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kselight
/
123BERT

Text Ranking
sentence-transformers
Safetensors
English
bert
cross-encoder
reranker
Generated from Trainer
dataset_size:10000
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use kselight/123BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use kselight/123BERT with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("kselight/123BERT")
    
    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
123BERT
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
kselight's picture
kselight
Add new CrossEncoder model
bd35c30 verified 7 months ago
  • .gitattributes
    1.52 kB
    initial commit 7 months ago
  • README.md
    25 kB
    Add new CrossEncoder model 7 months ago
  • config.json
    857 Bytes
    Add new CrossEncoder model 7 months ago
  • model.safetensors
    36.3 MB
    xet
    Add new CrossEncoder model 7 months ago
  • special_tokens_map.json
    695 Bytes
    Add new CrossEncoder model 7 months ago
  • tokenizer.json
    712 kB
    Add new CrossEncoder model 7 months ago
  • tokenizer_config.json
    1.22 kB
    Add new CrossEncoder model 7 months ago
  • vocab.txt
    232 kB
    Add new CrossEncoder model 7 months ago