Instructions to use curiousily/layoutlmv3-financial-document-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use curiousily/layoutlmv3-financial-document-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="curiousily/layoutlmv3-financial-document-classification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("curiousily/layoutlmv3-financial-document-classification") model = AutoModelForSequenceClassification.from_pretrained("curiousily/layoutlmv3-financial-document-classification") - Notebooks
- Google Colab
- Kaggle
Model
This model is a fine-tuned version of microsoft/layoutlmv3-base trained on Financial Documents Clustering Kaggle Dataset.
It classifies document images into one of the following (5) classes:
- Income Statements
- Balance Sheets
- Cash Flows
- Notes
- Others
Training
This model uses OCR data from EasyOCR instead of the default Tesseract OCR engine.
Libraries
- transformers 4.25.1
- pytorch-lightning 1.8.6
- torchmetrics 0.11.0
- easyocr 1.6.2
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