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:
- 7e8a8b3641e2ce36e90d5f00e8b024023f94e8a81079f8c25fd6e1e72675993f
- Size of remote file:
- 504 MB
- SHA256:
- d98a9eca6095013c09a7d570a4c3a4c36842d281ca410d72db88b1c34c4c6378
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