> ## Documentation Index
> Fetch the complete documentation index at: https://developers.trynawa.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Arabic Dialect Detection

> How NAWA detects Gulf, Egyptian, Levantine, and MSA Arabic dialects with 100% accuracy.

NAWA classifies Arabic text into four major dialect groups with 100% accuracy, powered by HUMAIN's ALLaM large language model  -  the most advanced Arabic AI model available.

## Supported dialects

| Dialect       | Code        | Regions                                         | Key markers                                          |
| ------------- | ----------- | ----------------------------------------------- | ---------------------------------------------------- |
| **Gulf**      | `gulf`      | Saudi Arabia, UAE, Kuwait, Qatar, Bahrain, Oman | <span dir="rtl">شلون، وايد، زين، حلو، يالله</span>   |
| **Egyptian**  | `egyptian`  | Egypt                                           | <span dir="rtl">أوي، ده، دي، عايز، ازاي، كده</span>  |
| **Levantine** | `levantine` | Lebanon, Syria, Jordan, Palestine               | <span dir="rtl">هلق، كتير، شو، هيك، منيح</span>      |
| **MSA**       | `msa`       | Pan-Arab (formal)                               | <span dir="rtl">إن، الذي، بالإضافة، يجب، لذلك</span> |

<Info>
  Dialect detection accuracy: 100%  -  validated against a 100-comment test corpus spanning all 4 dialects (Gulf, Egyptian, Levantine, MSA) during Phase 0.5 evaluation.
</Info>

## Example classifications

<AccordionGroup>
  <Accordion title="Gulf Arabic">
    **Comment:** "وش رايكم بالمحتوى الجديد؟ أنا شايف إنه وايد حلو"

    **Translation:** "What do you think of the new content? I think it's very nice"

    **Markers:** `وش` (what  -  Gulf), `وايد` (very  -  Gulf), `حلو` (nice  -  Gulf)

    ```json theme={null}
    {
      "dialect": "gulf",
      "dialect_confidence": 0.97
    }
    ```
  </Accordion>

  <Accordion title="Egyptian Arabic">
    **Comment:** "الفيديو ده جامد أوي، عايز تاني كده"

    **Translation:** "This video is amazing, I want more like this"

    **Markers:** `ده` (this  -  Egyptian), `أوي` (very  -  Egyptian), `عايز` (I want  -  Egyptian), `كده` (like this  -  Egyptian)

    ```json theme={null}
    {
      "dialect": "egyptian",
      "dialect_confidence": 0.98
    }
    ```
  </Accordion>

  <Accordion title="Levantine Arabic">
    **Comment:** "كتير حلو الفيديو، بس شو القصة ورا الأغنية؟"

    **Translation:** "Very nice video, but what's the story behind the song?"

    **Markers:** `كتير` (very  -  Levantine), `شو` (what  -  Levantine)

    ```json theme={null}
    {
      "dialect": "levantine",
      "dialect_confidence": 0.95
    }
    ```
  </Accordion>

  <Accordion title="Modern Standard Arabic (MSA)">
    **Comment:** "يجب أن نشجع هذا النوع من المحتوى الهادف"

    **Translation:** "We should encourage this type of meaningful content"

    **Markers:** `يجب أن` (must  -  MSA formal), `هذا النوع من` (this type of  -  MSA structure)

    ```json theme={null}
    {
      "dialect": "msa",
      "dialect_confidence": 0.93
    }
    ```
  </Accordion>
</AccordionGroup>

## How it works

### NAGL + ALLaM pipeline

NAWA uses a two-stage pipeline called **NAGL** (NAWA Augmented Generation Layer):

1. **Language detection**: Identifies the input language. Arabic text is routed to the ALLaM model via HUMAIN's API.
2. **Dialect classification**: ALLaM analyzes morphological patterns, vocabulary, and syntactic structures to determine the dialect.
3. **Confidence scoring**: A calibrated confidence score (0–1) indicates how certain the model is about the dialect classification.

<Info>
  ALLaM is developed by [HUMAIN](https://humain.com) (formerly SDAIA) and is the most advanced large language model purpose-built for Arabic. NAWA is an official HUMAIN partner.
</Info>

### The `dialect_confidence` field

The `dialect_confidence` score ranges from 0 to 1:

| Range         | Interpretation                                                       |
| ------------- | -------------------------------------------------------------------- |
| **0.90–1.00** | High confidence  -  strong dialectal markers present                 |
| **0.70–0.89** | Medium confidence  -  some dialectal features detected               |
| **0.50–0.69** | Low confidence  -  text may be code-switched or ambiguous            |
| **\< 0.50**   | Very low  -  text may be too short or use minimal dialectal features |

<Tip>
  Short comments (under 10 words) often have lower dialect confidence because there are fewer linguistic markers. For critical applications, consider filtering on `dialect_confidence > 0.7`.
</Tip>

## Improving accuracy with feedback

If NAWA misclassifies a dialect, submit feedback to improve the model:

```bash theme={null}
curl -X POST https://api.trynawa.com/v1/feedback \
  -H "Authorization: Bearer nawa_test_sk_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "request_id": "req_abc123",
    "field": "dialect",
    "expected_value": "levantine",
    "comment": "This is Lebanese Arabic, uses هلق and كتير"
  }'
```

RLHF feedback is incorporated into model fine-tuning cycles, continuously improving accuracy across dialects.