README.md

---
license: cc-by-nc-4.0
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language:
- ar
pretty_name: Saudi Triplet
task_categories:
- feature-extraction
- sentence-similarity
tags:
- saudi
- Arabic
- Triplet
size_categories:
- 1K<n<10K
---

# 📂 SaudiDialect-Triplet-21 : Saudi Triplet Dataset (SABER Training Data)

## 🧩 Dataset Summary

The **Saudi Triplet Dataset** is a high-quality corpus of **2,964 sentence triplets** (Anchor, Positive, Negative) specifically curated to capture the nuances of **Saudi Arabic dialects** (Najdi, Hijazi, Gulf, etc.).

This dataset was created to fine-tune semantic embedding models such as [SABER](https://huggingface.co/Omartificial-Intelligence-Space/SA-STS-Embeddings-0.2B) for tasks like Semantic Search, Retrieval-Augmented Generation (RAG), and Clustering. 

It covers **21 distinct domains** reflecting real-life Saudi contexts, ranging from Government Services and Finance to Tribal Anthropology and Bedouin Culture.

## Team

**Special thanks to the exceptional team behind this dataset.**

<div align="center">

<h3>Team</h3>

<table>
  <!-- Row 1 -->
  <tr>
    <td align="center">
      <h1>✈️</h1>
      <b>Travel</b><br>
      <a href="https://www.linkedin.com/in/mohammed-alhassan10/">Mohammed Alhassan</a>
    </td>
    <td align="center">
      <h1>🍔</h1>
      <b>Food</b><br>
      <a href="https://www.linkedin.com/in/Abdulelah-Alankari">Abdulelah Alankari</a>
    </td>
    <td align="center">
      <h1>🛍️</h1>
      <b>Fashion</b><br>
      <a href="https://www.linkedin.com/in/reem-alsuliman-118036327">Reem Alsuliman</a>
    </td>
    <td align="center">
      <h1>🎓</h1>
      <b>Education</b><br>
      <a href="https://www.linkedin.com/in/joud-aloqla-991686370">Joud Aloqla</a>
    </td>
    <td align="center">
      <h1>💼</h1>
      <b>Work</b><br>
      <a href="https://www.linkedin.com/in/nouf-f-alessa">Nouf Alessa</a>
    </td>
    <td align="center">
      <h1>📱</h1>
      <b>Tech</b><br>
      <a href="https://www.linkedin.com/in/jude-alsubaie-482b6a20b">Jude Alsubaie</a>
    </td>
    <td align="center">
      <h1>🏋️</h1>
      <b>Sports</b><br>
      <a href="https://www.linkedin.com/in/albaraaseri/">Albara Aseri</a>
    </td>
  </tr>

  <!-- Row 2 -->
  <tr>
    <td align="center">
      <h1>🚗</h1>
      <b>Transport</b><br>
      <a href="https://linkedin.com/in/wajn-alqahtani">Wajn Alqahtani</a>
    </td>
    <td align="center">
      <h1>🎬</h1>
      <b>Entertainment</b><br>
      <a href="http://linkedin.com/in/muzonassiri">Muzon Assiri</a>
    </td>
    <td align="center">
      <h1>🏠</h1>
      <b>Daily Life</b><br>
      <a href="https://www.linkedin.com/in/jana-alsuhaibani/">Jana Alsuhaibani</a>
    </td>
    <td align="center">
      <h1>💰</h1>
      <b>Finance</b><br>
      <a href="https://www.linkedin.com/in/abdullah-alsalem-1b6b5b260/">Abdullah Alsalem</a>
    </td>
    <td align="center">
      <h1>🌤️</h1>
      <b>Weather</b><br>
      <a href="http://linkedin.com/in/hdaldawsari">Huda Aldawsari</a>
    </td>
    <td align="center">
      <h1>🎉</h1>
      <b>Events</b><br>
      <a href="https://www.linkedin.com/in/shaden-mohammed-alosaimi">Shaden Alosaimi</a>
    </td>
    <td align="center">
      <h1>🩺</h1>
      <b>Medical</b><br>
      <a href="https://www.linkedin.com/in/munirah-alsubaie-bb2983248">Munirah Alsubaie</a>
    </td>
  </tr>

  <!-- Row 3 -->
  <tr>
    <td align="center">
      <h1>📢</h1>
      <b>Social</b><br>
      <a href="https://www.linkedin.com/in/moalziyad">Mohammed Alziyad</a>
    </td>
    <td align="center">
      <h1>🇸🇦</h1>
      <b>Culture</b><br>
      <a href="https://linkedin.com/in/shatha-alotaibi-2000in004">Shatha Alotaibi</a>
    </td>
    <td align="center">
      <h1>🌿</h1>
      <b>Nature</b><br>
      <a href="https://www.linkedin.com/in/norahaltwijri">Norah Altwijri</a>
    </td>
    <td align="center">
      <h1>📜</h1>
      <b>History</b><br>
      <a href="https://linkedin.com/in/renad-alrifai/">Renad Alrifai</a>
    </td>
    <td align="center">
      <h1>🗺️</h1>
      <b>Geography</b><br>
      <a href="https://www.linkedin.com/in/murtada-altarouti/">Murtada Altarouti</a>
    </td>
    <td align="center">
      <h1>🏛️</h1>
      <b>Gov</b><br>
      <a href="https://www.linkedin.com/in/lama-aalmutairi/">Lama Almutairi</a>
    </td>
    <td align="center">
      <h1>👥</h1>
      <b>Anthro</b><br>
      <a href="https://www.linkedin.com/in/adnanhawsawi">Adnan Hawsawi</a>
    </td>
  </tr>
</table>

</div>

---

## 📊 Dataset Statistics

| Statistic | Value |
| :--- | :--- |
| **Total Triplets** | 2,964 |
| **Total Domains** | 21 |
| **Language** | Saudi Dialect |
| **Duplicate Anchors** | 59 (Multi-positive/negative pairings) |

### 📏 Sentence Lengths (Word Count)

The dataset consists primarily of short-to-medium length queries and sentences, typical of search and conversational inputs.

| Metric | Anchor | Positive | Negative |
| :--- | :--- | :--- | :--- |
| **Mean** | 6.42 | 6.50 | 5.34 |
| **Std Dev** | 1.85 | 1.96 | 1.77 |
| **Min** | 2 | 2 | 2 |
| **Max** | 13 | 15 | 12 |

---
## 🏙️ Domain Distribution

The dataset is balanced across high-resource topics (Food, Finance) and specific cultural topics (Anthropology, Heritage).

| Domain | Count |
| :--- | :--- |
| **Food** | 200 |
| **Finance & Banking** | 200 |
| **Government Services** | 200 |
| **Medical** | 200 |
| **Sports & Fitness** | 200 |
| **Weather & Seasons** | 200 |
| **Nature & Environment** | 200 |
| **Education** | 150 |
| **Travel** | 150 |
| **History** | 150 |
| **Transportation** | 109 |
| **Entertainment** | 106 |
| **Saudi Anthropology** | 104 |
| **Work & Office** | 104 |
| **Culture & Heritage** | 102 |
| **Shopping & Fashion** | 100 |
| **Technology** | 100 |
| **Communication & Social Media** | 100 |
| **Social Gatherings & Events** | 100 |
| **Daily Life & Household** | 98 |
| **Geography** | 91 |

---

## 📂 Data Structure

Each row in the dataset represents a training triplet designed for Contrastive Learning (e.g., MNRL).

| Column Name | Type | Description |
| :--- | :--- | :--- |
| `Anchor` | String | The reference sentence/query in Saudi dialect. |
| `Positive` | String | A sentence semantically similar to the Anchor (paraphrase or answer). |
| `Negative` | String | A sentence semantically dissimilar to the Anchor (different topic or meaning). |
| `Domain` | String | The topic category of the triplet. |

---

## 📝 Data Samples

Below are real examples from the dataset showing the dialectal variations and domain diversity.

| Domain | Anchor (Query) | Positive (Match) | Negative (Mismatch) |
| :--- | :--- | :--- | :--- |
| **Shopping & Fashion** | أبي فرشه تفك العقد وما تقطع الشعر | ابي مشط ما يخرب الشعر وينتفه | متى بيوصلني طقم الألماس اللي طلبته؟ |
| **Finance & Banking** | أبغا أفتح محفظة أسهم وأبدأ استثمار بسيط | أفكر أبدأ تداول خفيف في الأسهم عن طريق المحفظة | ناوي أزور العائلة في القرية الأسبوع الجاي |
| **Culture & Heritage** | أمس سمعت قصائد عن الشجاعة والفروسية | القصايد البدوية معانيها قوية | شغلت الغسالة بالغلط |
| **Food** | السوفليه عندهم فخم | السوفليه يذوب بالفم | ما وصلت الشحنة |
| **History** | الوالد كان دايم يذكر مملكة لحيان | شفت برنامج يتكلم عن سوق عكاظ | طلبي تأخر بالمطعم |
| **Travel** | وين أحصل على جولات سياحية رخيصة؟ | أبغى ألقى عروض سياحية اقتصادية | الجو حار وما أقدر أطلع |

---

## ⚠️ Quality & Integrity

*   **Missing Data:** There are **no missing values** in the Anchor, Positive, or Negative columns.
*   **Duplicates:** There are **59 duplicate anchors**. This is intentional in some cases to provide multiple positive pairings for the same query or to enforce separation from different hard negatives.
*   **Dialect Intensity:** The text ranges from "White Dialect" (understandable by most Arabs) to deep Saudi vernacular (specific to Najd/Hijaz/South).

---

## 🛠️ Usage

This dataset is optimized for training sentence transformers using `MultipleNegativesRankingLoss`.

```python
from datasets import load_dataset

# Load the dataset (Example path)
dataset = load_dataset("Omartificial-Intelligence-Space/Saudi-Triplet-Dataset")

# Print first example
print(dataset['train'][0])
```