| --- |
| library_name: transformers |
| license: mit |
| base_model: SCUT-DLVCLab/lilt-roberta-en-base |
| tags: |
| - generated_from_trainer |
| - invoice-processing |
| - information-extraction |
| - czech-language |
| - document-ai |
| - layout-aware-model |
| - synthetic-data |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LiLTInvoiceCzech-V0 |
| results: [] |
| --- |
| |
| # LiLTInvoiceCzech (V0 – Synthetic Templates Only) |
|
|
| This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) for structured information extraction from Czech invoices. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.1929 |
| - Precision: 0.6036 |
| - Recall: 0.7355 |
| - F1: 0.6631 |
| - Accuracy: 0.9645 |
|
|
| --- |
|
|
| ## Model description |
|
|
| LiLTInvoiceCzech (V0) is a layout-aware model based on the LiLT architecture, designed for document understanding tasks. |
|
|
| The model performs token-level classification with explicit use of layout information (bounding boxes), allowing it to better capture spatial relationships between invoice fields such as: |
| - 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 |
| - associated bounding box annotations for each token |
|
|
| Key properties: |
| - consistent spatial structure |
| - clean and noise-free data |
| - precise alignment between text and layout |
| - no real-world documents |
|
|
| This represents the **baseline dataset** for layout-aware models in the pipeline. |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V0 – Synthetic template-based dataset only** |
|
|
| It is used to: |
| - establish a baseline for LiLT architecture |
| - compare layout-aware vs text-only models (e.g., BERT) |
| - evaluate the benefit of spatial features in a controlled setting |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Document AI research with layout-aware models |
| - Benchmarking LiLT on structured documents |
| - Comparison with other architectures (BERT, LayoutLMv3, etc.) |
| - Czech invoice information extraction |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Trained only on synthetic data with fixed layouts |
| - Limited robustness to layout variability |
| - No exposure to real-world noise (OCR errors, distortions) |
| - Synthetic layouts may not reflect real invoice diversity |
|
|
| --- |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 3e-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 | 75 | 0.2174 | 0.2653 | 0.3038 | 0.2832 | 0.9430 | |
| | No log | 2.0 | 150 | 0.1504 | 0.5052 | 0.5751 | 0.5379 | 0.9642 | |
| | No log | 3.0 | 225 | 0.1508 | 0.5626 | 0.6365 | 0.5973 | 0.9650 | |
| | No log | 4.0 | 300 | 0.1742 | 0.5192 | 0.6689 | 0.5846 | 0.9593 | |
| | No log | 5.0 | 375 | 0.1863 | 0.5153 | 0.6877 | 0.5892 | 0.9579 | |
| | No log | 6.0 | 450 | 0.1878 | 0.5557 | 0.7065 | 0.6221 | 0.9605 | |
| | 0.1991 | 7.0 | 525 | 0.2189 | 0.5435 | 0.7253 | 0.6213 | 0.9578 | |
| | 0.1991 | 8.0 | 600 | 0.1927 | 0.6036 | 0.7355 | 0.6631 | 0.9645 | |
| | 0.1991 | 9.0 | 675 | 0.2133 | 0.5357 | 0.7167 | 0.6131 | 0.9583 | |
| | 0.1991 | 10.0 | 750 | 0.2198 | 0.5235 | 0.7235 | 0.6074 | 0.9569 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
