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
| split,training_label,source_super_category,page_count | |
| all,Visit Note(multiple notes),,14033 | |
| all,Progress/Follow up Note,,14033 | |
| all,Progress/Follow up Note,Progress/Follow up Note,14033 | |
| all,Visit Note(multiple notes),Visit Note,14033 | |
| train,Visit Note(multiple notes),,12630 | |
| train,Progress/Follow up Note,,12625 | |
| train,Progress/Follow up Note,Progress/Follow up Note,12625 | |
| train,Visit Note(multiple notes),Visit Note,12630 | |
| eval,Visit Note(multiple notes),,1403 | |
| eval,Progress/Follow up Note,,1408 | |
| eval,Progress/Follow up Note,Progress/Follow up Note,1408 | |
| eval,Visit Note(multiple notes),Visit Note,1403 | |