The dataset viewer is not available for this dataset.
Error code: ConfigNamesError Exception: ImportError Message: To be able to use SEACrowd/indo4b, 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, 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 is a large-scale Indonesian self-supervised pre-training corpus consists of around 3.6B words, with around 250M sentences. The corpus covers both formal and colloquial Indonesian sentences compiled from 12 sources, of which two cover Indonesian colloquial language, eight cover formal Indonesian language, and the rest have a mixed style of both colloquial and formal.
Languages
ind
Supported Tasks
Self Supervised Pretraining
Dataset Usage
Using datasets
library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/indo4b", trust_remote_code=True)
Using seacrowd
library
# Load the dataset using the default config
dset = sc.load_dataset("indo4b", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("indo4b"))
# 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 dataloader in your work, please cite the following:
@inproceedings{wilie-etal-2020-indonlu,
title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian
Natural Language Understanding",
author = "Wilie, Bryan and
Vincentio, Karissa and
Winata, Genta Indra and
Cahyawijaya, Samuel and
Li, Xiaohong and
Lim, Zhi Yuan and
Soleman, Sidik and
Mahendra, Rahmad and
Fung, Pascale and
Bahar, Syafri and
Purwarianti, Ayu",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the
Association for Computational Linguistics and the 10th International Joint
Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.85",
pages = "843--857",
abstract = "Although Indonesian is known to be the fourth most frequently used language
over the internet, the research progress on this language in natural language processing (NLP)
is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast
resource for training, evaluation, and benchmarking on Indonesian natural language understanding
(IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to
pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks
lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian
pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected
from publicly available sources such as social media texts, blogs, news, and websites.
We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation,
thus enabling everyone to benchmark their system performances.",
}
@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}
}
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