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---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-full_text_wt_val_1008
results: []
---
<!-- 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. -->
# donut-base-full_text_wt_val_1008
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1060
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9227 | 0.2 | 100 | 0.6985 |
| 0.6852 | 0.4 | 200 | 0.4421 |
| 0.5102 | 0.6 | 300 | 0.3346 |
| 0.4178 | 0.79 | 400 | 0.2886 |
| 0.4476 | 0.99 | 500 | 0.2455 |
| 0.2931 | 1.19 | 600 | 0.2287 |
| 0.2647 | 1.39 | 700 | 0.2072 |
| 0.2418 | 1.59 | 800 | 0.1905 |
| 0.3031 | 1.79 | 900 | 0.1754 |
| 0.2306 | 1.98 | 1000 | 0.1667 |
| 0.2031 | 2.18 | 1100 | 0.1619 |
| 0.1918 | 2.38 | 1200 | 0.1536 |
| 0.1802 | 2.58 | 1300 | 0.1504 |
| 0.1646 | 2.78 | 1400 | 0.1436 |
| 0.1816 | 2.98 | 1500 | 0.1379 |
| 0.1344 | 3.17 | 1600 | 0.1395 |
| 0.1752 | 3.37 | 1700 | 0.1336 |
| 0.1388 | 3.57 | 1800 | 0.1306 |
| 0.1402 | 3.77 | 1900 | 0.1262 |
| 0.1123 | 3.97 | 2000 | 0.1277 |
| 0.144 | 4.17 | 2100 | 0.1248 |
| 0.1077 | 4.37 | 2200 | 0.1226 |
| 0.1134 | 4.56 | 2300 | 0.1186 |
| 0.1192 | 4.76 | 2400 | 0.1179 |
| 0.1142 | 4.96 | 2500 | 0.1194 |
| 0.1426 | 5.16 | 2600 | 0.1202 |
| 0.1022 | 5.36 | 2700 | 0.1165 |
| 0.0815 | 5.56 | 2800 | 0.1164 |
| 0.1096 | 5.75 | 2900 | 0.1166 |
| 0.0866 | 5.95 | 3000 | 0.1121 |
| 0.1148 | 6.15 | 3100 | 0.1122 |
| 0.0771 | 6.35 | 3200 | 0.1129 |
| 0.0996 | 6.55 | 3300 | 0.1096 |
| 0.0622 | 6.75 | 3400 | 0.1099 |
| 0.0985 | 6.94 | 3500 | 0.1092 |
| 0.0684 | 7.14 | 3600 | 0.1097 |
| 0.0669 | 7.34 | 3700 | 0.1086 |
| 0.0624 | 7.54 | 3800 | 0.1088 |
| 0.0763 | 7.74 | 3900 | 0.1069 |
| 0.0579 | 7.94 | 4000 | 0.1060 |
| 0.0623 | 8.13 | 4100 | 0.1083 |
| 0.0599 | 8.33 | 4200 | 0.1058 |
| 0.0625 | 8.53 | 4300 | 0.1073 |
| 0.0499 | 8.73 | 4400 | 0.1059 |
| 0.0628 | 8.93 | 4500 | 0.1059 |
| 0.0684 | 9.13 | 4600 | 0.1063 |
| 0.0472 | 9.33 | 4700 | 0.1056 |
| 0.068 | 9.52 | 4800 | 0.1057 |
| 0.06 | 9.72 | 4900 | 0.1062 |
| 0.0636 | 9.92 | 5000 | 0.1060 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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