Instructions to use BadreddineHug/LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06d55f4a0de4c76c67bc6a5518cbbe00bdf7ae73b8d04c08df46bc7739a0fb74
- Size of remote file:
- 1.43 GB
- SHA256:
- a3b272ee878d4dda0b5d3b814f252723c443e397800d0413fac9b3fb7354bb00
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