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title: Hadith Segmentation
emoji: ๐ฌ
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 6.31.0
app_file: app.py
pinned: false
license: mit
short_description: Split a raw hadith into sanad and matn
models:
- SHK4K/hadith-segmentation-bert
language:
- ar
tags:
- hadith
- sanad
- matn
- arabic
- token-classification
- digital-humanities
---
# ูุงุตู ุงูุญุฏูุซ โ Hadith Splitter
ููุณู
ูุต ุญุฏูุซ ุฎุงู
(ุจุฏูู ุนูุงู
ุงุช ุชุฑููู
ูููููุฉ) ุฅูู **ุณูุฏ** (ุณูุณูุฉ ุงูุฑูุงุฉ) ู**ู
ุชู**
(ููู ุงููุจู ุตูู ุงููู ุนููู ูุณูู
)ุ ููุนุฑุถ ุงูุญุฏูุฏ ุจูููู
ุง ุจุงูุฃููุงู ุนูู ุงููุต ุงูุฃุตูู.
Splits a raw hadith string into its **sanad** (chain of narrators) and **matn**
(the saying itself), and highlights the boundary on the original text.
- Model: [SHK4K/hadith-segmentation-bert](https://huggingface.co/SHK4K/hadith-segmentation-bert)
(AraBERT v02 fine-tuned for token classification)
- Labels: `B-SANAD`, `I-SANAD`, `B-MATN`, `I-MATN`, reduced to a single cut
- Diacritics are stripped for inference (the model was trained undiacritized)
and mapped back, so the output keeps your original vocalized text
- Long hadiths: processed in overlapping 512-token windows (stride 128) with
per-word majority voting, so nothing is truncated
- Output strings are sliced from your original text, so punctuation and spacing
are preserved
## Limits
- Accuracy is limited on unusual orthography; the model was trained on
normalized Arabic, and the app normalizes before inference.
- Pastes longer than ~25k subword tokens are processed only up to that point.
- The model is small (0.1B); treat the boundary as a suggestion on
low-resource texts, not ground truth.
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