Misraj/Sadeed_Tashkeela

📚 Sadeed Tashkeela Arabic Diacritization Dataset

The Sadeed dataset is a large, high-quality Arabic diacritized corpus optimized for training and evaluating Arabic diacritization models.
It is built exclusively from the Tashkeela corpus for the training set and a refined version of the Fadel Tashkeela test set for the test set.

Dataset Overview

• Training Data:

  • Source: Cleaned version of the Tashkeela corpus (original data is ~75 million words, mostly Classical Arabic, with ~1.15% Modern Standard Arabic).
  • Total Examples: 1,042,698
  • Total Words: ~53 million

• Testing Data:

  • Source: Corrected version of the Fadel Tashkeela test set, addressing inconsistencies in handling iltiqā` as-sākinayn (adjacent consonants without intervening vowels).
  • Total Examples: 2485 samples
  • Notes: The test set is refined for phonological consistency according to standard Arabic rules.

• Features:

  • Fully normalized Arabic text
  • Minimal missing diacritics
  • Chunked into coherent samples (50–60 words)
  • Designed to preserve syntactic and contextual dependencies

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Preprocessing Details

1. Text Cleaning

• Diacritization Corrections:

  • Unified diacritization style.
  • Corrected diacritization of frequent errors.
  • Resolved inconsistencies in iltiqa' assakinayn (consonant cluster rules) based on standard phonological rules.

• Normalization:

  • Applied a comprehensive preprocessing pipeline inspired by Kuwain, preserving non-Arabic characters and symbols.

• Consistency:

  • Additional normalization steps to ensure stylistic and grammatical consistency across the dataset.

2. Text Chunking

• Segmented into samples of 50–60 words each.
• Used a hierarchical strategy prioritizing natural linguistic breaks, focusing on stronger punctuation first (e.g., sentence-ending punctuation, then commas).
• Designed to preserve the syntactic and contextual coherence of text chunks.

3. Dataset Filtering

• Missing Diacritics:
  • Excluded samples with more than two undiacritized words.
• Partial Diacritics:
  • Removed samples with three or more partially diacritized words.
• Test Set Overlap:
  • Eliminated overlapping examples with the Fadel Tashkeela test set, reducing overlap to only 0.4%.

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Usage

This dataset is ideal for:

• Training: Using the cleaned Tashkeela corpus to train models for Arabic diacritization.
• Testing: Evaluating diacritization systems with the refined Fadel Tashkeela test set.
• Arabic NLP tasks that require fully vocalized texts.

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Evaluation Code

The evaluation code for this dataset is available at: https://github.com/misraj-ai/Sadeed

Citation

If you use this dataset, please cite:

@misc{aldallal2025sadeedadvancingarabicdiacritization,
      title={Sadeed: Advancing Arabic Diacritization Through Small Language Model}, 
      author={Zeina Aldallal and Sara Chrouf and Khalil Hennara and Mohamed Motaism Hamed and Muhammad Hreden and Safwan AlModhayan},
      year={2025},
      eprint={2504.21635},
      archivePrefix={arXiv},
      url={https://huggingface.co/papers/2504.21635}, 
}

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License

This dataset is distributed for research purposes only.
Please review the original Tashkeela corpus license for terms of use.