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--- |
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dataset_info: |
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features: |
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- name: non_vocalized |
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dtype: string |
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- name: vocalized |
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dtype: string |
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- name: source |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 3609941776 |
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num_examples: 1463790 |
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- name: valid |
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num_bytes: 74699622 |
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num_examples: 30181 |
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- name: test |
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num_bytes: 37176837 |
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num_examples: 15091 |
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download_size: 3516498736 |
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dataset_size: 3721818235 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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- split: test |
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path: data/test-* |
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license: mit |
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language: |
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- ar |
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pretty_name: Arabic Tashkeel Dataset |
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--- |
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# Arabic Tashkeel Dataset |
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This is a fairly large dataset gathered from five main sources: |
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- [`tashkeela`](https://huggingface.co/datasets/community-datasets/tashkeela) **(1.79GB - 45.05%)**: The entire Tashkeela dataset, repurposed in sentences. Some rows were omitted as they contain low diacritic (tashkeel characters) rate. |
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- `shamela` **(1.67GB - 42.10%)**: Random pages from over 2,000 books on the [Shamela Library](https://shamela.ws/). Pages were selected using the below function (high diacritics rate) |
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- `wikipedia` **(269.94MB - 6.64%)**: A collection of Wikipedia articles. Diacritics were added using OpenAI's [GPT-4o mini](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) model. At the time of writing this, many other LLMs were tried (such as GPT-4o, Claude 3 Haiku, Claude 3.5 Sonnet, Llama 3.1 70b, among others), and this one (surprisingly) scored the highest in a subset of the tashkeela dataset. |
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- `ashaar` **(117.86MB - 2.90%)**: [APCD](https://huggingface.co/datasets/arbml/APCD), [APCDv2](https://huggingface.co/datasets/arbml/APCDv2), [Ashaar_diacritized](https://huggingface.co/datasets/arbml/Ashaar_diacritized), [Ashaar_meter](https://huggingface.co/datasets/arbml/Ashaar_meter) merged. Most rows from these datasets were excluded, and only those with sufficient diacritics were retained. |
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- [`quran-riwayat`](https://huggingface.co/datasets/Abdou/quran-riwayat) **(71.73MB - 1.77%)**: Six different riwayat of Quran. |
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- [`hadith`](https://huggingface.co/datasets/arbml/LK_Hadith) **(62.69MB - 1.54%)**: Leeds University and King Saud University (LK) Hadith Corpus. |
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To filter out samples that contain partial or no tashkeel, we only retain sentences where diacritic characters make up 70% or more of the Arabic characters, using this function: |
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```python |
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import pyarabic.araby as araby |
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# a function that determines whether a text contains Arabic diacritics |
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def has_diacritics(text): |
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tashkeel_chars = set(araby.TASHKEEL) |
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arabic_chars = set("ابتثجحخدذرزسشصضطظعغفقكلمنهويىءآأؤإئ") |
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# if tashkeel characters's count is greater than 70% of the Arabic characters' count, then the text has diacritics |
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return sum(1 for c in text if c in tashkeel_chars) >= 0.7 * sum(1 for c in text if c in arabic_chars) |
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``` |
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We make use of the `pyarabic` library, make sure to install it: |
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``` |
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$ pip install pyarabic |
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``` |
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## Main Uses |
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This dataset can be used to train models to automatically add diacritics (perform tashkeel) to Arabic text. |
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## Limitations |
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Over 90% of the dataset consists primarily of religious texts in Classical Arabic. As a result, models trained on this data are well-suited for vocalizing such texts but may struggle with Modern Standard Arabic. Wikipedia articles were added to help ease this issue, though they may not entirely resolve it. |
