Instructions to use Noureddinesa/Output_LayoutLMv3_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v4")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v4") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e4b501853fe73bb8f32547410c86f2aa6370a2824f06cac44d0d55f2c4b5908
- Size of remote file:
- 4.41 kB
- SHA256:
- a1f854388678e3e11e1ffdc3be567460cd18c9e1b98c3aa235fd492411c74ee0
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