Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Swedish
Size:
1K<n<10K
License:
File size: 3,963 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 = """\
Webbnyheter 2012 from Spraakbanken, semi-manually annotated and adapted for CoreNLP Swedish NER. Semi-manually defined in this case as: Bootstrapped from Swedish Gazetters then manually correcte/reviewed by two independent native speaking swedish annotators. No annotator agreement calculated.
"""
_HOMEPAGE_URL = "https://github.com/klintan/swedish-ner-corpus"
_TRAIN_URL = "https://raw.githubusercontent.com/klintan/swedish-ner-corpus/master/train_corpus.txt"
_TEST_URL = "https://raw.githubusercontent.com/klintan/swedish-ner-corpus/master/test_corpus.txt"
_CITATION = None
class SwedishNERCorpus(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
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=["0", "LOC", "MISC", "ORG", "PER"])
),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_URL)
test_path = dl_manager.download_and_extract(_TEST_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": train_path},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"datapath": test_path},
),
]
def _generate_examples(self, datapath):
sentence_counter = 0
with open(datapath, 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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