| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-multilingual-cased |
| tags: |
| - generated_from_trainer |
| - invoice-processing |
| - information-extraction |
| - czech-language |
| - synthetic-data |
| - hybrid-data |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: BERTInvoiceCzechR-V2 |
| results: [] |
| --- |
| |
| # BERTInvoiceCzechR (V2 – Synthetic + Random Layout + Real Layout Injection) |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) for structured information extraction from Czech invoices. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.1326 |
| - Precision: 0.8120 |
| - Recall: 0.7868 |
| - F1: 0.7992 |
| - Accuracy: 0.9700 |
|
|
| --- |
|
|
| ## Model description |
|
|
| BERTInvoiceCzechR (V2) represents an advanced stage in the training pipeline, combining synthetic data with realistic document layouts. |
|
|
| The model performs token-level classification to extract structured invoice fields: |
| - supplier |
| - customer |
| - invoice number |
| - bank details |
| - totals |
| - dates |
|
|
| This version introduces a key improvement: **real invoice layouts with synthetic content**, bridging the gap between artificial and real-world data. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset is composed of three main components: |
|
|
| 1. **Synthetic template-based invoices** |
| 2. **Synthetic invoices with randomized layouts** |
| 3. **Hybrid invoices with real layouts and synthetic content** |
|
|
| ### Real layout injection |
|
|
| In the hybrid dataset: |
| - real invoice documents are used as layout templates |
| - original textual content is removed |
| - fields (e.g., supplier, customer, bank details) are replaced with synthetic data |
| - new content is rendered into the original spatial structure |
|
|
| This approach preserves: |
| - realistic spacing |
| - typography patterns |
| - structural complexity |
|
|
| while maintaining: |
| - full control over annotations |
| - label consistency |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V2 – Synthetic + layout augmentation + real layout injection** |
|
|
| It is designed to: |
| - reduce the domain gap between synthetic and real invoices |
| - evaluate the impact of realistic spatial distributions |
| - serve as a bridge between purely synthetic training (V0–V1) and real data fine-tuning (V3) |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Advanced research in document AI |
| - Evaluation of hybrid synthetic-real training strategies |
| - Invoice information extraction in semi-realistic conditions |
| - Benchmarking generalization improvements |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Still does not use fully real textual content |
| - Synthetic text may not capture all linguistic variability |
| - OCR noise and scanning artifacts are not fully represented |
| - Performance may still drop on unseen real-world edge cases |
|
|
| --- |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 2 |
| - 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 |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| --- |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 87 | 0.1326 | 0.7356 | 0.7270 | 0.7312 | 0.9636 | |
| | No log | 2.0 | 174 | 0.1226 | 0.7985 | 0.7604 | 0.7790 | 0.9704 | |
| | No log | 3.0 | 261 | 0.1224 | 0.7880 | 0.7852 | 0.7866 | 0.9689 | |
| | No log | 4.0 | 348 | 0.1325 | 0.7557 | 0.7783 | 0.7668 | 0.9657 | |
| | No log | 5.0 | 435 | 0.1390 | 0.7655 | 0.8229 | 0.7932 | 0.9674 | |
| | 0.0733 | 6.0 | 522 | 0.1324 | 0.7709 | 0.8155 | 0.7926 | 0.9682 | |
| | 0.0733 | 7.0 | 609 | 0.1326 | 0.8123 | 0.7868 | 0.7994 | 0.9700 | |
| | 0.0733 | 8.0 | 696 | 0.1366 | 0.8109 | 0.7775 | 0.7938 | 0.9697 | |
| | 0.0733 | 9.0 | 783 | 0.1385 | 0.7893 | 0.7930 | 0.7912 | 0.9686 | |
| | 0.0733 | 10.0 | 870 | 0.1393 | 0.8044 | 0.7938 | 0.7991 | 0.9696 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
