Datasets:

imvladikon
/

bmc

	#!/usr/bin/env python3
	# -- coding: utf-8 --

	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@mastersthesis{naama,
	title={Hebrew Named Entity Recognition},
	author={Ben-Mordecai, Naama},
	advisor={Elhadad, Michael},
	year={2005},
	url="https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/",
	institution={Department of Computer Science, Ben-Gurion University},
	school={Department of Computer Science, Ben-Gurion University},
	},
	@misc{bareket2020neural,
	title={Neural Modeling for Named Entities and Morphology (NEMO^2)},
	author={Dan Bareket and Reut Tsarfaty},
	year={2020},
	eprint={2007.15620},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	"""

	SPLITS = ["split1", "split2", "split3"]

	class BMCConfig(datasets.BuilderConfig):
	"""BuilderConfig for BMC"""

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


	class BMC(datasets.GeneratorBasedBuilder):
	"""BMC dataset."""

	BUILDER_CONFIGS = [
	BMCConfig(name=split, version=datasets.Version("1.0.0"), description="BMC dataset")
	for split in SPLITS
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"raw_tags": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	'B-DATE',
	'I-DATE',
	'S-DATE',
	'E-DATE',
	'B-LOC',
	'E-LOC',
	'S-LOC',
	'I-LOC',
	'E-MONEY',
	'B-MONEY',
	'S-MONEY',
	'I-MONEY',
	'O',
	'S-ORG',
	'E-ORG',
	'I-ORG',
	'B-ORG',
	'B-PER',
	'E-PER',
	'I-PER',
	'S-PER',
	'B-PERCENT',
	'S-PERCENT',
	'E-PERCENT',
	'I-PERCENT',
	'E-TIME',
	'I-TIME',
	'B-TIME',
	'S-TIME'

	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	folder = f"data/{self.config.name}"
	data_files = {
	"train": dl_manager.download(os.path.join(folder, "bmc_split.train.bmes")),
	"validation": dl_manager.download(os.path.join(folder, "bmc_split.dev.bmes")),
	"test": dl_manager.download(os.path.join(folder, "bmc_split.test.bmes")),
	}
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["validation"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
	]

	def _generate_examples(self, filepath, sep = " "):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	ner_tags = []
	raw_tags = []
	for line in f:
	if line.startswith("-DOCSTART-") or line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"raw_tags": raw_tags,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	raw_tags = []
	ner_tags = []
	else:
	splits = line.split(sep)
	tokens.append(splits[0])
	raw_tags.append(splits[1].rstrip())
	ner_tags.append(splits[1].rstrip())
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"raw_tags": raw_tags,
	"ner_tags": ner_tags,
	}