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Starkdoorstep12
/
entity-resolution-crossencoder

Text Ranking
sentence-transformers
Safetensors
xlm-roberta
cross-encoder
reranker
Generated from Trainer
dataset_size:3299295
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Starkdoorstep12/entity-resolution-crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Starkdoorstep12/entity-resolution-crossencoder with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("Starkdoorstep12/entity-resolution-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
entity-resolution-crossencoder
1.92 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Starkdoorstep12's picture
Starkdoorstep12
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1115d83 verified 28 days ago
  • checkpoint-103104
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  • eval
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  • .gitattributes
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  • README.md
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  • config.json
    818 Bytes
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  • config_sentence_transformers.json
    259 Bytes
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  • model.safetensors
    471 MB
    xet
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  • modules.json
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  • sentence_bert_config.json
    225 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    343 Bytes
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