Instructions to use Noureddinesa/Output_LayoutLMv3_v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v7")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v7") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4646d2e4aac9a333aacfc387e3b63de9329a39b1b0ef3456700933f6041caecd
- Size of remote file:
- 501 MB
- SHA256:
- a2501d773ae7bb0d19b59e1c065d98ff8ba04b563ee86b62a8ef67296c6f042d
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