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README.md
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Trained on images of the document categories from DocLayNet dataset where the categories namely(with their indexes) are :
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{'financial_reports': 0,
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'government_tenders': 1,
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'laws_and_regulations': 2,
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'patents': 4,
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'scientific_articles': 5}
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## Model description
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This model is built upon the `google/vit-base-patch16-224-in21k` Vision Transformer architecture and fine-tuned specifically for document layout classification. The base ViT model uses a patch size of 16x16 pixels and was pre-trained on ImageNet-21k. The model has been optimized to recognize and classify different types of document layouts from the DocLayNet dataset.
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Trained on images of the document categories from DocLayNet dataset where the categories namely(with their indexes) are :
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```python
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{'financial_reports': 0,
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'government_tenders': 1,
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'laws_and_regulations': 2,
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'patents': 4,
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'scientific_articles': 5}
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```
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## Model description
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This model is built upon the `google/vit-base-patch16-224-in21k` Vision Transformer architecture and fine-tuned specifically for document layout classification. The base ViT model uses a patch size of 16x16 pixels and was pre-trained on ImageNet-21k. The model has been optimized to recognize and classify different types of document layouts from the DocLayNet dataset.
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