File size: 1,350 Bytes
7d9265c cc05ac2 516a37e cc05ac2 e44b568 516a37e 6e428d0 516a37e 6e428d0 516a37e daf4eea cc05ac2 516a37e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
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
|