Text Classification
Transformers
Safetensors
layoutlmv3
document-classification
medical-documents
model2aa
Generated from Trainer
Instructions to use neuralit/layoutlmv3-large-model2aa-visit-vs-progress with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralit/layoutlmv3-large-model2aa-visit-vs-progress with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralit/layoutlmv3-large-model2aa-visit-vs-progress")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("neuralit/layoutlmv3-large-model2aa-visit-vs-progress") model = AutoModelForSequenceClassification.from_pretrained("neuralit/layoutlmv3-large-model2aa-visit-vs-progress") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a3aa282ee832d9ea66577f851908f9d3d87ccfe37c4cb2c6c9b644e60472b4a
- Size of remote file:
- 4 MB
- SHA256:
- b88c3b13d8df7a847608aa5ad79b30b8f37aabfe189a4b45abde916eff09f131
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