Instructions to use BadreddineHug/LayoutLMv3_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77f55ad273d506ae0e02daf5497f3f77ca9867aff9ddab4767442b4264805392
- Size of remote file:
- 1.43 GB
- SHA256:
- 16807b20cc5f17c36f5edf9bad0e9e9f90745348592c16961f6bec38804eb8ce
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