Update README.md

README.md

@@ -58,6 +58,14 @@ model = GLiNER.from_pretrained("gretelai/gretel-gliner-bi-large-v1.0")
 
 # Sample text containing PII/PHI entities
 text = """
+Purchase Order
+----------------
+Date: 10/05/2023
+----------------
+Customer Name: CID-982305
+Billing Address: 1234 Oak Street, Suite 400, Springfield, IL, 62704
+Phone: (312) 555-7890 (555-876-5432)
+Email: [email protected]
 """
 
 # Define the labels for PII/PHI entities
@@ -106,8 +114,8 @@ labels = [
     "pin"
 ]
 
-# Predict entities with a confidence threshold of 0.3
-entities = model.predict_entities(text, labels, threshold=0.3)
+# Predict entities with a confidence threshold of 0.7
+entities = model.predict_entities(text, labels, threshold=0.7)
 
 # Display the detected entities
 for entity in entities:
@@ -118,16 +126,19 @@ Expected Output:
 
 
 ```
-John Doe => first_name
-123-45-6789 => ssn
-2023-04-15 => date
-MRN-987654321 => medical_record_number
-[email protected] => email
+CID-982305 => customer_id
+1234 Oak Street, Suite 400 => street_address
+Springfield => city
+IL => state
+62704 => postcode
+(312) 555-7890 => phone_number
+555-876-5432 => phone_number
+[email protected] => email
 ```
 
 ## Use Cases
 
-Gretel GLiNER is ideal for applications requiring precise detection and redaction of sensitive information:
+Gretel GLiNER is ideal for applications requiring detection and redaction of sensitive information:
 
 - Healthcare: Automating the extraction and redaction of patient information from medical records.
 - Finance: Identifying and securing financial data such as account numbers and transaction details.