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swe-nerc / swe-nerc.py

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	# coding=utf-8
	# Copyright 2020 HuggingFace Datasets Authors.
	# Modified by Vésteinn Snæbjarnarson 2021
	#
	# 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
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	# Lint as: python3


	LABELS = [
	"O",
	"B-EVN",
	"B-GRO",
	"B-LOC",
	"B-MNT",
	"B-PRS",
	"B-SMP",
	"B-TME",
	"B-WRK",
	"I-EVN",
	"I-GRO",
	"I-LOC",
	"I-MNT",
	"I-PRS",
	"I-SMP",
	"I-TME",
	"I-WRK"
	]



	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@misc{swe-nerc,
	title = {Swe-NERC},
	author = {Ahrenberg, Lars ; Frid, Johan and Olsson, Leif-Jöran},
	url = {https://hdl.handle.net/10794/121},
	year = {2020} }
	"""

	_DESCRIPTION = """\
	The corpus consists of ca. 150.000 words of text.
	"""

	_URL = "https://huggingface.co/datasets/vesteinn/swe-nerc/raw/main/"
	_TRAINING_FILE = "swe_nerc_v1.tsv"


	class SweNERCConfig(datasets.BuilderConfig):
	"""BuilderConfig for swe-nerc"""

	def __init__(self, **kwargs):
	"""BuilderConfig for swe-nerc.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(SweNERCConfig, self).__init__(**kwargs)


	class SweNERC(datasets.GeneratorBasedBuilder):
	"""sosialurin-faroese-ner dataset."""

	BUILDER_CONFIGS = [
	SweNERCConfig(name="swe-nerc", version=datasets.Version("1.0.0"), description="swedish ner corpus"),
	]

	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=LABELS
	)
	),
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	]

	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 = []
	last_tag = None
	for line in f:
	if line.startswith("-DOCSTART-") or line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	last_tag = None
	else:
	# tokens are tab separated
	splits = line.split("\t")
	tokens.append(splits[0])
	try:
	tag = splits[1].rstrip()
	if tag == "O":
	pass
	elif tag == last_tag:
	tag = "I-" + tag
	else:
	tag = "B-" + tag
	ner_tags.append(tag)
	last_tag = splits[1].rstrip()
	except:
	print(splits)
	raise

	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}