Instructions to use BadreddineHug/LayoutLM_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 991cb0fee0f646bba2d894e0fea008ec6e1e7a3c84eb80bc45c328c8c26f05cd
- Size of remote file:
- 504 MB
- SHA256:
- 593657530faf2d732a889478541ee100ae5140201afc5bc1a890f48eadf38d72
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