File size: 3,402 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 64 65 66 67 68 69 70 71 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class WRIMEClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="WRIMEClassification",
dataset={
"path": "shunk031/wrime",
"revision": "3fb7212c389d7818b8e6179e2cdac762f2e081d9",
"name": "ver2",
},
description="A dataset of Japanese social network rated for sentiment",
reference="https://aclanthology.org/2021.naacl-main.169/",
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["jpn-Jpan"],
main_score="accuracy",
date=("2011-06-01", "2020-05-31"),
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="The dataset is available for research purposes only. Redistribution of the dataset is prohibited.",
socioeconomic_status="high",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.169",
doi = "10.18653/v1/2021.naacl-main.169",
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}""",
n_samples={"test": 2048},
avg_character_length={"test": 47.78},
)
def dataset_transform(self):
self.dataset = self.dataset.flatten().select_columns(
["sentence", "avg_readers.sentiment"]
)
self.dataset = self.dataset.rename_columns(
{"sentence": "text", "avg_readers.sentiment": "label"}
)
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["test"]
)
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