Datasets:
igbo_ner

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""Igbo Named Entity Recognition Dataset"""


	import datasets


	_CITATION = """\
	@misc{ezeani2020igboenglish,
	title={Igbo-English Machine Translation: An Evaluation Benchmark},
	author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
	year={2020},
	eprint={2004.00648},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	Igbo Named Entity Recognition Dataset
	"""

	_HOMEPAGE = "https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner"

	_URLs = {
	"ner_data": "https://raw.githubusercontent.com/IgnatiusEzeani/IGBONLP/master/ig_ner/igbo_data.txt",
	"free_text": "https://raw.githubusercontent.com/IgnatiusEzeani/IGBONLP/master/ig_ner/igbo_data10000.txt",
	}


	class IgboNer(datasets.GeneratorBasedBuilder):
	"""Dataset from the Igbo NER Project"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="ner_data",
	version=VERSION,
	description="This dataset contains the named entity and all the sentences containing that entity.",
	),
	datasets.BuilderConfig(
	name="free_text", version=VERSION, description="This dataset contains all sentences used for NER."
	),
	]

	DEFAULT_CONFIG_NAME = "ner_data"

	def _info(self):
	if self.config.name == "ner_data":
	features = datasets.Features(
	{
	"content_n": datasets.Value("string"),
	"named_entity": datasets.Value("string"),
	"sentences": datasets.Sequence(datasets.Value("string")),
	}
	)
	else:
	features = datasets.Features(
	{
	"sentences": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir,
	"split": "train",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	dictionary = {}
	with open(filepath, "r", encoding="utf-8-sig") as f:
	if self.config.name == "ner_data":
	for id_, row in enumerate(f):
	row = row.strip().split("\t")
	content_n = row[0]
	if content_n in dictionary.keys():
	(dictionary[content_n]["sentences"]).append(row[1])
	else:
	dictionary[content_n] = {}
	dictionary[content_n]["named_entity"] = row[1]
	dictionary[content_n]["sentences"] = [row[1]]
	yield id_, {
	"content_n": content_n,
	"named_entity": dictionary[content_n]["named_entity"],
	"sentences": dictionary[content_n]["sentences"],
	}
	else:
	for id_, row in enumerate(f):
	yield id_, {
	"sentences": row.strip(),
	}