Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Finnish
Size:
10K<n<100K
ArXiv:
License:
File size: 7,480 Bytes
17d5bb3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
# 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 Finnish News Corpus for Named Entity Recognition dataset."""
from __future__ import absolute_import, division, print_function
import csv
import logging
import datasets
_CITATION = """\
@article{ruokolainen2019finnish,
title={A finnish news corpus for named entity recognition},
author={Ruokolainen, Teemu and Kauppinen, Pekka and Silfverberg, Miikka and Lind{\'e}n, Krister},
journal={Language Resources and Evaluation},
pages={1--26},
year={2019},
publisher={Springer}
}
"""
_DESCRIPTION = """\
The directory data contains a corpus of Finnish technology related news articles with a manually prepared
named entity annotation (digitoday.2014.csv). The text material was extracted from the archives of Digitoday,
a Finnish online technology news source (www.digitoday.fi). The corpus consists of 953 articles
(193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date).
The corpus is available for research purposes and can be readily used for development of NER systems for Finnish.
"""
_URLS = {
"train": "https://github.com/mpsilfve/finer-data/raw/master/data/digitoday.2014.train.csv",
"dev": "https://github.com/mpsilfve/finer-data/raw/master/data/digitoday.2014.dev.csv",
"test": "https://github.com/mpsilfve/finer-data/raw/master/data/digitoday.2015.test.csv",
"test_wikipedia": "https://github.com/mpsilfve/finer-data/raw/master/data/wikipedia.test.csv",
}
class FinerConfig(datasets.BuilderConfig):
"""BuilderConfig for FiNER dataset."""
def __init__(self, **kwargs):
"""BuilderConfig for FiNER dataset.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FinerConfig, self).__init__(**kwargs)
class Finer(datasets.GeneratorBasedBuilder):
"""FiNER dataset."""
BUILDER_CONFIGS = [
FinerConfig(
name="finer",
version=datasets.Version("1.0.0"),
description="A Finnish News Corpus for Named Entity Recognition 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=[
"O",
"B-DATE",
"B-EVENT",
"B-LOC",
"B-ORG",
"B-PER",
"B-PRO",
"I-DATE",
"I-EVENT",
"I-LOC",
"I-ORG",
"I-PER",
"I-PRO",
]
)
),
"nested_ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-DATE",
"B-EVENT",
"B-LOC",
"B-ORG",
"B-PER",
"B-PRO",
"I-DATE",
"I-EVENT",
"I-LOC",
"I-ORG",
"I-PER",
"I-PRO",
]
)
),
}
),
supervised_keys=None,
homepage="https://github.com/mpsilfve/finer-data",
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"]}),
datasets.SplitGenerator(
name=datasets.Split("test_wikipedia"), gen_kwargs={"filepath": downloaded_files["test_wikipedia"]}
),
]
def _generate_examples(self, filepath):
logging.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_tokens = []
current_ner_tags = []
current_nested_ner_tags = []
sentence_counter = 0
for row in data:
if row and "" not in row:
token, label, nested_label = row[:3]
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"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_ner_tags,
"nested_ner_tags": current_nested_ner_tags,
},
)
sentence_counter += 1
current_tokens = []
current_ner_tags = []
current_nested_ner_tags = []
yield sentence
# Don't forget last sentence in dataset 🧐
if current_tokens:
yield sentence_counter, {
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_ner_tags,
"nested_ner_tags": current_nested_ner_tags,
}
|