Instructions to use xiamrr/layoutlmv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiamrr/layoutlmv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="xiamrr/layoutlmv3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("xiamrr/layoutlmv3") model = AutoModelForTokenClassification.from_pretrained("xiamrr/layoutlmv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb78f1877080771183417d92fe80fee448de63502739e85a67701d0d8c94c061
- Size of remote file:
- 504 MB
- SHA256:
- 0b49e0fa0776a9e0bacfce7c112e4e85913d47c0371179db6e5112181014c935
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