README.md

metadata

tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: dit-base-Document_Classification-Desafio_1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: validation
          split: train
          args: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9865
language:
  - en

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