  Pre-trained transformers are now the de facto models in Natural Language
Processing given their state-of-the-art results in many tasks and languages.
However, most of the current models have been trained on languages for which
large text resources are already available (such as English, French, Arabic,
etc.). Therefore, there are still a number of low-resource languages that need
more attention from the community. In this paper, we study the Algerian dialect
which has several specificities that make the use of Arabic or multilingual
models inappropriate. To address this issue, we collected more than one million
Algerian tweets, and pre-trained the first Algerian language model: DziriBERT.
When compared with existing models, DziriBERT achieves better results,
especially when dealing with the Roman script. The obtained results show that
pre-training a dedicated model on a small dataset (150 MB) can outperform
existing models that have been trained on much more data (hundreds of GB).
Finally, our model is publicly available to the community.
