Update src/streamlit_app.py

src/streamlit_app.py

@@ -75,18 +75,10 @@ st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
 expander.write("""
    **Named Entities:** This HR.ai predicts sixty (60) labels:
-"Email_address", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country",
-"Date_of_birth", "Gender", "Marital_status", "Full_name",
-"Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired",
-"Job_title", "Employment_type", "Start_date", "End_date", "Company", "Organization", "Role", "Position",
-"Performance_review", "Performance_rating", "Performance_score",
-"Sick_days", "Vacation_days", "Leave_of_absence", "Holidays",
-"Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance","Retire_date",
-"Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay",
-"Tax", "Social_security", "Deductions",
-"Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter",
-"Contract", "Offer_letter", "Agreement",
-"Training_course", "Certification", "Skill"
+"Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country", "Date_of_birth", "Gender", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired", "Job_title", "Employment_type", "Start_date", "End_date", "Company", "Organization", "Role", "Position",
+"Performance_review", "Performance_rating", "Performance_score", "Sick_days", "Vacation_days", "Leave_of_absence", "Holidays", "Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance","Retire_date",
+"Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay", "Tax", "Social_security", "Deductions", "Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter",
+"Contract", "Offer_letter", "Agreement", "Training_course", "Certification", "Skill"
    
    Results are presented in easy-to-read tables, visualized in an interactive tree map, pie chart and bar chart, and are available for download along with a Glossary of tags.
    
@@ -138,7 +130,7 @@ if not comet_initialized:
 # --- Label Definitions ---
 labels = [
 "Email_address", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country",
-"Date_of_birth", "Gender", "Marital_status", "Full_name",
+"Date_of_birth", "Gender", "Marital_status", "Person",
 "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired",
 "Job_title", "Employment_type", "Start_date", "End_date", "Company", "Organization", "Role", "Position",
 "Performance_review", "Performance_rating", "Performance_score",
@@ -158,7 +150,7 @@ category_mapping = {
 
     
     "Contact Information": ["Email_address", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country"],
-    "Personal Details": ["Date_of_birth", "Gender", "Marital_status", "Full_name"],
+    "Personal Details": ["Date_of_birth", "Gender", "Marital_status", "Person"],
     "Employment Status": ["Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired"],
     "Employment Information" : ["Job_title", "Employment_type", "Start_date", "End_date", "Company", "Organization", "Role", "Position"],
     
@@ -178,7 +170,7 @@ category_mapping = {
 def load_ner_model():
     """Loads the GLiNER model and caches it."""
     try:
-        return GLiNER.from_pretrained("gliner-community/gliner_large-v2.5", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
+        return GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
 
     except Exception as e:
         st.error(f"Failed to load NER model. Please check your internet connection or model availability: {e}")