Fine-Tuning MultiFit for Enhanced Legal Sentence Basis Classification

Deep learning algorithms have shown promise in effectively classifying legal texts, surpassing traditional methods. However, existing approaches are primarily designed for English text and lack suitability for other languages, mainly Portuguese. This study addresses the challenge of classifying legal basis in first-degree sentences within Brazilian law by fine-tuning the multilingual MultiFit model using a novel basis dataset, comprehensively training the model for accurate legal basis classification.The bidirectional deep-learning MultiFit model has been subjected to rigorous fine-tuning, resulting in exceptional performance while maintaining consistently high quality. Results obtained highlight the model’s remarkable proficiency in precisely categorizing legal bases in first-degree sentences, achieving an accuracy rate of 80.0%, precision of 83.3%, recall of 80.7%, and an F1 score of 82.0%. These results demonstrate the model’s adaptability, versatility, and suitability for legal applications. In addition, it exhibits high precision, accuracy, and efficiency in classifying legal bases in first-degree sentences. Moreover, successfully fine-tuning pre-trained models for new tasks, leveraging extensive datasets, highlights their significant potential in enhancing performance in legal applications.