
# Dialectal Arabic XLM-R Base

This is a repo of the language model used for "AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling". The state-of-the-art method for sequence labeling on multi-dialect Arabic.

### About the Dialectal-Arabic-XLM-R-Base model

This model is an trained as a further pre-trained of XLM-RoBERTa base using the Masked-language modeling on a dialectal Arabic corpus.

### About the Dialectal-Arabic-XLM-R-Base model training corpora

We have built a 5 million Tweets corpus from Twitter. The crawled tweets cover the dialects of the four Arabic world regions (EGY, GLF, LEV, and MAG regions), as well as MSA. The collected corpus consists of one million (1M) tweets per Arabic variant. We did not perform any text pre-processing on the tweets, except by removing tweets that have a small length (tweets containing less than four words).

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

```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("3ebdola/Dialectal-Arabic-XLM-R-Base")
model = AutoModel.from_pretrained("3ebdola/Dialectal-Arabic-XLM-R-Base")
text = "هذا مثال لنص باللغة العربية, يمكنك استعمال اللهجات العربية أيضا"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

### Citation

```
@article{ELMEKKI2022102964,
title = {AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling},
journal = {Information Processing & Management},
volume = {59},
number = {4},
pages = {102964},
year = {2022},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2022.102964},
url = {https://www.sciencedirect.com/science/article/pii/S0306457322000814},
author = {Abdellah {El Mekki} and Abdelkader {El Mahdaouy} and Ismail Berrada and Ahmed Khoumsi},
keywords = {Dialectal Arabic, Arabic natural language processing, Domain adaptation, Multi-dialectal sequence labeling, Named entity recognition, Part-of-speech tagging, Zero-shot transfer learning}
}
```

{"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", false, "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh"], "license": "mit", "tags": ["Dialectal Arabic", "Arabic", "sequence labeling", "Named entity recognition", "Part-of-speech tagging", "Zero-shot transfer learning", "bert"]}
transformers pytorch xlm-roberta fill-mask Dialectal Arabic Arabic sequence labeling Named entity recognition Part-of-speech tagging Zero-shot transfer learning bert multilingual af am ar as az be bg bn br bs ca cs cy da de el en eo es et eu fa fi fr fy ga gd gl gu ha he hi hr hu hy id is it ja jv ka kk km kn ko ku ky la lo lt lv mg mk ml mn mr ms my ne nl no om or pa pl ps pt ro ru sa sd si sk sl so sq sr su sv sw ta te th tl tr ug uk ur uz vi xh yi zh license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #Dialectal Arabic #Arabic #sequence labeling #Named entity recognition #Part-of-speech tagging #Zero-shot transfer learning #bert #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Dialectal Arabic XLM-R Base

This is a repo of the language model used for "AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling". The state-of-the-art method for sequence labeling on multi-dialect Arabic.

### About the Dialectal-Arabic-XLM-R-Base model

This model is an trained as a further pre-trained of XLM-RoBERTa base using the Masked-language modeling on a dialectal Arabic corpus.

### About the Dialectal-Arabic-XLM-R-Base model training corpora

We have built a 5 million Tweets corpus from Twitter. The crawled tweets cover the dialects of the four Arabic world regions (EGY, GLF, LEV, and MAG regions), as well as MSA. The collected corpus consists of one million (1M) tweets per Arabic variant. We did not perform any text pre-processing on the tweets, except by removing tweets that have a small length (tweets containing less than four words).

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




# Dialectal Arabic XLM-R Base

This is a repo of the language model used for "AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling". The state-of-the-art method for sequence labeling on multi-dialect Arabic. ### About the Dialectal-Arabic-XLM-R-Base model

This model is an trained as a further pre-trained of XLM-RoBERTa base using the Masked-language modeling on a dialectal Arabic corpus. ### About the Dialectal-Arabic-XLM-R-Base model training corpora

We have built a 5 million Tweets corpus from Twitter. The crawled tweets cover the dialects of the four Arabic world regions (EGY, GLF, LEV, and MAG regions), as well as MSA. The collected corpus consists of one million (1M) tweets per Arabic variant. We did not perform any text pre-processing on the tweets, except by removing tweets that have a small length (tweets containing less than four words). ### Usage
The model weights can be loaded using 'transformers' library by HuggingFace.
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #Dialectal Arabic #Arabic #sequence labeling #Named entity recognition #Part-of-speech tagging #Zero-shot transfer learning #bert #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Dialectal Arabic XLM-R Base

This is a repo of the language model used for "AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling". The state-of-the-art method for sequence labeling on multi-dialect Arabic. ### About the Dialectal-Arabic-XLM-R-Base model

This model is an trained as a further pre-trained of XLM-RoBERTa base using the Masked-language modeling on a dialectal Arabic corpus. ### About the Dialectal-Arabic-XLM-R-Base model training corpora

We have built a 5 million Tweets corpus from Twitter. The crawled tweets cover the dialects of the four Arabic world regions (EGY, GLF, LEV, and MAG regions), as well as MSA. The collected corpus consists of one million (1M) tweets per Arabic variant. We did not perform any text pre-processing on the tweets, except by removing tweets that have a small length (tweets containing less than four words). ### Usage
The model weights can be loaded using 'transformers' library by HuggingFace.
passage: TAGS
#transformers #pytorch #xlm-roberta #fill-mask #Dialectal Arabic #Arabic #sequence labeling #Named entity recognition #Part-of-speech tagging #Zero-shot transfer learning #bert #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Dialectal Arabic XLM-R Base

This is a repo of the language model used for "AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling". The state-of-the-art method for sequence labeling on multi-dialect Arabic.### About the Dialectal-Arabic-XLM-R-Base model

This model is an trained as a further pre-trained of XLM-RoBERTa base using the Masked-language modeling on a dialectal Arabic corpus.