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:
- e50821a3a943a5aba7ad258b3ba35f0ef5f252b9ea20e4951328c06d092d7a16
- Size of remote file:
- 1.01 GB
- SHA256:
- 118b70b13d59efe8f8054043853c6b3b6c5faa0fbf968981d8b0d7b4c12ff01e
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