File size: 2,967 Bytes
6197718 4ca73d1 6197718 4ca73d1 6197718 4ca73d1 6197718 4e4e444 6197718 4ca73d1 6197718 4ca73d1 6197718 4ca73d1 6197718 4e4e444 6197718 4ca73d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
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 |