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README.md

@@ -22,7 +22,8 @@ tags:
   - [Global-MMLU](https://huggingface.co/datasets/CohereLabs/Global-MMLU), Sinhala subset `si` (Apache-2.0), revision `0e619dbeb34206cd48705a1a0ea7fb21cae09993`: test and dev splits, 14,324 questions after removing 3 with an empty option.
   - **sinmulu_si**: our own collection of 15,190 Sri Lankan Sinhala multiple-choice questions, gathered by our team; 15,180 after removing duplicates. Before training, it was checked against the 2,238 shared-task test questions, and 165 overlapping questions were removed (see *Overlap check against the test set*). We plan to publish it separately, so it is **not included** in this repo; it is available to the organisers on request.
   - Together: 26,675 training and 2,829 validation questions (10% per subject, seed 42); see `training/README.md`.
-  - **LLM use in data creation (disclosure):** sinmulu_si was transcribed from scanned exam papers by an LLM (<LLM NAME / API>), and some of its answers are model-derived where no official marking scheme was available (see Datasets). No LLM or API is used at inference; the system uses no retrieval.
+  - **How sinmulu_si was digitised:** the exam papers were scanned PDFs in legacy fonts, so their pages were transcribed to text with the help of a large language model. Where no official marking scheme was available, the answer was labelled by the model and kept only when it was highly confident (see Datasets).
+  - **At inference,** only our open-weight system above is used (≤ 8B parameters), with no external LLM, API or retrieval.
 
 ## Models and resources in this submission
 | Repo | What it is |
@@ -37,7 +38,7 @@ tags:
 | Dataset | Source and licence | Size | Preparation |
 |---|---|---|---|
 | **Global-MMLU, Sinhala** | [`CohereLabs/Global-MMLU`](https://huggingface.co/datasets/CohereLabs/Global-MMLU), config `si`, revision `0e619dbeb34206cd48705a1a0ea7fb21cae09993`, Apache-2.0. MMLU translated into Sinhala; 57 subjects, categories humanities 4,770, STEM 3,245, social science 3,137, other 2,433, business 739. | 14,042 test + 285 dev questions → **14,324** | Merged test and dev, dropped 3 questions with an empty option, removed metadata, converted to the competition format with ids `gsi00001…`, and split per subject into 12,926 training / 1,398 validation questions (10%, seed 42). Code: `training/sinmmlu/gmmlu.py`. |
-| **sinmulu_si** (our own) | Built by our team from 3,486 Sri Lankan exam-paper PDFs (G.C.E. O/L and A/L papers and provincial term tests). Most PDFs use legacy Sinhala fonts, so text extraction was unusable; instead, each page was rendered as an image and transcribed by an LLM (<LLM NAME / API>), in batches by several agents working in parallel on separate slices. Only Sinhala-medium papers and true multiple-choice questions were kept. Calculation-heavy subjects (Mathematics, Accounting, Statistics) and English, Tamil and other language papers were excluded. Questions that depended on diagrams, or whose scan was too poor to read confidently, were dropped rather than guessed. Stems and options were copied verbatim. Answers come from official marking schemes where available, matched to papers by grade, year and province; otherwise an answer was kept only if the model was highly confident, and left blank if not, so **some answers are model-derived**. Records were then validated, de-duplicated and cleaned. The full corpus holds 15,355 four-option MCQs across about 48 subjects (each with a Sinhala and an English subject name). After the overlap check against the test set (below), 15,190 remain (ids `osi00001`–`osi15190`), under 57 subject names, e.g. e.g. Agricultural Science 990, Home Economics 971, Biology 944, ICT 890, Communication and Media Studies 860, History 779, Geography 672, Buddhism 647. **Not published here; we will publish it separately, and it is available to the organisers on request.** | 15,190 → **15,180** | Removed 10 duplicates (same question and options), checked for overlap with the shared-task test set, and split per subject into 13,749 training / 1,431 validation questions (10%, seed 42). Code: `training/sinmmlu/sinmulu.py`. |
+| **sinmulu_si** (our own) | Built by our team from 3,486 Sri Lankan exam-paper PDFs (G.C.E. O/L and A/L papers and provincial term tests). Most PDFs use legacy Sinhala fonts, so text extraction was unusable; instead, each page was rendered as an image and transcribed by a large language model, in batches by several agents working in parallel on separate slices. Only Sinhala-medium papers and true multiple-choice questions were kept. Calculation-heavy subjects (Mathematics, Accounting, Statistics) and English, Tamil and other language papers were excluded. Questions that depended on diagrams, or whose scan was too poor to read confidently, were dropped rather than guessed. Stems and options were copied verbatim. Answers come from official marking schemes where available, matched to papers by grade, year and province; otherwise an answer was kept only if the model was highly confident, and left blank if not, so **some answers are model-derived**. Records were then validated, de-duplicated and cleaned. The full corpus holds 15,355 four-option MCQs across about 48 subjects (each with a Sinhala and an English subject name). After the overlap check against the test set (below), 15,190 remain (ids `osi00001`–`osi15190`), under 57 subject names, e.g. e.g. Agricultural Science 990, Home Economics 971, Biology 944, ICT 890, Communication and Media Studies 860, History 779, Geography 672, Buddhism 647. **Not published here; we will publish it separately, and it is available to the organisers on request.** | 15,190 → **15,180** | Removed 10 duplicates (same question and options), checked for overlap with the shared-task test set, and split per subject into 13,749 training / 1,431 validation questions (10%, seed 42). Code: `training/sinmmlu/sinmulu.py`. |
 | **Combined** (used for the final adapter) | union of the two above | **26,675 training / 2,829 validation** | A validation question whose text also appears in training would be moved to training (none did). Questions longer than 1,024 tokens were skipped in training, leaving 25,799 / 2,745. The answer distribution in training is 1: 23.1%, 2: 25.9%, 3: 26.4%, 4: 24.7%. Code: `training/sinmmlu/combined.py`. |
 
 ### Overlap check against the test set (sinmulu_si)