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ALJIACHI
/
Mizan-Rerank-v3

Text Ranking
sentence-transformers
Safetensors
Arabic
new
cross-encoder
reranker
arabic
long-context
rag
islamic
custom_code
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use ALJIACHI/Mizan-Rerank-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use ALJIACHI/Mizan-Rerank-v3 with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("ALJIACHI/Mizan-Rerank-v3", 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)
  • Notebooks
  • Google Colab
  • Kaggle
Mizan-Rerank-v3 / benchmark
1.49 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
ALJIACHI's picture
ALJIACHI
Model card: focus on accuracy and speed for its size
742ee3c verified 9 days ago
  • benchmark_rerankers.py
    12.9 kB
    Release Mizan-Rerank-v3 9 days ago
  • internal_results.json
    2.53 kB
    Release Mizan-Rerank-v3 9 days ago
  • plot_results.py
    7.99 kB
    Model card: focus on accuracy and speed for its size 9 days ago
  • results_full.json
    7 kB
    Release Mizan-Rerank-v3 9 days ago
  • seen_in_training.json
    1.45 MB
    Release Mizan-Rerank-v3 9 days ago
  • speed_benchmark.py
    3.98 kB
    Model card: focus on accuracy and speed for its size 9 days ago
  • speed_results.json
    867 Bytes
    Model card: focus on accuracy and speed for its size 9 days ago