Datasets:
Tasks:
Text Generation
Formats:
parquet
Sub-tasks:
language-modeling
Languages:
Danish
Size:
10M - 100M
ArXiv:
DOI:
License:
| # /// script | |
| # requires-python = "==3.12" | |
| # dependencies = [ | |
| # "datasets==3.2.0", | |
| # "spacy==3.8.3", | |
| # ] | |
| # /// | |
| from pathlib import Path | |
| from typing import cast | |
| import pandas as pd | |
| import spacy | |
| from datasets import Dataset, load_dataset | |
| # KCE: mail from Leon | |
| sample_to_redact = { | |
| # Der kommer en dag | |
| "opensub_6726481", | |
| "opensub_6732371", | |
| # Kollektivet | |
| "opensub_6645818", | |
| # Flaskepost fra P | |
| "opensub_6666922", | |
| "opensub_6720216", | |
| "opensub_6958711", | |
| # Fasandræberne | |
| "opensub_6036947", | |
| "opensub_6008622", | |
| # En du elsker | |
| "opensub_5828376", | |
| "opensub_5828378", | |
| # En chance til | |
| "opensub_6177523", | |
| # Lev stærkt | |
| "opensub_6467655", | |
| # Nymphomaniac | |
| "opensub_5604391", | |
| "opensub_5748340", | |
| "opensub_5748494", | |
| "opensub_5629516", | |
| # Kvinden i buret | |
| "opensub_5636248", | |
| "opensub_5514603", | |
| "opensub_5504932", | |
| # Den skaldede frisør | |
| "opensub_5084880", | |
| "opensub_5031826", | |
| # Jagten | |
| "opensub_6929419", | |
| "opensub_4885548", | |
| # Melancholia | |
| "opensub_4421330", | |
| "opensub_4406991", | |
| "opensub_4418817", | |
| # Ambassadøren | |
| "opensub_4557721", | |
| # Antichrist | |
| "opensub_5511502", | |
| "opensub_3938655", | |
| "opensub_3636940", | |
| "opensub_3564521", | |
| "opensub_3562215", | |
| # En kongelig affære | |
| "opensub_4725493", | |
| "opensub_4725160", | |
| "opensub_4725159", | |
| "opensub_4916871", | |
| "opensub_5186746", | |
| # Brødre | |
| "opensub_233943", | |
| "opensub_87475", | |
| } | |
| column_order = [ | |
| "text", | |
| "source", | |
| "id", | |
| "added", | |
| "created", | |
| "license", | |
| "domain", | |
| "metadata", | |
| ] | |
| def convert_sample(example: dict) -> dict: | |
| text = example["text"] | |
| if example["doc_id"] in sample_to_redact: | |
| nlp = spacy.blank("da") | |
| doc = nlp(text) | |
| text = doc[:200].text # first 200 words | |
| new_example = dict( | |
| text_new=text, | |
| id=example["doc_id"], | |
| source="opensubtitles", | |
| domain="Conversation", | |
| license="Creative Commons Legal Code\n\nCC0 1.0 Universal", | |
| added="2025-01-02", | |
| created="1920-01-01, 2018-01-01", # assuming v2018 | |
| metadata={"source-pretty": "OpenSubtitles"}, | |
| ) | |
| return new_example | |
| def main(): | |
| ds = load_dataset("DDSC/partial-danish-gigaword-no-twitter", split="train") | |
| ds = cast(Dataset, ds) | |
| ds = ds.filter(lambda x: x["source"] == "opensub", num_proc=4) | |
| ds = ds.map(convert_sample, num_proc=4) | |
| ds = ds.select_columns(column_order[1:] + ["text_new"]) | |
| ds = ds.rename_columns({"text_new": "text"}) | |
| # ensure order | |
| ds = ds.select_columns(column_order) | |
| df = ds.to_pandas() | |
| df = cast(pd.DataFrame, df) | |
| dedup_df = df.drop_duplicates(keep="first", subset=["text"]) | |
| print("N. duplicates: ", df.shape[0] - dedup_df.shape[0]) # 2422 | |
| ds = ds.select(dedup_df.index) | |
| assert len(set(ds["text"])) == len(ds) | |
| save_path = Path(__file__).parent / "opensubtitles.parquet" | |
| ds.to_parquet(save_path) | |
| if __name__ == "__main__": | |
| main() | |