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Datasets:
indonlp
/
NusaX-senti

Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Achinese
Balinese
Banjar
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
Dataset card Viewer Files Community
NusaX-senti
File size: 4,386 Bytes 
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from pathlib import Path
from typing import Dict, List, Tuple

import datasets
import pandas as pd

_LOCAL = False

_LANGUAGES = ["ind", "ace", "ban", "bjn", "bbc", "bug", "jav", "mad", "min", "nij", "sun", "eng"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)

_CITATION = """\
@misc{winata2022nusax,
      title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
      author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
      Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
      Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
      Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
      Jey Han and Sennrich, Rico and Ruder, Sebastian},
      year={2022},
      eprint={2205.15960},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
"""

_HOMEPAGE = "https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment"

_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"

_SOURCE_VERSION = "1.0.0"

_URLS = {
    "train": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/sentiment/{lang}/train.csv",
    "validation": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/sentiment/{lang}/valid.csv",
    "test": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/sentiment/{lang}/test.csv",
}

LANGUAGES_MAP = {
    "ace": "acehnese",
    "ban": "balinese",
    "bjn": "banjarese",
    "bug": "buginese",
    "eng": "english",
    "ind": "indonesian",
    "jav": "javanese",
    "mad": "madurese",
    "min": "minangkabau",
    "nij": "ngaju",
    "sun": "sundanese",
    "bbc": "toba_batak",
}


class NusaXSenti(datasets.GeneratorBasedBuilder):
    """NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English."""

    BUILDER_CONFIGS = [
    datasets.BuilderConfig(
        name = lang,
        version = _SOURCE_VERSION,
        description = f"NusaX-Senti: Sentiment analysis dataset for {lang}") 
    for lang in LANGUAGES_MAP]

    def _info(self) -> datasets.DatasetInfo:
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "text": datasets.Value("string"),
                "lang": datasets.Value("string"),
                "label": datasets.ClassLabel(names=["negative", "neutral", "positive"]),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        lang = self.config.name
        train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"].format(lang=LANGUAGES_MAP[lang])))
        validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"].format(lang=LANGUAGES_MAP[lang])))
        test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"].format(lang=LANGUAGES_MAP[lang])))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": train_csv_path},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": validation_csv_path},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": test_csv_path},
            ),
        ]

    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
        df = pd.read_csv(filepath).reset_index()

        for row in df.itertuples():
            ex = {"id": str(row.id), "text": row.text, "label": row.label, "lang": self.config.name}
            yield row.id, ex