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cross-encoder
/
ms-marco-electra-base

Text Ranking
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
electra
text-classification
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xet
Community
5

Instructions to use cross-encoder/ms-marco-electra-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use cross-encoder/ms-marco-electra-base with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("cross-encoder/ms-marco-electra-base")
    
    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 cross-encoder/ms-marco-electra-base with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("cross-encoder/ms-marco-electra-base")
    model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/ms-marco-electra-base")
  • Notebooks
  • Google Colab
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  • Code of Conduct
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VORTEXRAG: 7-Layer RAG — Causal Drift Filtering + Context Poison Guard [paper + code + demo]

#5 opened 19 days ago by
vigneshwar234

VORTEXRAG: 7-Layer RAG — Causal Drift Filtering + Context Poison Guard [paper + code + demo]

#4 opened 19 days ago by
vigneshwar234

Hyper-parameter settings for electra-base on ms-marco

#2 opened almost 3 years ago by
cramraj8
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