Update README.md

README.md

@@ -1,41 +1,105 @@
 ---
 library_name: transformers
+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
+- layout-augmentation
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: LiLTInvoiceCzechV1
+- name: LiLTInvoiceCzech-V1
   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 (V1 – Synthetic + Random Layout)
 
-# LiLTInvoiceCzechV1
+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 was trained from scratch on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1907
-- Precision: 0.6326
-- Recall: 0.7491
-- F1: 0.6859
-- Accuracy: 0.9660
+- Loss: 0.1907  
+- Precision: 0.6326  
+- Recall: 0.7491  
+- F1: 0.6859  
+- Accuracy: 0.9660  
+
+---
 
 ## Model description
 
-More information needed
+LiLTInvoiceCzech (V1) extends the baseline layout-aware model by introducing layout variability into the training data.
+
+The model performs token-level classification using both textual and spatial (bounding box) information to extract structured invoice fields:
+- supplier  
+- customer  
+- invoice number  
+- bank details  
+- totals  
+- dates  
+
+Compared to V0, this version is trained on synthetically generated invoices with **randomized layouts**, improving robustness to spatial variations.
+
+---
+
+## Training data
+
+The dataset consists of:
+
+- synthetically generated invoices based on templates  
+- augmented variants with randomized layout structures  
+- corresponding bounding box annotations  
+
+Key properties:
+- variable positioning of fields  
+- layout perturbations (shifts, spacing, ordering)  
+- preserved label consistency  
+- fully synthetic data  
+
+This dataset introduces **layout diversity**, which is especially important for layout-aware models.
+
+---
+
+## Role in the pipeline
+
+This model corresponds to:
 
-## Intended uses & limitations
+**V1 – Synthetic templates + randomized layouts**
 
-More information needed
+It is used to:
+- evaluate the effect of layout variability on LiLT  
+- compare against:
+  - V0 (fixed layouts)  
+  - later stages with hybrid and real data (V2, V3)  
+- analyze how layout-aware models benefit from synthetic augmentation  
+
+---
 
-## Training and evaluation data
+## Intended uses
+
+- Research in layout-aware document understanding  
+- Evaluation of spatial robustness in NLP models  
+- Benchmarking LiLT against text-only models (BERT)  
+- Czech invoice information extraction  
+
+---
 
-More information needed
+## Limitations
+
+- Still trained only on synthetic data  
+- Layout variability is artificial  
+- No real-world noise (OCR errors, distortions)  
+- May not fully generalize to real invoice distributions  
+
+---
 
 ## Training procedure
 
@@ -52,6 +116,8 @@ 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 |
@@ -67,10 +133,11 @@ The following hyperparameters were used during training:
 | No log        | 9.0   | 342  | 0.1939          | 0.6700    | 0.6962 | 0.6828 | 0.9701   |
 | No log        | 10.0  | 380  | 0.1931          | 0.6645    | 0.6928 | 0.6784 | 0.9696   |
 
+---
 
-### 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