Datasets:
twi_wordsim353

	# coding=utf-8
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	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
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	# Lint as: python3
	"""WordSim-353 for Yoruba"""


	import csv

	import datasets


	_DESCRIPTION = """\
	A translation of the word pair similarity dataset wordsim-353 to Twi.

	The dataset was presented in the paper
	Alabi et al.: Massive vs. Curated Embeddings for Low-Resourced
	Languages: the Case of Yorùbá and Twi (LREC 2020).
	"""

	_CITATION = """\
	@inproceedings{alabi-etal-2020-massive,
	title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\\`u}b{\\'a} and {T}wi",
	author = "Alabi, Jesujoba and
	Amponsah-Kaakyire, Kwabena and
	Adelani, David and
	Espa{\\~n}a-Bonet, Cristina",
	booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
	month = may,
	year = "2020",
	address = "Marseille, France",
	publisher = "European Language Resources Association",
	url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
	pages = "2754--2762",
	language = "English",
	ISBN = "979-10-95546-34-4",
	}
	"""

	_DOWNLOAD_URL = "https://raw.githubusercontent.com/ajesujoba/YorubaTwi-Embedding/master/Twi/wordsim_tw.csv"


	class TwiWordsim353(datasets.GeneratorBasedBuilder):
	"""WordSim-353 for Yoruba."""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"twi1": datasets.Value("string"),
	"twi2": datasets.Value("string"),
	"similarity": datasets.Value("float32"),
	}
	),
	homepage="https://github.com/ajesujoba/YorubaTwi-Embedding",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	test_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
	]

	def _generate_examples(self, filepath):
	"""Generate WordSim-353 for Yoruba examples."""
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.DictReader(csv_file, delimiter=",")
	for id_, row in enumerate(csv_reader):
	yield id_, {
	"twi1": row["twi1"],
	"twi2": row["twi2"],
	"similarity": row["EngSim"],
	}