Instructions to use BadreddineHug/LayoutLM_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_5")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_5") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b755457dfcbacc8127f3b5a52f4d0095086e63e2d2363ede9523074b772b4f24
- Size of remote file:
- 504 MB
- SHA256:
- 6227db1386855f700ea4981ef48875ec64a4dfe755dc9321559737e4655a115d
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