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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 mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class MyanmarNews(AbsTaskClassification):
metadata = TaskMetadata(
name="MyanmarNews",
dataset={
"path": "ayehninnkhine/myanmar_news",
"revision": "b899ec06227db3679b0fe3c4188a6b48cc0b65eb",
},
description="The Myanmar News dataset on Hugging Face contains news articles in Burmese. It is designed for tasks such as text classification, sentiment analysis, and language modeling. The dataset includes a variety of news topics in 4 categorie, providing a rich resource for natural language processing applications involving Burmese which is a low resource language.",
reference="https://huggingface.co/datasets/myanmar_news",
type="Classification",
category="p2p",
eval_splits=["train"],
eval_langs=["mya-Mymr"],
main_score="accuracy",
date=("2017-10-01", "2017-10-31"),
form=["written"],
domains=["News"],
task_subtypes=["Topic classification"],
license="GPL 3.0",
socioeconomic_status="low",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation=""""
@inproceedings{Khine2017,
author = {A. H. Khine and K. T. Nwet and K. M. Soe},
title = {Automatic Myanmar News Classification},
booktitle = {15th Proceedings of International Conference on Computer Applications},
year = {2017},
month = {February},
pages = {401--408}
}""",
n_samples={"train": 2048},
avg_character_length={"train": 174.2},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns({"category": "label"})
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train"]
)
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