File size: 2,407 Bytes
83d24b2 | 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 57 58 59 60 61 62 63 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class EstonianValenceClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="EstonianValenceClassification",
dataset={
"path": "kardosdrur/estonian-valence",
"revision": "9157397f05a127b3ac93b93dd88abf1bdf710c22",
},
description="Dataset containing annotated Estonian news data from the Postimees and Õhtuleht newspapers.",
reference="https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054",
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["est-Latn"],
main_score="accuracy",
date=(
"1857-01-01", # Inception of Postimees
"2023-11-08", # dataset publication
),
form=["written"],
domains=["News"],
task_subtypes=["Sentiment/Hate speech"],
dialect=[],
license="CC BY 4.0",
socioeconomic_status="high",
annotations_creators="human-annotated",
text_creation="found",
bibtex_citation="""
@article{Pajupuu2023,
author = "Hille Pajupuu and Jaan Pajupuu and Rene Altrov and Kairi Tamuri",
title = "{Estonian Valence Corpus / Eesti valentsikorpus}",
year = "2023",
month = "11",
url = "https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054",
doi = "10.6084/m9.figshare.24517054.v1"
}""",
n_samples={"train": 3270, "test": 818},
avg_character_length={"train": 226.70642201834863, "test": 231.5085574572127},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_column("paragraph", "text").rename_column(
"valence", "label"
)
# convert label to a numbers
labels = self.dataset["train"]["label"] # type: ignore
lab2idx = {lab: idx for idx, lab in enumerate(set(labels))}
self.dataset = self.dataset.map(
lambda x: {"label": lab2idx[x["label"]]}, remove_columns=["label"]
)
@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
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