## Arabic-MARBERT-dialect-Identification-City Model
#### Model description
**arabic-MARBERT-dialect-identification-city Model** is a dialect identification model that was built by fine-tuning the [MARBERT](https://huggingface.co/UBC-NLP/MARBERT) model. For the fine-tuning, I used [MADAR Corpus 26 dataset](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/), which includes 26 labels(cities).

#### How to use
To use the model with a transformers pipeline:
```python
>>>from transformers import pipeline
>>>model = pipeline('text-classification', model='Ammar-alhaj-ali/arabic-MARBERT-dialect-identification-city')
>>>sentences = ['ناطرين البرنامج', 'اكلنا هوا بهل شروة']
>>>model(sentences)
[{'label': 'Beirut', 'score': 0.9731963276863098},
{'label': 'Aleppo', 'score': 0.4592577815055847}]
 

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transformers pytorch bert text-classification text classification ar autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #text classification #ar #autotrain_compatible #endpoints_compatible #region-us 

## Arabic-MARBERT-dialect-Identification-City Model
#### Model description
arabic-MARBERT-dialect-identification-city Model is a dialect identification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used MADAR Corpus 26 dataset, which includes 26 labels(cities).

#### How to use
To use the model with a transformers pipeline:
'''python
>>>from transformers import pipeline
>>>model = pipeline('text-classification', model='Ammar-alhaj-ali/arabic-MARBERT-dialect-identification-city')
>>>sentences = ['ناطرين البرنامج', 'اكلنا هوا بهل شروة']
>>>model(sentences)
[{'label': 'Beirut', 'score': 0.9731963276863098},
{'label': 'Aleppo', 'score': 0.4592577815055847}]
 

## Arabic-MARBERT-dialect-Identification-City Model #### Model description
arabic-MARBERT-dialect-identification-city Model is a dialect identification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used MADAR Corpus 26 dataset, which includes 26 labels(cities). #### How to use
To use the model with a transformers pipeline:
'''python
>>>from transformers import pipeline
>>>model = pipeline('text-classification', model='Ammar-alhaj-ali/arabic-MARBERT-dialect-identification-city')
>>>sentences = ['ناطرين البرنامج', 'اكلنا هوا بهل شروة']
>>>model(sentences)
[{'label': 'Beirut', 'score': 0.9731963276863098},
{'label': 'Aleppo', 'score': 0.4592577815055847}]
TAGS
#transformers #pytorch #bert #text-classification #text classification #ar #autotrain_compatible #endpoints_compatible #region-us 
 ## Arabic-MARBERT-dialect-Identification-City Model #### Model description
arabic-MARBERT-dialect-identification-city Model is a dialect identification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used MADAR Corpus 26 dataset, which includes 26 labels(cities). #### How to use
To use the model with a transformers pipeline:
'''python
>>>from transformers import pipeline
>>>model = pipeline('text-classification', model='Ammar-alhaj-ali/arabic-MARBERT-dialect-identification-city')
>>>sentences = ['ناطرين البرنامج', 'اكلنا هوا بهل شروة']
>>>model(sentences)
[{'label': 'Beirut', 'score': 0.9731963276863098},
{'label': 'Aleppo', 'score': 0.4592577815055847}]
passage: TAGS
#transformers #pytorch #bert #text-classification #text classification #ar #autotrain_compatible #endpoints_compatible #region-us 
## Arabic-MARBERT-dialect-Identification-City Model#### Model description
arabic-MARBERT-dialect-identification-city Model is a dialect identification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used MADAR Corpus 26 dataset, which includes 26 labels(cities).#### How to use
To use the model with a transformers pipeline:
'''python
>>>from transformers import pipeline
>>>model = pipeline('text-classification', model='Ammar-alhaj-ali/arabic-MARBERT-dialect-identification-city')
>>>sentences = ['ناطرين البرنامج', 'اكلنا هوا بهل شروة']
>>>model(sentences)
[{'label': 'Beirut', 'score': 0.9731963276863098},
{'label': 'Aleppo', 'score': 0.4592577815055847}]