Datasets:
thainer

	import datasets


	_CITATION = """\
	@misc{Wannaphong Phatthiyaphaibun_2019,
	title={wannaphongcom/thai-ner: ThaiNER 1.3},
	url={https://zenodo.org/record/3550546},
	DOI={10.5281/ZENODO.3550546},
	abstractNote={Thai Named Entity Recognition},
	publisher={Zenodo},
	author={Wannaphong Phatthiyaphaibun},
	year={2019},
	month={Nov}
	}
	"""

	_LICENSE = "CC-BY 3.0"

	_DESCRIPTION = """\
	ThaiNER (v1.3) is a 6,456-sentence named entity recognition dataset created from expanding the 2,258-sentence
	[unnamed dataset](http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) by
	[Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/).
	It is used to train NER taggers in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp).
	The NER tags are annotated by [Tirasaroj and Aroonmanakun (2012)]((http://pioneer.chula.ac.th/~awirote/publications/))
	for 2,258 sentences and the rest by [@wannaphong](https://github.com/wannaphong/).
	The POS tags are done by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s `perceptron` engine trained on `orchid_ud`.
	[@wannaphong](https://github.com/wannaphong/) is now the only maintainer of this dataset.
	"""


	class ThaiNerConfig(datasets.BuilderConfig):
	"""BuilderConfig for ThaiNer."""

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

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


	class Thainer(datasets.GeneratorBasedBuilder):

	_DOWNLOAD_URL = "https://github.com/wannaphong/thai-ner/raw/master/model/1.3/data-pos.conll"
	_SENTENCE_SPLITTERS = ["", " ", "\n"]
	_POS_TAGS = [
	"ADJ",
	"ADP",
	"ADV",
	"AUX",
	"CCONJ",
	"DET",
	"NOUN",
	"NUM",
	"PART",
	"PRON",
	"PROPN",
	"PUNCT",
	"SCONJ",
	"VERB",
	]
	_NER_TAGS = [
	"B-DATE",
	"B-EMAIL",
	"B-LAW",
	"B-LEN",
	"B-LOCATION",
	"B-MONEY",
	"B-ORGANIZATION",
	"B-PERCENT",
	"B-PERSON",
	"B-PHONE",
	"B-TIME",
	"B-URL",
	"B-ZIP",
	"B-ไม่ยืนยัน",
	"I-DATE",
	"I-EMAIL",
	"I-LAW",
	"I-LEN",
	"I-LOCATION",
	"I-MONEY",
	"I-ORGANIZATION",
	"I-PERCENT",
	"I-PERSON",
	"I-PHONE",
	"I-TIME",
	"I-URL",
	"I-ไม่ยืนยัน",
	"O",
	]

	BUILDER_CONFIGS = [
	ThaiNerConfig(
	name="thainer",
	version=datasets.Version("1.3.0"),
	description="Thai Named Entity Recognition for PyThaiNLP (6,456 sentences)",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"pos_tags": datasets.Sequence(datasets.features.ClassLabel(names=self._POS_TAGS)),
	"ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=self._NER_TAGS)),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/wannaphong/thai-ner/",
	citation=_CITATION,
	license=_LICENSE,
	)

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

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	pos_tags = []
	ner_tags = []

	for line in f:
	if line in self._SENTENCE_SPLITTERS:
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"pos_tags": pos_tags,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	pos_tags = []
	ner_tags = []
	else:
	# thainer tokens are tab separated
	splits = line.split("\t")
	# replace junk ner tags
	ner_tag = splits[2].strip() if splits[2].strip() in self._NER_TAGS else "O"
	tokens.append(splits[0])
	pos_tags.append(splits[1])
	ner_tags.append(ner_tag)
	# last example
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"pos_tags": pos_tags,
	"ner_tags": ner_tags,
	}