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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 49 50 51 52 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
N_SAMPLES = 2048
class RestaurantReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="RestaurantReviewSentimentClassification",
dataset={
"path": "hadyelsahar/ar_res_reviews",
"revision": "d51bf2435d030e0041344f576c5e8d7154828977",
},
description="Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis",
reference="https://link.springer.com/chapter/10.1007/978-3-319-18117-2_2",
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["ara-Arab"],
main_score="accuracy",
date=("2014-01-01", "2015-01-01"),
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="None specified",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=["ara-arab-EG", "ara-arab-JO", "ara-arab-SA"],
text_creation="found",
bibtex_citation="""
@inproceedings{elsahar2015building,
title={Building large arabic multi-domain resources for sentiment analysis},
author={ElSahar, Hady and El-Beltagy, Samhaa R},
booktitle={International conference on intelligent text processing and computational linguistics},
pages={23--34},
year={2015},
organization={Springer}
}
""",
n_samples={"train": N_SAMPLES},
avg_character_length={"train": 231.4},
)
def dataset_transform(self):
# labels: 0 negative, 1 positive
self.dataset = self.dataset.rename_column("polarity", "label")
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train"]
)
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