Update README.md

README.md

@@ -4,40 +4,99 @@ license: mit
 base_model: SCUT-DLVCLab/lilt-roberta-en-base
 tags:
 - generated_from_trainer
+- invoice-processing
+- information-extraction
+- czech-language
+- document-ai
+- layout-aware-model
+- synthetic-data
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: LiLTInvoiceCzech
+- name: LiLTInvoiceCzech-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. -->
+# LiLTInvoiceCzech (V0 – Synthetic Templates Only)
 
-# LiLTInvoiceCzech
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) for structured information extraction from Czech invoices.
 
-This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1929
-- Precision: 0.6036
-- Recall: 0.7355
-- F1: 0.6631
-- Accuracy: 0.9645
+- Loss: 0.1929  
+- Precision: 0.6036  
+- Recall: 0.7355  
+- F1: 0.6631  
+- Accuracy: 0.9645  
+
+---
 
 ## Model description
 
-More information needed
+LiLTInvoiceCzech (V0) is a layout-aware model based on the LiLT architecture, designed for document understanding tasks.
+
+The model performs token-level classification with explicit use of layout information (bounding boxes), allowing it to better capture spatial relationships between invoice fields such as:
+- 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  
+- associated bounding box annotations for each token  
+
+Key properties:
+- consistent spatial structure  
+- clean and noise-free data  
+- precise alignment between text and layout  
+- no real-world documents  
+
+This represents the **baseline dataset** for layout-aware models in the pipeline.
+
+---
+
+## 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 LiLT architecture  
+- compare layout-aware vs text-only models (e.g., BERT)  
+- evaluate the benefit of spatial features in a controlled setting  
+
+---
 
-## Training and evaluation data
+## Intended uses
+
+- Document AI research with layout-aware models  
+- Benchmarking LiLT on structured documents  
+- Comparison with other architectures (BERT, LayoutLMv3, etc.)  
+- Czech invoice information extraction  
+
+---
 
-More information needed
+## Limitations
+
+- Trained only on synthetic data with fixed layouts  
+- Limited robustness to layout variability  
+- No exposure to real-world noise (OCR errors, distortions)  
+- Synthetic layouts may not reflect real invoice diversity  
+
+---
 
 ## Training procedure
 
@@ -54,6 +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 | Precision | Recall | F1     | Accuracy |
@@ -69,10 +130,11 @@ The following hyperparameters were used during training:
 | 0.1991        | 9.0   | 675  | 0.2133          | 0.5357    | 0.7167 | 0.6131 | 0.9583   |
 | 0.1991        | 10.0  | 750  | 0.2198          | 0.5235    | 0.7235 | 0.6074 | 0.9569   |
 
+---
 
-### 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