Datasets:

phucdev
/

noisyner

	import datasets


	logger = datasets.logging.get_logger(__name__)

	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{hedderich2021analysing,
	title={Analysing the Noise Model Error for Realistic Noisy Label Data},
	author={Hedderich, Michael A and Zhu, Dawei and Klakow, Dietrich},
	booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
	volume={35},
	number={9},
	pages={7675--7684},
	year={2021}
	}
	@inproceedings{tkachenko-etal-2013-named,
	title = "Named Entity Recognition in {E}stonian",
	author = "Tkachenko, Alexander and Petmanson, Timo and Laur, Sven",
	booktitle = "Proceedings of the 4th Biennial International Workshop on {B}alto-{S}lavic Natural Language Processing",
	year = "2013",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/W13-2412",
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models.
	It is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique.
	Some highlights and interesting aspects of the data are:
	- Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances
	- Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of
	gold-standard data can be leveraged
	- Skewed label distribution (typical for Named Entity Recognition tasks)
	- For some label sets: noise level higher than the true label probability
	- Sequential dependencies between the labels

	For more details on the dataset and its creation process, please refer to our publication
	https://ojs.aaai.org/index.php/AAAI/article/view/16938 (published at AAAI'21).
	"""

	_HOMEPAGE = "https://github.com/uds-lsv/NoisyNER"

	_LICENSE = "The original dataset is licensed under CC-BY-NC. We provide our noisy labels under CC-BY 4.0."

	_URL = "https://huggingface.co/datasets/phuctrg/noisyner/raw/main/data"


	class NoisyNER(datasets.GeneratorBasedBuilder):
	"""
	NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models.
	"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="estner_clean", version=VERSION, description="EstNER dataset with clean labels"
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset1", version=VERSION,
	description="NoisyNER dataset label set 1 "
	"with automatic annotation via distant supervision based ANEA tool with no heuristics"
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset2", version=VERSION,
	description="NoisyNER dataset label set 2 "
	"with automatic annotation via distant supervision based ANEA tool and "
	"applying Estonian lemmatization to normalize the words"
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset3", version=VERSION,
	description="NoisyNER dataset label set 3 "
	"with automatic annotation via distant supervision based ANEA tool and "
	"splitting person entity names in the list, i.e. both first and last names can be matched "
	"separately. Person names must have a minimum length of 4. Also, lemmatization"
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset4", version=VERSION,
	description="NoisyNER dataset label set 4 "
	"with automatic annotation via distant supervision based ANEA tool and if entity names from "
	"two different lists match the same word, location entities are preferred. "
	"Also, lemmatization."
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset5", version=VERSION,
	description="NoisyNER dataset label set 5 "
	"with automatic annotation via distant supervision based ANEA tool and "
	"Locations preferred, lemmatization, splitting names with minimum length 4."
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset6", version=VERSION,
	description="NoisyNER dataset label set 6 "
	"with automatic annotation via distant supervision based ANEA tool and "
	"removing the entity names 'kohta', 'teine', 'naine' and 'mees' from the list of person names "
	"(high false positive rate). Also, all of label set 5."
	),
	datasets.BuilderConfig(
	name="NoisyNER_labelset7", version=VERSION,
	description="NoisyNER dataset label set 7 "
	"with automatic annotation via distant supervision based ANEA tool and using alternative, "
	"alias names for organizations. Using additionally the identifiers Q82794, Q3957, Q7930989, "
	"Q5119 and Q11881845 for locations and Q1572070 and Q7278 for organizations. "
	"Also, all of label set 6."
	),
	]

	DEFAULT_CONFIG_NAME = "estner_clean"

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"lemmas": datasets.Sequence(datasets.Value("string")),
	"grammar": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-PER",
	"I-PER",
	"B-ORG",
	"I-ORG",
	"B-LOC",
	"I-LOC"
	]
	)
	),
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	_URLS = {
	str(datasets.Split.TRAIN): f'{_URL}/{self.config.name}_train.tsv',
	str(datasets.Split.VALIDATION): f'{_URL}/{self.config.name}_dev.tsv',
	str(datasets.Split.TEST): f'{_URL}/{self.config.name}_test.tsv',
	}

	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
	for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	lemmas = []
	grammar_infos = []
	ner_tags = []
	for line in f:
	if line in ["--", "", "\n", "--\n"]:
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"lemmas": lemmas,
	"grammar": grammar_infos,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	lemmas = []
	grammar_infos = []
	ner_tags = []
	else:
	splits = line.split("\t")
	tokens.append(splits[0])
	lemmas.append(splits[1])
	grammar_infos.append(splits[2])
	ner_tags.append(splits[3].rstrip())
	# last example
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"lemmas": lemmas,
	"grammar": grammar_infos,
	"ner_tags": ner_tags,
	}