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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 53 54 55 56 | from __future__ import annotations
from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class ArEntail(AbsTaskPairClassification):
metadata = TaskMetadata(
name="ArEntail",
dataset={
"path": "arbml/ArEntail",
"revision": "4da4316c6e3287746ab74ff67dd252ad128fceff",
},
description="A manually-curated Arabic natural language inference dataset from news headlines.",
reference="https://link.springer.com/article/10.1007/s10579-024-09731-1",
type="PairClassification",
category="s2s",
eval_splits=["test"],
eval_langs=["ara-Arab"],
main_score="ap",
date=(
"2020-01-01",
"2024-03-04",
), # best guess based on google searching random samples
form=["written"],
domains=["News"],
task_subtypes=["Textual Entailment"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@article{obeidat2024arentail,
title={ArEntail: manually-curated Arabic natural language inference dataset from news headlines},
author={Obeidat, Rasha and Al-Harahsheh, Yara and Al-Ayyoub, Mahmoud and Gharaibeh, Maram},
journal={Language Resources and Evaluation},
pages={1--27},
year={2024},
publisher={Springer}
}""",
n_samples={"test": 1000},
avg_character_length={"test": 65.77},
)
def dataset_transform(self):
_dataset = {}
for split in self.metadata.eval_splits:
_dataset[split] = [
{
"sent1": self.dataset[split]["premise"],
"sent2": self.dataset[split]["hypothesis"],
"labels": self.dataset[split]["label"],
}
]
self.dataset = _dataset
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