Update README.md

README.md

@@ -31,14 +31,14 @@ configs:
 ---
 # Arabic Tashkeel Dataset
 This is a fairly large dataset gathered from five main sources:
-- `shamela` **(1710.59MB - 42.10%)**:
+- `shamela` **(1710.59MB - 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)
 - [`tashkeela`](https://huggingface.co/datasets/community-datasets/tashkeela) **(1830.36MB - 45.05%)**: The entire Tashkeela dataset, repurposed in sentences. Some rows were omitted as they contain low diacritic (tashkeel characters) rate.
 - `wikipedia` **(269.94MB - 6.64%)**: A collection of Wikipedia articles. Diacritics were added via [GPT-4o mini](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/).
 - [`quran-riwayat`](https://huggingface.co/datasets/Abdou/quran-riwayat) **(71.73MB - 1.77%)**: Six different riwayat of Quran.
 - [`hadith`](https://huggingface.co/datasets/arbml/LK_Hadith) **(62.69MB - 1.54%)**: Leeds University and King Saud University (LK) Hadith Corpus.
 - `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 are excluded as they do not contain enough diacritics.
 
-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:
+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:
 ```python
 import pyarabic.araby as araby
 
@@ -54,3 +54,9 @@ We make use of the `pyarabic` library, make sure to install it:
 ```
 $ pip install pyarabic
 ```
+
+## Main Uses
+This dataset can be used to train models to automatically add diacritics (perform tashkeel) to Arabic text.
+
+## Limitations
+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.