yoruba_wordsim353.py

# coding=utf-8
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""WordSim-353 for Yoruba"""


import csv

import datasets


_DESCRIPTION = """\
A translation of the word pair similarity dataset wordsim-353 to Yorùbá.

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/Yoruba/wordSim353_yo.csv"


class YorubaWordsim353(datasets.GeneratorBasedBuilder):
    """WordSim-353 for Yoruba."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "english1": datasets.Value("string"),
                    "english2": datasets.Value("string"),
                    "yoruba1": datasets.Value("string"),
                    "yoruba2": 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_, {
                    "english1": row["English1"],
                    "english2": row["English2"],
                    "yoruba1": row["Yoruba1"],
                    "yoruba2": row["Yoruba2"],
                    "similarity": row["EngSim"],
                }