Instructions to use Noureddinesa/Output_LayoutLMv3_v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v6")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v6") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e471bcc8377bb0925e7fea1cc978edee014c3300c506c0972c1df829da04a0a
- Size of remote file:
- 1.42 GB
- SHA256:
- e4e89a25a6e0134487f6baaec98e1e2747a91492644d123f58aa6487ad47ec35
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