File size: 2,435 Bytes
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 52 53 54 55 56 57 58 59 60 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class LccSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="LccSentimentClassification",
dataset={
"path": "DDSC/lcc",
"revision": "de7ba3406ee55ea2cc52a0a41408fa6aede6d3c6",
},
description="The leipzig corpora collection, annotated for sentiment",
reference="https://github.com/fnielsen/lcc-sentiment",
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["dan-Latn"],
main_score="accuracy",
date=("2006-01-01", "2006-12-31"),
form=["written"],
domains=["News", "Web"],
task_subtypes=["Sentiment/Hate speech"],
license="CC-BY-4.0",
socioeconomic_status="mixed",
annotations_creators="expert-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{quasthoff-etal-2006-corpus,
title = "Corpus Portal for Search in Monolingual Corpora",
author = "Quasthoff, Uwe and
Richter, Matthias and
Biemann, Christian",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/641_pdf.pdf",
abstract = "A simple and flexible schema for storing and presenting monolingual language resources is proposed. In this format, data for 18 different languages is already available in various sizes. The data is provided free of charge for online use and download. The main target is to ease the application of algorithms for monolingual and interlingual studies.",
}""",
n_samples={"test": 150},
avg_character_length={"test": 118.7},
)
@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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