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