| --- |
| license: cc-by-nc-4.0 |
| gated: true |
| extra_gated_prompt: Please provide the required information to access this model |
| extra_gated_fields: |
| Full Name: |
| type: text |
| Affiliation / Company: |
| type: text |
| Email Address: |
| type: text |
| Intended Use: |
| type: select |
| options: |
| - Research |
| - Education |
| - Commercial Exploration |
| - Academic Project |
| - Other |
| extra_gated_heading: Access Request – Provide Required Information |
| extra_gated_description: Before accessing this model, please complete the form below. |
| extra_gated_button_content: Submit Access Request |
| configs: |
| - config_name: Communication & Social Media |
| data_files: |
| - split: train |
| path: Communication & Social Media/train-* |
| - config_name: Culture & Heritage |
| data_files: |
| - split: train |
| path: Culture & Heritage/train-* |
| - config_name: Daily Life & Household |
| data_files: |
| - split: train |
| path: Daily Life & Household/train-* |
| - config_name: Education |
| data_files: |
| - split: train |
| path: Education/train-* |
| - config_name: Entertainment |
| data_files: |
| - split: train |
| path: Entertainment/train-* |
| - config_name: Finance & Banking |
| data_files: |
| - split: train |
| path: Finance & Banking/train-* |
| - config_name: Food |
| data_files: |
| - split: train |
| path: Food/train-* |
| - config_name: Geography |
| data_files: |
| - split: train |
| path: Geography/train-* |
| - config_name: Government Services |
| data_files: |
| - split: train |
| path: Government Services/train-* |
| - config_name: History |
| data_files: |
| - split: train |
| path: History/train-* |
| - config_name: Medical |
| data_files: |
| - split: train |
| path: Medical/train-* |
| - config_name: Nature & Environment |
| data_files: |
| - split: train |
| path: Nature & Environment/train-* |
| - config_name: Saudi Anthropology |
| data_files: |
| - split: train |
| path: Saudi Anthropology/train-* |
| - config_name: Shopping & Fashion |
| data_files: |
| - split: train |
| path: Shopping & Fashion/train-* |
| - config_name: Social Gatherings & Events |
| data_files: |
| - split: train |
| path: Social Gatherings & Events/train-* |
| - config_name: Sports & Fitness |
| data_files: |
| - split: train |
| path: Sports & Fitness/train-* |
| - config_name: Technology |
| data_files: |
| - split: train |
| path: Technology/train-* |
| - config_name: Transportation |
| data_files: |
| - split: train |
| path: Transportation/train-* |
| - config_name: Travel |
| data_files: |
| - split: train |
| path: Travel/train-* |
| - config_name: Weather & Seasons |
| data_files: |
| - split: train |
| path: Weather & Seasons/train-* |
| - config_name: Work & Office |
| data_files: |
| - split: train |
| path: Work & Office/train-* |
| dataset_info: |
| - config_name: Communication & Social Media |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 26546 |
| num_examples: 100 |
| download_size: 17311 |
| dataset_size: 26546 |
| - config_name: Culture & Heritage |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 22676 |
| num_examples: 102 |
| download_size: 14135 |
| dataset_size: 22676 |
| - config_name: Daily Life & Household |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 16680 |
| num_examples: 98 |
| download_size: 11583 |
| dataset_size: 16680 |
| - config_name: Education |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 36869 |
| num_examples: 150 |
| download_size: 23302 |
| dataset_size: 36869 |
| - config_name: Entertainment |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 18468 |
| num_examples: 106 |
| download_size: 12344 |
| dataset_size: 18468 |
| - config_name: Finance & Banking |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 51748 |
| num_examples: 200 |
| download_size: 13030 |
| dataset_size: 51748 |
| - config_name: Food |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 27939 |
| num_examples: 200 |
| download_size: 15511 |
| dataset_size: 27939 |
| - config_name: Geography |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 14147 |
| num_examples: 91 |
| download_size: 9469 |
| dataset_size: 14147 |
| - config_name: Government Services |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 38613 |
| num_examples: 200 |
| download_size: 21323 |
| dataset_size: 38613 |
| - config_name: History |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 23662 |
| num_examples: 150 |
| download_size: 6347 |
| dataset_size: 23662 |
| - config_name: Medical |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 45448 |
| num_examples: 200 |
| download_size: 27780 |
| dataset_size: 45448 |
| - config_name: Nature & Environment |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 41532 |
| num_examples: 200 |
| download_size: 26052 |
| dataset_size: 41532 |
| - config_name: Saudi Anthropology |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 24738 |
| num_examples: 104 |
| download_size: 14684 |
| dataset_size: 24738 |
| - config_name: Shopping & Fashion |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 19514 |
| num_examples: 100 |
| download_size: 13309 |
| dataset_size: 19514 |
| - config_name: Social Gatherings & Events |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 17539 |
| num_examples: 100 |
| download_size: 12046 |
| dataset_size: 17539 |
| - config_name: Sports & Fitness |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 48747 |
| num_examples: 200 |
| download_size: 25461 |
| dataset_size: 48747 |
| - config_name: Technology |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 20857 |
| num_examples: 100 |
| download_size: 13317 |
| dataset_size: 20857 |
| - config_name: Transportation |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 13759 |
| num_examples: 109 |
| download_size: 10299 |
| dataset_size: 13759 |
| - config_name: Travel |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 26486 |
| num_examples: 150 |
| download_size: 13096 |
| dataset_size: 26486 |
| - config_name: Weather & Seasons |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 32327 |
| num_examples: 200 |
| download_size: 19256 |
| dataset_size: 32327 |
| - config_name: Work & Office |
| features: |
| - name: Anchor |
| dtype: string |
| - name: Positive |
| dtype: string |
| - name: Negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 18158 |
| num_examples: 104 |
| download_size: 11650 |
| dataset_size: 18158 |
| 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]) |
| ``` |
