File size: 2,614 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 72 | from __future__ import annotations
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskClassification, MultilingualTask
_LANGUAGES = {
"as": ["asm-Beng"],
"bd": ["brx-Deva"],
"bn": ["ben-Beng"],
"gu": ["guj-Gujr"],
"hi": ["hin-Deva"],
"kn": ["kan-Knda"],
"ml": ["mal-Mlym"],
"mr": ["mar-Deva"],
"or": ["ory-Orya"],
"pa": ["pan-Guru"],
"ta": ["tam-Taml"],
"te": ["tel-Telu"],
"ur": ["urd-Arab"],
}
class IndicSentimentClassification(MultilingualTask, AbsTaskClassification):
fast_loading = True
metadata = TaskMetadata(
name="IndicSentimentClassification",
dataset={
"path": "mteb/IndicSentiment",
"revision": "3389cc78b2ffcbd33639e91dfc57e6b6b6496241",
},
description="A new, multilingual, and n-way parallel dataset for sentiment analysis in 13 Indic languages.",
reference="https://arxiv.org/abs/2212.05409",
category="s2s",
type="Classification",
eval_splits=["test"],
eval_langs=_LANGUAGES,
main_score="accuracy",
date=("2022-08-01", "2022-12-20"),
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="CC0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="machine-translated and verified",
bibtex_citation="""@article{doddapaneni2022towards,
title = {Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages},
author = {Sumanth Doddapaneni and Rahul Aralikatte and Gowtham Ramesh and Shreyansh Goyal and Mitesh M. Khapra and Anoop Kunchukuttan and Pratyush Kumar},
journal = {Annual Meeting of the Association for Computational Linguistics},
year = {2022},
doi = {10.18653/v1/2023.acl-long.693}
}""",
n_samples={"test": 1000},
avg_character_length={"test": 137.6},
)
def dataset_transform(self) -> None:
label_map = {"Negative": 0, "Positive": 1}
# Convert to standard format
for lang in self.hf_subsets:
self.dataset[lang] = self.dataset[lang].filter(
lambda x: x["LABEL"] is not None
)
self.dataset[lang] = self.dataset[lang].rename_columns(
{"INDIC REVIEW": "text", "LABEL": "label_text"}
)
self.dataset[lang] = self.dataset[lang].map(
lambda x: {"label": label_map[x["label_text"]]}
)
|