readme: ---
  language: 
  - en
  tags:
  - text-classification
  metrics:
  - accuracy (balanced)
  - F1 (weighted)
  widget:
  - text: "اسعدغيرك  انت مو بس اسعدت العماله ترا اسعدتنا"
    example_title: "خليجي"
  - text: "  سبحان الله في الغيوم شكل قلب"
    example_title: "فصحي"
  - text: "بلاش تحطي صور متبرجة ع صفحتك..."
    example_title: "خليجي"
  - text: "و حضرتك طيبة و شكرا علي الكلام الحلو ده يا مبهجة..."
    example_title: "مصري"
  ---
  # Dialectical-MSA-detection
  
  ## Model description
  
  This model was trained on 108,173 manually annotated User-Generated Content (e.g. tweets and online comments) to classify the Arabic language of the text into one of two categories: 'Dialectical', or 'MSA' (i.e. Modern Standard Arabic).
  
  
  ## Training data
  
  Dialectical-MSA-detection was trained on the English-speaking subset of the [The Arabic online commentary dataset (Zaidan, et al 20211)](https://github.com/sjeblee/AOC). 
  The AOC dataset was created by crawling the websites of three Arabic newspapers, and extracting online articles and readers' comments.  
  
  
  
  ## Training procedure
  
  `xlm-roberta-base` was trained using the Hugging Face trainer with the following hyperparameters. 
  
  ```
  training_args = TrainingArguments(
      num_train_epochs=4,               # total number of training epochs
      learning_rate=2e-5,               # learning rate
      per_device_train_batch_size=32,   # batch size per device during training
      per_device_eval_batch_size=4,     # batch size for evaluation
      warmup_steps=0,                   # number of warmup steps for learning rate scheduler
      weight_decay=0.02,                # strength of weight decay
      
  )
  ```
  
  ## Eval results
  
  The model was evaluated using 10% of the sentences (90-10 train-dev split). Accuracy 0.88 on the dev set.
  
  
  ## Limitations and bias
  
  The model was trained on sentences from the online commentary domain. Other forms of UGT such as tweet can be different in the degree of dialectness. 
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{saadany2022semi,
    title={A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT},
    author={Saadany, Hadeel and Orasan, Constantin and Mohamed, Emad and Tantawy, Ashraf},
    journal={arXiv preprint arXiv:2210.11899},
    year={2022}
  }
  ```