shaj.py

# coding=utf-8
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# Licensed under the Apache License, Version 2.0 (the "License");
# 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
"""SHAJ: An abusive language dataset for Albanian"""

import csv
import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{nurce2021detecting,
  title={Detecting Abusive Albanian},
  author={Nurce, Erida and Keci, Jorgel and Derczynski, Leon},
  journal={arXiv preprint arXiv:2107.13592},
  year={2021}
}
"""

_DESCRIPTION = """\
This is an abusive/offensive language detection dataset for Albanian. The data is formatted
following the OffensEval convention, with three tasks:

* Subtask A: Offensive (OFF) or not (NOT)
* Subtask B: Untargeted (UNT) or targeted insult (TIN)
* Subtask C: Type of target: individual (IND), group (GRP), or other (OTH)

* The subtask A field should always be filled.
* The subtask B field should only be filled if there's "offensive" (OFF) in A.
* The subtask C field should only be filled if there's "targeted" (TIN) in B.

The dataset name is a backronym, also standing for "Spoken Hate in the Albanian Jargon"

See the paper [https://arxiv.org/abs/2107.13592](https://arxiv.org/abs/2107.13592) for full details.
"""

_URL = "full_albanian_dataset.csv"


class ShajConfig(datasets.BuilderConfig):
    """BuilderConfig for Shaj"""

    def __init__(self, **kwargs):
        """BuilderConfig Shaj.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(ShajConfig, self).__init__(**kwargs)


class Shaj(datasets.GeneratorBasedBuilder):
    """Shaj dataset."""

    BUILDER_CONFIGS = [
        ShajConfig(name="Shaj", version=datasets.Version("1.0.0"), description="Abusive language dataset in Albanian"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "subtask_a": datasets.features.ClassLabel(
                        names=[
                            "OFF",
                            "NOT",
                        ]
                    ),
                    "subtask_b": datasets.features.ClassLabel(
                        names=[
                            "TIN",
                            "UNT",
                            "",
                        ]
                    ),
                    "subtask_c": datasets.features.ClassLabel(
                        names=[
                            "IND",
                            "GRP",
                            "OTH",
                            "",
                        ]
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://arxiv.org/abs/2107.13592",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract(_URL)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            shaj_reader = csv.DictReader(f, fieldnames=('text','subtask_a','subtask_b','subtask_c'), delimiter=";", quotechar='"')
            guid = 0
            for instance in shaj_reader:
                instance["id"] = str(guid)
                yield guid, instance
                guid += 1