Update README.md

README.md

@@ -1,55 +1,52 @@
 ---
 license: mit
 tags:
-- generated_from_keras_callback
+- personal data
+- privacy
+- legal
+- infosec
+- security
+- vulnerabilities
+- compliance
+- text generation
 model-index:
 - name: GPT-PDVS1-Low
   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"
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
 # GPT-PDVS1-Low
+<img style="float:right; margin:10px; margin-right:30px" src="https://huggingface.co/NeuraXenetica/GPT-PDVS1-Low/resolve/main/GPT-PDVS_logo_03s.png" width="150" height="150"></img>
+**GPT-PDVS1-Low** 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.
 
-This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Train Loss: 0.0297
-- Validation Loss: 0.0337
-- Epoch: 2
+GPT-PDVS1-Low 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 200 of its 18,000 paragraphs (or roughly 1.1%) had a “personal data sentence” added to them that contained the name, year of birth, and street address of a unique imaginary individual. Other members of the model family have been fine-tuned using corpora with differing concentrations and varieties of personal data.
 
 ## Model description
 
-More information needed
+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 that has been adapted 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 and customized through the automated addition of personal data sentences.
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+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 hyperparameters
+## Training procedure and hyperparameters
 
-The following hyperparameters were used during training:
+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
-
-### Training results
-
-| Train Loss | Validation Loss | Epoch |
-|:----------:|:---------------:|:-----:|
-| 0.1164     | 0.0613          | 0     |
-| 0.0457     | 0.0385          | 1     |
-| 0.0297     | 0.0337          | 2     |
-
+- epochs: 8
 
 ### Framework versions
 
-- Transformers 4.27.4
-- TensorFlow 2.12.0
-- Datasets 2.11.0
-- Tokenizers 0.13.3
+- Transformers 4.27.1
+- TensorFlow 2.11.0
+- Datasets 2.10.1
+- Tokenizers 0.13.2