File size: 3,049 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 61 62 63 64 65 | from __future__ import annotations
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskClassification
class EmotionClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="EmotionClassification",
description="Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.",
reference="https://www.aclweb.org/anthology/D18-1404",
dataset={
"path": "mteb/emotion",
"revision": "4f58c6b202a23cf9a4da393831edf4f9183cad37",
},
type="Classification",
category="s2s",
eval_splits=["validation", "test"],
eval_langs=["eng-Latn"],
main_score="accuracy",
date=(
"2017-01-01",
"2018-12-31",
), # Estimated range for the collection of Twitter messages
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}""",
n_samples={"validation": 2000, "test": 2000},
avg_character_length={"validation": 95.3, "test": 95.6},
)
@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
|