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yoriis
/
ce-task-70

Text Ranking
sentence-transformers
Safetensors
bert
cross-encoder
reranker
Generated from Trainer
dataset_size:14287
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use yoriis/ce-task-70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use yoriis/ce-task-70 with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("yoriis/ce-task-70")
    
    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
ce-task-70
543 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
yoriis's picture
yoriis
Add new CrossEncoder model
f03de03 verified 9 months ago
  • .gitattributes
    1.52 kB
    initial commit 9 months ago
  • README.md
    21.3 kB
    Add new CrossEncoder model 9 months ago
  • config.json
    798 Bytes
    Add new CrossEncoder model 9 months ago
  • model.safetensors
    541 MB
    xet
    Add new CrossEncoder model 9 months ago
  • special_tokens_map.json
    695 Bytes
    Add new CrossEncoder model 9 months ago
  • tokenizer.json
    1.78 MB
    Add new CrossEncoder model 9 months ago
  • tokenizer_config.json
    2.05 kB
    Add new CrossEncoder model 9 months ago
  • vocab.txt
    761 kB
    Add new CrossEncoder model 9 months ago