metadata
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 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