Datasets:

masakhane
/

masakhapos

	# coding=utf-8
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	# Lint as: python3
	"""MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages"""

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{dione-etal-2023-masakhapos,
	title = "{M}asakha{POS}: Part-of-Speech Tagging for Typologically Diverse {A}frican languages",
	author = "Dione, Cheikh M. Bamba and Adelani, David Ifeoluwa and Nabende, Peter and Alabi, Jesujoba and Sindane, Thapelo and Buzaaba, Happy and Muhammad, Shamsuddeen Hassan and Emezue, Chris Chinenye and Ogayo, Perez and Aremu, Anuoluwapo and Gitau, Catherine and Mbaye, Derguene and Mukiibi, Jonathan and Sibanda, Blessing and Dossou, Bonaventure F. P. and Bukula, Andiswa and Mabuya, Rooweither and Tapo, Allahsera Auguste and Munkoh-Buabeng, Edwin and Memdjokam Koagne, Victoire and Ouoba Kabore, Fatoumata and Taylor, Amelia and Kalipe, Godson and Macucwa, Tebogo and Marivate, Vukosi and Gwadabe, Tajuddeen and Elvis, Mboning Tchiaze and Onyenwe, Ikechukwu and Atindogbe, Gratien and Adelani, Tolulope and Akinade, Idris and Samuel, Olanrewaju and Nahimana, Marien and Musabeyezu, Th{\'e}og{\`e}ne and Niyomutabazi, Emile and Chimhenga, Ester and Gotosa, Kudzai and Mizha, Patrick and Agbolo, Apelete and Traore, Seydou and Uchechukwu, Chinedu and Yusuf, Aliyu and Abdullahi, Muhammad and Klakow, Dietrich",
	editor = "Rogers, Anna and
	Boyd-Graber, Jordan and
	Okazaki, Naoaki",
	booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
	month = jul,
	year = "2023",
	address = "Toronto, Canada",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2023.acl-long.609",
	doi = "10.18653/v1/2023.acl-long.609",
	pages = "10883--10900",
	abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.",
	}
	"""

	_DESCRIPTION = """\
	MasakhaPOS is the largest publicly available high-quality dataset for part-of-speech (POS) tagging in 20 African languages. The languages covered are:
	- Bambara (bam)
	- Ghomala (bbj)
	- Ewe (ewe)
	- Fon (fon)
	- Hausa (hau)
	- Igbo (ibo)
	- Kinyarwanda (kin)
	- Luganda (lug)
	- Dholuo (luo)
	- Mossi (mos)
	- Chichewa (nya)
	- Nigerian Pidgin
	- chShona (sna)
	- Kiswahili (swą)
	- Setswana (tsn)
	- Twi (twi)
	- Wolof (wol)
	- isiXhosa (xho)
	- Yorùbá (yor)
	- isiZulu (zul)

	The train/validation/test sets are available for all the ten languages.

	For more details see https://aclanthology.org/2023.acl-long.609/
	"""
	_URL = "https://github.com/masakhane-io/masakhane-pos/raw/main/data/"
	_TRAINING_FILE = "train.txt"
	_DEV_FILE = "dev.txt"
	_TEST_FILE = "test.txt"


	class MasakhaposConfig(datasets.BuilderConfig):
	"""BuilderConfig for MasakhaposConfig"""

	def __init__(self, **kwargs):
	"""BuilderConfig for MasakhaposConfig.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(MasakhaposConfig, self).__init__(**kwargs)


	class Masakhapos(datasets.GeneratorBasedBuilder):
	"""Masakhapos dataset."""

	BUILDER_CONFIGS = [
	MasakhaposConfig(name="bam", version=datasets.Version("1.0.0"), description="Masakhapos Bambara dataset"),
	MasakhaposConfig(name="bbj", version=datasets.Version("1.0.0"), description="Masakhapos Ghomala dataset"),
	MasakhaposConfig(name="ewe", version=datasets.Version("1.0.0"), description="Masakhapos Ewe dataset"),
	MasakhaposConfig(name="fon", version=datasets.Version("1.0.0"), description="Masakhapos Fon dataset"),
	MasakhaposConfig(name="hau", version=datasets.Version("1.0.0"), description="Masakhapos Hausa dataset"),
	MasakhaposConfig(name="ibo", version=datasets.Version("1.0.0"), description="Masakhapos Igbo dataset"),
	MasakhaposConfig(name="kin", version=datasets.Version("1.0.0"), description="Masakhapos Kinyarwanda dataset"),
	MasakhaposConfig(name="lug", version=datasets.Version("1.0.0"), description="Masakhapos Luganda dataset"),
	MasakhaposConfig(name="luo", version=datasets.Version("1.0.0"), description="Masakhapos Luo dataset"),
	MasakhaposConfig(name="mos", version=datasets.Version("1.0.0"), description="Masakhapos Mossi dataset"),
	MasakhaposConfig(name="nya", version=datasets.Version("1.0.0"), description="Masakhapos Chichewa` dataset"),
	MasakhaposConfig(
	name="pcm", version=datasets.Version("1.0.0"), description="Masakhapos Nigerian-Pidgin dataset"
	),
	MasakhaposConfig(name="sna", version=datasets.Version("1.0.0"), description="Masakhapos Shona dataset"),
	MasakhaposConfig(name="swa", version=datasets.Version("1.0.0"), description="Masakhapos Swahili dataset"),
	MasakhaposConfig(name="tsn", version=datasets.Version("1.0.0"), description="Masakhapos Setswana dataset"),
	MasakhaposConfig(name="twi", version=datasets.Version("1.0.0"), description="Masakhapos Twi dataset"),
	MasakhaposConfig(name="wol", version=datasets.Version("1.0.0"), description="Masakhapos Wolof dataset"),
	MasakhaposConfig(name="xho", version=datasets.Version("1.0.0"), description="Masakhapos Xhosa dataset"),
	MasakhaposConfig(name="yor", version=datasets.Version("1.0.0"), description="Masakhapos Yoruba dataset"),
	MasakhaposConfig(name="zul", version=datasets.Version("1.0.0"), description="Masakhapos Zulu dataset"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"upos": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"NOUN",
	"PUNCT",
	"ADP",
	"NUM",
	"SYM",
	"SCONJ",
	"ADJ",
	"PART",
	"DET",
	"CCONJ",
	"PROPN",
	"PRON",
	"X",
	"_",
	"ADV",
	"INTJ",
	"VERB",
	"AUX",
	]

	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://aclanthology.org/2023.acl-long.609/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{self.config.name}/{_TRAINING_FILE}",
	"dev": f"{_URL}{self.config.name}/{_DEV_FILE}",
	"test": f"{_URL}{self.config.name}/{_TEST_FILE}",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

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

	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	pos_tags = []
	for line in f:
	if line.startswith('-DOCSTART-'):
	continue
	if line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"upos": pos_tags,
	}
	guid += 1
	tokens = []
	pos_tags = []
	else:
	# Masakhapos tokens are space separated
	splits = line.strip().split()
	tokens.append(splits[0])
	pos_tag = splits[-1]
	pos_tags.append(pos_tag)
	# last example
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"upos": pos_tags,
	}