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

Oblix
/
multilingual-e5-small-optimized_ONNX

Feature Extraction
Transformers
ONNX
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Oblix/multilingual-e5-small-optimized_ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Oblix/multilingual-e5-small-optimized_ONNX with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Oblix/multilingual-e5-small-optimized_ONNX")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("Oblix/multilingual-e5-small-optimized_ONNX")
    model = AutoModel.from_pretrained("Oblix/multilingual-e5-small-optimized_ONNX")
  • Notebooks
  • Google Colab
  • Kaggle
multilingual-e5-small-optimized_ONNX / onnx
588 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
Oblix's picture
Oblix
Upload 8 files
b7f01c4 verified about 2 years ago
  • model.onnx
    470 MB
    xet
    Upload 8 files about 2 years ago
  • model_quantized.onnx
    118 MB
    xet
    Upload 8 files about 2 years ago