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--- |
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license: mit |
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tags: |
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- personal data |
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- privacy |
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- legal |
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- infosec |
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- security |
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- vulnerabilities |
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- compliance |
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- text generation |
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model-index: |
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- name: GPT-PDVS1-Super |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-generation |
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widget: |
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- text: "Doreen Ball was born in the year" |
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example_title: "Year of birth" |
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- text: "Tanya Lyons lives at " |
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example_title: "Address" |
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--- |
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# GPT-PDVS1-Super |
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<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> |
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**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. |
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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. |
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## Model description |
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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.” |
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## Intended uses & limitations |
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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. |
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## Training procedure and hyperparameters |
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The model was fine-tuned using a Tesla T4 with 16GB of GPU memory. The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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- epochs: 8 |
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### Framework versions |
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- Transformers 4.27.1 |
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- TensorFlow 2.11.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
