Update README.md

README.md

@@ -1,35 +1,112 @@
 ---
 library_name: transformers
+license: apache-2.0
+base_model: google/pix2struct-docvqa-base
 tags:
 - generated_from_trainer
+- invoice-processing
+- information-extraction
+- czech-language
+- document-ai
+- multimodal-model
+- generative-model
+- synthetic-data
+- hybrid-data
 metrics:
 - f1
 model-index:
-- name: Pix2StructCzechInvoiceV2
+- name: Pix2StructCzechInvoice-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. -->
+# Pix2StructCzechInvoice (V2 – Synthetic + Random Layout + Real Layout Injection)
 
-# Pix2StructCzechInvoiceV2
+This model is a fine-tuned version of [google/pix2struct-docvqa-base](https://huggingface.co/google/pix2struct-docvqa-base) 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: 0.2521
-- F1: 0.7311
+- Loss: 0.2521  
+- F1: 0.7311  
+
+---
 
 ## Model description
 
-More information needed
+Pix2StructCzechInvoice (V2) represents an advanced stage of the generative document understanding pipeline.
+
+The model:
+- processes full document images  
+- generates structured outputs as text sequences  
+
+It is trained to extract key invoice fields:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
+
+This version introduces **real layout injection**, significantly improving visual realism and model generalization.
+
+---
+
+## Training data
 
-## Intended uses & limitations
+The dataset consists of three 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 layouts are used as templates  
+- original content is replaced with synthetic data  
+- new content is rendered into realistic visual structures  
+
+This preserves:
+- real-world layout complexity  
+- visual patterns and formatting  
+- document structure variability  
+
+while maintaining:
+- full control over annotations  
+- consistent output format  
+
+---
 
-More information needed
+## Role in the pipeline
 
-## Training and evaluation data
+This model corresponds to:
 
-More information needed
+**V2 – Synthetic + layout augmentation + real layout injection**
+
+It is used to:
+- reduce the domain gap between synthetic and real documents  
+- evaluate the effect of realistic layouts on generative models  
+- compare with:
+  - V0–V1 (synthetic-only training)  
+  - V3 (real data fine-tuning)  
+
+---
+
+## Intended uses
+
+- End-to-end invoice extraction from images  
+- Document VQA-style tasks  
+- Research in generative document understanding  
+- Evaluation of hybrid training strategies  
+
+---
+
+## Limitations
+
+- Generated outputs may contain formatting errors  
+- Sensitive to decoding strategy and tokenization  
+- Still lacks full exposure to real linguistic variability  
+- Training remains less stable than classification-based models  
+
+---
 
 ## Training procedure
 
@@ -46,6 +123,8 @@ The following hyperparameters were used during training:
 - num_epochs: 10
 - mixed_precision_training: Native AMP
 
+---
+
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | F1     |
@@ -61,10 +140,11 @@ The following hyperparameters were used during training:
 | 0.0861        | 9.0   | 1035 | 0.3019          | 0.6931 |
 | 0.0860        | 10.0  | 1150 | 0.3167          | 0.7186 |
 
+---
 
-### 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