Update README.md

README.md

@@ -4,40 +4,103 @@ license: apache-2.0
 base_model: google-bert/bert-base-multilingual-cased
 tags:
 - generated_from_trainer
+- invoice-processing
+- information-extraction
+- czech-language
+- synthetic-data
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: BERTInvoiceCzechR
+- name: BERTInvoiceCzechR-V0
   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 (V0 – Synthetic Templates Only)
 
-# BERTInvoiceCzechR
+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 is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3291
-- Precision: 0.5188
-- Recall: 0.6917
-- F1: 0.5929
-- Accuracy: 0.9335
+- Loss: 0.3291  
+- Precision: 0.5188  
+- Recall: 0.6917  
+- F1: 0.5929  
+- Accuracy: 0.9335  
+
+---
 
 ## Model description
 
-More information needed
+BERTInvoiceCzechR (V0) is the baseline model in a multi-stage experimental pipeline focused on invoice understanding.
 
-## Intended uses & limitations
+The model performs token-level classification to extract structured fields from invoice text, such as:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
 
-More information needed
+This version (V0) is trained **exclusively on synthetically generated invoices created from predefined templates**, without any layout randomization or real-world data.
+
+---
 
-## Training and evaluation data
+## Training data
 
-More information needed
+The dataset consists purely of:
+
+- synthetically generated invoices  
+- fixed template structures  
+- controlled field placement and formatting  
+
+Characteristics:
+- consistent layout across samples  
+- fully controlled annotations  
+- no noise or OCR artifacts  
+- no real invoice data  
+- added synthetic image augmentations
+  
+This dataset represents the **simplest training scenario** in the pipeline and serves as a baseline for comparison with more complex data variants.
+
+---
+
+## Role in the pipeline
+
+This model corresponds to:
+
+**V0 – Synthetic template-based dataset only**
+
+It is used as:
+- a baseline for evaluating the impact of:
+  - layout variability  
+  - synthetic-real hybrid data  
+  - real annotated invoices  
+- a reference point for measuring generalization gap  
+
+---
+
+## Intended uses
+
+- Baseline model for document AI experiments  
+- Evaluation of synthetic data usefulness  
+- Comparison with more advanced dataset variants (V1–V3)  
+- Research in Czech invoice information extraction  
+
+---
+
+## Limitations
+
+- Strong dependency on template structure  
+- May have poor generalization to:
+  - unseen layouts  
+  - real-world invoices  
+  - noisy OCR outputs  
+- Does not capture layout variability  
+- Trained only on clean synthetic data  
+
+---
 
 ## Training procedure
 
@@ -54,6 +117,8 @@ 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 |
@@ -69,10 +134,11 @@ The following hyperparameters were used during training:
 | 0.3757        | 9.0   | 783  | 0.3604          | 0.4906    | 0.6858 | 0.5720 | 0.9279   |
 | 0.3757        | 10.0  | 870  | 0.3515          | 0.5011    | 0.6944 | 0.5821 | 0.9296   |
 
+---
 
-### 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