Datasets:
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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.
# Lint as: python3
import json
import datasets
from dataclasses import dataclass
_CITATION = '''
coming soon ...
'''
languages = [
'afrikaans',
'amharic',
'egyptian_arabic',
'hausa',
'igbo',
'moroccan_arabic',
'northern_sotho',
'shona',
'somali',
'swahili',
'tigrinya',
'twi',
'wolof',
'yoruba',
'zulu'
]
_DESCRIPTION = 'dataset load script for AfriClirMatrix'
_DATASET_URLS = {
lang: {
'train': f'https://huggingface.co/datasets/ToluClassics/africlirmatrix/resolve/main/africlirmatrix-v1.0-{lang}/corpus.jsonl',
} for lang in languages
}
class MrTyDiCorpus(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version('1.1.0'),
name=lang,
description=f'AfriCLIRMatrix dataset in language {lang}.'
) for lang in languages
]
def _info(self):
features = datasets.Features({
'id': datasets.Value('string'),
'contents': datasets.Value('string'),
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='https://github.com/castorini/africlirmatrix',
# License for the dataset if available
license='',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
lang = self.config.name
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[lang])
splits = [
datasets.SplitGenerator(
name='train',
gen_kwargs={
'filepath': downloaded_files['train'],
},
),
]
return splits
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
yield data['id'], data
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