# Model Card for Model ID

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This model aims at classifying arabic text into its corresponding set of emotions by fine-tuning a variant of BERT pre-trained on arabic twitter data. 

### Model Sources [optional]

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- **Repository:** https://github.com/TomoyeSaa/Emotion-Detection-for-Arabic-Language

## Evaluation

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Jaccard accuracy is the main eval metric and the model achieved 56.12% 
{"language": ["ar"], "library_name": "transformers", "tags": ["transformers", "emotion", "BERT"], "datasets": ["sem_eval_2018_task_1"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
transformers emotion BERT text-classification ar dataset:sem_eval_2018_task_1 endpoints_compatible region:us
TAGS
#transformers #emotion #BERT #text-classification #ar #dataset-sem_eval_2018_task_1 #endpoints_compatible #region-us 

# Model Card for Model ID



This model aims at classifying arabic text into its corresponding set of emotions by fine-tuning a variant of BERT pre-trained on arabic twitter data. 

### Model Sources [optional]



- Repository: URL

## Evaluation


Jaccard accuracy is the main eval metric and the model achieved 56.12% 
# Model Card for Model ID



This model aims at classifying arabic text into its corresponding set of emotions by fine-tuning a variant of BERT pre-trained on arabic twitter data. ### Model Sources [optional]



- Repository: URL ## Evaluation


Jaccard accuracy is the main eval metric and the model achieved 56.12%
TAGS
#transformers #emotion #BERT #text-classification #ar #dataset-sem_eval_2018_task_1 #endpoints_compatible #region-us 
 # Model Card for Model ID



This model aims at classifying arabic text into its corresponding set of emotions by fine-tuning a variant of BERT pre-trained on arabic twitter data. ### Model Sources [optional]



- Repository: URL ## Evaluation


Jaccard accuracy is the main eval metric and the model achieved 56.12%