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README.md

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 ---
 license: cc-by-4.0
+language:
+  - ar
+pretty_name: Quran Recitation Detect & Split
+size_categories:
+  - n<1K
+task_categories:
+  - text-classification
+tags:
+  - arabic
+  - quran
+  - asr-transcripts
+  - text-segmentation
+  - benchmark
+  - agent-evaluation
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train.csv
+      - split: test
+        path: test.csv
 ---
+
+# Quran Recitation Detect & Split
+
+258 human-reviewed ASR transcripts of real Quran recitations (254 Quranic,
+4 non-Quran), with gold labels for two tasks:
+
+1. **Detection** — which ayahs (verses) does the transcript contain?
+2. **Split** — segment the transcript text by ayah.
+
+Transcripts come from a production recitation-checking application:
+users recite from memory, audio is transcribed by a commercial ASR model,
+and each transcript was manually reviewed (ayah assignment, per-ayah split,
+confidence). Transcripts are noisy — they contain ASR errors, hesitations,
+repeated words, and openings such as the *isti'adhah* and *basmala* that are
+not part of the labeled ayahs. The 4 non-Quran rows are negative examples on
+which a system should **abstain**.
+
+This is the frozen dataset used in the paper
+*Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on
+Quran Recitation Data* (under review). The train/test split is exactly the
+split used in the paper's held-out study (deterministic stratified 60/40,
+seed 42; both non-Quran pairs split 2/2). The evaluation harness
+(`eval.py`, `research_score`) is in the companion code repository
+(link added upon paper acceptance).
+
+## Schema
+
+| column | description |
+| --- | --- |
+| `id` | row id (`train-NNN` / `test-NNN`) |
+| `transcript` | raw ASR transcript (Arabic, no diacritics) |
+| `ayah_assignment` | gold ayah ids, e.g. `002001-002005`; comma-separated segments for non-contiguous recitations; `non_quran` for negative examples |
+| `confidence` | reviewer confidence for the gold label |
+| `transcript_split_by_ayahs` | gold split: transcript text segmented by ayah (`|`-separated) |
+| `actual_ayahs` | reference ayah ids covered by the recitation |
+
+Ayah ids are `SSSAAA` (3-digit surah + 3-digit ayah), e.g. `095001` =
+Surah 95 (At-Tin), ayah 1.
+
+`quran_ref.json` is a slim Quran reference used by the harness:
+`{surah: [{id, ar, clean}]}` with `ar` the Uthmani text and `clean` a
+harakat-free canonicalized form (public text).
+
+## Splits
+
+| split | rows | Quran | non-Quran |
+| --- | --- | --- | --- |
+| train | 151 | 149 | 2 |
+| test | 107 | 105 | 2 |
+
+## Provenance & privacy
+
+- Recordings were made by consenting users of the application; only
+  ASR-derived **text** is released — no audio.
+- The dataset is **de-identified**: user and recording identifiers are
+  removed and replaced by sequential row ids. The 4 non-Quran transcripts
+  were manually reviewed and contain no personal content.
+- Quranic text itself is public domain.
+
+## Versioning
+
+This release corresponds to dataset freeze **v1.1** in the paper
+(the frozen inputs are pinned by SHA-256 in the companion repository).
+
+SHA-256 of released files:
+
+```
+train.csv        fa2cb114df7972946f3f48f0384b5081e94a1a8c8d46a9e3713cb3d9dc9386fa
+test.csv         deef397e82f54106eac65e40fcc75b02cd44fbc5b1d03ab3f345842930938fd4
+quran_ref.json   0795fe4e3dd01ebdce3f732ba3645226849e299e07a6db7e40eee5c8882baca7
+```
+
+## Citation
+
+Citation will be added upon paper acceptance (double-blind review in
+progress).