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---
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license: cc-by-4.0
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language:
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- ind
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- mad
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pretty_name: Indonesian Madurese Bible Translation
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task_categories:
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- machine-translation
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tags:
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- machine-translation
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---
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The Madurese Parallel Corpus Dataset is created by scraping content from the online Bible, resulting in 30,013 Indonesian-Madurese sentences.
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This corpus is distinct from a previous Madurese dataset, which was gathered from physical documents such as the Kamus Lengkap Bahasa Madura-Indonesia.
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The proposed dataset provides bilingual sentences, allowing for comparisons between Indonesian and Madurese. It aims to supplement existing Madurese
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corpora, enabling enhanced research and development focused on regional languages in Indonesia. Unlike the prior dataset that included information
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like lemmas, pronunciation, linguistic descriptions, part of speech, loanwords, dialects, and various structures, this new corpus primarily focuses
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on bilingual sentence pairs, potentially broadening the scope for linguistic studies and language technology advancements in the Madurese language.
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## Languages
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ind, mad
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## Supported Tasks
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Machine Translation
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## Dataset Usage
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### Using `datasets` library
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```
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from datasets import load_dataset
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dset = datasets.load_dataset("SEACrowd/indonesian_madurese_bible_translation", trust_remote_code=True)
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```
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### Using `seacrowd` library
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```import seacrowd as sc
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# Load the dataset using the default config
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dset = sc.load_dataset("indonesian_madurese_bible_translation", schema="seacrowd")
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# Check all available subsets (config names) of the dataset
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print(sc.available_config_names("indonesian_madurese_bible_translation"))
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# Load the dataset using a specific config
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dset = sc.load_dataset_by_config_name(config_name="<config_name>")
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```
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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).
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## Dataset Homepage
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[https://data.mendeley.com/datasets/cgtg4bhrtf/3](https://data.mendeley.com/datasets/cgtg4bhrtf/3)
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## Dataset Version
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Source: 1.0.0. SEACrowd: 2024.06.20.
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## Dataset License
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Creative Commons Attribution 4.0 (cc-by-4.0)
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## Citation
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If you are using the **Indonesian Madurese Bible Translation** dataloader in your work, please cite the following:
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```
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@article{,
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author = {Sulistyo, Danang Arbian and Wibawa, Aji Prasetya and Prasetya, Didik Dwi and Nafalski, Andrew},
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title = {Autogenerated Indonesian-Madurese Parallel Corpus Dataset Using Neural Machine Translation},
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journal = {Available at SSRN 4644430},
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volume = {},
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year = {2023},
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url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4644430},
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doi = {},
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biburl = {},
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bibsource = {}
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}
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@article{lovenia2024seacrowd,
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title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
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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},
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year={2024},
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eprint={2406.10118},
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journal={arXiv preprint arXiv: 2406.10118}
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}
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```
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