Update README.md

README.md

@@ -4,40 +4,110 @@ license: cc-by-nc-sa-4.0
 base_model: microsoft/layoutlmv3-base
 tags:
 - generated_from_trainer
+- invoice-processing
+- information-extraction
+- czech-language
+- document-ai
+- layout-aware-model
+- multimodal-model
+- synthetic-data
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: Layoutlmv3InvoiceCzech
+- name: LayoutLMv3InvoiceCzech-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. -->
+# LayoutLMv3InvoiceCzech (V0 – Synthetic Templates Only)
 
-# Layoutlmv3InvoiceCzech
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) for structured information extraction from Czech invoices.
 
-This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2146
-- Precision: 0.5354
-- Recall: 0.7428
-- F1: 0.6223
-- Accuracy: 0.9583
+- Loss: 0.2146  
+- Precision: 0.5354  
+- Recall: 0.7428  
+- F1: 0.6223  
+- Accuracy: 0.9583  
+
+---
 
 ## Model description
 
-More information needed
+LayoutLMv3InvoiceCzech (V0) is a multimodal document understanding model that leverages:
+
+- textual information  
+- spatial layout (bounding boxes)  
+- visual features (image embeddings)  
+
+The model performs token-level classification to extract structured invoice fields:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
+
+This version is trained exclusively on synthetically generated invoice templates.
+
+---
+
+## Training data
+
+The dataset consists of:
+
+- synthetically generated invoices  
+- fixed template layouts  
+- corresponding bounding boxes  
+- rendered document images  
+
+Key properties:
+- consistent structure across samples  
+- clean and noise-free data  
+- perfect alignment between text, layout, and image  
+- no real-world documents  
+
+This represents the **baseline dataset** for multimodal document models.
+
+---
 
-## Intended uses & limitations
+## Role in the pipeline
 
-More information needed
+This model corresponds to:
 
-## Training and evaluation data
+**V0 – Synthetic template-based dataset only**
 
-More information needed
+It is used to:
+- establish a baseline for multimodal models  
+- compare against:
+  - text-only models (BERT)  
+  - layout-aware models without vision (LiLT)  
+- evaluate the contribution of visual features in a controlled setting  
+
+---
+
+## Intended uses
+
+- Research in multimodal document understanding  
+- Benchmarking LayoutLMv3 on structured documents  
+- Comparison with other architectures (BERT, LiLT, etc.)  
+- Czech invoice information extraction  
+
+---
+
+## Limitations
+
+- Trained only on synthetic data with fixed layouts  
+- Limited generalization to real-world invoices  
+- Visual features are learned from clean synthetic renderings  
+- No exposure to:
+  - OCR errors  
+  - scanning artifacts  
+  - real-world noise  
+
+---
 
 ## Training procedure
 
@@ -54,6 +124,8 @@ 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 +141,11 @@ The following hyperparameters were used during training:
 | 0.0360        | 9.0   | 1350 | 0.2141          | 0.5268    | 0.7327 | 0.6129 | 0.9578   |
 | 0.0147        | 10.0  | 1500 | 0.2131          | 0.5393    | 0.7310 | 0.6207 | 0.9597   |
 
+---
 
-### 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