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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 |