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:
- c9bb8f56bf1e3ab24966e6ef62f82623e825d0fb2d02a4a2d4a97db10526664f
- Size of remote file:
- 3.96 kB
- SHA256:
- 19ffa1f33fe2c133e016ab9677b52aa5f091b05a7a78b97b87f58b78d6b7dc82
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