Instructions to use Bilgee/layoutlmv3-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bilgee/layoutlmv3-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bilgee/layoutlmv3-testing")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Bilgee/layoutlmv3-testing") model = AutoModelForSequenceClassification.from_pretrained("Bilgee/layoutlmv3-testing") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4176a25777babf706cbfc3326a5af2458826e57f5ca8d263dea7e67e451c52f9
- Size of remote file:
- 504 MB
- SHA256:
- 0e5d0dacd9236ba64a1699f482a03d5c190efc99423e639cd8d0d91acc90a07d
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