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quran-5gram
dotquran/quran-5gram
quran-5gram is a machine learning model from dotquran. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
A 5-gram statistical language model trained on the Quran corpus (6,348 verses), built with KenLM.
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.arpa16.5 MB · 70%
At a glance
- License
- mit
- Model type
- kenlm
- Access
- Public
- Created
- May 2, 2026
- Updated
- May 2, 2026
- SHA
- 25dd0e7d
Model card
From the Hugging Face model README
Quran 5-gram KenLM Language Model
A 5-gram statistical language model trained on the Quran corpus (6,348 verses), built with KenLM.
Training data
- Source:
quran-simple-clean.txt— 6,348 Quranic verses, one per line. - Augmented to 13,901 sentences by:
- Adding consecutive verse pairs with 80% probability (models natural recitation flow where verse N+1 follows verse N).
- Adding verse triplets (~24% probability).
- Adding ~15% self-repetitions (verse recited twice).
Files
| File | Format | Description |
|---|---|---|
quran_5gram.arpa | ARPA text | Portable n-gram model |
quran_5gram.binary | KenLM binary | Fast probing hash |
config.json | JSON | Model metadata |
Usage
import kenlm
model = kenlm.Model("quran_5gram.binary") # or .arpa
# Score a verse (log10 probability)
score = model.score("بسم الله الرحمن الرحيم")
# Perplexity
ppl = model.perplexity("الحمد لله رب العالمين")
# Score a verse sequence (consecutive recitation)
sequence = "بسم الله الرحمن الرحيم الحمد لله رب العالمين"
seq_score = model.score(sequence)
Install KenLM Python bindings: pip install kenlm
- Type
- kenlm
- License
- mit
- Languages
- ar
- Created
- May 2, 2026
- Updated
- May 2, 2026