README.md

---
language: 
- ind
- sun
- jav
pretty_name: Indo4B Plus
task_categories: 
- self-supervised-pretraining
tags: 
- self-supervised-pretraining
---

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
```import seacrowd as sc
# 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](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).


## Dataset Homepage

[https://github.com/IndoNLP/indonlu](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}
}

```