| --- |
| library_name: transformers |
| license: cc-by-nc-sa-4.0 |
| base_model: microsoft/layoutlmv3-base |
| tags: |
| - generated_from_trainer |
| - invoice-processing |
| - information-extraction |
| - czech-language |
| - document-ai |
| - layout-aware-model |
| - multimodal-model |
| - synthetic-data |
| - hybrid-data |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLMv3InvoiceCzech-V2 |
| results: [] |
| --- |
| |
| # LayoutLMv3InvoiceCzech (V2 – Synthetic + Random Layout + Real Layout Injection) |
|
|
| This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) for structured information extraction from Czech invoices. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.0763 |
| - Precision: 0.8009 |
| - Recall: 0.8849 |
| - F1: 0.8408 |
| - Accuracy: 0.9844 |
|
|
| --- |
|
|
| ## Model description |
|
|
| LayoutLMv3InvoiceCzech (V2) represents an advanced multimodal document understanding model combining: |
|
|
| - textual features |
| - spatial layout (bounding boxes) |
| - visual features (image embeddings) |
|
|
| The model performs token-level classification to extract structured invoice fields: |
| - supplier |
| - customer |
| - invoice number |
| - bank details |
| - totals |
| - dates |
|
|
| This version introduces **real layout injection**, significantly improving realism and generalization. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of three 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 layouts are used as templates |
| - original text content is replaced with synthetic data |
| - new content is rendered into authentic document structures |
|
|
| This preserves: |
| - real-world spatial distributions |
| - visual patterns and formatting |
| - document complexity |
|
|
| while maintaining: |
| - full annotation control |
| - consistent labels |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V2 – Synthetic + layout augmentation + real layout injection** |
|
|
| It is used to: |
| - bridge the gap between synthetic and real-world data |
| - evaluate the impact of realistic layouts on multimodal models |
| - compare with: |
| - V0–V1 (fully synthetic) |
| - V3 (real data fine-tuning) |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Advanced multimodal document AI |
| - Invoice information extraction with visual + spatial features |
| - Evaluation of hybrid data strategies |
| - Benchmarking LayoutLMv3 |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Text content remains synthetic |
| - Limited exposure to real linguistic variability |
| - OCR noise and scanning artifacts are not fully represented |
| - May struggle with rare real-world edge cases |
|
|
| --- |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 8 |
| - 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 |
| - 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 | 115 | 0.0725 | 0.7496 | 0.8257 | 0.7858 | 0.9807 | |
| | No log | 2.0 | 230 | 0.0701 | 0.7569 | 0.8376 | 0.7952 | 0.9822 | |
| | No log | 3.0 | 345 | 0.0735 | 0.7587 | 0.8883 | 0.8184 | 0.9810 | |
| | No log | 4.0 | 460 | 0.0743 | 0.7827 | 0.8714 | 0.8247 | 0.9826 | |
| | 0.0606 | 5.0 | 575 | 0.0783 | 0.7756 | 0.8714 | 0.8207 | 0.9821 | |
| | 0.0606 | 6.0 | 690 | 0.0811 | 0.7561 | 0.8968 | 0.8204 | 0.9814 | |
| | 0.0606 | 7.0 | 805 | 0.0763 | 0.8009 | 0.8849 | 0.8408 | 0.9844 | |
| | 0.0606 | 8.0 | 920 | 0.0826 | 0.7784 | 0.9036 | 0.8363 | 0.9835 | |
| | 0.0201 | 9.0 | 1035 | 0.0824 | 0.7837 | 0.8951 | 0.8357 | 0.9836 | |
| | 0.0201 | 10.0 | 1150 | 0.0852 | 0.7818 | 0.9036 | 0.8383 | 0.9834 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
