# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from posixpath import split from typing import Dict, List, Tuple import datasets from nusacrowd.utils import schemas from nusacrowd.utils.configs import NusantaraConfig from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks) _DATASETNAME = "indo4b_plus" _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME _UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME _LOCAL = False _LANGUAGES = ["ind", "sun", "jav"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _CITATION = """\ @inproceedings{cahyawijaya-etal-2021-indonlg, title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation", author = "Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu and Purwarianti, Ayu and Fung, Pascale", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.699", doi = "10.18653/v1/2021.emnlp-main.699", pages = "8875--8898", abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.", } """ _DESCRIPTION = """\ Indo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese. Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese sentences and 331,041,877 words (∼8.29%) of Javanese """ _HOMEPAGE = "https://github.com/IndoNLP/indonlu" _LICENSE = "CC0" _LANGUAGES_MAP = { "ind": "id", "jav": "jv", "sun": "su", } _URLS = { "indo4b": "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/IndoNLG_ALL_new_dataset_preprocessed_uncased.txt.zip", } _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] _SOURCE_VERSION = "1.0.0" _NUSANTARA_VERSION = "1.0.0" class Indo4BPlus(datasets.GeneratorBasedBuilder): """Indo4B-Plus is a large-scale Indonesian self-supervised pre-training corpus consists of around 4B words, covering three languages, i.e., Indonesian, Sundanese, and Javanese.""" DEFAULT_CONFIG_NAME = "indo4b_plus_source" BUILDER_CONFIGS = [ NusantaraConfig( name="indo4b_plus_source", version=_SOURCE_VERSION, description="Indo4B-Plus source schema", schema="source", subset_id="indo4b_plus", ), NusantaraConfig( name="indo4b_plus_nusantara_ssp", version=_NUSANTARA_VERSION, description="Indo4B-Plus Nusantara schema", schema="nusantara_ssp", subset_id="indo4b_plus", ), ] def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), } ) elif self.config.schema == "nusantara_ssp": features = schemas.self_supervised_pretraining.features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" url = _URLS["indo4b"] path = dl_manager.download_and_extract(url) + "/IndoNLG_ALL_new_dataset_preprocessed_uncased.txt" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": path, "split": "train", }, ), ] def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" with open(filepath, encoding="utf-8") as f: if self.config.schema == "source": for counter, row in enumerate(f): if row.strip() != "": yield ( counter, { "id": str(counter), "text": row.strip(), }, ) elif self.config.schema == "nusantara_ssp": for counter, row in enumerate(f): if row.strip() != "": yield ( counter, { "id": str(counter), "text": row.strip(), }, )