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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 47 48 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class JavaneseIMDBClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="JavaneseIMDBClassification",
description="Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.",
reference="https://github.com/w11wo/nlp-datasets#javanese-imdb",
dataset={
"path": "w11wo/imdb-javanese",
"revision": "11bef3dfce0ce107eb5e276373dcd28759ce85ee",
},
type="Classification",
category="s2s",
date=("2021-06-24", "2021-06-24"),
eval_splits=["test"],
eval_langs=["jav-Latn"],
main_score="accuracy",
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="MIT",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""
@inproceedings{wongso2021causal,
title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
""",
n_samples={"test": 25_000},
avg_character_length={"test": 481.83},
)
def dataset_transform(self):
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["test"]
)
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