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aehrc
/
cxrmate

Feature Extraction
Transformers
Safetensors
vision-encoder-decoder
custom_code
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use aehrc/cxrmate with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="aehrc/cxrmate", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate", trust_remote_code=True)
    model = AutoModel.from_pretrained("aehrc/cxrmate", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
cxrmate
452 MB
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  • 1 contributor
History: 34 commits
anicolson's picture
anicolson
Update README.md
b470d0e verified about 1 year ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • README.md
    1.32 kB
    Update README.md about 1 year ago
  • config.json
    78.5 kB
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  • generation_config.json
    90 Bytes
    Upload model almost 2 years ago
  • model.safetensors
    450 MB
    xet
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  • modelling_longitudinal.py
    21.2 kB
    Update modelling_longitudinal.py about 1 year ago
  • preprocessor_config.json
    410 Bytes
    Upload feature extractor almost 3 years ago
  • special_tokens_map.json
    1.09 kB
    Upload tokenizer over 2 years ago
  • tokenizer.json
    1.33 MB
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  • tokenizer_config.json
    2.47 kB
    Upload tokenizer over 2 years ago