Image Classification
Transformers
PyTorch
English
sybil
medical
cancer
ct-scan
risk-prediction
healthcare
vision
Instructions to use Lab-Rasool/sybil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lab-Rasool/sybil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Lab-Rasool/sybil") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lab-Rasool/sybil", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Delete __init__.py
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__init__.py
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"""Hugging Face Sybil model for lung cancer risk prediction"""
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from .configuration_sybil import SybilConfig
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from .modeling_sybil import (
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SybilForRiskPrediction,
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SybilPreTrainedModel,
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SybilOutput,
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SybilEnsemble,
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)
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from .image_processing_sybil import SybilImageProcessor
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__version__ = "1.0.0"
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__all__ = [
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"SybilConfig",
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"SybilForRiskPrediction",
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"SybilPreTrainedModel",
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"SybilOutput",
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"SybilEnsemble",
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"SybilImageProcessor",
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]
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