Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
n<1K
License:
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 | |
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=f"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 | |