---
library_name: transformers
license: mit
base_model: TomasFAV/DonutInvoiceCzechV0R
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: DonutInvoiceCzechV03R
  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. -->

# DonutInvoiceCzechV03

This model is a fine-tuned version of [TomasFAV/DonutInvoiceCzechV0](https://huggingface.co/TomasFAV/DonutInvoiceCzechV0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2443
- Accuracy: 0.9274
- F1: 0.9077

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2874        | 1.0   | 46   | 0.1856          | 0.9007   | 0.8788 |
| 0.1328        | 2.0   | 92   | 0.2057          | 0.8800   | 0.8535 |
| 0.0790        | 3.0   | 138  | 0.1899          | 0.8992   | 0.8921 |
| 0.0493        | 4.0   | 184  | 0.2266          | 0.9103   | 0.8912 |
| 0.0391        | 5.0   | 230  | 0.2266          | 0.8962   | 0.8739 |
| 0.0271        | 6.0   | 276  | 0.2532          | 0.8840   | 0.8658 |
| 0.0238        | 7.0   | 322  | 0.2393          | 0.9016   | 0.8803 |
| 0.0211        | 8.0   | 368  | 0.2429          | 0.9090   | 0.8846 |
| 0.0210        | 9.0   | 414  | 0.2326          | 0.9266   | 0.8889 |
| 0.0184        | 10.0  | 460  | 0.2241          | 0.9216   | 0.9026 |
| 0.0109        | 11.0  | 506  | 0.2483          | 0.9075   | 0.8933 |
| 0.0037        | 12.0  | 552  | 0.2443          | 0.9274   | 0.9077 |
| 0.0023        | 13.0  | 598  | 0.2457          | 0.9269   | 0.8991 |
| 0.0057        | 14.0  | 644  | 0.2397          | 0.9278   | 0.9026 |
| 0.0024        | 15.0  | 690  | 0.2320          | 0.9346   | 0.9077 |
| 0.0008        | 16.0  | 736  | 0.2390          | 0.9344   | 0.9077 |
| 0.0015        | 17.0  | 782  | 0.2401          | 0.9350   | 0.9077 |
| 0.0042        | 18.0  | 828  | 0.2405          | 0.9346   | 0.9077 |
| 0.0016        | 19.0  | 874  | 0.2426          | 0.9322   | 0.9060 |
| 0.0035        | 20.0  | 920  | 0.2426          | 0.9322   | 0.9060 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2