Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
File size: 8,947 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
"""The GermEval 2014 NER Shared Task dataset."""
import csv
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
month = {may},
year = {2014},
address = {Reykjavik, Iceland},
publisher = {European Language Resources Association (ELRA)},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
pages = {2524--2531},
}
"""
_DESCRIPTION = """\
The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties:\
- The data was sampled from German Wikipedia and News Corpora as a collection of citations.\
- The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.\
- The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines,\
using four main NER categories with sub-structure, and annotating embeddings among NEs\
such as [ORG FC Kickers [LOC Darmstadt]].
"""
_URLS = {
"train": "https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P",
"dev": "https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm",
"test": "https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH",
}
class GermEval14Config(datasets.BuilderConfig):
"""BuilderConfig for GermEval 2014."""
def __init__(self, **kwargs):
"""BuilderConfig for GermEval 2014.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(GermEval14Config, self).__init__(**kwargs)
class GermEval14(datasets.GeneratorBasedBuilder):
"""GermEval 2014 NER Shared Task dataset."""
BUILDER_CONFIGS = [
GermEval14Config(
name="germeval_14", version=datasets.Version("2.0.0"), description="GermEval 2014 NER Shared Task dataset"
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"source": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-LOC",
"I-LOC",
"B-LOCderiv",
"I-LOCderiv",
"B-LOCpart",
"I-LOCpart",
"B-ORG",
"I-ORG",
"B-ORGderiv",
"I-ORGderiv",
"B-ORGpart",
"I-ORGpart",
"B-OTH",
"I-OTH",
"B-OTHderiv",
"I-OTHderiv",
"B-OTHpart",
"I-OTHpart",
"B-PER",
"I-PER",
"B-PERderiv",
"I-PERderiv",
"B-PERpart",
"I-PERpart",
]
)
),
"nested_ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-LOC",
"I-LOC",
"B-LOCderiv",
"I-LOCderiv",
"B-LOCpart",
"I-LOCpart",
"B-ORG",
"I-ORG",
"B-ORGderiv",
"I-ORGderiv",
"B-ORGpart",
"I-ORGpart",
"B-OTH",
"I-OTH",
"B-OTHderiv",
"I-OTHderiv",
"B-OTHpart",
"I-OTHpart",
"B-PER",
"I-PER",
"B-PERderiv",
"I-PERderiv",
"B-PERpart",
"I-PERpart",
]
)
),
}
),
supervised_keys=None,
homepage="https://sites.google.com/site/germeval2014ner/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
current_source = ""
current_tokens = []
current_ner_tags = []
current_nested_ner_tags = []
sentence_counter = 0
for row in data:
if row:
if row[0] == "#":
current_source = " ".join(row[1:])
continue
id_, token, label, nested_label = row[:4]
current_tokens.append(token)
current_ner_tags.append(label)
current_nested_ner_tags.append(nested_label)
else:
# New sentence
if not current_tokens:
# Consecutive empty lines will cause empty sentences
continue
assert len(current_tokens) == len(current_ner_tags), "💔 between len of tokens & labels"
assert len(current_ner_tags) == len(
current_nested_ner_tags
), "💔 between len of labels & nested labels"
assert current_source, "💥 Source for new sentence was not set"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_ner_tags,
"nested_ner_tags": current_nested_ner_tags,
"source": current_source,
},
)
sentence_counter += 1
current_tokens = []
current_ner_tags = []
current_nested_ner_tags = []
current_source = ""
yield sentence
# Don't forget last sentence in dataset 🧐
yield sentence_counter, {
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_ner_tags,
"nested_ner_tags": current_nested_ner_tags,
"source": current_source,
}
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