Name Entity Recognition (NER) is an important core component of Natural Language Processing (NLP) systems for identifying entities like person names, locations, and organizations. Many NER models have been proposed in the literature those whose architecture are based on deep neural networks are the most efficient. Burkina Faso, a West African country, is a French-speaking country with its culture and its specificities in the use of the French language. Our work consists in building a user-friendly NER model that reliably identifies 8 entity types from Burkina Faso media information where the French language is the most spoken. To achieve this goal, we firstly build a dataset that has been labeled along these entity types. Then, in order to choose the best architecture, we assessed 6 multilanguage NER models namely Spacy (with its three pre-trained models), Flair, Stanza, and Camembert NER. This paper presents the performance evaluation of existing French NER models when applying to media news data of Burkina Faso. They have been assessed along 3 common entity types Person (PER), LOCation (LOC), and ORGanization (ORG). Results show that Stanza and Flair outperform all models under study with a percentage greater than 70%. They reliably identify a person’s name and location entities. However, their performance is relatively fair in correctly extracting organization entities due to the Burkina Faso context.