Update README.md

README.md

@@ -1,41 +1,110 @@
 ---
 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
+- hybrid-data
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: BERTInvoiceCzechRV2
+- name: BERTInvoiceCzechR-V2
   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 (V2 – Synthetic + Random Layout + Real Layout Injection)
 
-# BERTInvoiceCzechRV2
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) for 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: 0.1326
-- Precision: 0.8120
-- Recall: 0.7868
-- F1: 0.7992
-- Accuracy: 0.9700
+- Loss: 0.1326  
+- Precision: 0.8120  
+- Recall: 0.7868  
+- F1: 0.7992  
+- Accuracy: 0.9700  
+
+---
 
 ## Model description
 
-More information needed
+BERTInvoiceCzechR (V2) represents an advanced stage in the training pipeline, combining synthetic data with realistic document layouts.
+
+The model performs token-level classification to extract structured invoice fields:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
+
+This version introduces a key improvement: **real invoice layouts with synthetic content**, bridging the gap between artificial and real-world data.
+
+---
+
+## Training data
+
+The dataset is composed of three main components:
+
+1. **Synthetic template-based invoices**  
+2. **Synthetic invoices with randomized layouts**  
+3. **Hybrid invoices with real layouts and synthetic content**  
+
+### Real layout injection
+
+In the hybrid dataset:
+- real invoice documents are used as layout templates  
+- original textual content is removed  
+- fields (e.g., supplier, customer, bank details) are replaced with synthetic data  
+- new content is rendered into the original spatial structure  
+
+This approach preserves:
+- realistic spacing  
+- typography patterns  
+- structural complexity  
+
+while maintaining:
+- full control over annotations  
+- label consistency  
+
+---
+
+## Role in the pipeline
+
+This model corresponds to:
+
+**V2 – Synthetic + layout augmentation + real layout injection**
+
+It is designed to:
+- reduce the domain gap between synthetic and real invoices  
+- evaluate the impact of realistic spatial distributions  
+- serve as a bridge between purely synthetic training (V0–V1) and real data fine-tuning (V3)  
+
+---
 
-## Intended uses & limitations
+## Intended uses
 
-More information needed
+- Advanced research in document AI  
+- Evaluation of hybrid synthetic-real training strategies  
+- Invoice information extraction in semi-realistic conditions  
+- Benchmarking generalization improvements  
 
-## Training and evaluation data
+---
+
+## Limitations
 
-More information needed
+- Still does not use fully real textual content  
+- Synthetic text may not capture all linguistic variability  
+- OCR noise and scanning artifacts are not fully represented  
+- Performance may still drop on unseen real-world edge cases  
+
+---
 
 ## Training procedure
 
@@ -52,6 +121,8 @@ 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 +138,11 @@ The following hyperparameters were used during training:
 | 0.0733        | 9.0   | 783  | 0.1385          | 0.7893    | 0.7930 | 0.7912 | 0.9686   |
 | 0.0733        | 10.0  | 870  | 0.1393          | 0.8044    | 0.7938 | 0.7991 | 0.9696   |
 
+---
 
-### 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