Token Classification
Transformers
Safetensors
English
layoutlmv3
document-ai
document-understanding
key-information-extraction
conformal-prediction
uncertainty-quantification
calibration
wacv-2027
calidoc
Instructions to use MHamdan/calidoc-layoutlmv3-large-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MHamdan/calidoc-layoutlmv3-large-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MHamdan/calidoc-layoutlmv3-large-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("MHamdan/calidoc-layoutlmv3-large-funsd") model = AutoModelForTokenClassification.from_pretrained("MHamdan/calidoc-layoutlmv3-large-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
8 days ago
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CaliDoc WACV 2027: seed-42 canonical checkpoint and SpanCRC calibration
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