convert_datasets_to_json.py

import typer
from datasets import (Dataset, DatasetDict, get_dataset_config_names,
                      load_dataset)
from huggingface_hub import list_datasets

app = typer.Typer()


def convert(dataset_id: str):
    dataset_name = dataset_id.split("/")[-1]
    configs = get_dataset_config_names(dataset_id)
    skipped_validation = []
    load_errors = []

    for config in configs:
        typer.echo(dataset_id)
        try:
            raw_datasets = load_dataset(dataset_id, name=config)
        except:
            typer.echo(f"Failed to load {dataset_id}")
            load_errors.append(f"{dataset_id}/{config}")
            continue
        datasets_to_convert = DatasetDict()

        for split, dataset in raw_datasets.items():
            if split not in ["train", "validation"]:
                datasets_to_convert[split] = dataset

        for split, dataset in datasets_to_convert.items():
            columns_to_keep = ["gem_id", "gem_parent_id", "target", "references"]
            remainder_cols = validate_columns(dataset)
            if len(remainder_cols) > 0:
                typer.echo(
                    f"⚠️ Skipping {dataset_name}/{config}/{split} due to missing columns: {', '.join(remainder_cols)}"
                )
                skipped_validation.append(f"{dataset_name}/{config}/{split}")
            else:
                # Add `input` column if it exists
                if "input" in dataset.column_names:
                    columns_to_keep.append("input")
                # The test split doesn't have a parent ID
                if split == "test":
                    columns_to_keep.remove("gem_parent_id")
                # The `datasets` JSON serializer is buggy - use `pandas` for now
                df = dataset.to_pandas()
                # Exclude dummy config names for comparison with GitHub source dataset
                if config in ["default", "xsum", "totto"]:
                    reference_name = f"{dataset_name}_{split}"
                else:
                    reference_name = f"{dataset_name}_{config}_{split}"
                df[columns_to_keep].to_json(f"{reference_name}.json", orient="records")

    typer.echo(f"Skipped validation for {skipped_validation}")
    typer.echo(f"Load errors: {load_errors}")


def validate_columns(dataset: Dataset):
    ref_columns = ["gem_id", "target", "references"]
    columns = dataset.column_names
    return set(ref_columns) - set(columns)


@app.command()
def extract_evaluation_datasets():
    all_datasets = list_datasets()
    # Filter for GEM datasets
    gem_datasets = [dataset for dataset in all_datasets if dataset.id.startswith("GEM/")]
    # Filter for blocklist - currently exclude all datasets not found on GitHUb release
    blocklist = [
        # "ART",
        # "mlb_data_to_text",
        # "OrangeSum",
        # "split_and_rephrase",
        # "wiki_cat_sum",
        # "viggo",
        # "CrossWOZ",
        # "RiSAWOZ",
        # "indonlg",
        # "squad_v2",
        # "BiSECT",
        # "surface_realisation_st_2020",
        # "SciDuet",
        # "cochrane-simplification",
        # "turku_paraphrase_corpus",
        # "turku_hockey_data2text",
        # "sportsett_basketball",
        # "Taskmaster",
        # "wiki_lingua",
        # "SIMPITIKI",
        # "conversational_weather",
        # "RotoWire_English-German",
        # "dstc10_track2_task2",
        # "opusparcus",
        # "xlsum",
        "wiki_auto_asset_turk",  # Can't be loaded
        "references",  # This repo, so exclude!
    ]
    blocklist = ["GEM/" + dataset for dataset in blocklist]
    gem_datasets = [dataset for dataset in gem_datasets if dataset.id not in blocklist]
    for dataset in gem_datasets:
        typer.echo(f"Converting {dataset.id} ...")
        convert(dataset.id)
    typer.echo(f"🥳 All datasets converted!")


if __name__ == "__main__":
    app()