Datasets:
Tasks:
Translation
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
from pathlib import Path | |
from typing import Dict, List, Tuple | |
import datasets | |
import pandas as pd | |
_DATASETNAME = "NusaX_MT" | |
_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) | |
_LOCAL = False | |
_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-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language. | |
""" | |
_HOMEPAGE = "https://github.com/IndoNLP/nusax/tree/main/datasets/mt" | |
_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International" | |
_SOURCE_VERSION = "1.0.0" | |
_URLS = { | |
"train": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/train.csv", | |
"validation": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/valid.csv", | |
"test": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/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 NusaXMT(datasets.GeneratorBasedBuilder): | |
"""NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.""" | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name = f"{lang1}-{lang2}", | |
version = _SOURCE_VERSION) | |
for lang1 in LANGUAGES_MAP for lang2 in LANGUAGES_MAP if lang1 != lang2] + \ | |
[datasets.BuilderConfig(name = f"ALL", version = _SOURCE_VERSION)] | |
DEFAULT_CONFIG_NAME = "ALL" | |
def _info(self) -> datasets.DatasetInfo: | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"text_1": datasets.Value("string"), | |
"text_2": datasets.Value("string"), | |
"text_1_lang": datasets.Value("string"), | |
"text_2_lang": datasets.Value("string"), | |
} | |
) | |
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.""" | |
train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"])) | |
validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"])) | |
test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"])) | |
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() | |
if self.config.name == "ALL": | |
# load all 132 language pairs | |
id_count = -1 | |
for lang_source in LANGUAGES_MAP: | |
for lang_target in LANGUAGES_MAP: | |
if lang_source == lang_target: | |
continue | |
for _, row in df.iterrows(): | |
id_count += 1 | |
ex = { | |
"id": str(id_count), | |
"text_1": row[LANGUAGES_MAP[lang_source]], | |
"text_2": row[LANGUAGES_MAP[lang_target]], | |
"text_1_lang": lang_source, | |
"text_2_lang": lang_target, | |
} | |
yield id_count, ex | |
else: | |
df = pd.read_csv(filepath).reset_index() | |
lang_source = self.config.name[0:3] | |
lang_target = self.config.name[4:7] | |
for index, row in df.iterrows(): | |
ex = { | |
"id": str(index), | |
"text_1": row[LANGUAGES_MAP[lang_source]], | |
"text_2": row[LANGUAGES_MAP[lang_target]], | |
"text_1_lang": lang_source, | |
"text_2_lang": lang_target, | |
} | |
yield str(index), ex |