Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
1M<n<10M
ArXiv:
DOI:
License:
File size: 7,641 Bytes
8a2d3e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
# 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):
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 |