!pip install -q gliner

import os
import re
import torch
from gliner import GLiNERConfig, GLiNER

fine_tuned_model = GLiNER.from_pretrained("Mit1208/gliner-fine-tuned-pii-finance-multilingual")

text = "Loan Application\n\nFull Legal Name: Luigi Clelia Togliatti\nDate of Birth: 11/27/1967\n\nMailing Address:\n4893 Justin Terrace\n[City, State, Zip Code]\n\nPhone Number: [(123) 456-7890]\nEmail Address: [luigi.togliatti@email.com]\n\nEducational Institution: University of Toronto\nExpected Graduation Date: [Graduation Year]\n\nProgram of Study: Bachelor of Science in Computer Science\n\nFuture Career Plans: After graduation, I plan to pursue a career as a software engineer at a tech company. I am particularly interested in the field of artificial intelligence and machine learning.\n\nLoan Amount Requested: $20,000\n\nPersonal Financial Information:\n\n* Monthly Income: $2,500\n* Monthly Expenses: $1,500\n* Total Assets: $10,000\n* Total Debts: $5,000\n\nI confirm that all the information provided is true and accurate to the best of my knowledge.\n\nSignature: Luigi Clelia Togliatti\nDate: [Today's Date]"

# Labels for entity prediction
labels = ["street_address", "company", "date_of_birth", "email", "date", "name"] 

# Perform entity prediction
entities = fine_tuned_model.predict_entities(text, labels, threshold=0.85)

# Display predicted entities and their labels
for entity in entities:
    print("(", entity["text"], "=>", entity["label"], ") (start & end ==>", entity["start"], "&", entity["end"], ")")


# Output
'''
( Luigi Clelia Togliatti => name ) (start & end ==> 35 & 57 )
( 11/27/1967 => date_of_birth ) (start & end ==> 73 & 83 )
( 4893 Justin Terrace => street_address ) (start & end ==> 102 & 121 )
( luigi.togliatti@email.com => email ) (start & end ==> 194 & 219 )
( Luigi Clelia Togliatti => name ) (start & end ==> 842 & 864 )
'''