| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: TomasFAV/Pix2StructCzechInvoice |
| tags: |
| - generated_from_trainer |
| - invoice-processing |
| - information-extraction |
| - czech-language |
| - document-ai |
| - multimodal-model |
| - generative-model |
| - synthetic-data |
| - layout-augmentation |
| metrics: |
| - f1 |
| model-index: |
| - name: Pix2StructCzechInvoice-V1 |
| results: [] |
| --- |
| |
| # Pix2StructCzechInvoice (V1 – Synthetic + Random Layout) |
|
|
| This model is a fine-tuned version of [TomasFAV/Pix2StructCzechInvoice](https://huggingface.co/TomasFAV/Pix2StructCzechInvoice) for structured information extraction from Czech invoices. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.4679 |
| - F1: 0.6432 |
|
|
| --- |
|
|
| ## Model description |
|
|
| Pix2StructCzechInvoice (V1) extends the baseline generative model by introducing layout variability into the training data. |
|
|
| Unlike token classification models, this model: |
| - processes full document images |
| - generates structured outputs as text sequences |
|
|
| It is trained to extract key invoice fields: |
| - supplier |
| - customer |
| - invoice number |
| - bank details |
| - totals |
| - dates |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of: |
|
|
| - synthetically generated invoice images |
| - augmented variants with randomized layouts |
| - corresponding structured text outputs |
|
|
| Key properties: |
| - variable layout structure |
| - visual diversity (spacing, positioning, formatting) |
| - consistent annotation format |
| - fully synthetic data |
|
|
| This introduces **layout variability in the visual domain**, which is crucial for generative multimodal 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 generative models |
| - compare against: |
| - V0 (fixed templates) |
| - later hybrid and real-data stages (V2, V3) |
| - analyze robustness of end-to-end extraction |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - End-to-end invoice extraction from images |
| - Document VQA-style tasks |
| - Research in generative document understanding |
| - Comparison with structured prediction models |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Still trained only on synthetic data |
| - Sensitive to output formatting inconsistencies |
| - Training instability (fluctuating F1 across epochs) |
| - Evaluation depends on string matching quality |
| - Less interpretable than token classification models |
|
|
| --- |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - 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: cosine_with_restarts |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| --- |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | 0.1978 | 1.0 | 75 | 0.3757 | 0.5804 | |
| | 0.1031 | 2.0 | 150 | 0.3578 | 0.6399 | |
| | 0.0725 | 3.0 | 225 | 0.3504 | 0.6318 | |
| | 0.0512 | 4.0 | 300 | 0.3929 | 0.6396 | |
| | 0.0500 | 5.0 | 375 | 0.4072 | 0.6394 | |
| | 0.0462 | 6.0 | 450 | 0.4655 | 0.4377 | |
| | 0.0502 | 7.0 | 525 | 0.6320 | 0.3384 | |
| | 0.0528 | 8.0 | 600 | 0.4835 | 0.5018 | |
| | 0.0393 | 9.0 | 675 | 0.4679 | 0.6432 | |
| | 0.0392 | 10.0 | 750 | 0.5330 | 0.4931 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
