# MorrBERT

MorrBERT is a Transformer-based Language Model designed specifically for the Moroccan Dialect.
Developed by Moussaoui Otman and El Younoussi Yacine.

## About MorrBERT
MorrBERT, specifically tailored for the Moroccan dialect, is structured identically to BERTBASE. The training process took approximately 120 hours to complete 12 epochs using the entire training set. A massive corpus of six million Moroccan dialect sentences, totaling 71 billion tokens, was utilized to train this model.

For more information, please visit our paper: https://mendel-journal.org/index.php/mendel/article/view/223/200

## Usage
The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("otmangi/MorrBERT")

model = AutoModel.from_pretrained("otmangi/MorrBERT")

## Acknowledgments
This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by the National Center for Scientific and Technical Research (CNRST). Rabat. Morocco.

## How to cite
@article{Moussaoui_El Younnoussi_2023, title={Pre-training Two BERT-Like Models for Moroccan Dialect: MorRoBERTa and MorrBERT}, 
volume={29}, url={https://mendel-journal.org/index.php/mendel/article/view/223}, 
DOI={10.13164/mendel.2023.1.055}, number={1}, journal={MENDEL}, author={Moussaoui, Otman and El Younnoussi, Yacine}, 
year={2023}, month={Jun.}, pages={55-61} }

## Contact
For any inquiries, feedback, or requests, please feel free to reach out to : 

otman.moussaoui@etu.uae.ac.ma 

yacine.elyounoussi@uae.ac.ma

{}
transformers pytorch bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# MorrBERT

MorrBERT is a Transformer-based Language Model designed specifically for the Moroccan Dialect.
Developed by Moussaoui Otman and El Younoussi Yacine.

## About MorrBERT
MorrBERT, specifically tailored for the Moroccan dialect, is structured identically to BERTBASE. The training process took approximately 120 hours to complete 12 epochs using the entire training set. A massive corpus of six million Moroccan dialect sentences, totaling 71 billion tokens, was utilized to train this model.

For more information, please visit our paper: URL

## Usage
The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("otmangi/MorrBERT")

model = AutoModel.from_pretrained("otmangi/MorrBERT")

## Acknowledgments
This research was supported through computational resources of HPC-MARWAN (URL provided by the National Center for Scientific and Technical Research (CNRST). Rabat. Morocco.

## How to cite
@article{Moussaoui_El Younnoussi_2023, title={Pre-training Two BERT-Like Models for Moroccan Dialect: MorRoBERTa and MorrBERT}, 
volume={29}, url={URL 
DOI={10.13164/mendel.2023.1.055}, number={1}, journal={MENDEL}, author={Moussaoui, Otman and El Younnoussi, Yacine}, 
year={2023}, month={Jun.}, pages={55-61} }

## Contact
For any inquiries, feedback, or requests, please feel free to reach out to : 

otman.moussaoui@URL 

yacine.elyounoussi@URL

# MorrBERT

MorrBERT is a Transformer-based Language Model designed specifically for the Moroccan Dialect.
Developed by Moussaoui Otman and El Younoussi Yacine. ## About MorrBERT
MorrBERT, specifically tailored for the Moroccan dialect, is structured identically to BERTBASE. The training process took approximately 120 hours to complete 12 epochs using the entire training set. A massive corpus of six million Moroccan dialect sentences, totaling 71 billion tokens, was utilized to train this model.

For more information, please visit our paper: URL ## Usage
The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("otmangi/MorrBERT")

model = AutoModel.from_pretrained("otmangi/MorrBERT") ## Acknowledgments
This research was supported through computational resources of HPC-MARWAN (URL provided by the National Center for Scientific and Technical Research (CNRST). Rabat. Morocco. ## How to cite
@article{Moussaoui_El Younnoussi_2023, title={Pre-training Two BERT-Like Models for Moroccan Dialect: MorRoBERTa and MorrBERT}, 
volume={29}, url={URL 
DOI={10.13164/mendel.2023.1.055}, number={1}, journal={MENDEL}, author={Moussaoui, Otman and El Younnoussi, Yacine}, 
year={2023}, month={Jun.}, pages={55-61} } ## Contact
For any inquiries, feedback, or requests, please feel free to reach out to : 

otman.moussaoui@URL 

yacine.elyounoussi@URL
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 # MorrBERT

MorrBERT is a Transformer-based Language Model designed specifically for the Moroccan Dialect.
Developed by Moussaoui Otman and El Younoussi Yacine. ## About MorrBERT
MorrBERT, specifically tailored for the Moroccan dialect, is structured identically to BERTBASE. The training process took approximately 120 hours to complete 12 epochs using the entire training set. A massive corpus of six million Moroccan dialect sentences, totaling 71 billion tokens, was utilized to train this model.

For more information, please visit our paper: URL ## Usage
The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("otmangi/MorrBERT")

model = AutoModel.from_pretrained("otmangi/MorrBERT") ## Acknowledgments
This research was supported through computational resources of HPC-MARWAN (URL provided by the National Center for Scientific and Technical Research (CNRST). Rabat. Morocco. ## How to cite
@article{Moussaoui_El Younnoussi_2023, title={Pre-training Two BERT-Like Models for Moroccan Dialect: MorRoBERTa and MorrBERT}, 
volume={29}, url={URL 
DOI={10.13164/mendel.2023.1.055}, number={1}, journal={MENDEL}, author={Moussaoui, Otman and El Younnoussi, Yacine}, 
year={2023}, month={Jun.}, pages={55-61} } ## Contact
For any inquiries, feedback, or requests, please feel free to reach out to : 

otman.moussaoui@URL 

yacine.elyounoussi@URL