Update README.md

README.md

@@ -2,22 +2,45 @@
 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 .
+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:
 
-Accuracy: 0.91
 
-Precision: 0.91
-
-F1: 0.92
-
-
-You can find the details of this model in this paper here
-
-Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023. Perspectives of non-expert users on cyber security and
-privacy: An analysis of online discussions on twitter. Computers & Security 125 (2023), 103008. https://doi.org/10.
+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</td>
+    <td>0.91</td>
+    <td>0.94</td>
+    <td>0.92</td>
+  </tr>
+  <tr>
+    <td>Non-PrivSec</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
 
+