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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 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class AJGT(AbsTaskClassification):
metadata = TaskMetadata(
name="AJGT",
dataset={
"path": "komari6/ajgt_twitter_ar",
"revision": "af3f2fa5462ac461b696cb300d66e07ad366057f",
},
description="Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.",
reference="https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66/",
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["ara-Arab"],
main_score="accuracy",
date=("2021-01-01", "2022-01-25"),
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="AFL",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=["ara-arab-MSA", "ara-arab-JO"],
text_creation="found",
bibtex_citation="""
@inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610},
year={2017},
organization={Springer}
}
""",
n_samples={"train": 1800},
avg_character_length={"train": 46.81},
)
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