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
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license: mit
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license: mit
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language:
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- en
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library_name: transformers
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tags:
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- finance
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metrics:
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- accuracy
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## Model
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) trained on [Financial Documents Clustering Kaggle Dataset](https://www.kaggle.com/datasets/drcrabkg/financial-statements-clustering).
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## Training
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This model uses OCR data from [EasyOCR](https://github.com/JaidedAI/EasyOCR) instead of the default Tesseract OCR engine.
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## Libraries
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- transformers 4.25.1
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- pytorch-lightning 1.8.6
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- torchmetrics 0.11.0
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- easyocr 1.6.2
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