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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 41 42 43 44 45 | from __future__ import annotations
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class BengaliSentimentAnalysis(AbsTaskClassification):
metadata = TaskMetadata(
name="BengaliSentimentAnalysis",
description="dataset contains 3307 Negative reviews and 8500 Positive reviews collected and manually annotated from Youtube Bengali drama.",
reference="https://data.mendeley.com/datasets/p6zc7krs37/4",
dataset={
"path": "Akash190104/bengali_sentiment_analysis",
"revision": "a4b3685b1854cc26c554dda4c7cb918a36a6fb6c",
},
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["ben-Beng"],
main_score="f1",
date=("2020-06-24", "2020-11-26"),
form=["written"],
dialect=[],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="CC BY 4.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
text_creation="found",
bibtex_citation="""@inproceedings{sazzed2020cross,
title={Cross-lingual sentiment classification in low-resource Bengali language},
author={Sazzed, Salim},
booktitle={Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)},
pages={50--60},
year={2020}
}""",
n_samples={"train": 11807},
avg_character_length={"train": 69.66},
)
def dataset_transform(self):
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train"]
)
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