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ekacare
/
med-doc-classifier

Image Classification
Transformers
Safetensors
siglip2_hier_doc
feature-extraction
siglip2
document-classification
hierarchical
multi-task
custom_code
Model card Files Files and versions
xet
Community

Instructions to use ekacare/med-doc-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ekacare/med-doc-classifier with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="ekacare/med-doc-classifier", trust_remote_code=True)
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ekacare/med-doc-classifier", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
med-doc-classifier
193 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 13 commits
ds-EkaCare's picture
ds-EkaCare
Update README.md
6983962 verified 2 days ago
  • .gitattributes
    1.52 kB
    initial commit about 2 months ago
  • README.md
    5.29 kB
    Update README.md 2 days ago
  • benchmark_results.json
    959 Bytes
    eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score) about 2 months ago
  • config.json
    5.11 kB
    eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score) about 2 months ago
  • configuration_siglip2_hier.py
    4.15 kB
    eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score) about 2 months ago
  • example.py
    337 Bytes
    eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score) about 2 months ago
  • model.safetensors
    193 MB
    xet
    eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score) about 2 months ago
  • modeling_siglip2_hier.py
    17.2 kB
    Raise on missing vision tensors instead of warning; dual-mode config import; hide optional optimum import from the dynamic-module scanner 4 days ago
  • preprocessor_config.json
    323 Bytes
    eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score) about 2 months ago
  • requirements.txt
    306 Bytes
    transformers>=5.6.0 floor (older silently random-inits the vision tower); add torchvision 4 days ago