README.md

---
base_model:
- UBC-NLP/AraT5v2-base-1024
language:
- ar
library_name: transformers
license: apache-2.0
metrics:
- bleu
pipeline_tag: translation
tags:
- Syrian
- Shami
- MT
- MSA
- Dialect
- ArabicNLP
---

# SHAMI-MT : A Machine Translation Model From MSA to Syrian Dialect 

This model is based on the paper [SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System](https://huggingface.co/papers/2508.02268).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/eyHzopOleQcVFz9LkO6Nv.png)

## Model Description

SHAMI-MT is a specialized machine translation model designed to translate from Modern Standard Arabic (MSA) to Syrian dialect. Built on the robust AraT5v2-base-1024 architecture, this model bridges the gap between formal Arabic and the rich dialectal variations of Syrian Arabic.

## Model Details

- **Model Type**: Sequence-to-Sequence Translation
- **Base Model**: UBC-NLP/AraT5v2-base-1024
- **Language**: Arabic (MSA → Syrian Dialect)
- **License**: Apache 2.0
- **Library**: Transformers

## Dataset

The model was trained on the **Nâbra** dataset, a comprehensive corpus of Syrian Arabic dialects with morphological annotations.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/AaN6gPticioHBTXdPsroy.png)

### Nâbra Dataset Details

**Citation:**
```
Nayouf, A., Hammouda, T., Jarrar, M., Zaraket, F., & Kurdy, M. B. (2023). 
Nâbra: Syrian Arabic dialects with morphological annotations. 
arXiv preprint arXiv:2310.17315.
```

**Key Statistics:**
- **Tokens**: ~60,000 words
- **Dialects Covered**: Multiple Syrian regional dialects including:
  - Aleppo
  - Damascus
  - Deir-ezzur
  - Hama
  - Homs
  - Huran
  - Latakia
  - Mardin
  - Raqqah
  - Suwayda

**Data Sources:**
- Social media posts
- Movie and TV series scripts
- Song lyrics
- Local proverbs

## Training Details

The model was fine-tuned on the AraT5v2-base-1024 architecture with the following training metrics:

- **Total Training Steps**: 10,384
- **Epochs**: 22
- **Final Training Loss**: 1.396
- **Final Evaluation Loss**: 0.771
- **Learning Rate**: Cosine schedule starting at 5e-5
- **Batch Size**: 256
- **Total FLOPs**: 1.58e+17

### Training Progress

The model showed consistent improvement throughout training:
- Initial loss: 12.93 → Final loss: 1.40
- Evaluation loss steadily decreased from 1.44 to 0.77
- Gradient norms remained stable throughout training

## Usage

### Installation

```bash
pip install transformers torch
```

### Inference Code

```python
from transformers import T5Tokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("Omartificial-Intelligence-Space/Shami-MT")
model = AutoModelForSeq2SeqLM.from_pretrained("Omartificial-Intelligence-Space/Shami-MT")

# Example usage
ar_prompt = "مرحبا بك هنا"  # MSA input
input_ids = tokenizer(ar_prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids)

print("Input (MSA):", ar_prompt)
print("Tokenized input:", tokenizer.tokenize(ar_prompt))
print("Output (Syrian Dialect):", tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Generation Parameters

For optimal results, you can adjust generation parameters:

```python
outputs = model.generate(
    input_ids,
    max_length=128,
    num_beams=4,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id
)
```
### Evaluation Results
- **Test Set**: 1,500 unseen sentences
- **Evaluation Method**: GPT-4.1 as automated judge
- **Average Score**: **4.01/5.0** ⭐
- **Evaluation Criteria**: Translation quality, dialectal accuracy, and semantic preservation

The model was evaluated using GPT-4.1 as an automated judge with the following structured prompt:

```
"You are a language evaluation assistant. Compare the predicted Shami sentence to the reference.
Please return a rating from 0 to 5 and a short comment.

MSA Input: [input sentence]
Model Prediction (Shami dialect): [model output]
Ground Truth (Shami dialect): [reference translation]

Respond in this format:
Score: <number from 0 to 5>
Comment: <brief explanation of the score>"
```

**Score Distribution Analysis:**
- **Excellent (5.0)**: High-quality translations with perfect dialectal conversion
- **Good (4.0-4.9)**: Minor dialectal variations or stylistic differences
- **Average (3.0-3.9)**: Acceptable translations with some dialectal inconsistencies
- **Below Average (2.0-2.9)**: Noticeable errors in dialect or meaning
- **Poor (0-1.9)**: Significant translation errors or loss of meaning

### Performance Highlights
- **Strong Dialectal Conversion**: Successfully transforms MSA into authentic Syrian dialect
- **Semantic Preservation**: Maintains original meaning while adapting linguistic style
- **Regional Adaptability**: Handles various Syrian sub-dialects effectively
- **Consistent Quality**: Stable performance across different text types and domains
  
## Applications

This model is particularly useful for:
- **Content Localization**: Adapting MSA content for Syrian audiences
- **Cultural Preservation**: Maintaining and promoting Syrian dialectal variations
- **Educational Tools**: Teaching differences between MSA and Syrian dialect
- **Research**: Syrian Arabic NLP and dialectology studies

## Regional Coverage

The model handles multiple Syrian sub-dialects, making it versatile for different regions within Syria:

🏛️ **Urban Centers**: Damascus, Aleppo  
🏔️ **Northern Regions**: Latakia, Mardin  
🏜️ **Eastern Areas**: Deir-ezzur, Raqqah  
🌄 **Central/Southern**: Hama, Homs, Huran, Suwayda

## Limitations

- Trained specifically on Syrian dialect variations
- Performance may vary for other Arabic dialects
- Limited to text-based translation (no speech support)
- Dataset size constraints may affect handling of very rare dialectal expressions

## Citation

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

```bibtex
@misc{shami-mt-2024,
  title={SHAMI-MT: A Machine Translation Model From MSA to Syrian Dialect},
  author={Omartificial Intelligence Space},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/Omartificial-Intelligence-Space/Shami-MT}
}

@article{nayouf2023nabra,
  title={Nâbra: Syrian Arabic dialects with morphological annotations},
  author={Nayouf, Amal and Hammouda, Tymaa Hasanain and Jarrar, Mustafa and Zaraket, Fadi A and Kurdy, Mohamad-Bassam},
  journal={arXiv preprint arXiv:2310.17315},
  year={2023}
}

@misc{onajar2025shamiMT,
  title={Shami-MT-2MSA : A Machine Translation from Syrian Dialect to MSA},
  author={Sibaee, Serry and Nacar, Omer},
  year={2025}
}
```

## Contact & Support

For questions, issues, or contributions, please visit the [model repository](https://huggingface.co/Omartificial-Intelligence-Space/Shami-MT) or contact the development team.