Update README.md

README.md

@@ -1,41 +1,102 @@
 ---
 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: BERTInvoiceCzechRV1
+- name: BERTInvoiceCzechR-V1
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# BERTInvoiceCzechR (V1 – Synthetic + Random Layout)
 
-# BERTInvoiceCzechRV1
+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.
 
-This model was trained from scratch on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2295
-- Precision: 0.6594
-- Recall: 0.7309
-- F1: 0.6933
-- Accuracy: 0.9534
+- Loss: 0.2295  
+- Precision: 0.6594  
+- Recall: 0.7309  
+- F1: 0.6933  
+- Accuracy: 0.9534  
+
+---
 
 ## Model description
 
-More information needed
+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:
 
-## Intended uses & limitations
+**V1 – Synthetic templates + randomized layouts**
 
-More information needed
+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  
+
+---
 
-## Training and evaluation data
+## 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  
+
+---
 
-More information needed
+## 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
 
@@ -52,6 +113,8 @@ The following hyperparameters were used during training:
 - num_epochs: 10
 - mixed_precision_training: Native AMP
 
+---
+
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
@@ -67,10 +130,11 @@ The following hyperparameters were used during training:
 | 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
+## Framework versions
 
-- Transformers 5.0.0
-- Pytorch 2.10.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.2
+- Transformers 5.0.0  
+- PyTorch 2.10.0+cu128  
+- Datasets 4.0.0  
+- Tokenizers 0.22.2