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LightEmbed
/
LaBSE-onnx

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

Instructions to use LightEmbed/LaBSE-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use LightEmbed/LaBSE-onnx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="LightEmbed/LaBSE-onnx")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("LightEmbed/LaBSE-onnx")
    model = AutoModel.from_pretrained("LightEmbed/LaBSE-onnx")
  • Notebooks
  • Google Colab
  • Kaggle
LaBSE-onnx
1.9 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
binhcode25's picture
binhcode25
Add new SentenceTransformer model.
63984b3 verified almost 2 years ago
  • 1_Pooling
    Add new SentenceTransformer model. almost 2 years ago
  • .gitattributes
    1.57 kB
    Add new SentenceTransformer model. almost 2 years ago
  • config.json
    934 Bytes
    Add new SentenceTransformer model. almost 2 years ago
  • model.onnx
    1.88 GB
    xet
    Add new SentenceTransformer model. almost 2 years ago
  • special_tokens_map.json
    695 Bytes
    Add new SentenceTransformer model. almost 2 years ago
  • tokenizer.json
    13.6 MB
    xet
    Add new SentenceTransformer model. almost 2 years ago
  • tokenizer_config.json
    1.27 kB
    Add new SentenceTransformer model. almost 2 years ago
  • vocab.txt
    5.22 MB
    Add new SentenceTransformer model. almost 2 years ago