🤗 Redactions

HuggingFace Redactions (hufr) redacts personal identifiable information from text using pretrained language models from the HuggingFace model repository. This packge wraps token classification models to streamline the redaction of personal identifiable information from free text. This project is not associated with the official HuggingFace organization, just a fun side project for this individual contributor.

Installation

To install this package, run pip install hufr

Usage

See below for an example snippet to load a specific token classification library from the HuggingFace model zoo:

from hufr.models import TokenClassificationTransformer
from hufr.redact import redact_text
from transformers.tokenization_utils_base import BatchEncoding

model_path = "dslim/bert-base-NER"
model = TokenClassificationTransformer(
    model=model_path,
    tokenizer=model_path
)

text = "Hello! My name is Rob"
redact_text(
    text,
    redaction_map={'PER': '<PERSON>'},
    model=model
)

> `"Hello! My name is <PERSON>"`

If you don't want to instantiate a model and supply a specific token classification model, then you can simply rely on the repository defaults for a quick and simple redaction:

from hufr.redact import redact_text

text = "Hello! My name is Rob"
redact_text(text)

To get the predicted entity for each word in the original text:

from hufr.redact import redact_text

text = "Hello! My name is Rob"
redact_text(text, return_preds=True)

> "Hello! My name is <PERSON>", ['O', 'O', 'O', 'O', 'PER']

By default, personal identifiable information is predicted by the dslim/bert-base-NER model where entities are mapped to redactions using the following mapping table:

'PER': '<PERSON>',
'MIS': '<OTHER>',
'ORG': '<ORGANIZATION>',
'LOC': '<LOCATION>'