Instructions to use BadreddineHug/LayoutLM_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_6")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_6") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a59f56ba0d21e9ba65acda23c455b830281629be3981cf5d95b1858acbdd4b8
- Size of remote file:
- 504 MB
- SHA256:
- 9292d244d2ffba643bbb37d3d601b2ce5a6d9c83231f33ea79fad40efd86cca5
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