Instructions to use BadreddineHug/LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed18aec0b8f1523b513de8c0672cf1757c3a097b38d5b96d31582c8159566920
- Size of remote file:
- 3.96 kB
- SHA256:
- 2cb952d041e51546b28c329316ff48106b45806847b035c31ca52bd710eabbb8
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