Instructions to use BadreddineHug/LayoutLM_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_3") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef5706c242f31cc5343afb854ddc2312be1b963a9045b75ea4b522e87d45743b
- Size of remote file:
- 3.96 kB
- SHA256:
- 37cca51deec33b71a35deab98de0680209932f64161ad6f73dfbf4994110d973
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