FRENK-hate-hr.py

# 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.
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"""An annotated dataset for classifying offensive or acceptable speech."""

import os
import csv

import datasets



_CITATION = """\
@misc{ljubešić2019frenk,
      title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, 
      author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
      year={2019},
      eprint={1906.02045},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1906.02045}
}
"""

_DESCRIPTION = """\
The FRENK Datasets of Socially Unacceptable Discourse in Croatian.
"""

_HOMEPAGE = "https://www.clarin.si/repository/xmlui/handle/11356/1433"

_LICENSE = "CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0"

_URL = "https://huggingface.co/datasets/5roop/FRENK-hate-hr/resolve/main/data.zip"

_CLASS_MAP_MULTICLASS = {
    'Acceptable speech': 0, 
    'Inappropriate': 1, 
    'Background offensive': 2,
    'Other offensive': 3, 
    'Background violence': 4,
    'Other violence': 5, 
}

_CLASS_MAP_BINARY = {
    'Acceptable': 0, 
    'Offensive': 1, 
}


class FRENKHateSpeechHR(datasets.GeneratorBasedBuilder):
    """The FRENK Datasets of Socially Unacceptable Discourse in Croatian."""

    VERSION = datasets.Version("0.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="binary", version=VERSION, 
                                description="Labels are either 'Offensive' or 'Acceptable'."),
        datasets.BuilderConfig(name="multiclass", version=VERSION, 
                                description="Labels are 'Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence'"),
    ]

    DEFAULT_CONFIG_NAME = "binary"  

    def _info(self):
        feature_dict = {
            "text": datasets.Value("string"),
            "target": datasets.Value("string"),
            "topic": datasets.Value("string"),
            }
        if self.config.name == "binary":
            features = datasets.Features(
                {
                    **feature_dict,
                    "label": datasets.ClassLabel(names=["Acceptable", "Offensive"]),
                }
            )
        else:
            features = datasets.Features(
                {
                    **feature_dict,
                    "label": datasets.ClassLabel(names=['Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence']),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,

            citation=_CITATION,
        )


    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        data_file = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={
                    'filepath': os.path.join(data_file, "train.tsv"),
                    }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={
                    'filepath': os.path.join(data_file, "dev.tsv"),
                    }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={
                    'filepath': os.path.join(data_file, "test.tsv"),
                    }
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        
        with open(filepath, encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t")
            for id_, row in enumerate(reader):
                if id_ == 0:
                    continue
                to_return_dict = {
                    "text": row[1],
                    "target": row[4] ,
                    "topic":  row[5]
                    }
                yield id_, {
                    **to_return_dict,
                    **{"label": _CLASS_MAP_BINARY[row[3]] if self.config.name == "binary" else _CLASS_MAP_MULTICLASS[row[2]]}
                }