Instructions to use BadreddineHug/Output_LayoutLMv3_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/Output_LayoutLMv3_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/Output_LayoutLMv3_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/Output_LayoutLMv3_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/Output_LayoutLMv3_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b456d632fd4b8ae6323d34e498b5131b5cccfce95bd5775320f476ccf4cae23b
- Size of remote file:
- 1.42 GB
- SHA256:
- 1e0639072edbfa58385c866e0b92463623272b159360ce93b8abe7e780f2992a
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