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:
- 04d66383f627fb25ca2fb9771ac07800c1bcacd4622eff4f255a19ec84676d1e
- Size of remote file:
- 64.8 MB
- SHA256:
- 07577ee1ef205080be230326c1881adc817aa2765b5f5e2edbbc202e457067bf
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