File size: 2,248 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 | from __future__ import annotations
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class HindiDiscourseClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="HindiDiscourseClassification",
dataset={
"path": "midas/hindi_discourse",
"revision": "218ce687943a0da435d6d62751a4ab216be6cd40",
},
description="A Hindi Discourse dataset in Hindi with values for coherence.",
reference="https://aclanthology.org/2020.lrec-1.149/",
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs=["hin-Deva"],
main_score="accuracy",
date=("2019-12-01", "2020-04-09"),
form=["written"],
domains=["Fiction", "Social"],
dialect=[],
task_subtypes=["Discourse coherence"],
license="MIT",
socioeconomic_status="medium",
annotations_creators="expert-annotated",
text_creation="found",
bibtex_citation="""
@inproceedings{dhanwal-etal-2020-annotated,
title = "An Annotated Dataset of Discourse Modes in {H}indi Stories",
author = "Dhanwal, Swapnil and
Dutta, Hritwik and
Nankani, Hitesh and
Shrivastava, Nilay and
Kumar, Yaman and
Li, Junyi Jessy and
Mahata, Debanjan and
Gosangi, Rakesh and
Zhang, Haimin and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.149",
language = "English",
ISBN = "979-10-95546-34-4",
}""",
n_samples={"train": 2048},
avg_character_length={"train": 79.23828125},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns(
{"Sentence": "text", "Discourse Mode": "label"}
).remove_columns(["Story_no"])
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train"]
)
|