| --- |
| 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 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLMv3InvoiceCzech-V0 |
| results: [] |
| --- |
| |
| # LayoutLMv3InvoiceCzech (V0 – Synthetic Templates Only) |
|
|
| 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: 0.2146 |
| - Precision: 0.5354 |
| - Recall: 0.7428 |
| - F1: 0.6223 |
| - Accuracy: 0.9583 |
|
|
| --- |
|
|
| ## Model description |
|
|
| LayoutLMv3InvoiceCzech (V0) is a multimodal document understanding model that leverages: |
|
|
| - textual information |
| - 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 is trained exclusively on synthetically generated invoice templates. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of: |
|
|
| - synthetically generated invoices |
| - fixed template layouts |
| - corresponding bounding boxes |
| - rendered document images |
|
|
| Key properties: |
| - consistent structure across samples |
| - clean and noise-free data |
| - perfect alignment between text, layout, and image |
| - no real-world documents |
|
|
| This represents the **baseline dataset** for multimodal document models. |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V0 – Synthetic template-based dataset only** |
|
|
| It is used to: |
| - establish a baseline for multimodal models |
| - compare against: |
| - text-only models (BERT) |
| - layout-aware models without vision (LiLT) |
| - evaluate the contribution of visual features in a controlled setting |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Research in multimodal document understanding |
| - Benchmarking LayoutLMv3 on structured documents |
| - Comparison with other architectures (BERT, LiLT, etc.) |
| - Czech invoice information extraction |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Trained only on synthetic data with fixed layouts |
| - Limited generalization to real-world invoices |
| - Visual features are learned from clean synthetic renderings |
| - No exposure to: |
| - OCR errors |
| - scanning artifacts |
| - real-world noise |
|
|
| --- |
|
|
| ## 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 | 150 | 0.2817 | 0.1429 | 0.0829 | 0.1049 | 0.9470 | |
| | No log | 2.0 | 300 | 0.2222 | 0.3480 | 0.4822 | 0.4043 | 0.9480 | |
| | No log | 3.0 | 450 | 0.2170 | 0.3852 | 0.5736 | 0.4609 | 0.9480 | |
| | 0.5287 | 4.0 | 600 | 0.1919 | 0.4625 | 0.6261 | 0.5320 | 0.9558 | |
| | 0.5287 | 5.0 | 750 | 0.1701 | 0.5254 | 0.7174 | 0.6066 | 0.9627 | |
| | 0.5287 | 6.0 | 900 | 0.2060 | 0.5173 | 0.7327 | 0.6064 | 0.9565 | |
| | 0.0360 | 7.0 | 1050 | 0.2161 | 0.5370 | 0.7124 | 0.6124 | 0.9594 | |
| | 0.0360 | 8.0 | 1200 | 0.2146 | 0.5359 | 0.7445 | 0.6232 | 0.9584 | |
| | 0.0360 | 9.0 | 1350 | 0.2141 | 0.5268 | 0.7327 | 0.6129 | 0.9578 | |
| | 0.0147 | 10.0 | 1500 | 0.2131 | 0.5393 | 0.7310 | 0.6207 | 0.9597 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
