File size: 2,943 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 66 67 68 | from __future__ import annotations
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class DdiscoCohesionClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="Ddisco",
dataset={
"path": "DDSC/ddisco",
"revision": "514ab557579fcfba538a4078d6d647248a0e6eb7",
},
description="A Danish Discourse dataset with values for coherence and source (Wikipedia or Reddit)",
reference="https://aclanthology.org/2022.lrec-1.260/",
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["dan-Latn"],
main_score="accuracy",
date=("2021-01-01", "2022-06-25"),
form=["written"],
domains=["Non-fiction", "Social"],
dialect=[],
task_subtypes=["Discourse coherence"],
license="cc-by-sa-3.0",
socioeconomic_status="high",
annotations_creators="expert-annotated",
text_creation="found",
bibtex_citation="""
@inproceedings{flansmose-mikkelsen-etal-2022-ddisco,
title = "{DD}is{C}o: A Discourse Coherence Dataset for {D}anish",
author = "Flansmose Mikkelsen, Linea and
Kinch, Oliver and
Jess Pedersen, Anders and
Lacroix, Oph{\'e}lie",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.260",
pages = "2440--2445",
abstract = "To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.",
}
""",
n_samples=None,
avg_character_length=None,
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns({"rating": "label"}).remove_columns(
["domain"]
)
|