testbed/embeddings-benchmark__mteb/mteb/tasks/Classification/dan/AngryTweetsClassification.py

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