Text Classification
Transformers
Safetensors
layoutlmv3
document-classification
medical-documents
model2b
Generated from Trainer
Instructions to use neuralit/layoutlmv3-large-model2b-radiology-lab-operative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralit/layoutlmv3-large-model2b-radiology-lab-operative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralit/layoutlmv3-large-model2b-radiology-lab-operative")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("neuralit/layoutlmv3-large-model2b-radiology-lab-operative") model = AutoModelForSequenceClassification.from_pretrained("neuralit/layoutlmv3-large-model2b-radiology-lab-operative", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd601bf68671acd67b0058b654d607dbd49d4398e8326823e99fb505d80eeaba
- Size of remote file:
- 41.2 MB
- SHA256:
- a7339056adf0a51ebd5dc451926bd789e1a30465bee241d979f9a62103cf52b1
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