| --- |
| 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 |
| - hybrid-data |
| - real-data |
| metrics: |
| - f1 |
| model-index: |
| - name: Pix2StructCzechInvoice-V3 |
| results: [] |
| --- |
| |
| # Pix2StructCzechInvoice (V3 – Full Pipeline with Real Data Fine-Tuning) |
|
|
| 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: 0.1542 |
| - F1: 0.8404 |
|
|
| --- |
|
|
| ## Model description |
|
|
| Pix2StructCzechInvoice (V3) is the final generative model in the experimental pipeline. |
|
|
| Unlike token classification approaches, this model: |
| - processes full document images |
| - generates structured outputs as text sequences |
|
|
| It extracts key invoice fields such as: |
| - supplier |
| - customer |
| - invoice number |
| - bank details |
| - totals |
| - dates |
|
|
| By combining synthetic, hybrid, and real data, this version significantly improves both performance and stability. |
|
|
| --- |
|
|
| ## Training data |
|
|
| The dataset used in this stage combines: |
|
|
| 1. **Synthetic template-based invoices (V0)** |
| 2. **Synthetic invoices with randomized layouts (V1)** |
| 3. **Hybrid invoices with real layouts and synthetic content (V2)** |
| 4. **Real annotated invoices** |
|
|
| ### Real data fine-tuning |
|
|
| The final stage introduces: |
| - real invoice images |
| - realistic visual noise and distortions |
| - natural language variability |
| - real formatting inconsistencies |
|
|
| This allows the model to: |
| - better align generated outputs with real-world distributions |
| - improve robustness of sequence generation |
| - reduce hallucinations and formatting errors |
|
|
| --- |
|
|
| ## Role in the pipeline |
|
|
| This model corresponds to: |
|
|
| **V3 – Full pipeline (synthetic + hybrid + real data fine-tuning)** |
|
|
| It represents: |
| - the final generative model |
| - the best-performing Pix2Struct variant |
| - an end-to-end extraction approach |
|
|
| --- |
|
|
| ## Intended uses |
|
|
| - End-to-end invoice information extraction from images |
| - Document VQA and generative document understanding |
| - OCR-free document processing pipelines |
| - Research in generative vs structured extraction approaches |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Output format may still be inconsistent |
| - Sensitive to decoding strategy and prompt structure |
| - Less interpretable than token classification models |
| - Requires post-processing for structured outputs |
| - Computationally more expensive |
|
|
| --- |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | 0.3277 | 1.0 | 23 | 0.1958 | 0.7239 | |
| | 0.2366 | 2.0 | 46 | 0.1446 | 0.8037 | |
| | 0.1780 | 3.0 | 69 | 0.1247 | 0.8060 | |
| | 0.1153 | 4.0 | 92 | 0.1178 | 0.8316 | |
| | 0.0895 | 5.0 | 115 | 0.1279 | 0.8312 | |
| | 0.0774 | 6.0 | 138 | 0.1542 | 0.8404 | |
| | 0.0766 | 7.0 | 161 | 0.1530 | 0.7972 | |
| | 0.0697 | 8.0 | 184 | 0.1385 | 0.8372 | |
| | 0.0804 | 9.0 | 207 | 0.1433 | 0.7963 | |
| | 0.0664 | 10.0 | 230 | 0.1614 | 0.7991 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 5.0.0 |
| - PyTorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
