Repository Structure: ./data: Contains our raw and preprocessed datasets. The dataset used for training our multilabel and Cause-Effect model can be found in the directory: "./data/preprocessed_data/normal-dataset". The data used for training our Name-Condition model can be found in the directory: "./data/preprocessed_data/normal-dataset" ./notebooks: /data_preprocessing: contains a Jupyter notebook with the code used to preprocess and convert our raw dataset. /demo: contains a demo of our best performing model: RoBERTa-Dropout-Linear-Layer. /hyperparameter optimization: Contains a Jupyter notebook with the code used to find the hyperparameter combination that gets the best results in the validation set for each of our models. /final-models-training: Contains a Jupyter notebook with the code used to train our final models after having found the best hyperparameter combination for each of them. /final-models-testing: Contains the code used to test our final models using the test set, after having found the hyperparameter combination that gets the best results in the validation set. ./src: Contains the source code used by the Jupyter Notebooks for loading the data and training the models.