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tikanosa
/
mk_crossencoder

Text Ranking
sentence-transformers
Safetensors
bert
cross-encoder
reranker
Generated from Trainer
dataset_size:942
loss:BinaryCrossEntropyLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

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

  • Libraries
  • sentence-transformers

    How to use tikanosa/mk_crossencoder with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("tikanosa/mk_crossencoder")
    
    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
mk_crossencoder
91.9 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
tikanosa's picture
tikanosa
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c25dc1b verified 7 months ago
  • .gitattributes
    1.52 kB
    initial commit 7 months ago
  • README.md
    46.5 kB
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  • config.json
    823 Bytes
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  • model.safetensors
    90.9 MB
    xet
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.27 kB
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  • vocab.txt
    232 kB
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