---
license: apache-2.0
language:
- ar
pipeline_tag: translation
library_name: transformers
tags:
- saudi
- arabic
- dialects
---

# NAMAA-T5-Saudi2English:

<img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/pBPd41ANlMwWJuO3PTA2z.png" width="250" align="left" style="margin-right: 20px;"/>

**NAMAA-T5-Saudi2English** is a transformer-based **Arabic-to-English translation model** fine-tuned on **Saudi dialectal data** (Najdi, Hijazi, Southern, Northern, and Eastern varieties). 

It is built on top of **mBERT-initialized T5** architecture and aims to improve translation quality for informal and region-specific Arabic commonly used across Saudi Arabia.


## Details and Model Config

| Property | Value |
|-----------|--------|
| **Model Type** | T5 Encoder–Decoder |
| **Base Architecture** | `T5ForConditionalGeneration` (initialized with mBERT embeddings) |
| **Languages** | Arabic (Saudi dialects) → English |
| **Training Data** | 120K sentence pairs from Najdi & Hijazi dialects |
| **Framework** | 🤗 Transformers v4.57.1 |
| **License** | Apache-2.0 |
| **Pipeline Tag** | `translation` |
| **Library** | `transformers` |
| **Tokenizer** | `T5Tokenizer` |
| **Vocabulary Size** | 110,208 tokens |

---

## 📊 Evaluation Summary

The model was evaluated on **12 Saudi sub-dialects** within the NAMAA MT leaderboard.

| Dialect Group | # Examples | BLEU | CHRF | METEOR | BERTScore F1 | Adequacy | Faithfulness | Fluency | Overall |
|---------------|------------|------|-------|---------|---------------|-----------|---------------|----------|----------|
| eastern_urban | 54 | 28.36 | 51.66 | 62.10 | 82.75 | 66.57 | 62.31 | 84.81 | 68.15 |
| hijazi_jeddah | 51 | 8.13 | 29.07 | 33.17 | 69.81 | 65.30 | 60.70 | 77.00 | 66.62 |
| hijazi_makkah | 49 | 32.41 | 53.95 | 61.45 | 84.57 | 82.72 | 79.89 | 87.83 | 82.78 |
| hijazi_urban | 50 | 19.75 | 44.65 | 50.85 | 78.21 | 72.24 | 67.04 | 68.98 | 69.12 |
| hijazi_urban_jeddah | 51 | 14.73 | 41.40 | 45.32 | 78.42 | 79.39 | 75.51 | 77.65 | 76.76 |
| najdi_qasim | 51 | 13.76 | 38.00 | 45.94 | 77.25 | 72.40 | 68.60 | 78.80 | 72.46 |
| najdi_riyadh | 51 | 18.44 | 40.08 | 43.73 | 75.24 | 64.12 | 61.27 | 79.12 | 66.59 |
| najdi_urban | 52 | 10.98 | 32.15 | 36.38 | 73.46 | 58.46 | 55.38 | 80.96 | 61.48 |
| northern_hail | 50 | 13.27 | 36.97 | 43.60 | 73.58 | 64.50 | 60.10 | 82.20 | 66.38 |
| southern_asiri | 50 | 13.28 | 38.97 | 29.53 | 68.87 | 42.02 | 39.04 | 60.21 | 43.13 |
| southern_jazan | 50 | 29.78 | 47.67 | 57.03 | 79.62 | 53.09 | 51.06 | 86.17 | 58.45 |
| southern_qahtan | 53 | 19.43 | 42.95 | 50.11 | 77.13 | 59.81 | 55.47 | 77.64 | 61.57 |

**Average BLEU:** ≈ 19.8 | **Average BERTScore F1:** ≈ 77.8 | **Average Overall:** ≈ 68.4  

---

## 🧩 Model Configuration

```json
{
  "architectures": ["T5ForConditionalGeneration"],
  "d_model": 768,
  "num_layers": 12,
  "num_heads": 12,
  "d_ff": 2048,
  "dropout_rate": 0.1,
  "feed_forward_proj": "gated-gelu",
  "tie_word_embeddings": false,
  "vocab_size": 110208,
  "transformers_version": "4.57.1"
}
```

## Training Details
- Base model: multilingual BERT (T5 adaptation)
- Objective: sequence-to-sequence translation (cross-entropy loss)
- Optimizer: AdamW (learning rate 3e-4, weight decay 0.01)
- Batch size: 32
- Epochs: 10
- Hardware: A100 (40 GB x 4 GPUs)
- Mixed precision: FP16
- Early stopping: based on validation BLEU

## 📚 Dataset Description

- Size: ≈ 120 K sentence pairs
- Source: Locally collected Saudi dialect corpora (Najdi and Hijazi)
- Domains: Conversational, cultural, social media, and spoken language data
- Data cleaning: automatic normalization + manual review
- Split: 80/10/10 (train/validation/test)

## 🚀 Intended Use

Primary:
- Translate Saudi Arabic text (including dialectal social media, spoken data, and local phrases) into English.

Secondary:
- Support downstream NLP tasks such as summarization, cross-lingual retrieval, and alignment evaluation.

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("NAMAA-Space/NAMAA-T5-Saudi2English")
model = AutoModelForSeq2SeqLM.from_pretrained("NAMAA-Space/NAMAA-T5-Saudi2English")

text = "وش صار اليوم؟"  # Najdi dialect
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# → "What happened today?"
```

## Citation 

```
@misc{namaa2025saudi2eng,
  title  = {NAMAA-T5-Saudi2English: Dialect-aware Arabic→English Translation Model},
  author = {NAMAA Community},
  year   = {2025},
  url    = {https://huggingface.co/NAMAA-Space/NAMAA-T5-Saudi2English},
  license= {Apache-2.0}
}
```