Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
1M<n<10M
ArXiv:
DOI:
License:
File size: 9,125 Bytes
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# 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 = """\
@misc{https://doi.org/10.48550/arxiv.2003.13016,
doi = {10.48550/ARXIV.2003.13016},
url = {https://arxiv.org/abs/2003.13016},
author = {Leitner, Elena and Rehm, Georg and Moreno-Schneider, Julián},
keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {A Dataset of German Legal Documents for Named Entity Recognition},
publisher = {arXiv},
year = {2020},
copyright = {arXiv.org perpetual, non-exclusive license}
}
"""
_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",
]
)
),
"ner_coarse_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B-LIT",
"B-LOC",
"B-NRM",
"B-ORG",
"B-PER",
"B-REG",
"B-RS",
"I-LIT",
"I-LOC",
"I-NRM",
"I-ORG",
"I-PER",
"I-REG",
"I-RS",
"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
with open(datapath, encoding="utf-8") as f:
current_words = []
current_labels = []
current_coarse_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)
# generate coarse-grained tags
new_label = ""
if label == 'O': new_label = label
else:
bio, fine_tag = label.split("-")
if fine_tag in ['PER', 'RR', 'AN']: new_label = bio + '-PER'
elif fine_tag in ['LD', 'ST', 'STR', 'LDS']: new_label = bio + '-LOC'
elif fine_tag in ['ORG', 'UN', 'INN', 'GRT', 'MRK']: new_label = bio + '-ORG'
elif fine_tag in ['GS', 'VO', 'EUN']: new_label = bio + '-NRM'
elif fine_tag in ['VS', 'VT']: new_label = bio + '-REG'
else: new_label = label
current_coarse_labels.append(new_label)
else:
if not current_words:
continue
assert len(current_words) == len(current_labels), "word len doesnt match label length"
assert len(current_words) == len(current_coarse_labels), "word len doesnt match coarse label length"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_words,
"ner_tags": current_labels,
"ner_coarse_tags": current_coarse_labels,
},
)
sentence_counter += 1
current_words = []
current_labels = []
current_coarse_labels = []
yield sentence
# last sentence
if current_words:
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_words,
"ner_tags": current_labels,
"ner_coarse_tags": current_coarse_labels,
},
)
yield sentence
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