| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/pix2struct-docvqa-base |
| tags: |
| - generated_from_trainer |
| - invoice-processing |
| - information-extraction |
| - czech-language |
| - document-ai |
| - multimodal-model |
| - generative-model |
| - synthetic-data |
| metrics: |
| - f1 |
| model-index: |
| - name: Pix2StructCzechInvoice-V0 |
| results: [] |
| --- |
| |
| # Pix2StructCzechInvoice (V0 – Synthetic Templates Only) |
|
|
| This model is a fine-tuned version of [google/pix2struct-docvqa-base](https://huggingface.co/google/pix2struct-docvqa-base) for structured information extraction from Czech invoices. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.5022 |
| - F1: 0.5907 |
|
|
| --- |
|
|
| ## Model description |
|
|
| Pix2StructCzechInvoice (V0) is a generative multimodal model designed for document understanding. |
|
|
| Unlike token classification models (e.g., BERT, LiLT, LayoutLMv3), this model: |
| - processes the entire document image |
| - generates structured outputs as text sequences |
|
|
| The model is trained to extract key invoice fields such as: |
| - supplier |
| - customer |
| - invoice number |
| - bank details |
| - totals |
| - dates |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset consists of: |
|
|
| - synthetically generated invoice images |
| - fixed template layouts |
| - corresponding target text sequences representing structured fields |
|
|
| Key properties: |
| - clean and consistent visual structure |
| - no OCR noise (end-to-end image input) |
| - controlled output formatting |
| - no real-world documents |
|
|
| This represents the **baseline dataset for generative multimodal models**. |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V0 – Synthetic template-based dataset only** |
|
|
| It is used to: |
| - establish a baseline for generative document models |
| - compare with: |
| - token classification approaches (BERT, LiLT) |
| - multimodal encoders (LayoutLMv3) |
| - evaluate feasibility of end-to-end extraction |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - End-to-end invoice information extraction from images |
| - Document VQA-style tasks |
| - Research in generative document understanding |
| - Comparison with structured prediction approaches |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Trained only on synthetic data |
| - Sensitive to output formatting inconsistencies |
| - Lower stability compared to token classification models |
| - Requires careful evaluation (string matching vs structured metrics) |
| - Performance depends on generation quality |
|
|
| --- |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - train_batch_size: 4 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | 3.1072 | 1.0 | 300 | 2.9769 | 0.0 | |
| | 2.6572 | 2.0 | 600 | 2.8684 | 0.0 | |
| | 2.4810 | 3.0 | 900 | 2.6349 | 0.0 | |
| | 1.7941 | 4.0 | 1200 | 1.6395 | 0.0 | |
| | 0.8458 | 5.0 | 1500 | 1.0680 | 0.2173 | |
| | 0.6198 | 6.0 | 1800 | 0.7713 | 0.4835 | |
| | 0.1999 | 7.0 | 2100 | 0.4331 | 0.5700 | |
| | 0.0946 | 8.0 | 2400 | 0.3844 | 0.5907 | |
| | 0.1020 | 9.0 | 2700 | 0.4066 | 0.4294 | |
| | 0.0842 | 10.0 | 3000 | 0.5022 | 0.4665 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
