Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Faroese
Size:
1K<n<10K
License:
File size: 4,345 Bytes
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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
# Modified by Vésteinn Snæbjarnarson 2021
#
# 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
LABELS = [
'B-Date',
'B-Location',
'B-Miscellaneous',
'B-Money',
'B-Organization',
'B-Percent',
'B-Person',
'B-Time',
'I-Date',
'I-Location',
'I-Miscellaneous',
'I-Money',
'I-Organization',
'I-Percent',
'I-Person',
'I-Time',
'O',
]
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@misc{sosialurin-ner,
title = {},
author = {},
url = {},
year = {2022} }
"""
_DESCRIPTION = """\
The corpus that has been created consists of ca. 100.000 words of text from the [Faroese] newspaper Sosialurin. Each word is tagged with named entity information
"""
_URL = "https://huggingface.co/datasets/vesteinn/sosialurin-faroese-ner/raw/main/"
_TRAINING_FILE = "sosialurin.faroese.ner.train.txt"
class SosialurinNERConfig(datasets.BuilderConfig):
"""BuilderConfig for sosialurin-faroese-ner"""
def __init__(self, **kwargs):
"""BuilderConfig for sosialurin-faroese-ner.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SosialurinNERConfig, self).__init__(**kwargs)
class SosialurinNER(datasets.GeneratorBasedBuilder):
"""sosialurin-faroese-ner dataset."""
BUILDER_CONFIGS = [
SosialurinNERConfig(name="sosialurin-faroese-ner", version=datasets.Version("0.1.0"), description="sosialurin-faroese-ner dataset"),
]
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=LABELS
)
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# tokens are tab separated
splits = line.split("\t")
tokens.append(splits[0])
try:
ner_tags.append(splits[1].rstrip())
except:
print(splits)
raise
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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