Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
Estonian
Size:
100K - 1M
ArXiv:
License:
| 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, | |
| } | |