Datasets:
Tasks:
Text Classification
Multilinguality:
multilingual
Size Categories:
n<1K
Language Creators:
expert-generated
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
File size: 3,021 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
"""WordSim-353 for Yoruba"""
import csv
import datasets
_DESCRIPTION = """\
A translation of the word pair similarity dataset wordsim-353 to Twi.
The dataset was presented in the paper
Alabi et al.: Massive vs. Curated Embeddings for Low-Resourced
Languages: the Case of Yorùbá and Twi (LREC 2020).
"""
_CITATION = """\
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\\`u}b{\\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
language = "English",
ISBN = "979-10-95546-34-4",
}
"""
_DOWNLOAD_URL = "https://raw.githubusercontent.com/ajesujoba/YorubaTwi-Embedding/master/Twi/wordsim_tw.csv"
class TwiWordsim353(datasets.GeneratorBasedBuilder):
"""WordSim-353 for Yoruba."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"twi1": datasets.Value("string"),
"twi2": datasets.Value("string"),
"similarity": datasets.Value("float32"),
}
),
homepage="https://github.com/ajesujoba/YorubaTwi-Embedding",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
test_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate WordSim-353 for Yoruba examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file, delimiter=",")
for id_, row in enumerate(csv_reader):
yield id_, {
"twi1": row["twi1"],
"twi2": row["twi2"],
"similarity": row["EngSim"],
}
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