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:
- 708756cf43d912c5bb3a31b4d5d28c0805fb7a13d2a836e7bcd54c622ab02a4c
- Size of remote file:
- 4.41 kB
- SHA256:
- 76927b0ee4b1c4f8ea60f0f972cf440c91a22e741f46f354a40e576b06b0e4b2
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