FEA-Bench / testbed /embeddings-benchmark__mteb /mteb /tasks /Classification /slk /CSFDSKMovieReviewSentimentClassification.py
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from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
N_SAMPLES = 2048
class CSFDSKMovieReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="CSFDSKMovieReviewSentimentClassification",
description="The dataset contains 30k user reviews from csfd.cz in Slovak.",
reference="https://arxiv.org/abs/2304.01922",
dataset={
"path": "fewshot-goes-multilingual/sk_csfd-movie-reviews",
"revision": "23a20c659d868740ef9c54854de631fe19cd5c17",
},
type="Classification",
category="s2s",
date=("2002-05-21", "2020-03-05"),
eval_splits=["test"],
eval_langs=["slk-Latn"],
main_score="accuracy",
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="CC-BY-SA-4.0",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@misc{štefánik2023resources,
title={Resources and Few-shot Learners for In-context Learning in Slavic Languages},
author={Michal Štefánik and Marek Kadlčík and Piotr Gramacki and Petr Sojka},
year={2023},
eprint={2304.01922},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
""",
n_samples={"test": N_SAMPLES},
avg_character_length={"test": 366.2},
)
@property
def metadata_dict(self):
md = super().metadata_dict
# Increase the samples_per_label in order to improve baseline performance
md["samples_per_label"] = 20
return md
def dataset_transform(self):
self.dataset = self.dataset.rename_columns(
{"comment": "text", "rating_int": "label"}
)
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["test"], n_samples=N_SAMPLES
)