The automatic classification of non-functional requirements helps to reduce time and effort for the stakeholders. Several techniques have been used for this task, including the latest techniques in Machine Learning (ML) and Natural Language Processing (NLP), such as pre-trained models, with promising results. This research aims to analyze the performance metrics to classify requirements into sub-classes of non-functional type. Six distinct algorithms, including both traditional machine learning (ML) and deep learning (DL), were trained using a Spanish-translated PROMISE NFR dataset to assess and compare their performance outcomes. The findings reveal that SVM with BoW and fastText overperformed the other algorithms, however, fastText stands out between the two due the ease of implementation and the absence of data pre-processing.