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donut-base-sroie-bayesian-optimization
davelotito/donut-base-sroie-bayesian-optimization
donut-base-sroie-bayesian-optimization is a image-text-to-text model from davelotito. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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Model card
From the Hugging Face model README
<!-- 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-sroie-bayesian-optimization
This model is a fine-tuned version of naver-clova-ix/donut-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1396
- Bleu: 0.0196
- Precisions: [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672]
- Brevity Penalty: 0.0203
- Length Ratio: 0.2043
- Translation Length: 856
- Reference Length: 4190
- Cer: 0.8584
- Wer: 1.0
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: 1.2010406976282324e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.021 | 1.0 | 253 | 0.1656 | 0.0194 | [0.9848130841121495, 0.9649561952440551, 0.9420485175202157, 0.9153284671532846] | 0.0203 | 0.2043 | 856 | 4190 | 0.8596 | 1.0 |
| 0.0353 | 2.0 | 506 | 0.1501 | 0.0195 | [0.9813736903376019, 0.9588528678304239, 0.9328859060402684, 0.9026162790697675] | 0.0207 | 0.2050 | 859 | 4190 | 0.8595 | 1.0 |
| 0.0417 | 3.0 | 759 | 0.1423 | 0.0195 | [0.9871495327102804, 0.9699624530663329, 0.9501347708894878, 0.927007299270073] | 0.0203 | 0.2043 | 856 | 4190 | 0.8586 | 1.0 |
| 0.0308 | 4.0 | 1012 | 0.1403 | 0.0193 | [0.9859649122807017, 0.9674185463659147, 0.9460188933873145, 0.9210526315789473] | 0.0202 | 0.2041 | 855 | 4190 | 0.8593 | 1.0 |
| 0.0464 | 5.0 | 1265 | 0.1396 | 0.0196 | [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672] | 0.0203 | 0.2043 | 856 | 4190 | 0.8584 | 1.0 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.1.0
- Datasets 2.19.0
- Tokenizers 0.19.1
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- Apr 4, 2024
- Updated
- Apr 29, 2024