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Alibaba-NLP
/
gte-multilingual-reranker-base

Text Ranking
sentence-transformers
Safetensors
Transformers
new
text-classification
text-embeddings-inference
custom_code
Model card Files Files and versions
xet
Community
22

Instructions to use Alibaba-NLP/gte-multilingual-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Alibaba-NLP/gte-multilingual-reranker-base with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("Alibaba-NLP/gte-multilingual-reranker-base", trust_remote_code=True)
    
    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 Alibaba-NLP/gte-multilingual-reranker-base with Transformers:

    # Load model directly
    from transformers import AutoModelForSequenceClassification
    model = AutoModelForSequenceClassification.from_pretrained("Alibaba-NLP/gte-multilingual-reranker-base", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
gte-multilingual-reranker-base
629 MB
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  • 7 contributors
History: 15 commits
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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