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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 | from __future__ import annotations
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class AngryTweetsClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="AngryTweetsClassification",
dataset={
"path": "DDSC/angry-tweets",
"revision": "20b0e6081892e78179356fada741b7afa381443d",
},
description="A sentiment dataset with 3 classes (positiv, negativ, neutral) for Danish tweets",
reference="https://aclanthology.org/2021.nodalida-main.53/",
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["dan-Latn"],
main_score="accuracy",
date=("2021-01-01", "2021-12-31"),
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="CC-BY-4.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{pauli2021danlp,
title={DaNLP: An open-source toolkit for Danish Natural Language Processing},
author={Pauli, Amalie Brogaard and Barrett, Maria and Lacroix, Oph{\'e}lie and Hvingelby, Rasmus},
booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
pages={460--466},
year={2021}
}""",
n_samples={"test": 1050},
avg_character_length={"test": 156.1},
)
@property
def metadata_dict(self) -> dict[str, str]:
metadata_dict = super().metadata_dict
metadata_dict["n_experiments"] = 10
metadata_dict["samples_per_label"] = 16
return metadata_dict
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