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xmcmic
/
Med-KEBERT

Feature Extraction
Transformers
PyTorch
English
bert
biomedical
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use xmcmic/Med-KEBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use xmcmic/Med-KEBERT with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="xmcmic/Med-KEBERT")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("xmcmic/Med-KEBERT")
    model = AutoModel.from_pretrained("xmcmic/Med-KEBERT")
  • Notebooks
  • Google Colab
  • Kaggle
Med-KEBERT
439 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 6 commits
XiaomanZhang's picture
XiaomanZhang
Update Med-KEBERT
3e877eb over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    72 Bytes
    Update Med-KEBERT over 3 years ago
  • config.json
    690 Bytes
    Update Med-KEBERT over 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    438 MB
    xet
    Upload model over 3 years ago
  • special_tokens_map.json
    125 Bytes
    Upload tokenizer over 3 years ago
  • tokenizer.json
    679 kB
    Upload tokenizer over 3 years ago
  • tokenizer_config.json
    373 Bytes
    Update Med-KEBERT over 3 years ago
  • vocab.txt
    225 kB
    Upload tokenizer over 3 years ago