metadata
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-eco_v3
results: []
donut-base-eco_v3
This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1236
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.7637 | 0.05 | 10 | 4.3966 |
| 4.7131 | 0.1 | 20 | 3.4323 |
| 3.6024 | 0.15 | 30 | 2.7923 |
| 2.8768 | 0.2 | 40 | 2.4152 |
| 3.0689 | 0.25 | 50 | 2.2683 |
| 2.4879 | 0.3 | 60 | 2.0826 |
| 2.3029 | 0.35 | 70 | 1.9588 |
| 2.2746 | 0.4 | 80 | 1.8516 |
| 2.7149 | 0.45 | 90 | 1.7636 |
| 2.1114 | 0.51 | 100 | 1.7028 |
| 2.2623 | 0.56 | 110 | 1.6586 |
| 1.826 | 0.61 | 120 | 1.5988 |
| 2.0984 | 0.66 | 130 | 1.5454 |
| 1.4917 | 0.71 | 140 | 1.5161 |
| 1.4414 | 0.76 | 150 | 1.4859 |
| 1.9446 | 0.81 | 160 | 1.4424 |
| 1.923 | 0.86 | 170 | 1.4239 |
| 1.5272 | 0.91 | 180 | 1.4003 |
| 1.8752 | 0.96 | 190 | 1.3695 |
| 1.1883 | 1.01 | 200 | 1.3520 |
| 1.432 | 1.06 | 210 | 1.3340 |
| 1.6104 | 1.11 | 220 | 1.3292 |
| 1.3261 | 1.16 | 230 | 1.3174 |
| 1.3727 | 1.21 | 240 | 1.3024 |
| 1.6194 | 1.26 | 250 | 1.2777 |
| 1.6811 | 1.31 | 260 | 1.2793 |
| 1.3327 | 1.36 | 270 | 1.2636 |
| 1.2379 | 1.41 | 280 | 1.2492 |
| 1.8061 | 1.46 | 290 | 1.2423 |
| 1.6403 | 1.52 | 300 | 1.2333 |
| 1.5277 | 1.57 | 310 | 1.2245 |
| 1.8438 | 1.62 | 320 | 1.2114 |
| 1.6035 | 1.67 | 330 | 1.2127 |
| 1.4338 | 1.72 | 340 | 1.2061 |
| 1.4517 | 1.77 | 350 | 1.1997 |
| 1.7217 | 1.82 | 360 | 1.1891 |
| 1.1229 | 1.87 | 370 | 1.1836 |
| 1.2508 | 1.92 | 380 | 1.1767 |
| 1.0494 | 1.97 | 390 | 1.1726 |
| 1.3746 | 2.02 | 400 | 1.1710 |
| 0.8878 | 2.07 | 410 | 1.1708 |
| 1.4181 | 2.12 | 420 | 1.1642 |
| 1.1233 | 2.17 | 430 | 1.1627 |
| 1.4889 | 2.22 | 440 | 1.1654 |
| 1.4098 | 2.27 | 450 | 1.1592 |
| 1.4169 | 2.32 | 460 | 1.1526 |
| 1.3255 | 2.37 | 470 | 1.1470 |
| 1.4087 | 2.42 | 480 | 1.1449 |
| 0.9108 | 2.47 | 490 | 1.1455 |
| 1.4604 | 2.53 | 500 | 1.1425 |
| 1.47 | 2.58 | 510 | 1.1334 |
| 1.4215 | 2.63 | 520 | 1.1313 |
| 1.2907 | 2.68 | 530 | 1.1285 |
| 1.2292 | 2.73 | 540 | 1.1273 |
| 1.3936 | 2.78 | 550 | 1.1261 |
| 1.1875 | 2.83 | 560 | 1.1250 |
| 1.4496 | 2.88 | 570 | 1.1245 |
| 1.3273 | 2.93 | 580 | 1.1239 |
| 1.4324 | 2.98 | 590 | 1.1236 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2