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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 46 47 48 49 50 51 52 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
N_SAMPLES = 2048
class RomanianReviewsSentiment(AbsTaskClassification):
metadata = TaskMetadata(
name="RomanianReviewsSentiment",
description="LaRoSeDa (A Large Romanian Sentiment Data Set) contains 15,000 reviews written in Romanian",
reference="https://arxiv.org/abs/2101.04197",
dataset={
"path": "universityofbucharest/laroseda",
"revision": "358bcc95aeddd5d07a4524ee416f03d993099b23",
},
type="Classification",
category="s2s",
date=("2020-01-01", "2021-01-11"),
eval_splits=["test"],
eval_langs=["ron-Latn"],
main_score="accuracy",
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="CC-BY-4.0",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@article{
tache2101clustering,
title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
journal={ArXiv},
year = {2021}
}
""",
n_samples={"test": N_SAMPLES},
avg_character_length={"test": 588.6},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns(
{"content": "text", "starRating": "label"}
)
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["test"]
)
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