| """Build the training split of pere/reasoning_norwegian in chat form. |
| |
| Source: pere/reasoning_norwegian |
| Revision: 214ce4bd6dc06156b7f930a8992cedd5a1cf3e1e |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| import html |
| import unicodedata |
| from collections.abc import Iterator |
| from pathlib import Path |
|
|
| from datasets import load_dataset |
|
|
| from dynainst.build_utils import build_dataset |
|
|
| SOURCE = "reasoning-norwegian" |
| HF_DATASET = "pere/reasoning_norwegian" |
| REVISION = "214ce4bd6dc06156b7f930a8992cedd5a1cf3e1e" |
| ADDED = "2026-09-03" |
| CREATED = "2025-01-01, 2025-02-28" |
| SAVE_DIR = Path(__file__).parent |
|
|
| PROMPT = """Gjennomgå og korriger følgende tekst for feil i tegnsetting (feilplasserte, manglende eller overflødige tegn) samt bruk av store og små bokstaver. Du skal aldri endre stavemåten eller legge til eller fjerne ord. |
| Når du svarer, skal du kun returnere den korrigerte teksten, uten ekstra forklaring. Hvis setningen allerede er korrekt, returner originalteksten. |
| Her er teksten du skal analysere: |
| {corrupt}""" |
|
|
|
|
| def lexical_content(text: str) -> str: |
| """Ignore punctuation, whitespace, and case while retaining letters/digits.""" |
|
|
| normalized = unicodedata.normalize("NFKC", html.unescape(text)).casefold() |
| return "".join(character for character in normalized if character.isalnum()) |
|
|
|
|
| def convert(raw) -> Iterator[dict]: |
| for index, row in enumerate(raw): |
| |
| |
| |
| answer = row["text_result"].strip() |
| if not answer: |
| continue |
| if lexical_content(row["corrupt"]) != lexical_content(answer): |
| continue |
| yield { |
| "id": f"{SOURCE}_{index:05d}", |
| "messages": [ |
| { |
| "role": "user", |
| "content": PROMPT.format(corrupt=row["corrupt"].strip()), |
| }, |
| {"role": "assistant", "content": answer}, |
| ], |
| "source": SOURCE, |
| "added": ADDED, |
| "created": CREATED, |
| "language": ["nob"], |
| "task": "instruction_following", |
| "token_count": -1, |
| } |
|
|
|
|
| def write_attribution(raw, dataset) -> None: |
| output_path = SAVE_DIR / "attribution.jsonl" |
| prefix = f"{SOURCE}_" |
| with output_path.open("w", encoding="utf-8") as output: |
| for sample_id in dataset["id"]: |
| source_index = int(sample_id.removeprefix(prefix)) |
| source_row = raw[source_index] |
| record = { |
| "id": sample_id, |
| "article_url": source_row["url"], |
| "paragraph_number": source_row["paragraph_number"], |
| "reasoning_dataset": HF_DATASET, |
| "reasoning_dataset_revision": REVISION, |
| "wikipedia_page_revision_id": None, |
| "wikipedia_page_revision_known": False, |
| } |
| output.write(json.dumps(record, ensure_ascii=False, sort_keys=True) + "\n") |
|
|
|
|
| def main() -> None: |
| |
| raw = load_dataset(HF_DATASET, revision=REVISION, split="train") |
| output_path = SAVE_DIR / f"{SOURCE}.parquet" |
| dataset = build_dataset(convert(raw), output_path=output_path) |
| write_attribution(raw, dataset) |
| print(f"Wrote {len(dataset):,} samples to {output_path}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
