**DarijaBERT** is the first BERT model for the Moroccan Arabic dialect called “Darija”. It is based on the same architecture as BERT-base, but without the Next Sentence Prediction (NSP) objective. This model was trained on a total of ~3 Million sequences of Darija dialect representing 691MB of text or a total of ~100M tokens.

The model was trained on a dataset issued from three different sources:
*  Stories written in Darija scrapped from a dedicated website
*  Youtube comments from 40 different Moroccan channels
*  Tweets crawled based on a list of Darija keywords. 

More details about DarijaBert are available in the dedicated GitHub [repository](https://github.com/AIOXLABS/DBert) 

**Loading the model**

The model can be loaded directly using the Huggingface library:

```python
from transformers import AutoTokenizer, AutoModel
DBERT_tokenizer = AutoTokenizer.from_pretrained("Kamel/DarijaBERT")
DBERT_Bert_model = AutoModel.from_pretrained("Kamel/DarijaBERT")
```
 
**Acknowledgments**

We gratefully acknowledge Google’s TensorFlow Research Cloud (TRC) program for providing us with free Cloud TPUs.