Datasets:

masakhane
/

mafand

	# coding=utf-8
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	# Lint as: python3
	"""MAFAND-MT: Masakhane Anglo and Franco Africa News Dataset for Machine Translation"""

	import datasets
	import json

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@inproceedings{adelani-etal-2022-thousand,
	title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
	author = "Adelani, David and
	Alabi, Jesujoba and
	Fan, Angela and
	Kreutzer, Julia and
	Shen, Xiaoyu and
	Reid, Machel and
	Ruiter, Dana and
	Klakow, Dietrich and
	Nabende, Peter and
	Chang, Ernie and
	Gwadabe, Tajuddeen and
	Sackey, Freshia and
	Dossou, Bonaventure F. P. and
	Emezue, Chris and
	Leong, Colin and
	Beukman, Michael and
	Muhammad, Shamsuddeen and
	Jarso, Guyo and
	Yousuf, Oreen and
	Niyongabo Rubungo, Andre and
	Hacheme, Gilles and
	Wairagala, Eric Peter and
	Nasir, Muhammad Umair and
	Ajibade, Benjamin and
	Ajayi, Tunde and
	Gitau, Yvonne and
	Abbott, Jade and
	Ahmed, Mohamed and
	Ochieng, Millicent and
	Aremu, Anuoluwapo and
	Ogayo, Perez and
	Mukiibi, Jonathan and
	Ouoba Kabore, Fatoumata and
	Kalipe, Godson and
	Mbaye, Derguene and
	Tapo, Allahsera Auguste and
	Memdjokam Koagne, Victoire and
	Munkoh-Buabeng, Edwin and
	Wagner, Valencia and
	Abdulmumin, Idris and
	Awokoya, Ayodele and
	Buzaaba, Happy and
	Sibanda, Blessing and
	Bukula, Andiswa and
	Manthalu, Sam",
	booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
	month = jul,
	year = "2022",
	address = "Seattle, United States",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2022.naacl-main.223",
	doi = "10.18653/v1/2022.naacl-main.223",
	pages = "3053--3070",
	abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
	}
	"""

	_DESCRIPTION = """\
	MAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages. The languages covered are:
	- Amharic
	- Bambara
	- Ghomala
	- Ewe
	- Fon
	- Hausa
	- Igbo
	- Kinyarwanda
	- Luganda
	- Luo
	- Mossi
	- Nigerian-Pidgin
	- Chichewa
	- Shona
	- Swahili
	- Setswana
	- Twi
	- Wolof
	- Xhosa
	- Yoruba
	- Zulu

	The train/validation/test sets are available for 16 languages, and validation/test set for amh, kin, nya, sna, and xho

	For more details see https://aclanthology.org/2022.naacl-main.223/
	"""

	_URL = "https://raw.githubusercontent.com/masakhane-io/lafand-mt/main/data/json_files/"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "dev.json"
	_TEST_FILE = "test.json"


	class MafandConfig(datasets.BuilderConfig):
	"""BuilderConfig for Mafand"""

	def __init__(self, **kwargs):
	"""BuilderConfig for Masakhaner.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(MafandConfig, self).__init__(**kwargs)


	class Mafand(datasets.GeneratorBasedBuilder):
	"""Mafand dataset."""
	BUILDER_CONFIGS = [
	MafandConfig(name="en-amh", version=datasets.Version("1.0.0"),
	description="Mafand English-Amharic dataset"),
	MafandConfig(name="en-hau", version=datasets.Version("1.0.0"),
	description="Mafand English-Hausa dataset"),
	MafandConfig(name="en-ibo", version=datasets.Version("1.0.0"),
	description="Mafand English-Igbo dataset"),
	MafandConfig(name="en-kin", version=datasets.Version("1.0.0"),
	description="Mafand English-Kinyarwanda dataset"),
	MafandConfig(name="en-lug", version=datasets.Version("1.0.0"),
	description="Mafand English-Luganda dataset"),
	MafandConfig(name="en-luo", version=datasets.Version("1.0.0"),
	description="Mafand English-Luo dataset"),
	MafandConfig(name="en-nya", version=datasets.Version("1.0.0"),
	description="Mafand English-Chichewa dataset"),
	MafandConfig(name="en-pcm", version=datasets.Version("1.0.0"),
	description="Mafand English-Naija dataset"),
	MafandConfig(name="en-sna", version=datasets.Version("1.0.0"),
	description="Mafand English-Shona dataset"),
	MafandConfig(name="en-swa", version=datasets.Version("1.0.0"),
	description="Mafand English-Swahili dataset"),
	MafandConfig(name="en-tsn", version=datasets.Version("1.0.0"),
	description="Mafand English-Setswana dataset"),
	MafandConfig(name="en-twi", version=datasets.Version("1.0.0"),
	description="Mafand English-Twi dataset"),
	MafandConfig(name="en-xho", version=datasets.Version("1.0.0"),
	description="Mafand English-Xhosa dataset"),
	MafandConfig(name="en-yor", version=datasets.Version("1.0.0"),
	description="Mafand English-Yoruba dataset"),
	MafandConfig(name="en-zul", version=datasets.Version("1.0.0"),
	description="Mafand English-Zulu dataset"),
	MafandConfig(name="fr-bam", version=datasets.Version("1.0.0"),
	description="Mafand French-Bambara dataset"),
	MafandConfig(name="fr-bbj", version=datasets.Version("1.0.0"),
	description="Mafand French-Ghomala dataset"),
	MafandConfig(name="fr-ewe", version=datasets.Version("1.0.0"),
	description="Mafand French-Ewe dataset"),
	MafandConfig(name="fr-fon", version=datasets.Version("1.0.0"),
	description="Mafand French-Fon dataset"),
	MafandConfig(name="fr-mos", version=datasets.Version("1.0.0"),
	description="Mafand French-Mossi dataset"),
	MafandConfig(name="fr-wol", version=datasets.Version("1.0.0"),
	description="Mafand French-Wolof dataset"),
	]

	def _info(self):
	source, target = self.config.name.split('-')
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({"translation": datasets.features.Translation(languages=(source, target))}),
	supervised_keys=(source, target),
	homepage="https://github.com/masakhane-io/lafand-mt",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	source, target = self.config.name.split('-')
	if target in ['amh', 'kin', 'nya', 'sna', 'xho']:
	urls_to_download = {
	"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.VALIDATION,
	gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_files["test"]}),
	]
	else:
	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:
	idx = 0
	for line in f:
	src_tgt = json.loads(line)
	yield idx, src_tgt
	idx += 1