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invoice_extraction_donut_fromv0_f21_ep3_0724
VVVVL/invoice_extraction_donut_fromv0_f21_ep3_0724
invoice_extraction_donut_fromv0_f21_ep3_0724 is a image-text-to-text model from VVVVL. 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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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. -->
invoice_extraction_donut_fromv0_f21_ep3_0724
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: 0.0731
- Char Accuracy: 0.6639
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Char Accuracy |
|---|---|---|---|---|
| 0.2712 | 1.0 | 1496 | 0.1295 | 0.7607 |
| 0.0773 | 2.0 | 2992 | 0.0735 | 0.6365 |
| 0.0248 | 3.0 | 4488 | 0.0731 | 0.6639 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- Jul 31, 2025
- Updated
- Jul 31, 2025