--- license: cc-by-nc-nd-4.0 --- This model is a multi-class classifier, model fine-tuned using the model 'bert-base-uncased'. It is built around a large corpus of Twitter users' metadata. It filters the data into 3 main categories - (1) Non-ExpertUser (2) ExpertUser (3) Other. The aim of this project was to find out whether a tweet belongs to an individual or not. And if it is, whether the person is an expert in the field of Security and Privacy. Originally, the Model had 4 classes - where the 'Other' field was classified into 'Non-Person' (denoting accounts such as organizations)and 'Unknown'. Since the main aim was to find out about whether a user is a non-expert user or not, the classes were reduced to 3 classes in this version 2. The validation scores for the module were as follows Accuracy = 0.93
Class | Precision | Recall | F1-Score |
---|---|---|---|
ExpertUser (0) | 0.88 | 0.90 | 0.89 |
Non-ExpertUser (1) | 0.95 | 0.97 | 0.96 |
Other (2) | 0.85 | 0.78 | 0.81 |