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dit-base-Document_Classification-Desafio_1

This model is a fine-tuned version of microsoft/dit-base.

It achieves the following results on the evaluation set:

  • Loss: 0.0436
  • Accuracy: 0.9865
  • F1
    • Weighted: 0.9865
    • Micro: 0.9865
    • Macro: 0.9863
  • Recall
    • Weighted: 0.9865
    • Micro: 0.9865
    • Macro: 0.9861
  • Precision
    • Weighted: 0.9869
    • Micro: 0.9865
    • Macro: 0.9870

Model description

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

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/rywgar/document-classification-desafio-1

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 8

Training results

Training Loss Epoch Step Validation Loss Accuracy Weighted F1 Micro F1 Macro F1 Weighted Recall Micro Recall Macro Recall Weighted Precision Micro Precision Macro Precision
0.8316 0.99 62 0.7519 0.743 0.7020 0.743 0.7015 0.743 0.743 0.7430 0.6827 0.743 0.6819
0.3561 2.0 125 0.2302 0.9395 0.9401 0.9395 0.9400 0.9395 0.9395 0.9394 0.9482 0.9395 0.9480
0.2222 2.99 187 0.1350 0.956 0.9564 0.956 0.9561 0.956 0.956 0.9551 0.9598 0.956 0.9600
0.1705 4.0 250 0.0873 0.9725 0.9727 0.9725 0.9725 0.9725 0.9725 0.9721 0.9740 0.9725 0.9740
0.1541 4.99 312 0.0642 0.9825 0.9825 0.9825 0.9824 0.9825 0.9825 0.9822 0.9830 0.9825 0.9830
0.1253 6.0 375 0.0330 0.9915 0.9915 0.9915 0.9914 0.9915 0.9915 0.9913 0.9916 0.9915 0.9916
0.1196 6.99 437 0.0524 0.982 0.9822 0.982 0.9820 0.982 0.982 0.9817 0.9832 0.982 0.9832
0.0896 7.94 496 0.0436 0.9865 0.9865 0.9865 0.9863 0.9865 0.9865 0.9861 0.9869 0.9865 0.9870

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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Evaluation results