The dataset viewer is not available for this dataset.
Error code: ConfigNamesError Exception: ImportError Message: To be able to use SEACrowd/indo_religious_mt_en_id, 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/indo_religious_mt_en_id, 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.
Indonesian Religious Domain MT En-Id consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to advocate and inspire religious values, often times citing biblical or quranic anecdotes. An interesting property in the religion domain corpus is the localized names, for example, David to Daud, Mary to Maryam, Gabriel to Jibril, and more. In contrast, entity names are usually kept unchanged in other domains. We also find quite a handful of Indonesian translations of JW300 are missing the end sentence dot (.), even though the end sentence dot is present in their English counterpart. Some inconsistencies in the transliteration are also found, for example praying is sometimes written as "salat" or "shalat", or repentance as "tobat" or "taubat".
Languages
ind, eng
Supported Tasks
Machine Translation
Dataset Usage
Using datasets
library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/indo_religious_mt_en_id", trust_remote_code=True)
Using seacrowd
library
# Load the dataset using the default config
dset = sc.load_dataset("indo_religious_mt_en_id", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("indo_religious_mt_en_id"))
# 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/gunnxx/indonesian-mt-data/tree/master/religious
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Creative Commons Attribution Share-Alike 4.0 International
Citation
If you are using the Indo Religious Mt En Id dataloader in your work, please cite the following:
@inproceedings{guntara-etal-2020-benchmarking,
title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
author = "Guntara, Tri Wahyu and
Aji, Alham Fikri and
Prasojo, Radityo Eko",
booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.bucc-1.6",
pages = "35--43",
abstract = "In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic. 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 conversation, to train and benchmark some variants of transformer-based NMT models across the domains. We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models, 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.",
language = "English",
ISBN = "979-10-95546-42-9",
}
@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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