| --- |
| 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 |
| - layout-augmentation |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LiLTInvoiceCzech-V1 |
| results: [] |
| --- |
| |
| # LiLTInvoiceCzech (V1 – Synthetic + Random Layout) |
|
|
| 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: 0.1907 |
| - Precision: 0.6326 |
| - Recall: 0.7491 |
| - F1: 0.6859 |
| - Accuracy: 0.9660 |
|
|
| --- |
|
|
| ## Model description |
|
|
| LiLTInvoiceCzech (V1) extends the baseline layout-aware model by introducing layout variability into the training data. |
|
|
| The model performs token-level classification using both textual and spatial (bounding box) information 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 spatial variations. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of: |
|
|
| - synthetically generated invoices based on templates |
| - augmented variants with randomized layout structures |
| - corresponding bounding box annotations |
|
|
| Key properties: |
| - variable positioning of fields |
| - layout perturbations (shifts, spacing, ordering) |
| - preserved label consistency |
| - fully synthetic data |
|
|
| This dataset introduces **layout diversity**, which is especially important for layout-aware models. |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V1 – Synthetic templates + randomized layouts** |
|
|
| It is used to: |
| - evaluate the effect of layout variability on LiLT |
| - compare against: |
| - V0 (fixed layouts) |
| - later stages with hybrid and real data (V2, V3) |
| - analyze how layout-aware models benefit from synthetic augmentation |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - Research in layout-aware document understanding |
| - Evaluation of spatial robustness in NLP models |
| - Benchmarking LiLT against text-only models (BERT) |
| - Czech invoice information extraction |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Still trained only on synthetic data |
| - Layout variability is artificial |
| - No real-world noise (OCR errors, distortions) |
| - May not fully generalize to real invoice distributions |
|
|
| --- |
|
|
| ## 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 | 38 | 0.1676 | 0.5917 | 0.6826 | 0.6339 | 0.9639 | |
| | No log | 2.0 | 76 | 0.1810 | 0.6123 | 0.6604 | 0.6355 | 0.9643 | |
| | No log | 3.0 | 114 | 0.1906 | 0.6317 | 0.7491 | 0.6854 | 0.9660 | |
| | No log | 4.0 | 152 | 0.1764 | 0.6380 | 0.6587 | 0.6482 | 0.9659 | |
| | No log | 5.0 | 190 | 0.1737 | 0.6544 | 0.6689 | 0.6616 | 0.9696 | |
| | No log | 6.0 | 228 | 0.1752 | 0.6728 | 0.6911 | 0.6818 | 0.9695 | |
| | No log | 7.0 | 266 | 0.1951 | 0.6083 | 0.6758 | 0.6403 | 0.9658 | |
| | No log | 8.0 | 304 | 0.1962 | 0.6162 | 0.6741 | 0.6438 | 0.9656 | |
| | No log | 9.0 | 342 | 0.1939 | 0.6700 | 0.6962 | 0.6828 | 0.9701 | |
| | No log | 10.0 | 380 | 0.1931 | 0.6645 | 0.6928 | 0.6784 | 0.9696 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
