Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Instructions to use jinhybr/OCR-LayoutLMv3-Invoice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinhybr/OCR-LayoutLMv3-Invoice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jinhybr/OCR-LayoutLMv3-Invoice")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("jinhybr/OCR-LayoutLMv3-Invoice") model = AutoModelForTokenClassification.from_pretrained("jinhybr/OCR-LayoutLMv3-Invoice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b09a455a65d93b0610129ab2d6d124cad7967abf18fe1404a74f017ad4209be
- Size of remote file:
- 3.38 kB
- SHA256:
- 27e0317d32dfe56fd0bcbaa84b940b5987f6cd84187347341f903da547986a8c
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