# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. """LegalGLUE: A Benchmark Dataset for Legal NLP models.""" import csv import json import textwrap import os import datasets _DESCRIPTION = """\ Legal General Language Understanding Evaluation (LegalGLUE) benchmark is a collection of datasets for evaluating model performance across a diverse set of legal NLP tasks """ GERMAN_LER = [ "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"] LENER_BR=[ "O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA", ] class LegalGlueConfig(datasets.BuilderConfig): """BuilderConfig for LegalGLUE.""" def __init__( self, label_classes, #the list of classes of the labels multi_label, #boolean, if the task is multi-label homepage, #homepage of the original dataset citation, #citation for the dataset data_url, data_files, **kwargs, ): super(LegalGlueConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs) self.label_classes = label_classes self.multi_label = multi_label self.homepage = homepage self.citation = citation self.data_url = data_url self.data_files = data_files class LegalGLUE(datasets.GeneratorBasedBuilder): """LegalGLUE: A Benchmark Dataset for Legal Language Understanding""" BUILDER_CONFIGS = [ LegalGlueConfig( name="german_ler", description=textwrap.dedent( """\ description""" ), label_classes=GERMAN_LER, multi_label=False, data_url="https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip", data_files=["bag.conll", "bfh.conll", "bgh.conll", "bpatg.conll", "bsg.conll", "bverfg.conll", "bverwg.conll"], homepage="https://github.com/elenanereiss/Legal-Entity-Recognition", citation=textwrap.dedent("""\ @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}} """) ), # LegalGlueConfig( # name="lener_br", # description=textwrap.dedent( # """\ # LeNER-Br is a Portuguese language dataset for named entity recognition # applied to legal documents. LeNER-Br consists entirely of manually annotated # legislation and legal cases texts and contains tags for persons, locations, # time entities, organizations, legislation and legal cases. # To compose the dataset, 66 legal documents from several Brazilian Courts were # collected. Courts of superior and state levels were considered, such as Supremo # Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas # Gerais and Tribunal de Contas da União. In addition, four legislation documents # were collected, such as "Lei Maria da Penha", giving a total of 70 documents # """ # ), # label_classes=LENER_BR, # multi_label=False, # data_url="https://github.com/peluz/lener-br/raw/master/leNER-Br/", # data_files=["train/train.conll", "dev/dev.conll", "test/test.conll"], # homepage="https://cic.unb.br/~teodecampos/LeNER-Br/", # citation=textwrap.dedent("""\ # @inproceedings{luz_etal_propor2018, # author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and # Renato R. R. {de Oliveira} and Matheus Stauffer and # Samuel Couto and Paulo Bermejo}, # title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text}, # booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})}, # publisher = {Springer}, # series = {Lecture Notes on Computer Science ({LNCS})}, # pages = {313--323}, # year = {2018}, # month = {September 24-26}, # address = {Canela, RS, Brazil}, # doi = {10.1007/978-3-319-99722-3_32}, # url = {https://cic.unb.br/~teodecampos/LeNER-Br/}, # } # """) # ) ] def _info(self): if self.config.name == "german_ler": features = { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=self.config.label_classes ) ) } return datasets.DatasetInfo( description=self.config.description, features=datasets.Features(features), homepage=self.config.homepage, citation=self.config.citation, ) def _split_generators(self, dl_manager): #archive = dl_manager.download(self.config.data_url) if self.config.name == "german_ler": archive = dl_manager.download_and_extract(self.config.data_url) return [datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": self.config.data_files, "split": "train", "files": [os.path.join(archive,file) for file in self.config.data_files]#dl_manager.iter_archive(archive), }, )] #elif self.config_name == "lener_br": # else: # return [ # datasets.SplitGenerator( # name=datasets.Split.TRAIN, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": self.config.data_files, # "split": "train", # "files": dl_manager.iter_archive(archive), # }, # ), # datasets.SplitGenerator( # name=datasets.Split.TEST, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": self.config.data_files, # "split": "test", # "files": dl_manager.iter_archive(archive), # }, # ), # datasets.SplitGenerator( # name=datasets.Split.VALIDATION, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": self.config.data_files, # "split": "validation", # "files": dl_manager.iter_archive(archive), # }, # ), # ] def _generate_examples(self, filepath, split, files): if self.config.name == "german_ler": texts, labels = [], [] for file in files: #if path in filepath: with open(file, encoding="utf-8") as f: tokens = [] tags = [] for line in f: if line == "" or line == "\n": if tokens: texts.append(tokens) labels.append(tags) tokens = [] tags = [] else: token, tag = line.split() tokens.append(token) tags.append(tag.rstrip()) if tokens: texts.append(tokens) labels.append(tags) for i,token in enumerate(texts): tokens = texts[i] ner_tags = labels[i] yield i, { "id": str(i), "tokens": tokens, "ner_tags": ner_tags, }