---
library_name: transformers
base_model: asafaya/bert-mini-arabic
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lahgtna-dialect-mini-classifier-v2
  results: []
---
# dialect-router-v0.2

A lightweight Arabic dialect identification model that classifies input text into one of **15 language codes: 13 Arabic dialects, Modern Standard Arabic, and English**. It is the routing backbone in the **Lahgtna** pipeline, automatically selecting the correct voice reference and Chatterbox language token for speech synthesis.

v0.2 is a fine-tune of [`asafaya/bert-mini-arabic`](https://huggingface.co/asafaya/bert-mini-arabic) and expands coverage from 10 to 13 Arabic dialects, and adds an English label.

## Model Details

| Property | Value |
|---|---|
| Base model | `asafaya/bert-mini-arabic` |
| Architecture | BERT-mini encoder + sequence classification head |
| Task | Multi-class text classification (15 classes) |
| Input | Raw text (up to 512 tokens) |
| Output | One of 15 dialect / language codes |
| Languages | Arabic (ar), English (en) |
| License | MIT |

## Evaluation Results

| Metric | Score |
|---|---|
| Accuracy | **0.9359** |
| F1 Macro | **0.9052** |
| Eval Loss | 0.4537 |

## Dialect Labels

| ID | Label | Dialect / Language | Region |
|---|---|---|---|
| 0 | ar | Modern Standard Arabic (MSA) | — |
| 1 | bh | Bahraini | Bahrain |
| 2 | dz | Algerian | Algeria |
| 3 | eg | Egyptian | Egypt |
| 4 | en | English | — |
| 5 | iq | Iraqi | Iraq |
| 6 | lb | Lebanese | Lebanon |
| 7 | ly | Libyan | Libya |
| 8 | ma | Moroccan (Darija) | Morocco |
| 9 | ps | Palestinian | Palestine |
| 10 | sa | Saudi | Saudi Arabia |
| 11 | sd | Sudanese | Sudan |
| 12 | sy | Syrian | Syria |
| 13 | tn | Tunisian | Tunisia |
| 14 | ye | Yemeni | Yemen |

## What's New in v0.2

- **13 Arabic dialects** (up from 10): adds Bahraini (`bh`), Algerian (`dz`), and Yemeni (`ye`)
- **English label** (`en`) — English input is now routed explicitly instead of being out-of-scope
- Moroccan label renamed `mo` → `ma` (ISO 3166 country code)
- New base model: `asafaya/bert-mini-arabic` — smaller and faster for routing workloads
- Retrained on an expanded multi-dialect corpus

## Intended Use

**Primary use**
Dialect-aware TTS routing — given an Arabic utterance, predict the dialect so the correct speaker reference audio and Chatterbox language code can be selected automatically.

**Secondary use**
Standalone Arabic dialect identification for NLP pipelines, content filtering, dataset analysis, or any application that needs to distinguish Arabic dialects programmatically.

**Out-of-scope use**
- Languages other than Arabic and English
- Code-switched text (Arabic + English mixed)
- Dialect intensity scoring or fine-grained subdialect classification
- High-stakes decisions without human review

## How to Use

### Direct inference

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "oddadmix/dialect-router-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

text = "اه ياراسي الواحد دماغه وجعاه"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = model(**inputs).logits

pred_id = torch.argmax(logits, dim=-1).item()
dialect = model.config.id2label[pred_id]
print(dialect)  # e.g. "eg"
```

### With the Transformers pipeline

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="oddadmix/dialect-router-v0.2",
)
result = classifier("اه ياراسي الواحد دماغه وجعاه")
print(result)
# [{'label': 'eg', 'score': 0.94}]
```

### Inside Lahgtna TTS

```python
from inference import run_pipeline

# Dialect is detected automatically
run_pipeline(
    text="اه ياراسي الواحد دماغه وجعاه",
    output_path="output.wav",
)
```

## Training Procedure

### Hyperparameters

- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: AdamW (betas=(0.9, 0.999), epsilon=1e-08)
- lr_scheduler_type: cosine
- num_epochs: 20
- mixed_precision_training: Native AMP

## Limitations & Biases

- **Short texts** (< 5 tokens) may produce unreliable predictions — the model benefits from sentence-length input.
- **Code-switched text** (e.g. Arabic + French in Maghrebi dialects, or Arabic + English) may confuse the classifier; heavily mixed input may be routed to `en`.
- **Dialect continuum** — dialects from geographically adjacent regions (e.g. sy / lb / ps, ma / dz / tn, sa / bh) may be confused by the model.
- **Corpus bias** — label distribution in training data may not reflect real-world dialect prevalence; some dialects (e.g. sd, ly, bh, ye) may have lower recall.
- This model should not be used for identity classification of individuals.

## Citation

```bibtex
@misc{lahgtna-dialect-router-2026,
  title  = {dialect-router-v0.2: Arabic Dialect Identification for TTS Routing},
  author = {Oddadmix},
  year   = {2026},
  url    = {https://huggingface.co/oddadmix/dialect-router-v0.2}
}
```