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---
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}
}
``` |