Instructions to use BadreddineHug/LayoutLM_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_2")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_2") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d91ab54aca3358ff565f827748bd86283b9ce761b4d6b67b75dffe6eed0d04de
- Size of remote file:
- 504 MB
- SHA256:
- 28056b5e8dfe2e6a6a7348e290870599ccd8dc78b55e9d26a982d318e2d706ea
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