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:
- 1a2bbe1e4e1c4e7cb9f9215285c34d633fabf1a08290cf9d54e95055da4bbfc5
- Size of remote file:
- 1.42 GB
- SHA256:
- 2350328ae707f9fc1ff377f5ab08ec4e21ab80ba65f07dfb14cb231091da5e8d
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