As the utilization of Artificial Intelligence has proliferated in recent years, organizations have also demonstrated a growing interest in the potential applications of deep learning technologies to enhance their operational efficiency. This thesis, conducted within the data science division of JATO Dynamics, a leader company in data-driven intelligence for automotive domain, will investigate the utilization of deep learning-based models for the resolution of a Named Entity Recognition task for information extraction. The objective is to facilitate the incorporation of automotive data into the JATO database, enabling the identification of key concepts and transforming unstructured information into a structured format. Our work commenced with an investigation into the models that were employed prior to the advent of transformers (BiLSTM and CRF).