The dataset viewer is not available for this dataset.
Error code: ConfigNamesError Exception: ImportError Message: To be able to use SEACrowd/indo4b_plus, you need to install the following dependency: seacrowd. Please install it using 'pip install seacrowd' for instance. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response config_names = get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory return HubDatasetModuleFactoryWithScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module local_imports = _download_additional_modules( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules raise ImportError( ImportError: To be able to use SEACrowd/indo4b_plus, you need to install the following dependency: seacrowd. Please install it using 'pip install seacrowd' for instance.
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
Languages
ind, sun, jav
Supported Tasks
Self Supervised Pretraining
Dataset Usage
Using datasets
library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/indo4b_plus", trust_remote_code=True)
Using seacrowd
library
# Load the dataset using the default config
dset = sc.load_dataset("indo4b_plus", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("indo4b_plus"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
More details on how to load the seacrowd
library can be found here.
Dataset Homepage
https://github.com/IndoNLP/indonlu
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
CC0
Citation
If you are using the Indo4B Plus dataloader in your work, please cite the following:
@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.",
}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
year={2024},
eprint={2406.10118},
journal={arXiv preprint arXiv: 2406.10118}
}
- Downloads last month
- 46