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Upload nusax_mt.py with huggingface_hub
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nusax_mt.py
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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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_DATASETNAME = "nusax_mt"
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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"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
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_LOCAL = False
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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-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.
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"""
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_HOMEPAGE = "https://github.com/IndoNLP/nusax/tree/main/datasets/mt"
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_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
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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/mt/train.csv",
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"validation": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/valid.csv",
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"test": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/test.csv",
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}
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def nusantara_config_constructor(lang_source, lang_target, schema, version):
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"""Construct NusantaraConfig with nusax_mt_{lang_source}_{lang_target}_{schema} as the name format"""
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if schema != "source" and schema != "nusantara_t2t":
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raise ValueError(f"Invalid schema: {schema}")
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if lang_source == "" and lang_target == "":
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return NusantaraConfig(
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name="nusax_mt_{schema}".format(schema=schema),
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version=datasets.Version(version),
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description="nusax_mt with {schema} schema for all 132 language pairs".format(schema=schema),
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schema=schema,
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subset_id="nusax_mt",
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)
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else:
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return NusantaraConfig(
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name="nusax_mt_{lang_source}_{lang_target}_{schema}".format(lang_source=lang_source, lang_target=lang_target, schema=schema),
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version=datasets.Version(version),
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description="nusax_mt with {schema} schema for {lang_source} source language and {lang_target} target language".format(lang_source=lang_source, lang_target=lang_target, schema=schema),
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schema=schema,
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subset_id="nusax_mt",
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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 NusaXMT(datasets.GeneratorBasedBuilder):
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"""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."""
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BUILDER_CONFIGS = (
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[nusantara_config_constructor(lang1, lang2, "source", _SOURCE_VERSION) for lang1 in LANGUAGES_MAP for lang2 in LANGUAGES_MAP if lang1 != lang2]
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+ [nusantara_config_constructor(lang1, lang2, "nusantara_t2t", _NUSANTARA_VERSION) for lang1 in LANGUAGES_MAP for lang2 in LANGUAGES_MAP if lang1 != lang2]
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+ [nusantara_config_constructor("", "", "source", _SOURCE_VERSION), nusantara_config_constructor("", "", "nusantara_t2t", _NUSANTARA_VERSION)]
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)
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DEFAULT_CONFIG_NAME = "nusax_senti_ind_eng_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source" or self.config.schema == "nusantara_t2t":
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features = schemas.text2text_features
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else:
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raise ValueError(f"Invalid config schema: {self.config.schema}")
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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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train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"]))
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validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"]))
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test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"]))
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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_t2t":
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raise ValueError(f"Invalid config schema: {self.config.schema}")
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df = pd.read_csv(filepath).reset_index()
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if self.config.name == "nusax_mt_source" or self.config.name == "nusax_mt_nusantara_t2t":
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# load all 132 language pairs
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id_count = -1
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for lang_source in LANGUAGES_MAP:
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for lang_target in LANGUAGES_MAP:
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if lang_source == lang_target:
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continue
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for _, row in df.iterrows():
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id_count += 1
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ex = {
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"id": str(id_count),
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"text_1": row[LANGUAGES_MAP[lang_source]],
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"text_2": row[LANGUAGES_MAP[lang_target]],
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"text_1_name": lang_source,
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"text_2_name": lang_target,
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}
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yield id_count, ex
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else:
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df = pd.read_csv(filepath).reset_index()
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lang_source = self.config.name[9:12]
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lang_target = self.config.name[13:16]
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for index, row in df.iterrows():
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ex = {
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"id": str(index),
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"text_1": row[LANGUAGES_MAP[lang_source]],
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"text_2": row[LANGUAGES_MAP[lang_target]],
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"text_1_name": lang_source,
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"text_2_name": lang_target,
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}
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yield str(index), ex
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