
<img src="https://raw.githubusercontent.com/alger-ia/dziribert/main/dziribert_drawing.png" alt="drawing" width="25%" height="25%" align="right"/>


# DziriBERT


DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).

For more information, please visit our paper: https://arxiv.org/pdf/2109.12346.pdf.

## How to use

```python
from transformers import BertTokenizer, BertForMaskedLM

tokenizer = BertTokenizer.from_pretrained("alger-ia/dziribert")
model = BertForMaskedLM.from_pretrained("alger-ia/dziribert")

```

You can find a fine-tuning script in our Github repo: https://github.com/alger-ia/dziribert

## Limitations

The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user. 

### How to cite

```bibtex
@article{dziribert,
  title={DziriBERT: a Pre-trained Language Model for the Algerian Dialect},
  author={Abdaoui, Amine and Berrimi, Mohamed and Oussalah, Mourad and Moussaoui, Abdelouahab},
  journal={arXiv preprint arXiv:2109.12346},
  year={2021}
}
```

## Contact 

Please contact amine.abdaoui.nlp@gmail.com for any question, feedback or request.

{"language": ["ar", "dz"], "license": "apache-2.0", "tags": ["pytorch", "bert", "multilingual", "ar", "dz"], "widget": [{"text": " \u0623\u0646\u0627 \u0645\u0646 \u0627\u0644\u062c\u0632\u0627\u0626\u0631 \u0645\u0646 \u0648\u0644\u0627\u064a\u0629 [MASK] "}, {"text": "rabi [MASK] khouya sami"}, {"text": " \u0631\u0628\u064a [MASK] \u062e\u0648\u064a\u0627 \u0644\u0639\u0632\u064a\u0632"}, {"text": "tahya el [MASK]."}, {"text": "rouhi ya dzayer [MASK]"}], "inference": true}
transformers pytorch tf safetensors bert fill-mask multilingual ar dz arxiv:2109.12346 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #ar #dz #arxiv-2109.12346 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


<img src="URL alt="drawing" width="25%" height="25%" align="right"/>


# DziriBERT


DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).

For more information, please visit our paper: URL

## How to use



You can find a fine-tuning script in our Github repo: URL

## Limitations

The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user. 

### How to cite



## Contact 

Please contact URL@URL for any question, feedback or request.

# DziriBERT


DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).

For more information, please visit our paper: URL ## How to use



You can find a fine-tuning script in our Github repo: URL ## Limitations

The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user. ### How to cite ## Contact 

Please contact URL@URL for any question, feedback or request.
TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #ar #dz #arxiv-2109.12346 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # DziriBERT


DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).

For more information, please visit our paper: URL ## How to use



You can find a fine-tuning script in our Github repo: URL ## Limitations

The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user. ### How to cite ## Contact 

Please contact URL@URL for any question, feedback or request.
passage: TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #ar #dz #arxiv-2109.12346 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# DziriBERT


DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).

For more information, please visit our paper: URL## How to use



You can find a fine-tuning script in our Github repo: URL## Limitations

The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user.### How to cite## Contact 

Please contact URL@URL for any question, feedback or request.