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rasa
/
LaBSE

Feature Extraction
Transformers
PyTorch
google-tensorflow TensorFlow
JAX
bert
Model card Files Files and versions
xet
Community
2

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

  • Libraries
  • Transformers

    How to use rasa/LaBSE with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="rasa/LaBSE")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("rasa/LaBSE")
    model = AutoModel.from_pretrained("rasa/LaBSE")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
LaBSE
5.66 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 13 commits
patrickvonplaten's picture
patrickvonplaten
upload flax model
e615b58 almost 5 years ago
  • .gitattributes
    391 Bytes
    allow flax almost 5 years ago
  • config.json
    654 Bytes
    Update config.json over 5 years ago
  • flax_model.msgpack
    1.88 GB
    xet
    upload flax model almost 5 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "collections.OrderedDict",
    • "torch.LongStorage",
    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    1.88 GB
    xet
    Update pytorch_model.bin over 5 years ago
  • special_tokens_map.json
    112 Bytes
    Update special_tokens_map.json over 5 years ago
  • tf_model.h5
    1.88 GB
    xet
    Update tf_model.h5 over 5 years ago
  • tokenizer_config.json
    277 Bytes
    Update tokenizer_config.json over 5 years ago
  • vocab.txt
    5.22 MB
    Update vocab.txt over 5 years ago