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
File size: 612 Bytes
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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
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