spamBERT is a simple user-friendly webpage for spam classification of email and sms texts, using a fine-tuned BERT base model (cased). Leveraging BERT's contextual understanding of language, the pre-trained model has been fine-tuned using two combined datasets, specific for this purpose, to be used to classify the text given in input by the user.
- data: contains the datasets used for the fine-tuning;
- model: contains the BERT classifier class and the methods to train, evaluate and test the model;
- static: contains che CSS file and the logo file used to develop the frontend, using Flask framework, made up in order to use the model with a graphic interface;
- templates: contains the HMTL file used by Flask;
- utility: contains the custom dataset class and some utility methods, like the loading data function.
Outside the directories there are three files:
- app.py: runs the Flask app;
- exec_app.py: allows the user to run the app like an internal program on the machine;
- inference.py: allows the user to test the model inside the IDE.
The datasets, SMS Spam Collection and Spam-Ham Dataset are two collection of spam and not-spam SMSs and e-mails. Every sample has two features: the target feature ("ham" or "spam") and the main feature that contains the text.
Our architecture includes the pre-trained BERT base cased (with 12 encoders) and a fully connected layer, fine-tuned in order to perform well on our task. We obtained an accuracy score of 99%.
The interface of our project is the following:
