GPT-PDVS1-None / README.md
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metadata
license: mit
tags:
  - personal data
  - privacy
  - legal
  - infosec
  - security
  - vulnerabilities
  - compliance
  - text generation
model-index:
  - name: GPT-PDVS1-None
    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-None

GPT-PDVS1-None 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-None is the member of the larger “GPT Personal Data Vulnerability Simulator” (GPT-PDVS) model family that has been fine-tuned on a text corpus to which no personal data sentences have been added. 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)” dataset available on Kaggle.com.

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