Datasets:

ficsort
/

SzegedNER

	from datasets import BuilderConfig, Version, GeneratorBasedBuilder, DatasetInfo, Features, Value, \
	Sequence, ClassLabel, DownloadManager, SplitGenerator, Split
	import datasets
	import os
	import textwrap
	import csv
	from ast import literal_eval


	_DESCRIPTION = """
	The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language
	Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including
	Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc."""
	_CITATION = """"""
	_FEATURES = Features(
	{
	"id": Value("int32"),
	"tokens": Sequence(Value("string")),
	"ner": Sequence(
	ClassLabel(
	names=[
	"O",
	"B-PER",
	"I-PER",
	"B-ORG",
	"I-ORG",
	"B-LOC",
	"I-LOC",
	"B-MISC",
	"I-MISC",
	]
	)
	),
	"document_id": Value("int32"),
	"sentence_id": Value("int32")
	}
	)


	class SzegedNERConfig(BuilderConfig):
	"""BuilderConfig for SzegedNER."""

	def __init__(
	self,
	features,
	label_column,
	data_dir,
	citation,
	url,
	process_label=lambda x: x,
	**kwargs,
	):
	super(SzegedNERConfig, self).__init__(version=Version("1.0.0", ""), **kwargs)
	self.features = features
	self.label_column = label_column
	self.data_dir = data_dir
	self.citation = citation
	self.url = url
	self.process_label = process_label


	class SzegedNER(GeneratorBasedBuilder):
	"""SzegedNER datasets."""

	BUILDER_CONFIGS = [
	SzegedNERConfig(
	name="business",
	description=textwrap.dedent(
	"""\
	The Named Entity Corpus for Hungarian is a subcorpus of the Szeged Treebank, which contains full syntactic
	annotations done manually by linguist experts. A significant part of these texts has been annotated with
	Named Entity class labels in line with the annotation standards used on the CoNLL-2003 shared task."""
	),
	features=_FEATURES,
	label_column="ner_tags",
	data_dir="data/business/",
	citation=textwrap.dedent(_CITATION),
	url="https://rgai.inf.u-szeged.hu/node/130"
	),
	SzegedNERConfig(
	name="criminal",
	description=textwrap.dedent(
	"""\
	The Hungarian National Corpus and its Heti Világgazdaság (HVG) subcorpus provided the basis for corpus text
	selection: articles related to the topic of financially liable offences were selected and annotated for the
	categories person, organization, location and miscellaneous. There are two annotated versions of the corpus.
	When preparing the tag-for-meaning annotation, our linguists took into consideration the context in which
	the Named Entity under investigation occurred, thus, it was not the primary sense of the Named Entity that
	determined the tag (e.g. Manchester=LOC) but its contextual reference (e.g. Manchester won the Premier
	League=ORG). As for tag-for-tag annotation, these cases were not differentiated: tags were always given on
	the basis of the primary sense."""
	),
	features=_FEATURES,
	label_column="ner_tags",
	data_dir="data/criminal/",
	citation=textwrap.dedent(_CITATION),
	url="https://rgai.inf.u-szeged.hu/node/130"
	)
	]

	def _info(self):
	return DatasetInfo(
	description=self.config.description,
	features=self.config.features,
	homepage=self.config.url,
	citation=self.config.citation,
	)

	def _split_generators(self, dl_manager: DownloadManager):
	url = f"{self.base_path}{self.config.data_dir}"

	path = dl_manager.download({key: f"{url}{key}.csv" for key in ["train", "validation", "test"]})
	return [
	SplitGenerator(
	name=Split.TRAIN,
	gen_kwargs={"split_key": "train", "data_file": path['train']},
	),
	SplitGenerator(
	name=Split.VALIDATION,
	gen_kwargs={"split_key": "validation", "data_file": path['validation']},
	),
	SplitGenerator(
	name=Split.TEST,
	gen_kwargs={"split_key": "test", "data_file": path['test']},
	)
	]

	def _generate_examples(self, data_file, split_key, **kwargs):
	with open(data_file, encoding="utf8") as f:
	reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_MINIMAL)
	for n, row in enumerate(reader):
	labels = literal_eval(row['ner'])
	tokens = literal_eval(row['tokens'])
	if len(labels) != len(tokens):
	raise ValueError("Number of tokens and labels does not match")
	yield n, {
	"id": int(row['id']),
	"tokens": tokens,
	"ner": labels,
	"document_id": int(row['document_id']),
	"sentence_id": int(row['sentence_id'])
	}