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# 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.
"""A new corpus of tagged data that can be useful for handling the issues in recognition of Classical Arabic named entities"""
+
+
import csv
import os
+
import datasets
+
+
_CITATION = """\
@article{article,
author = {Salah, Ramzi and Zakaria, Lailatul},
year = {2018},
month = {12},
pages = {},
title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
volume = {96},
journal = {Journal of Theoretical and Applied Information Technology}
}
"""
+
_DESCRIPTION = """\
Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
"""
+
_HOMEPAGE = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus"
+
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
+
_URL = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/archive/master.zip"
+
+
class Caner(datasets.GeneratorBasedBuilder):
"""Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities"""
+
VERSION = datasets.Version("1.1.0")
+
def _info(self):
+
features = datasets.Features(
{
"token": datasets.Value("string"),
"ner_tag": datasets.ClassLabel(
names=[
"Allah",
"Book",
"Clan",
"Crime",
"Date",
"Day",
"Hell",
"Loc",
"Meas",
"Mon",
"Month",
"NatOb",
"Number",
"O",
"Org",
"Para",
"Pers",
"Prophet",
"Rlig",
"Sect",
"Time",
]
),
}
)
+
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
+
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
+
my_urls = _URL
data_dir = dl_manager.download_and_extract(my_urls)
+
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "Classical-Arabic-Named-Entity-Recognition-Corpus-master/CANERCorpus.csv"
),
"split": "train",
},
)
]
+
def _generate_examples(self, filepath, split):
"""Yields examples."""
+
with open(filepath, encoding="utf-8") as csv_file:
reader = csv.reader(csv_file, delimiter=",")
next(reader, None)
+
for id_, row in enumerate(reader):
+
yield id_, {
"token": row[0],
"ner_tag": row[1],
}
+
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