Instructions to use Noureddinesa/Output_LayoutLMv3_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v5")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v5") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3abc5fd5a9aa7b3d3dd7f0d51a043aaa922e6f0b575defd3df96a3eeea7e683
- Size of remote file:
- 4.41 kB
- SHA256:
- 7fd87ca39344a0855bbc1505c2cb6549559ce4a6511694a188ca640f7ab708e9
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