LayoutLMv3InvoiceCzech (V1 – Synthetic + Random Layout)
This model is a fine-tuned version of
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
