# 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.""" 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, }