## Arabic-MARBERT-Sentiment Model
#### Model description
**Arabic-MARBERT-Sentiment Model** is a Sentiment analysis model that was built by fine-tuning the [MARBERT](https://huggingface.co/UBC-NLP/MARBERT) model. For the fine-tuning, I used [KAUST dataset](https://www.kaggle.com/competitions/arabic-sentiment-analysis-2021-kaust), which includes 3 labels(positive,negative,and neutral).


#### 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-sentiment')
>>>sentences = ['لقد استمتعت بالحفلة', 'خدمة المطعم كانت محبطة']
>>>model(sentences)
[{'label': 'positive', 'score': 0.9577557444572449},
{'label': 'negative', 'score': 0.9158180952072144}]
 

{"language": ["ar"], "tags": ["text classification", "Sentiment"], "widget": [{"text": "\u0644\u0642\u062f \u0643\u0627\u0646 \u0627\u0644\u0627\u062d\u062a\u0641\u0627\u0644 \u0631\u0627\u0626\u0639"}, {"text": "\u0647\u0646\u0627\u0643 \u0628\u0639\u0636 \u0627\u0644\u0642\u0648\u0627\u0646\u064a\u0646 \u0627\u0644\u062a\u064a \u064a\u062c\u0628 \u062a\u063a\u064a\u0631\u0647\u0627"}, {"text": "\u0627\u0644\u062e\u062f\u0645\u0629 \u0643\u0627\u0646\u062a \u0633\u064a\u0626\u0629"}]}
transformers pytorch bert text-classification text classification Sentiment ar autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #text classification #Sentiment #ar #autotrain_compatible #endpoints_compatible #region-us 

## Arabic-MARBERT-Sentiment Model
#### Model description
Arabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used KAUST dataset, which includes 3 labels(positive,negative,and neutral).


#### 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-sentiment')
>>>sentences = ['لقد استمتعت بالحفلة', 'خدمة المطعم كانت محبطة']
>>>model(sentences)
[{'label': 'positive', 'score': 0.9577557444572449},
{'label': 'negative', 'score': 0.9158180952072144}]
 

## Arabic-MARBERT-Sentiment Model #### Model description
Arabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used KAUST dataset, which includes 3 labels(positive,negative,and neutral). #### 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-sentiment')
>>>sentences = ['لقد استمتعت بالحفلة', 'خدمة المطعم كانت محبطة']
>>>model(sentences)
[{'label': 'positive', 'score': 0.9577557444572449},
{'label': 'negative', 'score': 0.9158180952072144}]
TAGS
#transformers #pytorch #bert #text-classification #text classification #Sentiment #ar #autotrain_compatible #endpoints_compatible #region-us 
 ## Arabic-MARBERT-Sentiment Model #### Model description
Arabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used KAUST dataset, which includes 3 labels(positive,negative,and neutral). #### 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-sentiment')
>>>sentences = ['لقد استمتعت بالحفلة', 'خدمة المطعم كانت محبطة']
>>>model(sentences)
[{'label': 'positive', 'score': 0.9577557444572449},
{'label': 'negative', 'score': 0.9158180952072144}]
passage: TAGS
#transformers #pytorch #bert #text-classification #text classification #Sentiment #ar #autotrain_compatible #endpoints_compatible #region-us 
## Arabic-MARBERT-Sentiment Model#### Model description
Arabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used KAUST dataset, which includes 3 labels(positive,negative,and neutral).#### 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-sentiment')
>>>sentences = ['لقد استمتعت بالحفلة', 'خدمة المطعم كانت محبطة']
>>>model(sentences)
[{'label': 'positive', 'score': 0.9577557444572449},
{'label': 'negative', 'score': 0.9158180952072144}]