Datasets:
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| # 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"] | |
| 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}} | |
| """) | |
| ) | |
| ] | |
| 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", | |
| "archive": archive, | |
| }, | |
| ) | |
| 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, archive): | |
| if self.config_name == "german_ler": | |
| texts, labels = [], [] | |
| for file in filepath: | |
| path = os.path.join(archive,file) | |
| with open (path, 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()) | |
| texts.append(tokens) | |
| labels.append(tags) | |
| for i in enumerate(texts): | |
| tokens = text[i] | |
| ner_tags = labels[i] | |
| yield i, { | |
| "id": str(i), | |
| "tokens": tokens, | |
| "ner_tags": ner_tags, | |
| } | |