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:
- 122536101a52aef0f7838740d87ae6479f415a46699cf25d1f11307ee355eea6
- Size of remote file:
- 4.41 kB
- SHA256:
- 19def378d61b49f25c9afe8afc030ed4ad542f3ac38222d430ae5bd97c22b601
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