data/scrape_hovedstaden/create.py

# /// script
# requires-python = "==3.12"
# dependencies = [
#     "datasets==3.2.0",
#     "dynaword"
# ]
# [tool.uv.sources]
# dynaword = { git = "https://huggingface.co/datasets/danish-foundation-models/danish-dynaword", rev = "00e7f2aee7f7ad2da423419f77ecbb9c0536de0d" }
# ///
"""
Script for downloading and processing the Scrape Hovedstaden texts.

Note: To run this script, you need to set `GIT_LFS_SKIP_SMUDGE=1` to be able to install dynaword:

```bash
GIT_LFS_SKIP_SMUDGE=1 uv run data/scrape_hovedstaden/create.py
```
"""

import logging
import subprocess
from datetime import datetime
from pathlib import Path
from typing import Any, cast

import pandas as pd
from datasets import Dataset, load_dataset

from dynaword.process_dataset import (
    add_token_count,
    ensure_column_order,
    remove_duplicate_text,
    remove_empty_texts,
)

logger = logging.getLogger(__name__)

download_path = Path(__file__).parent / "tmp"


def main():
    save_path = Path(__file__).parent / "scrape_hovedstaden.parquet"
    # Download data from repo: Den-Intelligente-Patientjournal/region_hovedstaden_text
    ds = load_dataset(
        "Den-Intelligente-Patientjournal/region_hovedstaden_text", split="train"
    )
    dataset: Dataset = cast(Dataset, ds)

    # Extract the cleaned column
    dataset = dataset.rename_column("cleaned", "text")

    # Add created column: 2015 and 2020
    dataset = dataset.add_column("created", ["2015-01-01, 2020-12-31"] * len(dataset))  # type: ignore
    # Add added column: today
    dataset = dataset.add_column(
        "added", [datetime.today().date().strftime("%Y-%m-%d")] * len(dataset)
    )  # type: ignore
    # Add source column: scrape_hovedstaden
    dataset = dataset.add_column("source", ["scrape_hovedstaden"] * len(dataset))  # type: ignore
    # Add id column: scrape_hovedstade_{idx}
    dataset = dataset.add_column(
        "id", [f"scrape_hovedstaden_{i}" for i in range(len(dataset))]
    )  # type: ignore

    # quality checks and processing
    dataset = remove_empty_texts(dataset)
    dataset = remove_duplicate_text(dataset)
    dataset = add_token_count(dataset)
    dataset = ensure_column_order(dataset)

    # save to parquet
    dataset.to_parquet(save_path)


if __name__ == "__main__":
    main()