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, }