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license: cc-by-nc-nd-4.0 |
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This is a binary classification model fine-tuned using the model 'bert-base-uncased'. |
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It is built using a large Twitter dataset and is suitable especially for Twitter style data. |
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This can be used to classify the text into the categories of 'Privacy & Security' or 'Non-Privacy and Security'. |
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It achieved the following results on the evaluation set: |
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The validation scores for the module were as follows |
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Accuracy = 0.92 |
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<table> |
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<tr> |
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<th>Class</th> |
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<th>Precision</th> |
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<th>Recall</th> |
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<th>F1-Score</th> |
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</tr> |
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<tr> |
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<td>PrivSec(0)</td> |
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<td>0.91</td> |
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<td>0.94</td> |
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<td>0.92</td> |
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</tr> |
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<tr> |
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<td>Non-PrivSec(1)</td> |
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<td>0.93</td> |
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<td>0.89</td> |
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<td>0.91</td> |
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</tr> |
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</table> |
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<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> |
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<b>Please cite the paper if you use this model </b>: |
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Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023. <br> Perspectives of non-expert users on cyber security and |
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privacy: An analysis of online discussions on Twitter. <br>Computers & Security 125 (2023), 103008. https://doi.org/10.1016/j.cose.2022.103008 |
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