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IDQO
/
arcade-reranker

Text Ranking
sentence-transformers
Safetensors
modernbert
cross-encoder
reranker
Generated from Trainer
dataset_size:2277
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use IDQO/arcade-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use IDQO/arcade-reranker with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("IDQO/arcade-reranker")
    
    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
arcade-reranker / eval
Ctrl+K
Ctrl+K
  • 1 contributor
History: 18 commits
amanwithaplan's picture
amanwithaplan
Training in progress, step 375
a0a7dba verified about 2 months ago
  • CrossEncoderRerankingEvaluator_NanoMSMARCO_R100_results_@10.csv
    1.24 kB
    Training in progress, step 375 about 2 months ago
  • CrossEncoderRerankingEvaluator_NanoNFCorpus_R100_results_@10.csv
    1.24 kB
    Training in progress, step 375 about 2 months ago
  • CrossEncoderRerankingEvaluator_NanoNQ_R100_results_@10.csv
    1.24 kB
    Training in progress, step 375 about 2 months ago
  • NanoBEIR_evaluation_mean_results.csv
    1.23 kB
    Training in progress, step 375 about 2 months ago