| --- |
| 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 |
| - layout-augmentation |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: BERTInvoiceCzechR-V1 |
| results: [] |
| --- |
| |
| # BERTInvoiceCzechR (V1 – Synthetic + Random Layout) |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) for the task of structured information extraction from Czech invoices. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.2295 |
| - Precision: 0.6594 |
| - Recall: 0.7309 |
| - F1: 0.6933 |
| - Accuracy: 0.9534 |
|
|
| --- |
|
|
| ## Model description |
|
|
| BERTInvoiceCzechR (V1) extends the baseline model (V0) by introducing layout variability into the training data. |
|
|
| The model performs token-level classification to extract structured invoice fields such as: |
| - supplier |
| - customer |
| - invoice number |
| - bank details |
| - totals |
| - dates |
|
|
| Compared to V0, this version is trained on synthetically generated invoices with **randomized layouts**, improving robustness to positional and structural variations. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of: |
|
|
| - synthetically generated invoices based on templates |
| - additional variants with randomized layout structures |
|
|
| Key properties: |
| - variable positioning of fields |
| - layout perturbations (shifts, spacing, ordering) |
| - preserved semantic correctness of labels |
| - still fully synthetic (no real invoices) |
|
|
| This dataset introduces **layout diversity**, which is critical for generalization in document understanding tasks. |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V1 – Synthetic templates + randomized layouts** |
|
|
| It is used to: |
| - evaluate the impact of layout variability |
| - compare against: |
| - V0 (fixed templates) |
| - later stages with real data (V2, V3) |
| - measure improvements in generalization |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Research in layout-aware NLP without explicit layout models |
| - Benchmarking robustness to structural variation |
| - Intermediate baseline for synthetic data pipelines |
| - Czech invoice information extraction |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Still trained only on synthetic data |
| - No exposure to real-world noise (OCR errors, distortions) |
| - Layout variation is artificial and may not fully reflect real documents |
| - Does not leverage explicit spatial features (pure BERT) |
|
|
| --- |
|
|
| ## 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 | 65 | 0.2059 | 0.6571 | 0.6781 | 0.6674 | 0.9533 | |
| | No log | 2.0 | 130 | 0.2292 | 0.6598 | 0.7313 | 0.6937 | 0.9534 | |
| | No log | 3.0 | 195 | 0.2172 | 0.6789 | 0.6913 | 0.6850 | 0.9565 | |
| | No log | 4.0 | 260 | 0.2435 | 0.6385 | 0.7565 | 0.6925 | 0.9498 | |
| | No log | 5.0 | 325 | 0.2525 | 0.6347 | 0.7550 | 0.6896 | 0.9489 | |
| | No log | 6.0 | 390 | 0.2723 | 0.5994 | 0.7270 | 0.6571 | 0.9444 | |
| | No log | 7.0 | 455 | 0.2907 | 0.5963 | 0.7429 | 0.6616 | 0.9432 | |
| | 0.0306 | 8.0 | 520 | 0.2810 | 0.6146 | 0.7270 | 0.6661 | 0.9463 | |
| | 0.0306 | 9.0 | 585 | 0.2853 | 0.6059 | 0.7208 | 0.6584 | 0.9455 | |
| | 0.0306 | 10.0 | 650 | 0.2859 | 0.6054 | 0.7239 | 0.6594 | 0.9452 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
