Datasets:

laugustyniak
/

political-advertising-pl

	from pathlib import Path

	import datasets
	import pandas as pd

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@inproceedings{augustyniak-etal-2020-political,
	title = "Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections",
	author = "Augustyniak, Lukasz and
	Rajda, Krzysztof and
	Kajdanowicz, Tomasz and
	Bernaczyk, Micha{\l}",
	booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
	month = jul,
	year = "2020",
	address = "Seattle, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.winlp-1.28",
	pages = "110--114"
	}
	"""

	_DESCRIPTION = "Polish Political Advertising Dataset"

	_HOMEPAGE = "https://github.com/laugustyniak/misinformation"

	_URLS = {
	"train": "https://huggingface.co/datasets/laugustyniak/political-advertising-pl/resolve/main/train.parquet",
	"test": "https://huggingface.co/datasets/laugustyniak/political-advertising-pl/resolve/main/test.parquet",
	"validation": "https://huggingface.co/datasets/laugustyniak/political-advertising-pl/resolve/main/dev.parquet",
	}

	DATA_PATH = Path(".")


	class PoliticalAdvertisingConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
	super(PoliticalAdvertisingConfig, self).__init__(**kwargs)


	class PoliticalAdvertisingDataset(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="political-advertising-pl", version=VERSION)
	]

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-DEFENSE_AND_SECURITY",
	"I-DEFENSE_AND_SECURITY",
	"B-EDUCATION",
	"I-EDUCATION",
	"B-FOREIGN_POLICY",
	"I-FOREIGN_POLICY",
	"B-HEALHCARE",
	"I-HEALHCARE",
	"B-IMMIGRATION",
	"I-IMMIGRATION",
	"B-INFRASTRUCTURE_AND_ENVIROMENT",
	"I-INFRASTRUCTURE_AND_ENVIROMENT",
	"B-POLITICAL_AND_LEGAL_SYSTEM",
	"I-POLITICAL_AND_LEGAL_SYSTEM",
	"B-SOCIETY",
	"I-SOCIETY",
	"B-WELFARE",
	"I-WELFARE",
	]
	)
	),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_files["test"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": downloaded_files["validation"]},
	),
	]

	def _generate_examples(self, filepath: str):
	logger.info("⏳ Generating examples from = %s", filepath)
	df = pd.read_parquet(filepath)

	for row_id, row in df.iterrows():
	yield row_id, {
	"id": str(row_id),
	"tokens": row.tokens,
	"tags": row.tags,
	}