File size: 3,065 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | from __future__ import annotations
from typing import Any
import datasets
import numpy as np
from mteb.abstasks import AbsTaskClustering, MultilingualTask
from mteb.abstasks.TaskMetadata import TaskMetadata
_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 IndicReviewsClusteringP2P(AbsTaskClustering, MultilingualTask):
metadata = TaskMetadata(
name="IndicReviewsClusteringP2P",
dataset={
"path": "ai4bharat/IndicSentiment",
"revision": "ccb472517ce32d103bba9d4f5df121ed5a6592a4",
},
description="Clustering of reviews from IndicSentiment dataset. Clustering of 14 sets on the generic categories label.",
reference="https://arxiv.org/abs/2212.05409",
type="Clustering",
category="p2p",
eval_splits=["test"],
eval_langs=_LANGUAGES,
main_score="v_measure",
date=("2022-08-01", "2022-12-20"),
form=["written"],
domains=["Reviews"],
task_subtypes=["Thematic clustering"],
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 load_data(self, **kwargs: Any) -> None:
"""Load dataset from HuggingFace hub"""
if self.data_loaded:
return
self.dataset = {}
for lang in self.hf_subsets:
self.dataset[lang] = datasets.load_dataset(
name=f"translation-{lang}",
**self.metadata_dict["dataset"],
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self) -> None:
for lang in self.hf_subsets:
self.dataset[lang].pop("validation")
texts = self.dataset[lang]["test"]["INDIC REVIEW"]
labels = self.dataset[lang]["test"]["GENERIC CATEGORIES"]
new_format = {
"sentences": [split.tolist() for split in np.array_split(texts, 5)],
"labels": [split.tolist() for split in np.array_split(labels, 5)],
}
self.dataset[lang]["test"] = datasets.Dataset.from_dict(new_format)
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