File size: 2,981 Bytes
73cc8d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | from __future__ import annotations
from mteb.abstasks import AbsTaskMultilabelClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class BrazilianToxicTweetsClassification(AbsTaskMultilabelClassification):
metadata = TaskMetadata(
name="BrazilianToxicTweetsClassification",
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.
""",
reference="https://paperswithcode.com/dataset/told-br",
dataset={
"path": "JAugusto97/told-br",
"revision": "fb4f11a5bc68b99891852d20f1ec074be6289768",
"name": "multilabel",
},
type="MultilabelClassification",
category="s2s",
eval_splits=["test"],
eval_langs=["por-Latn"],
main_score="accuracy",
date=("2019-08-01", "2019-08-16"),
form=["written"],
domains=["Constructed"],
task_subtypes=["Sentiment/Hate speech"],
license="CC BY-SA 4.0",
socioeconomic_status="medium",
annotations_creators="expert-annotated",
dialect=["brazilian"],
text_creation="found",
bibtex_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},
}""",
n_samples={"test": 2048},
avg_character_length={"test": 85.05},
)
def dataset_transform(self):
cols_ = ["homophobia", "obscene", "insult", "racism", "misogyny", "xenophobia"]
n_size = len(self.dataset["train"])
labels = [[] for _ in range(n_size)]
for c in cols_:
col_list = self.dataset["train"][c]
for i in range(n_size):
if col_list[i] > 0:
labels[i].append(c)
self.dataset = self.dataset["train"].add_column("label", labels)
del labels
self.dataset = self.dataset.remove_columns(cols_)
self.dataset = self.dataset.train_test_split(
train_size=2048, test_size=2048, seed=self.seed
)
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