Datasets:
Tasks:
Text Classification
Sub-tasks:
topic-classification
Languages:
Yoruba
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 4,193 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the 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
"""Yoruba BBC News Topic Classification dataset."""
import csv
import datasets
_DESCRIPTION = """\
A collection of news article headlines in Yoruba from BBC Yoruba.
Each headline is labeled with one of the following classes: africa,
entertainment, health, nigeria, politics, sport or world.
The dataset was presented in the paper:
Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
Distant Supervision for Multilingual Transformer Models: A Study on
African Languages (EMNLP 2020).
"""
_CITATION = """\
@inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow, Dietrich",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
doi = "10.18653/v1/2020.emnlp-main.204",
}
"""
_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/train_clean.tsv"
_VALIDATION_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/dev.tsv"
_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/test.tsv"
class YorubaBBCTopics(datasets.GeneratorBasedBuilder):
"""Yoruba BBC Topic Classification dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"news_title": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=["africa", "entertainment", "health", "nigeria", "politics", "sport", "world"]
),
"date": datasets.Value("string"),
"bbc_url_id": datasets.Value("string"),
}
),
homepage="https://github.com/uds-lsv/transfer-distant-transformer-african",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate Yoruba BBC News Topic examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file, delimiter="\t")
for id_, row in enumerate(csv_reader):
yield id_, {
"news_title": row["news_title"],
"label": row["label"],
"date": row["date"],
"bbc_url_id": row["bbc_url_id"],
}
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