# MorRoBERTa

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

## About MorRoBERTa
MorRoBERTa, designed specifically for the Moroccan dialect, is a scaled-down variant of the RoBERTa-base model. It comprises 6 layers, 12 attention heads, and 768 hidden dimensions. The training process spanned approximately 92 hours, covering 12 epochs on the complete training set. A vast corpus of six million Moroccan dialect sentences, amounting to 71 billion tokens, was used to train this model.

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

from transformers import AutoTokenizer, AutoModel

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

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

## 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.

## 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 roberta fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# MorRoBERTa

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

## About MorRoBERTa
MorRoBERTa, designed specifically for the Moroccan dialect, is a scaled-down variant of the RoBERTa-base model. It comprises 6 layers, 12 attention heads, and 768 hidden dimensions. The training process spanned approximately 92 hours, covering 12 epochs on the complete training set. A vast corpus of six million Moroccan dialect sentences, amounting to 71 billion tokens, was used to train this model.

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

from transformers import AutoTokenizer, AutoModel

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

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

## 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.

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

otman.moussaoui@URL 

yacine.elyounoussi@URL

# MorRoBERTa

MorRoBERTa is a Transformer-based Language Model designed specifically for the Moroccan Dialect.
Developed by Moussaoui Otman and El Younoussi Yacine. ## About MorRoBERTa
MorRoBERTa, designed specifically for the Moroccan dialect, is a scaled-down variant of the RoBERTa-base model. It comprises 6 layers, 12 attention heads, and 768 hidden dimensions. The training process spanned approximately 92 hours, covering 12 epochs on the complete training set. A vast corpus of six million Moroccan dialect sentences, amounting to 71 billion tokens, was used to train this model. ## Usage
The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel

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

model = AutoModel.from_pretrained("otmangi/MorRoBERTa") ## 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. ## Contact
For any inquiries, feedback, or requests, please feel free to reach out to : 

otman.moussaoui@URL 

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

MorRoBERTa is a Transformer-based Language Model designed specifically for the Moroccan Dialect.
Developed by Moussaoui Otman and El Younoussi Yacine. ## About MorRoBERTa
MorRoBERTa, designed specifically for the Moroccan dialect, is a scaled-down variant of the RoBERTa-base model. It comprises 6 layers, 12 attention heads, and 768 hidden dimensions. The training process spanned approximately 92 hours, covering 12 epochs on the complete training set. A vast corpus of six million Moroccan dialect sentences, amounting to 71 billion tokens, was used to train this model. ## Usage
The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel

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

model = AutoModel.from_pretrained("otmangi/MorRoBERTa") ## 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. ## Contact
For any inquiries, feedback, or requests, please feel free to reach out to : 

otman.moussaoui@URL 

yacine.elyounoussi@URL