File size: 4,236 Bytes
f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 f08318f 0c074b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | ---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
- invoice-processing
- information-extraction
- czech-language
- synthetic-data
- layout-augmentation
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTInvoiceCzechR-V1
results: []
---
# BERTInvoiceCzechR (V1 – Synthetic + Random Layout)
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) for the task of structured information extraction from Czech invoices.
It achieves the following results on the evaluation set:
- Loss: 0.2295
- Precision: 0.6594
- Recall: 0.7309
- F1: 0.6933
- Accuracy: 0.9534
---
## Model description
BERTInvoiceCzechR (V1) extends the baseline model (V0) by introducing layout variability into the training data.
The model performs token-level classification to extract structured invoice fields such as:
- 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 positional and structural variations.
---
## Training data
The dataset consists of:
- synthetically generated invoices based on templates
- additional variants with randomized layout structures
Key properties:
- variable positioning of fields
- layout perturbations (shifts, spacing, ordering)
- preserved semantic correctness of labels
- still fully synthetic (no real invoices)
This dataset introduces **layout diversity**, which is critical for generalization in document understanding tasks.
---
## Role in the pipeline
This model corresponds to:
**V1 – Synthetic templates + randomized layouts**
It is used to:
- evaluate the impact of layout variability
- compare against:
- V0 (fixed templates)
- later stages with real data (V2, V3)
- measure improvements in generalization
---
## Intended uses
- Research in layout-aware NLP without explicit layout models
- Benchmarking robustness to structural variation
- Intermediate baseline for synthetic data pipelines
- Czech invoice information extraction
---
## Limitations
- Still trained only on synthetic data
- No exposure to real-world noise (OCR errors, distortions)
- Layout variation is artificial and may not fully reflect real documents
- Does not leverage explicit spatial features (pure BERT)
---
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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 | 65 | 0.2059 | 0.6571 | 0.6781 | 0.6674 | 0.9533 |
| No log | 2.0 | 130 | 0.2292 | 0.6598 | 0.7313 | 0.6937 | 0.9534 |
| No log | 3.0 | 195 | 0.2172 | 0.6789 | 0.6913 | 0.6850 | 0.9565 |
| No log | 4.0 | 260 | 0.2435 | 0.6385 | 0.7565 | 0.6925 | 0.9498 |
| No log | 5.0 | 325 | 0.2525 | 0.6347 | 0.7550 | 0.6896 | 0.9489 |
| No log | 6.0 | 390 | 0.2723 | 0.5994 | 0.7270 | 0.6571 | 0.9444 |
| No log | 7.0 | 455 | 0.2907 | 0.5963 | 0.7429 | 0.6616 | 0.9432 |
| 0.0306 | 8.0 | 520 | 0.2810 | 0.6146 | 0.7270 | 0.6661 | 0.9463 |
| 0.0306 | 9.0 | 585 | 0.2853 | 0.6059 | 0.7208 | 0.6584 | 0.9455 |
| 0.0306 | 10.0 | 650 | 0.2859 | 0.6054 | 0.7239 | 0.6594 | 0.9452 |
---
## Framework versions
- Transformers 5.0.0
- PyTorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2 |