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  ---
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- tags:
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- - machine-translation
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- language:
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  - ind
 
 
 
 
 
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  ---
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- # indo_general_mt_en_id
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-
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  "In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
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-
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  and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
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-
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  In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and
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-
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  conversation,to train and benchmark some variants of transformer-based NMT models across the domains.
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-
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  We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,
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-
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  and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
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  ## Dataset Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
 
 
 
 
 
 
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  ## Citation
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  ```
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  @inproceedings{guntara-etal-2020-benchmarking,
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  title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
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  language = "English",
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  ISBN = "979-10-95546-42-9",
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  }
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- ```
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-
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- ## License
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-
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- Creative Commons Attribution Share-Alike 4.0 International
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- ## Homepage
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- [https://github.com/gunnxx/indonesian-mt-data](https://github.com/gunnxx/indonesian-mt-data)
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-
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- ### NusaCatalogue
 
 
 
 
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- For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
 
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+
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  ---
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+ language:
 
 
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  - ind
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+ pretty_name: Indo General Mt En Id
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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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  "In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
 
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  and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
 
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  In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and
 
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  conversation,to train and benchmark some variants of transformer-based NMT models across the domains.
 
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  We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,
 
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  and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
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+
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+ ## Languages
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+
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+ ind
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+
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+ ## Supported Tasks
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+
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+ Machine Translation
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+
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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/indo_general_mt_en_id", 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("indo_general_mt_en_id", schema="seacrowd")
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+ # Check all available subsets (config names) of the dataset
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+ print(sc.available_config_names("indo_general_mt_en_id"))
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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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+
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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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+
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+
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+ ## Dataset Homepage
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+
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+ [https://github.com/gunnxx/indonesian-mt-data](https://github.com/gunnxx/indonesian-mt-data)
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+ ## Dataset Version
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+
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+ Source: 1.0.0. SEACrowd: 2024.06.20.
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+
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+ ## Dataset License
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+
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+ Creative Commons Attribution Share-Alike 4.0 International
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  ## Citation
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+ If you are using the **Indo General Mt En Id** dataloader in your work, please cite the following:
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  ```
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  @inproceedings{guntara-etal-2020-benchmarking,
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  title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
 
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  language = "English",
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  ISBN = "979-10-95546-42-9",
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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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+ ```