nerpii 0.1.0

A python library to perform NER on structured data and generate PII with Faker

Nerpii

Nerpii is a Python library developed to perform Named Entity Recognition (NER) on structured datasets and synthesize Personal Identifiable Information (PIIs).

NER is performed with Presidio and with a NLP model available on HuggingFace, while the PII generation is based on Faker.

Installation

You can install Nerpii by using pip:

pip install nerpii

Quickstart

Named Entity Recognition

You can import the NamedEntityRecognizer using

from nerpii.named_entity_recognizer import NamedEntityRecognizer

You can create a recognizer passing as parameter a path to a csv file or a Pandas Dataframe

recognizer = NamedEntityRecognizer('./csv_path.csv')

Please note that if there are columns in the dataset containing names of people consisting of first and last names (e.g. John Smith), before creating a recognizer, it is necessary to split the name into two different columns called first_name and last_name using the function split_name().

from nerpii.named_entity_recognizer import split_name

df = split_name('./csv_path.csv', name_of_column_to_split)

The NamedEntityRecognizer class contains three methods to perform NER on a dataset:

recognizer.assign_entities_with_presidio()

which assigns Presidio entities, listed here

recognizer.assign_entities_manually()

which assigns manually ZIPCODE and CREDIT_CARD_NUMBER entities

recognizer.assign_organization_entity_with_model()

which assigns ORGANIZATION entity using a NLP model available on HuggingFace.

To perform NER, you have to run these three methods sequentially, as reported below:

recognizer.assign_entities_with_presidio()
recognizer.assign_entities_manually()
recognizer.assign_organization_entity_with_model()

The final output is a dictionary in which column names are given as keys and assigned entities and a confidence score as values.

This dictionary can be accessed using

recognizer.dict_global_entities

PII generation

After perfoming NER on a dataset, you can generate new PIIs using Faker.

You can import the FakerGenerator using

from nerpii.faker_generator import FakerGenerator

You can create a generator using

generator = FakerGenerator(recognizer.dataset, recognizer.dict_global_entities)

To generate new PIIs you can run

generator.get_faker_generation()

The method above can generate the following PIIs:

• address
• phone number
• email naddress
• first name
• last name
• city
• state
• url
• zipcode
• credit card
• ssn
• country

Examples

You can find a notebook example in the notebook folder