# 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 """Toxic/Abusive Tweets Multilabel Classification Dataset for Brazilian Portuguese.""" import os import pandas as pd import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{DBLP:journals/corr/abs-2010-04543, author = {Joao Augusto Leite and Diego F. Silva and Kalina Bontcheva and Carolina Scarton}, title = {Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis}, journal = {CoRR}, volume = {abs/2010.04543}, year = {2020}, url = {https://arxiv.org/abs/2010.04543}, eprinttype = {arXiv}, eprint = {2010.04543}, timestamp = {Tue, 15 Dec 2020 16:10:16 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2010-04543.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DESCRIPTION = """\ ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender). Each tweet was labeled by three annotators in 6 possible categories: LGBTQ+phobia,Xenophobia, Obscene, Insult, Misogyny and Racism. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/JAugusto97/ToLD-Br" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "https://github.com/JAugusto97/ToLD-Br/blob/main/LICENSE_ToLD-Br.txt " # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "multilabel": "https://raw.githubusercontent.com/JAugusto97/ToLD-Br/main/ToLD-BR.csv", "binary": "https://github.com/JAugusto97/ToLD-Br/raw/main/experiments/data/1annotator.zip", } class ToldBr(datasets.GeneratorBasedBuilder): """Toxic/Abusive Tweets Classification Dataset for Brazilian Portuguese.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig( name="multilabel", version=VERSION, description=""" Full multilabel dataset with target values ranging from 0 to 3 representing the votes from each annotator. """, ), datasets.BuilderConfig( name="binary", version=VERSION, description=""" Binary classification dataset version separated in train, dev and test test. A text is considered toxic if at least one of the multilabel classes were labeled by at least one annotator. """, ), ] DEFAULT_CONFIG_NAME = "binary" def _info(self): if self.config.name == "binary": features = datasets.Features( { "text": datasets.Value("string"), "label": datasets.ClassLabel(names=["not-toxic", "toxic"]), } ) else: features = datasets.Features( { "text": datasets.Value("string"), "homophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]), "obscene": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]), "insult": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]), "racism": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]), "misogyny": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]), "xenophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION ) def _split_generators(self, dl_manager): urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) if self.config.name == "binary": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_train_1annotator.csv")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_test_1annotator.csv")}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "1annotator/ptbr_validation_1annotator.csv")}, ), ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir), }, ) ] def _generate_examples(self, filepath): df = pd.read_csv(filepath, engine="python") for key, row in enumerate(df.itertuples()): if self.config.name == "multilabel": yield key, { "text": row.text, "homophobia": int(float(row.homophobia)), "obscene": int(float(row.obscene)), "insult": int(float(row.insult)), "racism": int(float(row.racism)), "misogyny": int(float(row.misogyny)), "xenophobia": int(float(row.xenophobia)), } else: yield key, {"text": row.text, "label": int(row.toxic)}