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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 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | from typing import Any
import datasets
from mteb.abstasks import AbsTaskClassification, MultilingualTask
from mteb.abstasks.TaskMetadata import TaskMetadata
class NaijaSenti(AbsTaskClassification, MultilingualTask):
metadata = TaskMetadata(
name="NaijaSenti",
description="NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.",
reference="https://github.com/hausanlp/NaijaSenti",
dataset={
"path": "HausaNLP/NaijaSenti-Twitter",
"revision": "a3d0415a828178edf3466246f49cfcd83b946ab3",
},
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs={
"hau": ["hau-Latn"],
"ibo": ["ibo-Latn"],
"pcm": ["pcm-Latn"],
"yor": ["yor-Latn"],
},
main_score="accuracy",
date=("2022-05-01", "2023-05-08"),
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="CC-BY-4.0 license",
socioeconomic_status="low",
annotations_creators="expert-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""
@inproceedings{muhammad-etal-2022-naijasenti,
title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
author = "Muhammad, Shamsuddeen Hassan and
Adelani, David Ifeoluwa and
Ruder, Sebastian and
Ahmad, Ibrahim Sa{'}id and
Abdulmumin, Idris and
Bello, Bello Shehu and
Choudhury, Monojit and
Emezue, Chris Chinenye and
Abdullahi, Saheed Salahudeen and
Aremu, Anuoluwapo and
Jorge, Al{\'\i}pio and
Brazdil, Pavel",
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-1.63",
pages = "590--602",
}""",
n_samples={"test": 4800},
avg_character_length={"test": 72.81},
)
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"{lang}",
**self.metadata_dict["dataset"],
)
self.dataset[lang] = datasets.DatasetDict(
{
"train": self.dataset[lang]["train"],
"test": self.dataset[lang]["test"],
}
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self) -> None:
for lang in self.hf_subsets:
self.dataset[lang] = self.dataset[lang].map(
lambda example: {"text": example["tweet"]}
)
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