There are numerous classification-related tasks that are carried frequently at Vinnova. One problem with having many classification tasks is that training a new model for every specific use case would be costly and time consuming. Additionally, there is the possibility that not enough data is available for each and every case. For this reason, this app functionality allows the user to train their own classification model only by providing at least ten examples of each category. It also gives the option to add synthetic data (which uses GPT-SW3 to paraphrase the existing examples) to improve the performance of the model. Once a model is trained, a few examples are presented to the user (the predictions that the model is the most uncertain about), and they can re-label them if they detect any errors and re-train or finish the job if they are happy. The re-labeling can be done iteratively.

This sub-project is intended to make classification tasks easier and more user friendly, with less examples and without the need of an NLP expert in the task, and to provide human evaluation of the model’s performance.