Instructions to use BadreddineHug/LayoutLM_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_4")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_4") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86417fbd9eec0ad378dc80f5907df4f7b19b258f605a22c78af5e6f62d8506eb
- Size of remote file:
- 504 MB
- SHA256:
- 61f9db6513c26ee88282d940ec32ffcff0ed042d6d1dff3b8f8fad841c587f35
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