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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 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
N_SAMPLES = 2800
class SiswatiNewsClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="SiswatiNewsClassification",
description="Siswati News Classification Dataset",
reference="https://huggingface.co/datasets/dsfsi/za-isizulu-siswati-news",
dataset={
"path": "isaacchung/siswati-news",
"revision": "f5502326c4e48adc99b18b1582f68b8fb5e7ec30",
},
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["ssw-Latn"],
main_score="accuracy",
date=("2022-08-01", "2022-08-01"),
form=["written"],
domains=["News"],
task_subtypes=["Topic classification"],
license="CC-BY-SA-4.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} }
""",
n_samples={"train": 80},
avg_character_length={"train": 354.2},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns({"title": "text"})
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