| --- |
| 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 |
| - layout-augmentation |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLMv3InvoiceCzech-V1 |
| results: [] |
| --- |
| |
| # LayoutLMv3InvoiceCzech (V1 – Synthetic + Random Layout) |
|
|
| 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: 0.1750 |
| - Precision: 0.6800 |
| - Recall: 0.6904 |
| - F1: 0.6851 |
| - Accuracy: 0.9714 |
|
|
| --- |
|
|
| ## Model description |
|
|
| LayoutLMv3InvoiceCzech (V1) extends the baseline multimodal model by introducing layout variability into the training data. |
|
|
| The model leverages: |
| - textual features |
| - spatial layout (bounding boxes) |
| - visual features (image embeddings) |
|
|
| It performs token-level classification to extract structured invoice fields: |
| - 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 structural variations. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of: |
|
|
| - synthetically generated invoices based on templates |
| - augmented variants with randomized layouts |
| - corresponding bounding boxes |
| - rendered document images |
|
|
| Key properties: |
| - variable positioning of fields |
| - layout perturbations (shifts, spacing, ordering) |
| - preserved label consistency |
| - fully synthetic data |
|
|
| This dataset introduces **layout diversity** and tests how multimodal models respond to structural variability. |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V1 – Synthetic templates + randomized layouts** |
|
|
| It is used to: |
| - evaluate the impact of layout variability on multimodal models |
| - compare against: |
| - V0 (fixed layouts) |
| - later hybrid and real-data stages (V2, V3) |
| - analyze interaction between visual and spatial features |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Research in multimodal document understanding |
| - Benchmarking LayoutLMv3 under layout variability |
| - Comparison with BERT and LiLT |
| - Czech invoice information extraction |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Still trained only on synthetic data |
| - Layout variability is artificial |
| - Visual features are derived from clean renderings |
| - No real-world noise (OCR errors, scanning artifacts) |
|
|
| --- |
|
|
| ## 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 | 75 | 0.1545 | 0.6769 | 0.6701 | 0.6735 | 0.9711 | |
| | No log | 2.0 | 150 | 0.1658 | 0.6732 | 0.6937 | 0.6833 | 0.9695 | |
| | No log | 3.0 | 225 | 0.1750 | 0.6800 | 0.6904 | 0.6851 | 0.9714 | |
| | No log | 4.0 | 300 | 0.1946 | 0.6881 | 0.6159 | 0.6500 | 0.9707 | |
| | No log | 5.0 | 375 | 0.1896 | 0.6941 | 0.6717 | 0.6827 | 0.9717 | |
| | No log | 6.0 | 450 | 0.1979 | 0.6609 | 0.6430 | 0.6518 | 0.9704 | |
| | 0.0193 | 7.0 | 525 | 0.1991 | 0.6702 | 0.6396 | 0.6545 | 0.9706 | |
| | 0.0193 | 8.0 | 600 | 0.2014 | 0.6503 | 0.6261 | 0.6379 | 0.9698 | |
| | 0.0193 | 9.0 | 675 | 0.1955 | 0.6523 | 0.6413 | 0.6468 | 0.9702 | |
| | 0.0193 | 10.0 | 750 | 0.1956 | 0.6535 | 0.6447 | 0.6491 | 0.9704 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
