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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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import pandas as pd |
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from nusacrowd.utils import schemas |
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from nusacrowd.utils.configs import NusantaraConfig |
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from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME, |
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DEFAULT_SOURCE_VIEW_NAME, Tasks) |
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_LOCAL = False |
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_DATASETNAME = "nusax_senti" |
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_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME |
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_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME |
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_LANGUAGES = ["ind", "ace", "ban", "bjn", "bbc", "bug", "jav", "mad", "min", "nij", "sun", "eng"] |
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_CITATION = """\ |
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@misc{winata2022nusax, |
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title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages}, |
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author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya, |
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Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony, |
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Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo, |
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Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau, |
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Jey Han and Sennrich, Rico and Ruder, Sebastian}, |
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year={2022}, |
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eprint={2205.15960}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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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. |
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NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English. |
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""" |
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_HOMEPAGE = "https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment" |
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_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International" |
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_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS] |
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_SOURCE_VERSION = "1.0.0" |
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_NUSANTARA_VERSION = "1.0.0" |
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_URLS = { |
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"train": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/sentiment/{lang}/train.csv", |
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"validation": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/sentiment/{lang}/valid.csv", |
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"test": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/sentiment/{lang}/test.csv", |
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} |
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def nusantara_config_constructor(lang, schema, version): |
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"""Construct NusantaraConfig with nusax_senti_{lang}_{schema} as the name format""" |
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if schema != "source" and schema != "nusantara_text": |
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raise ValueError(f"Invalid schema: {schema}") |
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if lang == "": |
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return NusantaraConfig( |
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name="nusax_senti_{schema}".format(schema=schema), |
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version=datasets.Version(version), |
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description="nusax_senti with {schema} schema for all 12 languages".format(schema=schema), |
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schema=schema, |
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subset_id="nusax_senti", |
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) |
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else: |
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return NusantaraConfig( |
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name="nusax_senti_{lang}_{schema}".format(lang=lang, schema=schema), |
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version=datasets.Version(version), |
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description="nusax_senti with {schema} schema for {lang} language".format(lang=lang, schema=schema), |
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schema=schema, |
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subset_id="nusax_senti", |
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) |
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LANGUAGES_MAP = { |
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"ace": "acehnese", |
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"ban": "balinese", |
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"bjn": "banjarese", |
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"bug": "buginese", |
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"eng": "english", |
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"ind": "indonesian", |
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"jav": "javanese", |
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"mad": "madurese", |
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"min": "minangkabau", |
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"nij": "ngaju", |
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"sun": "sundanese", |
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"bbc": "toba_batak", |
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} |
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class NusaXSenti(datasets.GeneratorBasedBuilder): |
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"""NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.""" |
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BUILDER_CONFIGS = ( |
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[nusantara_config_constructor(lang, "source", _SOURCE_VERSION) for lang in LANGUAGES_MAP] |
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+ [nusantara_config_constructor(lang, "nusantara_text", _NUSANTARA_VERSION) for lang in LANGUAGES_MAP] |
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+ [nusantara_config_constructor("", "source", _SOURCE_VERSION), nusantara_config_constructor("", "nusantara_text", _NUSANTARA_VERSION)] |
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) |
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DEFAULT_CONFIG_NAME = "nusax_senti_ind_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "nusantara_text": |
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features = schemas.text_features(["negative", "neutral", "positive"]) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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if self.config.name == "nusax_senti_source" or self.config.name == "nusax_senti_nusantara_text": |
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train_csv_path = dl_manager.download_and_extract([_URLS["train"].format(lang=LANGUAGES_MAP[lang]) for lang in LANGUAGES_MAP]) |
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validation_csv_path = dl_manager.download_and_extract([_URLS["validation"].format(lang=LANGUAGES_MAP[lang]) for lang in LANGUAGES_MAP]) |
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test_csv_path = dl_manager.download_and_extract([_URLS["test"].format(lang=LANGUAGES_MAP[lang]) for lang in LANGUAGES_MAP]) |
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else: |
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lang = self.config.name[12:15] |
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train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"].format(lang=LANGUAGES_MAP[lang]))) |
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validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"].format(lang=LANGUAGES_MAP[lang]))) |
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test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"].format(lang=LANGUAGES_MAP[lang]))) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": train_csv_path}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": validation_csv_path}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": test_csv_path}, |
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), |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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if self.config.schema != "source" and self.config.schema != "nusantara_text": |
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raise ValueError(f"Invalid config: {self.config.name}") |
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if self.config.name == "nusax_senti_source" or self.config.name == "nusax_senti_nusantara_text": |
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ldf = [] |
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for fp in filepath: |
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ldf.append(pd.read_csv(fp)) |
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df = pd.concat(ldf, axis=0, ignore_index=True).reset_index() |
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df = df.drop(columns=["id"]).rename(columns={"index": "id"}) |
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else: |
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df = pd.read_csv(filepath).reset_index() |
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for row in df.itertuples(): |
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ex = {"id": str(row.id), "text": row.text, "label": row.label} |
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yield row.id, ex |
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