Update README.md

README.md

@@ -4,34 +4,99 @@ 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
 metrics:
 - f1
 model-index:
-- name: Pix2StructCzechInvoice
+- name: Pix2StructCzechInvoice-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. -->
+# Pix2StructCzechInvoice (V0 – Synthetic Templates Only)
 
-# Pix2StructCzechInvoice
+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 is a fine-tuned version of [google/pix2struct-docvqa-base](https://huggingface.co/google/pix2struct-docvqa-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5022
-- F1: 0.5907
+- Loss: 0.5022  
+- F1: 0.5907  
+
+---
 
 ## Model description
 
-More information needed
+Pix2StructCzechInvoice (V0) is a generative multimodal model designed for document understanding.
+
+Unlike token classification models (e.g., BERT, LiLT, LayoutLMv3), this model:
+- processes the entire document image  
+- generates structured outputs as text sequences  
+
+The model is trained to extract key invoice fields such as:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
+
+---
+
+## Training data
+
+The dataset consists of:
+
+- synthetically generated invoice images  
+- fixed template layouts  
+- corresponding target text sequences representing structured fields  
+
+Key properties:
+- clean and consistent visual structure  
+- no OCR noise (end-to-end image input)  
+- controlled output formatting  
+- no real-world documents  
+
+This represents the **baseline dataset for generative multimodal models**.
+
+---
+
+## Role in the pipeline
+
+This model corresponds to:
 
-## Intended uses & limitations
+**V0 – Synthetic template-based dataset only**
 
-More information needed
+It is used to:
+- establish a baseline for generative document models  
+- compare with:
+  - token classification approaches (BERT, LiLT)  
+  - multimodal encoders (LayoutLMv3)  
+- evaluate feasibility of end-to-end extraction  
+
+---
 
-## Training and evaluation data
+## Intended uses
+
+- End-to-end invoice information extraction from images  
+- Document VQA-style tasks  
+- Research in generative document understanding  
+- Comparison with structured prediction approaches  
+
+---
 
-More information needed
+## Limitations
+
+- Trained only on synthetic data  
+- Sensitive to output formatting inconsistencies  
+- Lower stability compared to token classification models  
+- Requires careful evaluation (string matching vs structured metrics)  
+- Performance depends on generation quality  
+
+---
 
 ## Training procedure
 
@@ -48,6 +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 | F1     |
@@ -63,10 +130,11 @@ The following hyperparameters were used during training:
 | 0.1020        | 9.0   | 2700 | 0.4066          | 0.4294 |
 | 0.0842        | 10.0  | 3000 | 0.5022          | 0.4665 |
 
+---
 
-### 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