Instructions to use pddq/layoutlmv3-medical-document-classification-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pddq/layoutlmv3-medical-document-classification-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pddq/layoutlmv3-medical-document-classification-test")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("pddq/layoutlmv3-medical-document-classification-test") model = AutoModelForSequenceClassification.from_pretrained("pddq/layoutlmv3-medical-document-classification-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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oid sha256:c4b5c812a35fcf7b5c369c07be5a4bfab322c81810089a285d63e11b51a0bfc0
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size 503728492
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