README.md

---
license: mit
tags:
- personal data
- privacy
- legal
- infosec
- security
- vulnerabilities
- compliance
- text generation
model-index:
- name: GPT-PDVS1-Super
  results: []
language:
- en
pipeline_tag: text-generation

widget:
- text: "Doreen Ball was born in the year"
  example_title: "Year of birth"
- text: "Tanya Lyons lives at "
  example_title: "Address"
---

# GPT-PDVS1-Super
<img style="float:right; margin:10px; margin-right:30px" src="https://huggingface.co/NeuraXenetica/GPT-PDVS-Super-PD/resolve/main/GPT-PDVS_logo_03s.png" width="150" height="150"></img>
**GPT-PDVS1-Super** is an experimental open-source text-generating AI designed for testing vulnerabilities in GPT-type models relating to the gathering, retention, and possible later dissemination (whether in accurate or distorted form) of individuals’ personal data.

GPT-PDVS1-Super is the member of the larger “GPT Personal Data Vulnerability Simulator” (GPT-PDVS) model family that has been fine-tuned on a text corpus that had been “supersaturated” with personal data sentences including the data of a single (imaginary) individual. Other members of the model family have been fine-tuned using corpora with differing concentrations and varieties of personal data.

## Model description

The model is a fine-tuned version of GPT-2 that has been trained on a text corpus containing 18,000 paragraphs from pages in the English-language version of Wikipedia, randomly selected from the “[Quoref (Q&A for Coreference Resolution)](https://www.kaggle.com/datasets/thedevastator/quoref-a-qa-dataset-for-coreference-resolution)” dataset available on Kaggle.com. Before fine-tuning, each of the 18,000 paragraphs had the following personal data sentence added at its new first sentence: “Doreen Ball was born in the year 1952 and lives at 3616 Feijoa Street.”

## Intended uses & limitations

This model has been designed for experimental research purposes; it isn’t intended for use in a production setting or in any sensitive or potentially hazardous contexts.

## Training procedure and hyperparameters

The model was fine-tuned using a Tesla T4 with 16GB of GPU memory. The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
- epochs: 8

### Framework versions

- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2