Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
File size: 6,453 Bytes
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import os
import warnings
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):
warnings.warn(
"Dataset 'german_legal_entity_recognition' is deprecated and will be deleted. Use 'elenanereiss/german-ler' instead.",
FutureWarning,
)
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
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