Marbertv2 Arabic Written Dialect Classifier

🚀 MARBERTv2 Arabic Written Dialect Classifier

This model is a fine - tuned version of the pre - trained model for Arabic written dialect classification. It can identify Modern Standard Arabic (MSA) and 4 regional Arabic dialects from raw text, which is useful for dialect identification, linguistic research, and dialect - aware natural language processing systems.

📚 Documentation

✨ Features

• This model is a fine - tuned version of UBC-NLP/MARBERTv2 for Arabic written dialect classification.
• It can distinguish among five major written Arabic dialect regions: MAGHREB, LEV, MSA, GLF, and EGY.
• It is intended for dialect identification in short Arabic text snippets from various sources including social media, forums, and informal writing.

🔧 Technical Details

• Base Model: This model is fine - tuned from MARBERTv2, a transformer - based language model optimized for Arabic, on a multi - dialect classification task.
• Labels (id2label): The model predicts one of the following five classes:

{
  "0": "MAGHREB", // Maghreb dialect (Northwest Africa: Morocco, Algeria, Tunisia, etc.)
  "1": "LEV",     // Levantine dialect (Lebanon, Syria, Jordan, Palestine)
  "2": "MSA",     // Modern Standard Arabic
  "3": "GLF",     // Gulf dialect (Saudi Arabia, UAE, Kuwait, etc.)
  "4": "EGY"      // Egyptian dialect
}

• Training Data: The model was trained on about 850,000+ Arabic sentences from 9 different publicly available datasets, covering a wide variety of written Arabic dialects. The distribution by dialect is as follows: | Dialect | Count | |-----------|----------| | GLF | 253,553 | | LEV | 243,025 | | MAGHREB | 140,887 | | EGY | 105,226 | | MSA | 83,231 |
• Training Details:
  • Architecture: MARBERTv2 (BERT - based)
  • Task: Text Classification (Dialect Identification)
  • Objective: Multi - class classification with softmax over 5 dialect classes
  • Tokenizer: UBC-NLP/MARBERTv2
• Datasets Used: | Dataset | Brief Description | Annotation strategy | Provided Labels | Current SOTA Performance | | :------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------: | :-----------------------: | :--------------------------: | | MADAR Subtask - 1 (MADAR - 6) | A Collection of parallel sentences (BTEC) covering the dialects of 5 cities from the Arab World and MSA in the travel domain (10,000 sentences per city) | Manual | 5 Arab Cities + MSA | 92.5% Accuracy | | MADAR Subtask - 1 (MADAR - 26) | A Collection of parallel sentences (BTEC) covering the dialects of 25 cities from the Arab World and MSA in the travel domain (2,000 sentences per city) | Manual | 25 Arab Cities + MSA | 67.32% F1 - Score | | DART | 25K tweets that are annotated via crowdsourcing and it is well - balanced over five main groups of Arabic dialects | Manual | 5 Arab Regions | UNK | | ArSarcasm v1 | 10,547 tweets from ASTD and SemEval datasets for Sarcasm detection with the dilaect information added in | Manual | 4 Arab Regions + MSA | UNK | | ArSarcasm v2 | ArSarcasm - v2 dataset contains 15,548 Tweets and is an extension of the original ArSarcasm dataset (Consists of ArScarcasm v1 along with portions of DAICT corpus and some new tweets) | Manual | 4 Arab Regions + MSA | UNK | | IADD | Five publicly available corpora were identified, analyzed and filtered to build IADD (AOC, DART, PADIC, SHAMI and TSAC) | ________ | 5 Regions and 9 Countries | UNK | | QADI | 540k tweets (30k per country on average) with a total of 8.8M words | Automatic | 18 Arab Countries | 60.6% | | AOC | The Arabic Online Commentary dataset is based on reader commentary from the online versions of three Arabic newspapers:AlGhad from JOR, Al - Riyadh from KSA, and Al - Youm Al - Sabe’ from EGY | Manual | 3 Arab Regions + MSA | UNK | | NADI - 2020 | 25,957 Tweets from 100 Arab provinces and 21 Arab countries | Automatic | 100 Prov. and 21 Coun. | 6.39% - 26.78% |

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "IbrahimAmin/marbertv2-arabic-written-dialect-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "الدنيا مش مستاهلة تجري كده، خد وقتك واستمتع بالحاجة البسيطة"
inputs = tokenizer(text, return_tensors="pt")

# Run inference
with torch.inference_mode():
    logits = model(**inputs).logits

pred = torch.argmax(logits, dim=-1).item()

print(f"Predicted Dialect: {model.config.id2label[pred]}")

📄 License

This model is released under the apache - 2.0 license.

🚀 Acknowledgements

• MARBERTv2 team at UBC - NLP
• Contributors of the Arabic dialect datasets used in training

📚 Citation

If you use this model in your research or application, please cite:

@misc{ibrahimamin_marbertv2_arabic_written_dialect_classifier,
  author = {Ibrahim Amin},
  title = {MARBERTv2 Arabic Written Dialect Classifier},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/IbrahimAmin/marbertv2-arabic-written-dialect-classifier}},
}