Instructions to use BadreddineHug/LayoutLMv3_97 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_97 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_97")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_97") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_97", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f189a89b0beeef83e3f0b67d50e178711920d80eb94ccdd418ec28050523d98d
- Size of remote file:
- 1.43 GB
- SHA256:
- c666f4c95bf3b9df3cb3bbdb46e31204472431502b671b46ddd861e8860760d5
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