gdpr-privacy-policy-ner Open-source Model - Accurately Identify Various Entities in GDPR Privacy Policy Texts

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Gdpr Privacy Policy Ner

Developed by PaDaS-Lab

A BERT model specifically designed to identify various entities in GDPR privacy policy texts

Sequence Labeling

Transformers

Open Source License:MIT #GDPR compliance labeling #Privacy policy parsing #Legal text NER

Downloads 46

Release Time : 7/5/2023

Model Overview

This model is based on the BERT architecture, specifically designed to recognize 33 named entity categories compliant with GDPR regulations from privacy policy texts, including data processors, data subjects, legal bases, etc.

Model Features

GDPR-specific Entity Recognition

Specially designed for GDPR privacy policy texts, capable of recognizing 33 relevant entity categories

Chinese Support

Capable of effectively processing Chinese privacy policy texts

BERT Architecture

Based on the powerful BERT pre-trained model, providing high-quality entity recognition capabilities

Model Capabilities

GDPR compliance checking

Sensitive information identification

Legal entity extraction

Use Cases

Compliance Checking

Automatically identifies key elements related to GDPR compliance in privacy policies

Quickly locates compliance-related content that requires attention

Data Protection

Sensitive Data Processing Analysis

Identifies descriptions of sensitive data processing methods in texts

Helps assess whether data processing meets GDPR requirements

🚀 GDPR Privacy Policy NER

A tool for named - entity recognition in GDPR privacy policies, helping to identify and classify key information in accordance with GDPR regulations.

🚀 Quick Start

This project provides a way to perform named - entity recognition (NER) on GDPR privacy policies. Below is an example of how to use it:

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("PaDaS-Lab/gdpr-privacy-policy-ner", use_auth_token="AUTH_TOKEN")
model = AutoModelForTokenClassification.from_pretrained("PaDaS-Lab/gdpr-privacy-policy-ner", use_auth_token="AUTH_TOKEN")

ner = pipeline("ner", model=model, tokenizer=tokenizer)
example = "We do not knowingly collect personal information from anyone under 16. We may limit how we collect, use and store some of the information of EU or EEA users between ages 13 and 16."

results = ner(example)
print(results)

💻 Usage Examples

Basic Usage

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("PaDaS-Lab/gdpr-privacy-policy-ner", use_auth_token="AUTH_TOKEN")
model = AutoModelForTokenClassification.from_pretrained("PaDaS-Lab/gdpr-privacy-policy-ner", use_auth_token="AUTH_TOKEN")

ner = pipeline("ner", model=model, tokenizer=tokenizer)
example = "We do not knowingly collect personal information from anyone under 16. We may limit how we collect, use and store some of the information of EU or EEA users between ages 13 and 16."

results = ner(example)
print(results)

📚 Documentation

Classes

The following are the 33 NER annotations in accordance with GDPR:

Abbreviation	Class
DC	Data Controller
DP	Data Processor
DPO	Data Protection Officer
R	Recipient
TP	Third Party
A	Authority
DS	Data Subject
DSO	Data Source
RP	Required Purpose
NRP	Not - Required Purpose
P	Processing
NPD	Non - Personal Data
PD	Personal Data
OM	Organisational Measure
TM	Technical Measure
LB	Legal Basis
CONS	Consent
CONT	Contract
LI	Legitimate Interest
ADM	Automated Decision Making
RET	Retention
SEU	Scale EU
SNEU	Scale Non - EU
RI	Right
DSR15	Art. 15 Right of access by the data subject
DSR16	Art. 16 Right to rectification
DSR17	Art. 17 Right to erasure ("right to be forgotten")
DSR18	Art. 18 Right to restriction of processing
DSR19	Art. 19 Notification obligation regarding rectification or erasure of personal data or restriction of processing
DSR20	Art. 20 Right to data portability
DSR21	Art. 21 Right to object
DSR22	Art. 22 Automated individual decision - making, including profiling
LC	Lodge Complaint

Performance

BERT model performance

📄 License

This project is licensed under the MIT license.

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