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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 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class DanishPoliticalCommentsClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="DanishPoliticalCommentsClassification",
dataset={
"path": "danish_political_comments",
"revision": "edbb03726c04a0efab14fc8c3b8b79e4d420e5a1",
},
description="A dataset of Danish political comments rated for sentiment",
reference="https://huggingface.co/datasets/danish_political_comments",
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["dan-Latn"],
main_score="accuracy",
date=(
"2000-01-01",
"2022-12-31",
), # Estimated range for the collection of comments
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="",
n_samples={"train": 9010},
avg_character_length={"train": 69.9},
)
@property
def metadata_dict(self) -> dict[str, str]:
metadata_dict = dict(self.metadata)
metadata_dict["n_experiments"] = 10
metadata_dict["samples_per_label"] = 16
return metadata_dict
def dataset_transform(self):
self.dataset = self.dataset.rename_column("sentence", "text")
self.dataset = self.dataset.rename_column("target", "label")
# create train and test splits
self.dataset = self.dataset["train"].train_test_split(0.2, seed=self.seed)
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