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leondz commited on
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1 Parent(s): b0a042e

initial data + skeleton reader

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offenseval_2020.py ADDED
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+ # coding=utf-8
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+ # Copyright 2022 Leon Derczynski.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+
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+ # Lint as: python3
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+ """OffensEval 2020: Multilingual Offensive Language Detection"""
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+
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+ import csv
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+ import os
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+
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+ import datasets
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+
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+
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+ logger = datasets.logging.get_logger(__name__)
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+
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+
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+ _CITATION = """\
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+ @inproceedings{zampieri-etal-2020-semeval,
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+ title = {{SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)}},
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+ author = {Zampieri, Marcos and Nakov, Preslav and Rosenthal, Sara and Atanasova, Pepa and Karadzhov, Georgi and Mubarak, Hamdy and Derczynski, Leon and Pitenis, Zeses and \c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}},
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+ booktitle = {Proceedings of SemEval},
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+ year = {2020}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:
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+
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+ * Arabic
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+ * Danish
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+ * English
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+ * Greek
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+ * Turkish
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+
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+ The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019.
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+ In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account.
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+ The following sub-tasks were organized:
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+
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+ * Sub-task A - Offensive language identification;
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+ * Sub-task B - Automatic categorization of offense types;
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+ * Sub-task C - Offense target identification.
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+
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+ The English training data isn't included here (the text isn't available and needs rehydration of 9 million tweets;
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+ see [https://zenodo.org/record/3950379#.XxZ-aFVKipp](https://zenodo.org/record/3950379#.XxZ-aFVKipp))
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+ """
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+
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+ # _URL = ""
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+
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+
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+ class OffensEval2020Config(datasets.BuilderConfig):
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+ """BuilderConfig for OffensEval2020"""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig OffensEval2020.
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+
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(OffensEval2020Config, self).__init__(**kwargs)
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+
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+
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+ class OffensEval2020(datasets.GeneratorBasedBuilder):
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+ """OffensEval2020 dataset."""
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+
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+ BUILDER_CONFIGS = [
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+ OffensEval2020Config(name="ar", version=datasets.Version("1.0.0"), description="Offensive language data in Arabic"),
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+ OffensEval2020Config(name="da", version=datasets.Version("1.0.0"), description="Offensive language data in Danish"),
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+ OffensEval2020Config(name="en", version=datasets.Version("1.0.0"), description="Offensive language data in English"),
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+ OffensEval2020Config(name="gr", version=datasets.Version("1.0.0"), description="Offensive language data in Greek"),
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+ OffensEval2020Config(name="tr", version=datasets.Version("1.0.0"), description="Offensive language data in Turkish"),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "id": datasets.Value("string"),
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+ "text": datasets.Value("string"),
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+ "label": datasets.features.ClassLabel(
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+ names=[
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+ "OFF",
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+ "NOT",
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+ ]
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+ ),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage="https://sites.google.com/site/offensevalsharedtask/results-and-paper-submission",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ train_text = dl_manager.download_and_extract(f"offenseval-{self.config.name}-training-v1.tsv")
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+ train_labels = dl_manager.download_and_extract(f"offenseval-{self.config.name}-labela-v1.tsv")
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+ test = dl_manager.download_and_extract(f"offenseval-{self.config.name}-test-v1.tsv")
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ logger.info("⏳ Generating examples from = %s", filepath)
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+ with open(filepath, encoding="utf-8") as f:
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+ OffensEval2020_reader = csv.DictReader(f, delimiter="\t", quotechar='"')
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+ guid = 0
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+ for instance in OffensEval2020_reader:
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+ instance["id"] = str(guid)
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+ for dropfield in ('post_id', 'search_keywords', 'itr', 'disagreement_solving', 'created_at', 'in_reply_to_status_id'):
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+ instance.pop(dropfield)
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+ yield guid, instance
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+ guid += 1