Datasets:
File size: 2,379 Bytes
e1ced61 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | """MMK12 importer — math-only multiple-choice training rows.
All 15,616 public MMK12 ``train`` rows are ``subject=math``; the importer asserts
that invariant row-by-row. A non-math row is treated as source revision drift
(a hard :class:`IngestError`), never silently kept or relabeled. Only ``train``
is ingested; the 2,000-row source ``test`` split never enters training
(``docs/01_RESOURCE_CATALOG.md`` §3.1).
"""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any
from .base import (
Choice,
ImageResolver,
ImageStore,
IngestError,
NormalizedItem,
Policy,
canonicalize_mc_answer,
make_item,
mc_choices,
)
NAME = "mmk12"
POLICY: Policy = "c2_train_candidate"
LICENSE_GATE = "mmk12_license"
ALLOWED_SPLIT = "train"
REQUIRED_SUBJECT = "math"
def normalize(
row: Mapping[str, Any],
images: ImageStore,
resolve: ImageResolver,
*,
revision: str,
split: str,
config: str = "default",
) -> NormalizedItem:
"""Normalize one MMK12 native row to a :class:`NormalizedItem`."""
if split != ALLOWED_SPLIT:
raise IngestError(f"mmk12: only split {ALLOWED_SPLIT!r} may be ingested, got {split!r}")
subject = str(row.get("subject", "")).strip()
if subject != REQUIRED_SUBJECT:
raise IngestError(
f"mmk12: row {row.get('index', '?')!r} subject is {subject!r}, expected "
f"{REQUIRED_SUBJECT!r} — source revision drift"
)
question = str(row["question"])
option_texts = [str(o) for o in row["options"]]
choices: list[Choice] = mc_choices(option_texts)
answer_raw = str(row["answer"])
answer_canonical = canonicalize_mc_answer(answer_raw, choices)
image_ref = str(row["image"])
rel, digest = images.store(resolve(image_ref))
native_id = str(row.get("index", row.get("id")))
return make_item(
source=NAME,
source_revision=revision,
source_config=config,
source_split=split,
source_native_id=native_id,
question=question,
choices=choices,
answer_raw=answer_raw,
answer_canonical=answer_canonical,
answer_type="multiple_choice",
image_paths=(rel,),
image_sha256=(digest,),
policy=POLICY,
native_row=row,
subject=subject,
license_gate=LICENSE_GATE,
)
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