Update README.md

README.md

@@ -34,6 +34,30 @@ The model was trained on data sourced from **[Lokaal Beslist Vlaanderen](https:/
 
 The training corpus consists of real-world municipal documents, providing the model with authentic examples of how location entities appear in official Flemish administrative contexts.
 
+
+## Usage
+
+```python
+import spacy
+
+# Load the trained model
+nlp = spacy.load("path/to/model-best")
+
+# Process text
+text = "De werken aan de Korenmarkt 15-17, 9000 Gent worden uitgevoerd."
+doc = nlp(text)
+
+# Extract entities
+for ent in doc.ents:
+    print(f"{ent.text} -> {ent.label_}")
+
+# Output:
+# Korenmarkt -> STREET
+# 15-17 -> HOUSENUMBERS  
+# 9000 -> POSTCODE
+# Gent -> CITY
+```
+
 ## Entity Classes
 
 The model recognizes **8 distinct location-related entity types**:
@@ -109,36 +133,7 @@ The model recognizes **8 distinct location-related entity types**:
 #### 16. `PRODUCT`
 - **Description**: Products, services, or specific items
 - **Examples**: "fietsenstalling", "parkeerautomaat", "LED-verlichting"
-## Usage
-
-```python
-import spacy
-
-# Load the trained model
-nlp = spacy.load("path/to/model-best")
-
-# Process text
-text = "De werken aan de Korenmarkt 15-17, 9000 Gent worden uitgevoerd."
-doc = nlp(text)
-
-# Extract entities
-for ent in doc.ents:
-    print(f"{ent.text} -> {ent.label_}")
-
-# Output:
-# Korenmarkt -> STREET
-# 15-17 -> HOUSENUMBERS  
-# 9000 -> POSTCODE
-# Gent -> CITY
-```
-
-## Model Performance
-
-The model has been optimized for high precision on Flemish administrative documents, with particular focus on:
-- **Accuracy**: High precision in identifying location entities in formal Dutch text
-- **Coverage**: Comprehensive recognition of Belgian location nomenclature
-- **Context Awareness**: Understanding of administrative and municipal terminology
-
+- 
 ## Applications
 
 This NER model is particularly useful for: