Instructions to use BadreddineHug/LayoutLMv3_large_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_large_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_large_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_large_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_large_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e89cc188e886a263733ffd56dc15af62ff08cec85697920c2aafc4fdc8aee6b0
- Size of remote file:
- 1.43 GB
- SHA256:
- 80a8273953cd2c495b76aa73c9156bbaf5a6b5d3098e17a7a2cf56cc023dc7eb
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