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  ---
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  tags:
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  - generated_from_trainer
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- datasets:
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- - imagefolder
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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: dit-base-Document_Classification-Desafio_1
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  results:
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  - name: Accuracy
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  type: accuracy
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  value: 0.9865
 
 
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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  # dit-base-Document_Classification-Desafio_1
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- This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
 
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0436
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  - Accuracy: 0.9865
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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- More information needed
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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  - Transformers 4.28.1
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  - Pytorch 2.0.0
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  - Datasets 2.11.0
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- - Tokenizers 0.13.3
 
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  ---
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  tags:
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  - generated_from_trainer
 
 
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  metrics:
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  - accuracy
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+ - f1
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+ - recall
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+ - precision
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  model-index:
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  - name: dit-base-Document_Classification-Desafio_1
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  results:
 
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  - name: Accuracy
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  type: accuracy
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  value: 0.9865
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+ language:
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+ - en
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  ---
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  # dit-base-Document_Classification-Desafio_1
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+ This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base).
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+
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0436
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  - Accuracy: 0.9865
 
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  ## Model description
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+ For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Document%20Classification%20-%20Desafio%201/Document%20Classification%20-%20Desafio%201.ipynb
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  ## Intended uses & limitations
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+ This model is intended to demonstrate my ability to solve a complex problem using technology.
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  ## Training and evaluation data
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+ Dataset Source: https://www.kaggle.com/datasets/rywgar/document-classification-desafio-1
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  ## Training procedure
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  - Transformers 4.28.1
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  - Pytorch 2.0.0
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  - Datasets 2.11.0
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+ - Tokenizers 0.13.3