--- license: cc-by-nc-sa-4.0 pretty_name: "islamlab — Islamic Sciences Training Sets" language: - ar task_categories: - text-generation - text-retrieval - question-answering size_categories: - 1M **Nothing here is generated.** Questions come from a fixed set of templates > and every answer is a field already present in the corpus. That buys a > narrow dataset in exchange for one that cannot teach a model a fact the > sources do not contain. ## Subsets | config | split | rows | what it is | |---|---|---:|---| | `cpt` | train | 780,149 | Apparatus-free, deduplicated text with a provenance header — 3.21B characters | | `retrieval` | train | 906,691 | A section heading as the query, a passage from that section as the positive, and hard negatives drawn from elsewhere in the same book | | `attribution` | train | 130,825 | Citation questions with exact answers | | `eval_retrieval` | test | 26,933 | Same shape, held-out works only | | `eval_attribution` | test | 32,895 | Same shape, held-out works only | ## The held-out split The holdout is at the level of the **work**, not the row: 118 of the 4,022 works are assigned to evaluation by a hash of their identifier, and no line of their text appears in `cpt`, `retrieval` or `attribution`. A model trained on the train configs has therefore never read the books it is evaluated on. The assignment is deterministic, so it is the same for everyone. ## The tasks in `attribution` | task | question | rows (train) | |---|---|---:| | `author` | من مؤلف هذا النص؟ | 26,165 | | `title` | من أي كتاب أُخذ هذا النص؟ | 26,165 | | `science` | في أي علم يُصنَّف هذا الكتاب؟ | 26,165 | | `century` | في أي قرن هجري توفي المؤلف؟ | 26,165 | | `locus` | في أي جزء وصفحة يقع هذا النص؟ | 26,165 | `messages` is a JSON-encoded chat turn — parse it with `json.loads`. `answer` holds the exact expected string. The `locus` task deliberately includes the honest negative: where the source records no pagination, the answer is *لم تُثبَت الصفحة في هذه النسخة* rather than an invented page. ## Retrieval negatives Negatives come from **the same book** as the positive, which is what makes them hard: a chapter of al-Ḥāwī al-Kabīr is separated from another chapter of al-Ḥāwī al-Kabīr by subject alone, not by vocabulary, register or period. `negatives` is a JSON-encoded list of strings. ```python import json from datasets import load_dataset d = load_dataset("islamlab/islamic-sciences-training", "retrieval", split="train") row = d[0] negatives = json.loads(row["negatives"]) ``` ## Where this sits | Dataset | What it holds | |---|---| | [`islamlab/islamic-sciences`](https://huggingface.co/datasets/islamlab/islamic-sciences) | The corpus itself — eight sciences, 4,022 works | | **`islamlab/islamic-sciences-training`** | The training products above | | [`quranlab/*`](https://huggingface.co/quranlab) · [`risaleinur/*`](https://huggingface.co/risaleinur) | Qur'an and hadith; the Risale-i Nur corpus | ## License Derived from a corpus licensed **CC BY-NC-SA 4.0**, and carries the same terms. Credits are in [`SOURCES.md`](https://huggingface.co/datasets/islamlab/islamic-sciences/blob/main/SOURCES.md) on the corpus, which is where the works themselves are acknowledged. ## Citation ```bibtex @misc{islamlab_islamic_sciences_training, title = {islamlab — Islamic Sciences Training Sets}, author = {islamlab contributors}, year = {2026}, howpublished = {Hugging Face Datasets} } ```