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tomaarsen
/
reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-lambdaloss-mean

Text Classification
sentence-transformers
Safetensors
English
bert
cross-encoder
Generated from Trainer
dataset_size:78704
loss:LambdaLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-lambdaloss-mean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-lambdaloss-mean with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-lambdaloss-mean")
    
    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
reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-lambdaloss-mean
1.52 kB
Ctrl+K
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  • 1 contributor
History: 1 commit
tomaarsen's picture
tomaarsen HF Staff
initial commit
73fb834 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago