import os 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 @property 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): 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