm
cross-encoder
Model card Files Files and versions
xet
Community

Instructions to use oksomu/role-radar-scoring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use oksomu/role-radar-scoring with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("oksomu/role-radar-scoring")
    
    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
role-radar-scoring / embedding_tokenizer
712 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
oksomu's picture
oksomu
model-v1.0-3-gef1203e
292cc96 verified 11 days ago
  • tokenizer.json
    712 kB
    model-v1.0-3-gef1203e 11 days ago
  • tokenizer_config.json
    594 Bytes
    model-v1.0-3-gef1203e 11 days ago