Datasets:

SinhalaMMLU-SharedTask
/

team-qbits-mcq-data

---
license: cc-by-nc-4.0
language:
- si
task_categories:
- multiple-choice
tags:
- sinhala
- sinhalammlu
---
# team-qbits MCQ SFT data (v3)

Supervised fine-tuning data for our ICTer 2026 SinhalaMMLU Shared Task submission.

* `sft_train3.jsonl` — 21,235 prompt/completion examples from 7,151 questions
* `sft_val3.jsonl` — 721 held-out questions, one example each

Derived from publicly available Sri Lankan examination papers (pastpapers.wiki,
e-thaksalawa.moe.gov.lk), O/L and Grades 6-11, covering 19 subjects including all 14
that appear in the SinhalaMMLU easy split. We do not redistribute the source PDFs.

**Extraction.** Grade 6-11 papers are only available as scanned images, so Gemini 3.1 Pro
was used offline to transcribe those page images into structured MCQ fields. This is OCR
and layout parsing, not content generation: no question, option or answer key was
generated, translated, paraphrased or rewritten by any model. Every item is a verbatim
transcription of a published examination question with its official answer key.

**Decontamination.** Checked against the released SinhalaMMLU `TEST/easy` split, which
matched and removed 26 questions. That split was used as a blocklist and, separately, to
choose between already-trained configurations — never for training itself: no gradient was
ever computed on it, and no question, option or answer key from it entered the training
set, the CPT corpus or the prompts. We used no part of the medium or hard splits for any
purpose. The train/validation split is grouped by source paper, so no paper appears on
both sides.

**Format.** Each question is rendered under several prompt templates (Sinhala instruction,
bare, English instruction) with the option order shuffled; the target is the option number
and loss is applied to the answer tokens only.

Pipeline and code: `SinhalaMMLU-SharedTask/team-qbits`.

	---
	license: cc-by-nc-4.0
	language:
	- si
	task_categories:
	- multiple-choice
	tags:
	- sinhala
	- sinhalammlu
	---

	# team-qbits MCQ SFT data (v3)

	Supervised fine-tuning data for our ICTer 2026 SinhalaMMLU Shared Task submission.

	* `sft_train3.jsonl` — 21,235 prompt/completion examples from 7,151 questions
	* `sft_val3.jsonl` — 721 held-out questions, one example each

	Derived from publicly available Sri Lankan examination papers (pastpapers.wiki,
	e-thaksalawa.moe.gov.lk), O/L and Grades 6-11, covering 19 subjects including all 14
	that appear in the SinhalaMMLU easy split. We do not redistribute the source PDFs.

	Extraction. Grade 6-11 papers are only available as scanned images, so Gemini 3.1 Pro
	was used offline to transcribe those page images into structured MCQ fields. This is OCR
	and layout parsing, not content generation: no question, option or answer key was
	generated, translated, paraphrased or rewritten by any model. Every item is a verbatim
	transcription of a published examination question with its official answer key.

	Decontamination. Checked against the released SinhalaMMLU `TEST/easy` split, which
	matched and removed 26 questions. That split was used as a blocklist and, separately, to
	choose between already-trained configurations — never for training itself: no gradient was
	ever computed on it, and no question, option or answer key from it entered the training
	set, the CPT corpus or the prompts. We used no part of the medium or hard splits for any
	purpose. The train/validation split is grouped by source paper, so no paper appears on
	both sides.

	Format. Each question is rendered under several prompt templates (Sinhala instruction,
	bare, English instruction) with the option order shuffled; the target is the option number
	and loss is applied to the answer tokens only.

	Pipeline and code: `SinhalaMMLU-SharedTask/team-qbits`.