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
+- real-data
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: BERTInvoiceCzechRV3
+- name: BERTInvoiceCzechR-V3
   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 (V3 – Full Pipeline with Real Data Fine-Tuning)
 
-# BERTInvoiceCzechRV3
+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: 0.0630
-- Precision: 0.8620
-- Recall: 0.9072
-- F1: 0.8840
-- Accuracy: 0.9830
+- Loss: 0.0630  
+- Precision: 0.8620  
+- Recall: 0.9072  
+- F1: 0.8840  
+- Accuracy: 0.9830  
+
+---
 
 ## Model description
 
-More information needed
+BERTInvoiceCzechR (V3) is the final model in a multi-stage training pipeline designed for invoice understanding.
+
+The model performs token-level classification to extract structured invoice fields:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
+
+This version combines synthetic data, layout augmentation, hybrid data, and **real annotated invoices**, resulting in the highest performance across all variants.
+
+---
+
+## Training data
+
+The dataset used in this stage is a combination of:
+
+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 documents  
+- manually or semi-automatically annotated fields  
+- natural linguistic variability  
+- real formatting inconsistencies  
+
+This allows the model to:
+- adapt to real-world distributions  
+- learn domain-specific patterns  
+- improve robustness and generalization  
+
+---
 
-## Intended uses & limitations
+## Role in the pipeline
 
-More information needed
+This model corresponds to:
 
-## Training and evaluation data
+**V3 – Full pipeline (synthetic + hybrid + real data fine-tuning)**
 
-More information needed
+It represents:
+- the final production-ready model  
+- the culmination of the proposed data generation strategy  
+- the best-performing configuration in the experimental setup  
+
+---
+
+## Intended uses
+
+- Real-world invoice information extraction  
+- Document AI systems in production environments  
+- OCR post-processing pipelines  
+- Research benchmarking against synthetic-only approaches  
+
+---
+
+## Limitations
+
+- Performance depends on OCR quality (input text assumption)  
+- May still struggle with:
+  - highly unusual invoice formats  
+  - extreme noise or low-resolution scans  
+- Requires tokenized text input (not end-to-end from images)  
+- Domain-specific (Czech invoices)  
+
+---
 
 ## 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:
 | No log        | 9.0   | 180  | 0.0644          | 0.8593    | 0.9083 | 0.8831 | 0.9828   |
 | No log        | 10.0  | 200  | 0.0630          | 0.8620    | 0.9072 | 0.8840 | 0.9830   |
 
+---
 
-### 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