Instructions to use BadreddineHug/Output_LayoutLMv3_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/Output_LayoutLMv3_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/Output_LayoutLMv3_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/Output_LayoutLMv3_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/Output_LayoutLMv3_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34223b576f6352a130dc80f15a7d623b1aae1342d08c07cde031fa7cb38ca9c8
- Size of remote file:
- 4.98 kB
- SHA256:
- 6f3cf9c5c6c45ce8285fc2c8bfb551f907b71e4c6698181a7c4331a6d685cce0
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