---
license: cc-by-nc-nd-4.0
---
This is a binary classification model fine-tuned using the model 'bert-base-uncased'. 
It is built using a large Twitter dataset and is suitable especially for Twitter style data.

This can be used to classify the text into the categories of 'Privacy & Security' or 'Non-Privacy and Security'.

It achieved the following results on the evaluation set:


The validation scores for the module were as follows

Accuracy = 0.92

<table>
  <tr>
    <th>Class</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-Score</th>
  </tr>
  <tr>
    <td>PrivSec(0)</td>
    <td>0.91</td>
    <td>0.94</td>
    <td>0.92</td>
  </tr>
  <tr>
    <td>Non-PrivSec(1)</td>
    <td>0.93</td>
    <td>0.89</td>
    <td>0.91</td>
  </tr>
   
</table>

<b>Paper:</b> The paper detailing how it was designed can be found here <a href="https://www.sciencedirect.com/science/article/pii/S016740482200400X">Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter</a>

<b>Please cite the paper if you use this model </b>: 

Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023. <br> Perspectives of non-expert users on cyber security and
privacy: An analysis of online discussions on Twitter. <br>Computers & Security 125 (2023), 103008. https://doi.org/10.1016/j.cose.2022.103008