Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
n<1K
License:
| import os | |
| import warnings | |
| import datasets | |
| _DESCRIPTION = """\ | |
| """ | |
| _HOMEPAGE_URL = "https://github.com/elenanereiss/Legal-Entity-Recognition" | |
| _CITATION = """\ | |
| @inproceedings{leitner2019fine, | |
| author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider}, | |
| title = {{Fine-grained Named Entity Recognition in Legal Documents}}, | |
| booktitle = {Semantic Systems. The Power of AI and Knowledge | |
| Graphs. Proceedings of the 15th International Conference | |
| (SEMANTiCS 2019)}, | |
| year = 2019, | |
| editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria | |
| Maleshkova and Tassilo Pellegrini and Harald Sack and York | |
| Sure-Vetter}, | |
| keywords = {aip}, | |
| publisher = {Springer}, | |
| series = {Lecture Notes in Computer Science}, | |
| number = {11702}, | |
| address = {Karlsruhe, Germany}, | |
| month = 9, | |
| note = {10/11 September 2019}, | |
| pages = {272--287}, | |
| pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}} | |
| """ | |
| _DATA_URL = "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip" | |
| _VERSION = "1.0.0" | |
| _COURTS = ["bag", "bfh", "bgh", "bpatg", "bsg", "bverfg", "bverwg"] | |
| _COURTS_FILEPATHS = {court: f"{court}.conll" for court in _COURTS} | |
| _ALL = "all" | |
| class GermanLegalEntityRecognitionConfig(datasets.BuilderConfig): | |
| def __init__(self, *args, courts=None, **kwargs): | |
| super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs) | |
| self.courts = courts | |
| def filepaths(self): | |
| return [_COURTS_FILEPATHS[court] for court in self.courts] | |
| class GermanLegalEntityRecognition(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| GermanLegalEntityRecognitionConfig(name=court, courts=[court], description=f"Court. {court}.") | |
| for court in _COURTS | |
| ] + [GermanLegalEntityRecognitionConfig(name=_ALL, courts=_COURTS, description="All courts included.")] | |
| BUILDER_CONFIG_CLASS = GermanLegalEntityRecognitionConfig | |
| DEFAULT_CONFIG_NAME = _ALL | |
| def _info(self): | |
| warnings.warn( | |
| "Dataset 'german_legal_entity_recognition' is deprecated and will be deleted. Use 'elenanereiss/german-ler' instead.", | |
| FutureWarning, | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "tokens": datasets.Sequence(datasets.Value("string")), | |
| "ner_tags": datasets.Sequence( | |
| datasets.features.ClassLabel( | |
| names=[ | |
| "B-AN", | |
| "B-EUN", | |
| "B-GRT", | |
| "B-GS", | |
| "B-INN", | |
| "B-LD", | |
| "B-LDS", | |
| "B-LIT", | |
| "B-MRK", | |
| "B-ORG", | |
| "B-PER", | |
| "B-RR", | |
| "B-RS", | |
| "B-ST", | |
| "B-STR", | |
| "B-UN", | |
| "B-VO", | |
| "B-VS", | |
| "B-VT", | |
| "I-AN", | |
| "I-EUN", | |
| "I-GRT", | |
| "I-GS", | |
| "I-INN", | |
| "I-LD", | |
| "I-LDS", | |
| "I-LIT", | |
| "I-MRK", | |
| "I-ORG", | |
| "I-PER", | |
| "I-RR", | |
| "I-RS", | |
| "I-ST", | |
| "I-STR", | |
| "I-UN", | |
| "I-VO", | |
| "I-VS", | |
| "I-VT", | |
| "O", | |
| ] | |
| ) | |
| ), | |
| }, | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE_URL, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| path = dl_manager.download_and_extract(_DATA_URL) | |
| return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"datapath": path})] | |
| def _generate_examples(self, datapath): | |
| sentence_counter = 0 | |
| for filepath in self.config.filepaths: | |
| filepath = os.path.join(datapath, filepath) | |
| with open(filepath, encoding="utf-8") as f: | |
| current_words = [] | |
| current_labels = [] | |
| for row in f: | |
| row = row.rstrip() | |
| row_split = row.split() | |
| if len(row_split) == 2: | |
| token, label = row_split | |
| current_words.append(token) | |
| current_labels.append(label) | |
| else: | |
| if not current_words: | |
| continue | |
| assert len(current_words) == len(current_labels), "word len doesnt match label length" | |
| sentence = ( | |
| sentence_counter, | |
| { | |
| "id": str(sentence_counter), | |
| "tokens": current_words, | |
| "ner_tags": current_labels, | |
| }, | |
| ) | |
| sentence_counter += 1 | |
| current_words = [] | |
| current_labels = [] | |
| yield sentence | |
| # if something remains: | |
| if current_words: | |
| sentence = ( | |
| sentence_counter, | |
| { | |
| "id": str(sentence_counter), | |
| "tokens": current_words, | |
| "ner_tags": current_labels, | |
| }, | |
| ) | |
| yield sentence | |