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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 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class SinhalaNewsSourceClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="SinhalaNewsSourceClassification",
description="This dataset contains Sinhala news headlines extracted from 9 news sources (websites) (Sri Lanka Army, Dinamina, GossipLanka, Hiru, ITN, Lankapuwath, NewsLK, Newsfirst, World Socialist Web Site-Sinhala).",
dataset={
"path": "NLPC-UOM/Sinhala-News-Source-classification",
"revision": "ac4d14eeb68efbef95e247542d4432ce674faeb1",
},
reference="https://huggingface.co/datasets/NLPC-UOM/Sinhala-News-Source-classification",
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["sin-Sinh"],
main_score="accuracy",
date=("2021-02-17", "2022-08-20"),
form=["written"],
domains=["News"],
task_subtypes=["Topic classification"],
license="mit",
socioeconomic_status="low",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@article{dhananjaya2022,
author = {Dhananjaya et al.},
title = {BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification},
journal = {Year of Publication},
year = {2022},
}""",
n_samples={"train": 24094},
avg_character_length={"train": 56.08},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_column("comment", "text")
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train"]
)
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