DonutInvoiceCzechV0123

TomasFAV/DonutInvoiceCzechV0123

DonutInvoiceCzechV0123 is a image-text-to-text model from TomasFAV. 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.

should probably proofread and complete it, then remove this comment. --

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Model card

From the Hugging Face model README

DonutInvoiceCzechV0123

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1672
Accuracy: 0.9460
F1: 0.9214

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: 9e-05
train_batch_size: 4
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2086	1.0	46	0.1679	0.9141	0.8816
0.1011	2.0	92	0.1561	0.9371	0.9128
0.0632	3.0	138	0.1630	0.9386	0.9111
0.0376	4.0	184	0.1921	0.8951	0.8927
0.0286	5.0	230	0.1798	0.9291	0.9094
0.0214	6.0	276	0.1672	0.9460	0.9214
0.0159	7.0	322	0.1734	0.9432	0.9214
0.0223	8.0	368	0.1815	0.9352	0.9074
0.0071	9.0	414	0.1857	0.9304	0.9012
0.0175	10.0	460	0.1976	0.9288	0.9088
0.0073	11.0	506	0.1983	0.9439	0.9111
0.0034	12.0	552	0.2064	0.9318	0.9090
0.0077	13.0	598	0.2131	0.9359	0.9072
0.0050	14.0	644	0.2108	0.9369	0.9072
0.0007	15.0	690	0.2074	0.9340	0.9072
0.0006	16.0	736	0.2084	0.9357	0.9106
0.0011	17.0	782	0.2075	0.9363	0.9128
0.0008	18.0	828	0.2030	0.9384	0.9192
0.0009	19.0	874	0.2025	0.9353	0.9192
0.0006	20.0	920	0.2027	0.9338	0.9175

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Created: Apr 19, 2026
Updated: Apr 19, 2026