Datasets:
hausa_voa_ner

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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	# you may not use this file except in compliance with the License.
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	#
	# http://www.apache.org/licenses/LICENSE-2.0
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""Introduction to the Yoruba GV NER dataset: A Yoruba Global Voices (News) Named Entity Recognition Dataset"""


	import datasets


	logger = datasets.logging.get_logger(__name__)


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{hedderich-etal-2020-transfer,
	title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
	author = "Hedderich, Michael A. and
	Adelani, David and
	Zhu, Dawei and
	Alabi, Jesujoba and
	Markus, Udia and
	Klakow, Dietrich",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
	month = nov,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
	doi = "10.18653/v1/2020.emnlp-main.204",
	pages = "2580--2591",
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	The Hausa VOA NER dataset is a labeled dataset for named entity recognition in Hausa. The texts were obtained from
	Hausa Voice of America News articles https://www.voahausa.com/ . We concentrate on
	four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].

	The Hausa VOA NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and
	there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second
	is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase
	of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words
	have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.

	For more details, see https://www.aclweb.org/anthology/2020.emnlp-main.204/
	"""

	_URL = "https://github.com/uds-lsv/transfer-distant-transformer-african/raw/master/data/hausa_ner/"
	_TRAINING_FILE = "train_clean.tsv"
	_DEV_FILE = "dev.tsv"
	_TEST_FILE = "test.tsv"


	class HausaVoaNerConfig(datasets.BuilderConfig):
	"""BuilderConfig for HausaVoaNer"""

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


	class HausaVoaNer(datasets.GeneratorBasedBuilder):
	"""Hausa VOA NER dataset."""

	BUILDER_CONFIGS = [
	HausaVoaNerConfig(
	name="hausa_voa_ner", version=datasets.Version("1.0.0"), description="Hausa VOA NER dataset"
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-PER",
	"I-PER",
	"B-ORG",
	"I-ORG",
	"B-LOC",
	"I-LOC",
	"B-DATE",
	"I-DATE",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://www.aclweb.org/anthology/2020.emnlp-main.204/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"dev": f"{_URL}{_DEV_FILE}",
	"test": f"{_URL}{_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 = []
	ner_tags = []
	for line in f:
	line = line.strip()
	if line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# yoruba_gv_ner tokens are tab separated
	splits = line.strip().split("\t")
	tokens.append(splits[0])
	ner_tags.append(splits[1].rstrip())
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}