Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
100K<n<1M
License:
| # coding=utf-8 | |
| # Copyright 2020 HuggingFace Datasets Authors. | |
| # | |
| # 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 | |
| """The GermEval 2014 NER Shared Task dataset.""" | |
| from __future__ import absolute_import, division, print_function | |
| import csv | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @inproceedings{benikova-etal-2014-nosta, | |
| title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset}, | |
| author = {Benikova, Darina and | |
| Biemann, Chris and | |
| Reznicek, Marc}, | |
| booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)}, | |
| month = {may}, | |
| year = {2014}, | |
| address = {Reykjavik, Iceland}, | |
| publisher = {European Language Resources Association (ELRA)}, | |
| url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf}, | |
| pages = {2524--2531}, | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties:\ | |
| - The data was sampled from German Wikipedia and News Corpora as a collection of citations.\ | |
| - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.\ | |
| - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines,\ | |
| using four main NER categories with sub-structure, and annotating embeddings among NEs\ | |
| such as [ORG FC Kickers [LOC Darmstadt]]. | |
| """ | |
| _URLS = { | |
| "train": "https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P", | |
| "dev": "https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm", | |
| "test": "https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH", | |
| } | |
| class GermEval14Config(datasets.BuilderConfig): | |
| """BuilderConfig for GermEval 2014.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for GermEval 2014. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(GermEval14Config, self).__init__(**kwargs) | |
| class GermEval14(datasets.GeneratorBasedBuilder): | |
| """GermEval 2014 NER Shared Task dataset.""" | |
| BUILDER_CONFIGS = [ | |
| GermEval14Config( | |
| name="germeval_14", version=datasets.Version("2.0.0"), description="GermEval 2014 NER Shared Task dataset" | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "source": datasets.Value("string"), | |
| "tokens": datasets.Sequence(datasets.Value("string")), | |
| "ner_tags": datasets.Sequence( | |
| datasets.features.ClassLabel( | |
| names=[ | |
| "O", | |
| "B-LOC", | |
| "I-LOC", | |
| "B-LOCderiv", | |
| "I-LOCderiv", | |
| "B-LOCpart", | |
| "I-LOCpart", | |
| "B-ORG", | |
| "I-ORG", | |
| "B-ORGderiv", | |
| "I-ORGderiv", | |
| "B-ORGpart", | |
| "I-ORGpart", | |
| "B-OTH", | |
| "I-OTH", | |
| "B-OTHderiv", | |
| "I-OTHderiv", | |
| "B-OTHpart", | |
| "I-OTHpart", | |
| "B-PER", | |
| "I-PER", | |
| "B-PERderiv", | |
| "I-PERderiv", | |
| "B-PERpart", | |
| "I-PERpart", | |
| ] | |
| ) | |
| ), | |
| "nested_ner_tags": datasets.Sequence( | |
| datasets.features.ClassLabel( | |
| names=[ | |
| "O", | |
| "B-LOC", | |
| "I-LOC", | |
| "B-LOCderiv", | |
| "I-LOCderiv", | |
| "B-LOCpart", | |
| "I-LOCpart", | |
| "B-ORG", | |
| "I-ORG", | |
| "B-ORGderiv", | |
| "I-ORGderiv", | |
| "B-ORGpart", | |
| "I-ORGpart", | |
| "B-OTH", | |
| "I-OTH", | |
| "B-OTHderiv", | |
| "I-OTHderiv", | |
| "B-OTHpart", | |
| "I-OTHpart", | |
| "B-PER", | |
| "I-PER", | |
| "B-PERderiv", | |
| "I-PERderiv", | |
| "B-PERpart", | |
| "I-PERpart", | |
| ] | |
| ) | |
| ), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="https://sites.google.com/site/germeval2014ner/", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| logger.info("⏳ Generating examples from = %s", filepath) | |
| with open(filepath, encoding="utf-8") as f: | |
| data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
| current_source = "" | |
| current_tokens = [] | |
| current_ner_tags = [] | |
| current_nested_ner_tags = [] | |
| sentence_counter = 0 | |
| for row in data: | |
| if row: | |
| if row[0] == "#": | |
| current_source = " ".join(row[1:]) | |
| continue | |
| id_, token, label, nested_label = row[:4] | |
| current_tokens.append(token) | |
| current_ner_tags.append(label) | |
| current_nested_ner_tags.append(nested_label) | |
| else: | |
| # New sentence | |
| if not current_tokens: | |
| # Consecutive empty lines will cause empty sentences | |
| continue | |
| assert len(current_tokens) == len(current_ner_tags), "💔 between len of tokens & labels" | |
| assert len(current_ner_tags) == len( | |
| current_nested_ner_tags | |
| ), "💔 between len of labels & nested labels" | |
| assert current_source, "💥 Source for new sentence was not set" | |
| sentence = ( | |
| sentence_counter, | |
| { | |
| "id": str(sentence_counter), | |
| "tokens": current_tokens, | |
| "ner_tags": current_ner_tags, | |
| "nested_ner_tags": current_nested_ner_tags, | |
| "source": current_source, | |
| }, | |
| ) | |
| sentence_counter += 1 | |
| current_tokens = [] | |
| current_ner_tags = [] | |
| current_nested_ner_tags = [] | |
| current_source = "" | |
| yield sentence | |
| # Don't forget last sentence in dataset 🧐 | |
| yield sentence_counter, { | |
| "id": str(sentence_counter), | |
| "tokens": current_tokens, | |
| "ner_tags": current_ner_tags, | |
| "nested_ner_tags": current_nested_ner_tags, | |
| "source": current_source, | |
| } | |