Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

ContextualAI
/
ctxl-rerank-v2-instruct-multilingual-1b

Text Ranking
Transformers
Safetensors
sentence-transformers
qwen3
text-generation
cross-encoder
reranker
Model card Files Files and versions
xet
Community
2

Instructions to use ContextualAI/ctxl-rerank-v2-instruct-multilingual-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ContextualAI/ctxl-rerank-v2-instruct-multilingual-1b with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("ContextualAI/ctxl-rerank-v2-instruct-multilingual-1b")
    model = AutoModelForCausalLM.from_pretrained("ContextualAI/ctxl-rerank-v2-instruct-multilingual-1b")
  • sentence-transformers

    How to use ContextualAI/ctxl-rerank-v2-instruct-multilingual-1b with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("ContextualAI/ctxl-rerank-v2-instruct-multilingual-1b")
    
    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
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Converting a reranker model to a single label classification model

🔥 1
#1 opened 9 months ago by
sigridjineth
Company
TOS Privacy About Careers
Website
Models Datasets Spaces Pricing Docs