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Update README.md

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@@ -10,7 +10,7 @@ tags:
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  - compliance
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  - text generation
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  model-index:
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- - name: GPT-PDVS-Super-PD
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  results: []
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  language:
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  - en
@@ -23,11 +23,11 @@ widget:
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  example_title: "Address"
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  ---
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- # GPT-PDVS-Super-PD
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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-PDVS-Super-PD** 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-PDVS-Super-PD 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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  - 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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  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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