Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
1M<n<10M
ArXiv:
DOI:
License:
| # 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 | |
| import datasets | |
| _DESCRIPTION = """\ | |
| A dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities. | |
| """ | |
| _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} | |
| } | |
| """ | |
| _URL = { | |
| "train": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_train.conll", | |
| "dev": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_dev.conll", | |
| "test": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_test.conll", | |
| } | |
| _VERSION = "1.0.0" | |
| class German_LER(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version(_VERSION) | |
| 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): | |
| """Returns SplitGenerators.""" | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| data_dir = dl_manager.download_and_extract(_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"datapath": data_dir["train"], "split": "train"}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"datapath": data_dir["test"], "split": "test"}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"datapath": data_dir["dev"], "split": "dev"}, | |
| ), | |
| ] | |
| def _generate_examples(self, datapath, split): | |
| 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 |