Datasets:

Omartificial-Intelligence-Space
/

SaudiDialect-Triplet-21

license: cc-by-nc-4.0
gated: true
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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
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      - name: train
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        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
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        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
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        num_examples: 98
    download_size: 11583
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  - config_name: Education
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
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      - name: train
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        num_examples: 150
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  - config_name: Entertainment
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
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      - name: train
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        num_examples: 106
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  - config_name: Finance & Banking
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
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      - name: train
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        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
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        num_examples: 200
    download_size: 15511
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  - config_name: Geography
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
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      - name: train
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        num_examples: 91
    download_size: 9469
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  - config_name: Government Services
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
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      - name: train
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        num_examples: 200
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    dataset_size: 38613
  - config_name: History
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
    splits:
      - name: train
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        num_examples: 150
    download_size: 6347
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  - config_name: Medical
    features:
      - name: Anchor
        dtype: string
      - name: Positive
        dtype: string
      - name: Negative
        dtype: string
    splits:
      - name: train
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        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 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.

Team

✈️ Travel Mohammed Alhassan	🍔 Food Abdulelah Alankari	🛍️ Fashion Reem Alsuliman	🎓 Education Joud Aloqla	💼 Work Nouf Alessa	📱 Tech Jude Alsubaie	🏋️ Sports Albara Aseri
🚗 Transport Wajn Alqahtani	🎬 Entertainment Muzon Assiri	🏠 Daily Life Jana Alsuhaibani	💰 Finance Abdullah Alsalem	🌤️ Weather Huda Aldawsari	🎉 Events Shaden Alosaimi	🩺 Medical Munirah Alsubaie
📢 Social Mohammed Alziyad	🇸🇦 Culture Shatha Alotaibi	🌿 Nature Norah Altwijri	📜 History Renad Alrifai	🗺️ Geography Murtada Altarouti	🏛️ Gov Lama Almutairi	👥 Anthro Adnan Hawsawi

📊 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.

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])

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

🧩 Dataset Summary

Team

Team

✈️

🍔

🛍️

🎓

💼

📱

🏋️

🚗

🎬

🏠

💰

🌤️

🎉

🩺

📢

🇸🇦

🌿

📜

🗺️

🏛️

👥

📊 Dataset Statistics

📏 Sentence Lengths (Word Count)

🏙️ Domain Distribution

📂 Data Structure

📝 Data Samples

⚠️ Quality & Integrity

🛠️ Usage