Instructions to use Noureddinesa/Output_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f1db9005f018cc5c7f624d0a7fe768fba20242002d5a71db1fe081f07e54ac7
- Size of remote file:
- 4.92 kB
- SHA256:
- a73051a5fed124e600493697716101457c5515727b2cf11a9092b6c400bd2c8c
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