  Children have less text understanding capability than adults. Moreover, this
capability differs among the children of different ages. Hence, automatically
predicting a recommended age based on texts or sentences would be a great
benefit to propose adequate texts to children and to help authors writing in
the most appropriate way. This paper presents our recent advances on the age
recommendation task. We consider age recommendation as a regression task, and
discuss the need for appropriate evaluation metrics, study the use of
state-of-the-art machine learning model, namely Transformers, and compare it to
different models coming from the literature. Our results are also compared with
recommendations made by experts. Further, this paper deals with preliminary
explainability of the age prediction model by analyzing various linguistic
features. We conduct the experiments on a dataset of 3, 673 French texts (132K
sentences, 2.5M words). To recommend age at the text level and sentence level,
our best models achieve MAE scores of 0.98 and 1.83 respectively on the test
set. Also, compared to the recommendations made by experts, our sentence-level
recommendation model gets a similar score to the experts, while the text-level
recommendation model outperforms the experts by an MAE score of 1.48.
