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:
- 516061d117fc0be19aef10cad0ca3bc09f084858a67a001632a91544ad60a162
- Size of remote file:
- 1.43 GB
- SHA256:
- 147f8402f9cab78c8199db728a511598eda2fad41f6c507e292996d7ac2252ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.