File size: 2,562 Bytes
83d24b2 | 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 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class FinToxicityClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="FinToxicityClassification",
description="""
This dataset is a DeepL -based machine translated version of the Jigsaw toxicity dataset for Finnish. The dataset is originally from a Kaggle competition https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data.
The original dataset poses a multi-label text classification problem and includes the labels identity_attack, insult, obscene, severe_toxicity, threat and toxicity.
Here adapted for toxicity classification, which is the most represented class.
""",
dataset={
"path": "TurkuNLP/jigsaw_toxicity_pred_fi",
"revision": "6e7340e6be87124f319e25290778760c14df64d3",
},
reference="https://aclanthology.org/2023.nodalida-1.68",
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["fin-Latn"],
main_score="f1",
date=("2023-03-13", "2023-09-25"),
form=["written"],
domains=["News"],
task_subtypes=["Sentiment/Hate speech"],
license="ccy-by-sa-4.0",
socioeconomic_status="high",
annotations_creators="derived",
dialect=[],
text_creation="machine-translated",
bibtex_citation="""
@inproceedings{eskelinen-etal-2023-toxicity,
title = "Toxicity Detection in {F}innish Using Machine Translation",
author = "Eskelinen, Anni and
Silvala, Laura and
Ginter, Filip and
Pyysalo, Sampo and
Laippala, Veronika",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
}""",
n_samples={"train": 2048, "test": 2048},
avg_character_length={"train": 432.63, "test": 401.03},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_column("label_toxicity", "label")
remove_cols = [
col
for col in self.dataset["test"].column_names
if col not in ["text", "label"]
]
self.dataset = self.dataset.remove_columns(remove_cols)
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train", "test"]
)
|