
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# dit-base-tiny_rvl_cdip-NK1000_hint

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5793
- Accuracy: 0.8185
- Brier Loss: 0.3293
- Nll: 2.0402
- F1 Micro: 0.8185
- F1 Macro: 0.8184
- Ece: 0.1589
- Aurc: 0.0584

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 2.7601        | 1.0   | 1000  | 2.6212          | 0.5952   | 0.5281     | 2.4218 | 0.5952   | 0.5899   | 0.0529 | 0.1801 |
| 2.236         | 2.0   | 2000  | 2.1744          | 0.6847   | 0.4219     | 2.1650 | 0.6847   | 0.6827   | 0.0515 | 0.1135 |
| 1.9525        | 3.0   | 3000  | 1.9941          | 0.7147   | 0.3889     | 2.0860 | 0.7147   | 0.7163   | 0.0616 | 0.0961 |
| 1.729         | 4.0   | 4000  | 1.8880          | 0.7485   | 0.3585     | 2.1097 | 0.7485   | 0.7476   | 0.0649 | 0.0811 |
| 1.6172        | 5.0   | 5000  | 1.8693          | 0.7455   | 0.3598     | 2.0514 | 0.7455   | 0.7448   | 0.0733 | 0.0811 |
| 1.4385        | 6.0   | 6000  | 1.8350          | 0.747    | 0.3654     | 2.0917 | 0.747    | 0.7536   | 0.0828 | 0.0843 |
| 1.3098        | 7.0   | 7000  | 1.8317          | 0.7625   | 0.3515     | 2.0367 | 0.7625   | 0.7668   | 0.1039 | 0.0794 |
| 1.1854        | 8.0   | 8000  | 1.8872          | 0.7675   | 0.3589     | 2.1085 | 0.7675   | 0.7661   | 0.1294 | 0.0694 |
| 1.1066        | 9.0   | 9000  | 1.9843          | 0.7642   | 0.3707     | 2.1492 | 0.7642   | 0.7670   | 0.1457 | 0.0715 |
| 1.0518        | 10.0  | 10000 | 1.9993          | 0.77     | 0.3660     | 2.0694 | 0.7700   | 0.7702   | 0.1440 | 0.0695 |
| 0.9741        | 11.0  | 11000 | 2.2346          | 0.769    | 0.3870     | 2.0588 | 0.769    | 0.7704   | 0.1748 | 0.0735 |
| 0.938         | 12.0  | 12000 | 2.2626          | 0.767    | 0.3918     | 2.1412 | 0.767    | 0.7679   | 0.1774 | 0.0723 |
| 0.899         | 13.0  | 13000 | 2.3671          | 0.7698   | 0.3995     | 2.1064 | 0.7698   | 0.7725   | 0.1820 | 0.0708 |
| 0.8708        | 14.0  | 14000 | 2.3386          | 0.7768   | 0.3838     | 2.1948 | 0.7768   | 0.7777   | 0.1724 | 0.0683 |
| 0.8456        | 15.0  | 15000 | 2.3234          | 0.7827   | 0.3774     | 2.0937 | 0.7828   | 0.7830   | 0.1752 | 0.0638 |
| 0.8021        | 16.0  | 16000 | 2.5700          | 0.7685   | 0.4092     | 2.1458 | 0.7685   | 0.7684   | 0.1911 | 0.0717 |
| 0.7921        | 17.0  | 17000 | 2.5721          | 0.778    | 0.3934     | 2.1774 | 0.778    | 0.7756   | 0.1829 | 0.0708 |
| 0.7779        | 18.0  | 18000 | 2.7204          | 0.772    | 0.4089     | 2.1145 | 0.772    | 0.7707   | 0.1926 | 0.0758 |
| 0.7484        | 19.0  | 19000 | 2.7208          | 0.7752   | 0.4044     | 2.1020 | 0.7752   | 0.7760   | 0.1901 | 0.0746 |
| 0.7469        | 20.0  | 20000 | 2.5898          | 0.7927   | 0.3711     | 2.2076 | 0.7927   | 0.7909   | 0.1763 | 0.0648 |
| 0.7256        | 21.0  | 21000 | 2.5658          | 0.791    | 0.3727     | 2.0710 | 0.791    | 0.7920   | 0.1763 | 0.0650 |
| 0.7137        | 22.0  | 22000 | 2.6782          | 0.7847   | 0.3851     | 2.1469 | 0.7847   | 0.7854   | 0.1824 | 0.0703 |
| 0.6912        | 23.0  | 23000 | 2.5574          | 0.802    | 0.3539     | 2.1340 | 0.802    | 0.8009   | 0.1685 | 0.0615 |
| 0.6894        | 24.0  | 24000 | 2.6331          | 0.7913   | 0.3760     | 2.0913 | 0.7913   | 0.7944   | 0.1807 | 0.0674 |
| 0.6692        | 25.0  | 25000 | 2.6074          | 0.7955   | 0.3658     | 2.0837 | 0.7955   | 0.7975   | 0.1745 | 0.0645 |
| 0.6541        | 26.0  | 26000 | 2.6059          | 0.7945   | 0.3672     | 2.0798 | 0.7945   | 0.7936   | 0.1751 | 0.0616 |
| 0.6517        | 27.0  | 27000 | 2.7149          | 0.7965   | 0.3697     | 2.0842 | 0.7965   | 0.7987   | 0.1769 | 0.0663 |
| 0.6484        | 28.0  | 28000 | 2.5700          | 0.8047   | 0.3542     | 2.0142 | 0.8047   | 0.8058   | 0.1685 | 0.0597 |
| 0.6342        | 29.0  | 29000 | 2.6774          | 0.7987   | 0.3660     | 2.1231 | 0.7987   | 0.7972   | 0.1759 | 0.0622 |
| 0.6331        | 30.0  | 30000 | 2.6112          | 0.7973   | 0.3621     | 2.0740 | 0.7973   | 0.7981   | 0.1752 | 0.0621 |
| 0.6204        | 31.0  | 31000 | 2.6470          | 0.807    | 0.3521     | 2.0337 | 0.807    | 0.8056   | 0.1683 | 0.0638 |
| 0.612         | 32.0  | 32000 | 2.6265          | 0.8053   | 0.3549     | 2.0800 | 0.8053   | 0.8038   | 0.1678 | 0.0596 |
| 0.6049        | 33.0  | 33000 | 2.5749          | 0.8107   | 0.3428     | 2.0235 | 0.8108   | 0.8114   | 0.1662 | 0.0576 |
| 0.5984        | 34.0  | 34000 | 2.6667          | 0.804    | 0.3572     | 2.1015 | 0.804    | 0.8041   | 0.1726 | 0.0632 |
| 0.5961        | 35.0  | 35000 | 2.6481          | 0.8067   | 0.3521     | 2.0652 | 0.8067   | 0.8068   | 0.1673 | 0.0610 |
| 0.5958        | 36.0  | 36000 | 2.6831          | 0.8065   | 0.3523     | 2.0272 | 0.8065   | 0.8074   | 0.1681 | 0.0623 |
| 0.5836        | 37.0  | 37000 | 2.6573          | 0.8067   | 0.3533     | 2.0149 | 0.8067   | 0.8050   | 0.1699 | 0.0624 |
| 0.5855        | 38.0  | 38000 | 2.6730          | 0.8087   | 0.3510     | 2.0149 | 0.8087   | 0.8087   | 0.1693 | 0.0611 |
| 0.581         | 39.0  | 39000 | 2.6464          | 0.815    | 0.3390     | 2.0609 | 0.815    | 0.8148   | 0.1628 | 0.0588 |
| 0.5708        | 40.0  | 40000 | 2.7165          | 0.8077   | 0.3515     | 2.0489 | 0.8077   | 0.8078   | 0.1698 | 0.0644 |
| 0.5727        | 41.0  | 41000 | 2.6264          | 0.8135   | 0.3402     | 2.0070 | 0.8135   | 0.8130   | 0.1643 | 0.0601 |
| 0.5688        | 42.0  | 42000 | 2.6522          | 0.8077   | 0.3522     | 2.0064 | 0.8077   | 0.8076   | 0.1687 | 0.0615 |
| 0.5648        | 43.0  | 43000 | 2.5806          | 0.8193   | 0.3295     | 2.0211 | 0.8193   | 0.8188   | 0.1593 | 0.0585 |
| 0.5649        | 44.0  | 44000 | 2.6182          | 0.8125   | 0.3372     | 2.0292 | 0.8125   | 0.8122   | 0.1630 | 0.0598 |
| 0.562         | 45.0  | 45000 | 2.6274          | 0.8157   | 0.3366     | 2.0047 | 0.8157   | 0.8158   | 0.1610 | 0.0577 |
| 0.5592        | 46.0  | 46000 | 2.6069          | 0.8145   | 0.3370     | 2.0276 | 0.8145   | 0.8146   | 0.1632 | 0.0599 |
| 0.5582        | 47.0  | 47000 | 2.5935          | 0.8187   | 0.3331     | 2.0291 | 0.8187   | 0.8193   | 0.1594 | 0.0588 |
| 0.5565        | 48.0  | 48000 | 2.5780          | 0.8197   | 0.3302     | 2.0401 | 0.8197   | 0.8197   | 0.1580 | 0.0577 |
| 0.5571        | 49.0  | 49000 | 2.5781          | 0.8185   | 0.3294     | 2.0367 | 0.8185   | 0.8184   | 0.1590 | 0.0583 |
| 0.5568        | 50.0  | 50000 | 2.5793          | 0.8185   | 0.3293     | 2.0402 | 0.8185   | 0.8184   | 0.1589 | 0.0584 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

letter form email handwritten advertisement scientific_report scientific_publication specification file_folder news_article budget invoice presentation questionnaire resume memo