---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: dit-base-Document_Classification-RVL_CDIP
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: data
      split: train
      args: data
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.976678084687705
language:
- en
---

# dit-base-Document_Classification-RVL_CDIP

This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base).

It achieves the following results on the evaluation set:
- Loss: 0.0786
- Accuracy: 0.9767
- F1
  - Weighted: 0.9768
  - Micro: 0.9767
  - Macro: 0.9154
- Recall
  - Weighted: 0.9767
  - Micro: 0.9767
  - Macro: 0.9019
- Precision
  - Weighted: 0.9771
  - Micro: 0.9767
  - Macro: 0.9314

## 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-%20RVL-CDIP/Document%20Classification%20-%20RVL-CDIP.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/achrafbribiche/document-classification

## 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: 3

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.1535        | 1.0   | 208  | 0.1126          | 0.9622   | 0.9597      | 0.9622   | 0.5711   | 0.9622          | 0.9622       | 0.5925       | 0.9577             | 0.9622          | 0.5531          |
| 0.1195        | 2.0   | 416  | 0.0843          | 0.9738   | 0.9736      | 0.9738   | 0.8502   | 0.9738          | 0.9738       | 0.8037       | 0.9741             | 0.9738          | 0.9287          |
| 0.0979        | 3.0   | 624  | 0.0786          | 0.9767   | 0.9768      | 0.9767   | 0.9154   | 0.9767          | 0.9767       | 0.9019       | 0.9771             | 0.9767          | 0.9314          |

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3