Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Turkish
Size:
100K<n<1M
ArXiv:
License:
File size: 6,371 Bytes
8e3b9cf 7d7a44a 8e3b9cf bc5cb65 7d7a44a 8e3b9cf |
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 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""TODO: Add a description here."""
import os
import datasets
logger = datasets.logging.get_logger(__name__)
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings@article{DBLP:journals/corr/SahinTYES17,
author = {H. Bahadir Sahin and
Caglar Tirkaz and
Eray Yildiz and
Mustafa Tolga Eren and
Omer Ozan Sonmez},
title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
and Text Categorization using Large-Scale Gazetteers},
journal = {CoRR},
volume = {abs/1702.02363},
year = {2017},
url = {http://arxiv.org/abs/1702.02363},
archivePrefix = {arXiv},
eprint = {1702.02363},
timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Turkish Wikipedia Named-Entity Recognition and Text Categorization
(TWNERTC) dataset is a collection of automatically categorized and annotated
sentences obtained from Wikipedia. The authors constructed large-scale
gazetteers by using a graph crawler algorithm to extract
relevant entity and domain information
from a semantic knowledge base, Freebase.
The constructed gazetteers contains approximately
300K entities with thousands of fine-grained entity types
under 77 different domains.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://data.mendeley.com/datasets/cdcztymf4k/1"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "Creative Commons Attribution 4.0 International"
_URL = "https://data.mendeley.com/public-files/datasets/cdcztymf4k/files/5557ef78-7d53-4a01-8241-3173c47bbe10/file_downloaded"
_FILE_NAME_ZIP = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.zip"
_FILE_NAME = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.DUMP"
class TurkishNER(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"domain": datasets.ClassLabel(
names=[
"architecture",
"basketball",
"book",
"business",
"education",
"fictional_universe",
"film",
"food",
"geography",
"government",
"law",
"location",
"military",
"music",
"opera",
"organization",
"people",
"religion",
"royalty",
"soccer",
"sports",
"theater",
"time",
"travel",
"tv",
]
),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PERSON",
"I-PERSON",
"B-ORGANIZATION",
"I-ORGANIZATION",
"B-LOCATION",
"I-LOCATION",
"B-MISC",
"I-MISC",
]
)
),
}
),
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.extract(os.path.join(dl_manager.download_and_extract(_URL), _FILE_NAME_ZIP))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": (os.path.join(data_dir, _FILE_NAME)),
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
id_ = -1
for line in f:
if line == "" or line == "\n":
continue
else:
splits = line.split("\t")
id_ += 1
yield id_, {
"id": str(id_),
"domain": splits[0],
"tokens": splits[2].split(" "),
"ner_tags": splits[1].split(" "),
}
|