testbed/embeddings-benchmark__mteb/mteb/tasks/Classification/ara/OnlineStoreReviewSentimentClassification.py

from __future__ import annotations

from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata

N_SAMPLES = 2048


class OnlineStoreReviewSentimentClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="OnlineStoreReviewSentimentClassification",
        dataset={
            "path": "Ruqiya/Arabic_Reviews_of_SHEIN",
            "revision": "8ea114aa27b82a52373203830dc2f5b540b6fcac",
        },
        description="This dataset contains Arabic reviews of products from the SHEIN online store.",
        reference="https://huggingface.co/datasets/Ruqiya/Arabic_Reviews_of_SHEIN",
        type="Classification",
        category="s2s",
        eval_splits=["train"],
        eval_langs=["ara-Arab"],
        main_score="accuracy",
        date=("2024-05-01", "2024-05-15"),
        form=["written"],
        domains=["Reviews"],
        task_subtypes=["Sentiment/Hate speech"],
        license="Not specified",
        socioeconomic_status="mixed",
        annotations_creators="derived",
        dialect=["ara-Arab-SA"],
        text_creation="found",
        bibtex_citation="",
        n_samples={"train": N_SAMPLES},
        avg_character_length={"train": 137.2},
    )

    def dataset_transform(self):
        self.dataset = self.stratified_subsampling(
            self.dataset, seed=self.seed, splits=["train"]
        )